From 55e27160f5ef7e2b18a06bb0c09a7ce4e1d29600 Mon Sep 17 00:00:00 2001 From: Bryce Date: Tue, 2 Jan 2024 21:06:39 -0800 Subject: [PATCH] build: vendorize refiners so we can still work in conda envs --- Makefile | 13 + imaginairy/api/generate_compvis.py | 9 +- imaginairy/api/generate_refiners.py | 9 +- imaginairy/modules/refiners_sd.py | 50 +- imaginairy/utils/model_manager.py | 16 +- imaginairy/vendored/refiners/LICENSE | 21 + .../vendored/{ => refiners}/__init__.py | 0 .../vendored/refiners/fluxion/__init__.py | 3 + .../refiners/fluxion/adapters/__init__.py | 3 + .../refiners/fluxion/adapters/adapter.py | 101 +++ .../refiners/fluxion/adapters/lora.py | 130 ++++ .../vendored/refiners/fluxion/context.py | 53 ++ .../refiners/fluxion/layers/__init__.py | 110 +++ .../refiners/fluxion/layers/activations.py | 77 +++ .../refiners/fluxion/layers/attentions.py | 246 +++++++ .../refiners/fluxion/layers/basics.py | 207 ++++++ .../vendored/refiners/fluxion/layers/chain.py | 586 ++++++++++++++++ .../vendored/refiners/fluxion/layers/conv.py | 96 +++ .../refiners/fluxion/layers/converter.py | 45 ++ .../refiners/fluxion/layers/embedding.py | 21 + .../refiners/fluxion/layers/linear.py | 49 ++ .../refiners/fluxion/layers/maxpool.py | 43 ++ .../refiners/fluxion/layers/module.py | 264 +++++++ .../vendored/refiners/fluxion/layers/norm.py | 88 +++ .../refiners/fluxion/layers/padding.py | 8 + .../refiners/fluxion/layers/pixelshuffle.py | 8 + .../refiners/fluxion/layers/sampling.py | 99 +++ .../refiners/fluxion/model_converter.py | 644 ++++++++++++++++++ imaginairy/vendored/refiners/fluxion/utils.py | 206 ++++++ .../refiners/foundationals/__init__.py | 0 .../refiners/foundationals/clip/__init__.py | 0 .../clip/bpe_simple_vocab_16e6.txt.gz | Bin 0 -> 1356917 bytes .../refiners/foundationals/clip/common.py | 48 ++ .../refiners/foundationals/clip/concepts.py | 148 ++++ .../foundationals/clip/image_encoder.py | 179 +++++ .../foundationals/clip/text_encoder.py | 195 ++++++ .../refiners/foundationals/clip/tokenizer.py | 121 ++++ .../refiners/foundationals/dinov2/__init__.py | 29 + .../refiners/foundationals/dinov2/dinov2.py | 148 ++++ .../refiners/foundationals/dinov2/vit.py | 373 ++++++++++ .../latent_diffusion/__init__.py | 40 ++ .../latent_diffusion/auto_encoder.py | 221 ++++++ .../latent_diffusion/cross_attention.py | 175 +++++ .../foundationals/latent_diffusion/freeu.py | 94 +++ .../latent_diffusion/image_prompt.py | 465 +++++++++++++ .../foundationals/latent_diffusion/lora.py | 146 ++++ .../foundationals/latent_diffusion/model.py | 115 ++++ .../latent_diffusion/multi_diffusion.py | 98 +++ .../preprocessors/__init__.py | 0 .../preprocessors/informative_drawings.py | 107 +++ .../latent_diffusion/range_adapter.py | 68 ++ .../reference_only_control.py | 143 ++++ .../foundationals/latent_diffusion/restart.py | 110 +++ .../latent_diffusion/schedulers/__init__.py | 11 + .../latent_diffusion/schedulers/ddim.py | 57 ++ .../latent_diffusion/schedulers/ddpm.py | 34 + .../latent_diffusion/schedulers/dpm_solver.py | 117 ++++ .../latent_diffusion/schedulers/scheduler.py | 128 ++++ .../self_attention_guidance.py | 101 +++ .../stable_diffusion_1/__init__.py | 17 + .../stable_diffusion_1/controlnet.py | 179 +++++ .../stable_diffusion_1/image_prompt.py | 53 ++ .../stable_diffusion_1/model.py | 173 +++++ .../stable_diffusion_1/multi_diffusion.py | 41 ++ .../self_attention_guidance.py | 41 ++ .../stable_diffusion_1/t2i_adapter.py | 37 + .../stable_diffusion_1/unet.py | 288 ++++++++ .../stable_diffusion_xl/__init__.py | 13 + .../stable_diffusion_xl/image_prompt.py | 53 ++ .../stable_diffusion_xl/model.py | 154 +++++ .../stable_diffusion_xl/multi_diffusion.py | 21 + .../self_attention_guidance.py | 41 ++ .../stable_diffusion_xl/t2i_adapter.py | 48 ++ .../stable_diffusion_xl/text_encoder.py | 85 +++ .../stable_diffusion_xl/unet.py | 285 ++++++++ .../latent_diffusion/t2i_adapter.py | 215 ++++++ .../segment_anything/__init__.py | 0 .../segment_anything/image_encoder.py | 368 ++++++++++ .../segment_anything/mask_decoder.py | 264 +++++++ .../foundationals/segment_anything/model.py | 170 +++++ .../segment_anything/prompt_encoder.py | 192 ++++++ .../segment_anything/transformer.py | 158 +++++ imaginairy/vendored/refiners/py.typed | 0 imaginairy/vendored/refiners/readme.txt | 1 + requirements-dev.txt | 12 +- setup.py | 5 +- 86 files changed, 9548 insertions(+), 42 deletions(-) create mode 100644 imaginairy/vendored/refiners/LICENSE rename imaginairy/vendored/{ => refiners}/__init__.py (100%) create mode 100644 imaginairy/vendored/refiners/fluxion/__init__.py create mode 100644 imaginairy/vendored/refiners/fluxion/adapters/__init__.py create mode 100644 imaginairy/vendored/refiners/fluxion/adapters/adapter.py create mode 100644 imaginairy/vendored/refiners/fluxion/adapters/lora.py create mode 100644 imaginairy/vendored/refiners/fluxion/context.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/__init__.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/activations.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/attentions.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/basics.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/chain.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/conv.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/converter.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/embedding.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/linear.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/maxpool.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/module.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/norm.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/padding.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/pixelshuffle.py create mode 100644 imaginairy/vendored/refiners/fluxion/layers/sampling.py create mode 100644 imaginairy/vendored/refiners/fluxion/model_converter.py create mode 100644 imaginairy/vendored/refiners/fluxion/utils.py create mode 100644 imaginairy/vendored/refiners/foundationals/__init__.py create mode 100644 imaginairy/vendored/refiners/foundationals/clip/__init__.py create mode 100644 imaginairy/vendored/refiners/foundationals/clip/bpe_simple_vocab_16e6.txt.gz create mode 100644 imaginairy/vendored/refiners/foundationals/clip/common.py create mode 100644 imaginairy/vendored/refiners/foundationals/clip/concepts.py create mode 100644 imaginairy/vendored/refiners/foundationals/clip/image_encoder.py create mode 100644 imaginairy/vendored/refiners/foundationals/clip/text_encoder.py create mode 100644 imaginairy/vendored/refiners/foundationals/clip/tokenizer.py create mode 100644 imaginairy/vendored/refiners/foundationals/dinov2/__init__.py create mode 100644 imaginairy/vendored/refiners/foundationals/dinov2/dinov2.py create mode 100644 imaginairy/vendored/refiners/foundationals/dinov2/vit.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/__init__.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/auto_encoder.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/cross_attention.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/freeu.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/image_prompt.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/lora.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/model.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/multi_diffusion.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/preprocessors/__init__.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/preprocessors/informative_drawings.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/range_adapter.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/reference_only_control.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/restart.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/__init__.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/ddim.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/ddpm.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/dpm_solver.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/scheduler.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/self_attention_guidance.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/__init__.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/controlnet.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/image_prompt.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/multi_diffusion.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/self_attention_guidance.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/t2i_adapter.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/unet.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/__init__.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/image_prompt.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/model.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/multi_diffusion.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/self_attention_guidance.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/t2i_adapter.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/text_encoder.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/unet.py create mode 100644 imaginairy/vendored/refiners/foundationals/latent_diffusion/t2i_adapter.py create mode 100644 imaginairy/vendored/refiners/foundationals/segment_anything/__init__.py create mode 100644 imaginairy/vendored/refiners/foundationals/segment_anything/image_encoder.py create mode 100644 imaginairy/vendored/refiners/foundationals/segment_anything/mask_decoder.py create mode 100644 imaginairy/vendored/refiners/foundationals/segment_anything/model.py create mode 100644 imaginairy/vendored/refiners/foundationals/segment_anything/prompt_encoder.py create mode 100644 imaginairy/vendored/refiners/foundationals/segment_anything/transformer.py create mode 100644 imaginairy/vendored/refiners/py.typed create mode 100644 imaginairy/vendored/refiners/readme.txt diff --git a/Makefile b/Makefile index e63a926..4ceef51 100644 --- a/Makefile +++ b/Makefile @@ -201,6 +201,19 @@ vendorize_normal_map: make af +vendorize_refiners: + export REPO=git@github.com:finegrain-ai/refiners.git PKG=refiners COMMIT=20c229903f53d05dc1c44659ec97603660ef964c && \ + make download_repo REPO=$$REPO PKG=$$PKG COMMIT=$$COMMIT && \ + mkdir -p ./imaginairy/vendored/$$PKG && \ + rm -rf ./imaginairy/vendored/$$PKG/* && \ + cp -R ./downloads/refiners/src/refiners/* ./imaginairy/vendored/$$PKG/ && \ + cp ./downloads/refiners/LICENSE ./imaginairy/vendored/$$PKG/ && \ + rm -rf ./imaginairy/vendored/$$PKG/training_utils && \ + echo "vendored from $$REPO @ $$COMMIT" | tee ./imaginairy/vendored/$$PKG/readme.txt + find ./imaginairy/vendored/refiners/ -type f -name "*.py" -exec sed -i '' 's/from refiners/from imaginairy.vendored.refiners/g' {} + &&\ + find ./imaginairy/vendored/refiners/ -type f -name "*.py" -exec sed -i '' 's/import refiners/import imaginairy.vendored.refiners/g' {} + &&\ + make af + vendorize: ## vendorize a github repo. `make vendorize REPO=git@github.com:openai/CLIP.git PKG=clip` mkdir -p ./downloads diff --git a/imaginairy/api/generate_compvis.py b/imaginairy/api/generate_compvis.py index a7f87ed..7e1106a 100644 --- a/imaginairy/api/generate_compvis.py +++ b/imaginairy/api/generate_compvis.py @@ -12,17 +12,17 @@ if TYPE_CHECKING: logger = logging.getLogger(__name__) -def _generate_single_image_compvis( +def _generate_single_image( prompt: "ImaginePrompt", debug_img_callback=None, progress_img_callback=None, progress_img_interval_steps=3, progress_img_interval_min_s=0.1, - half_mode=None, add_caption=False, # controlnet, finetune, naive, auto inpaint_method="finetune", return_latent=False, + dtype=None, ): import torch.nn from PIL import Image, ImageOps @@ -96,7 +96,7 @@ def _generate_single_image_compvis( weights_location=prompt.model_weights, config_path=prompt.model_architecture, control_weights_locations=control_modes, - half_mode=half_mode, + half_mode=dtype == torch.float16, for_inpainting=for_inpainting and inpaint_method == "finetune", ) is_controlnet_model = hasattr(model, "control_key") @@ -502,7 +502,6 @@ def _generate_composition_image( ): from PIL import Image - from imaginairy.api.generate_refiners import generate_single_image from imaginairy.utils import default, get_default_dtype cutoff = normalize_image_size(cutoff) @@ -532,7 +531,7 @@ def _generate_composition_image( }, ) - result = generate_single_image(composition_prompt, dtype=dtype) + result = _generate_single_image(composition_prompt, dtype=dtype) img = result.images["generated"] while img.width < target_width: from imaginairy.enhancers.upscale_realesrgan import upscale_image diff --git a/imaginairy/api/generate_refiners.py b/imaginairy/api/generate_refiners.py index a933fe1..254d9cf 100644 --- a/imaginairy/api/generate_refiners.py +++ b/imaginairy/api/generate_refiners.py @@ -27,7 +27,6 @@ def generate_single_image( ): import torch.nn from PIL import Image, ImageOps - from refiners.foundationals.latent_diffusion.schedulers import DDIM, DPMSolver from tqdm import tqdm from imaginairy.api.generate import ( @@ -61,6 +60,10 @@ def generate_single_image( prepare_image_for_outpaint, ) from imaginairy.utils.safety import create_safety_score + from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers import ( + DDIM, + DPMSolver, + ) if dtype is None: dtype = torch.float16 @@ -513,7 +516,9 @@ def prep_control_input( if not control_config: msg = f"Unknown control mode: {control_input.mode}" raise ValueError(msg) - from refiners.foundationals.latent_diffusion import SD1ControlnetAdapter + from imaginairy.vendored.refiners.foundationals.latent_diffusion import ( + SD1ControlnetAdapter, + ) controlnet = SD1ControlnetAdapter( # type: ignore name=control_input.mode, diff --git a/imaginairy/modules/refiners_sd.py b/imaginairy/modules/refiners_sd.py index 7eafce1..62e483d 100644 --- a/imaginairy/modules/refiners_sd.py +++ b/imaginairy/modules/refiners_sd.py @@ -5,42 +5,52 @@ import math from functools import lru_cache from typing import Any, List, Literal -import refiners.fluxion.layers as fl import torch -from refiners.fluxion.layers.attentions import ScaledDotProductAttention -from refiners.fluxion.layers.chain import ChainError -from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL -from refiners.foundationals.latent_diffusion.model import ( +from torch import Tensor, device as Device, dtype as DType, nn +from torch.nn import functional as F + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.schema import WeightedPrompt +from imaginairy.utils.feather_tile import rebuild_image, tile_image +from imaginairy.vendored.refiners.fluxion.layers.attentions import ( + ScaledDotProductAttention, +) +from imaginairy.vendored.refiners.fluxion.layers.chain import ChainError +from imaginairy.vendored.refiners.foundationals.clip.text_encoder import ( + CLIPTextEncoderL, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.model import ( TLatentDiffusionModel, ) -from refiners.foundationals.latent_diffusion.schedulers.ddim import DDIM -from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler -from refiners.foundationals.latent_diffusion.self_attention_guidance import ( +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.ddim import ( + DDIM, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.scheduler import ( + Scheduler, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.self_attention_guidance import ( SelfAttentionMap, ) -from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import ( +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import ( Controlnet, SD1ControlnetAdapter, ) -from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import ( +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.model import ( SD1Autoencoder, SD1UNet, StableDiffusion_1 as RefinerStableDiffusion_1, StableDiffusion_1_Inpainting as RefinerStableDiffusion_1_Inpainting, ) -from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import ( +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import ( SDXLAutoencoder, StableDiffusion_XL as RefinerStableDiffusion_XL, ) -from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import ( +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import ( DoubleTextEncoder, ) -from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet -from torch import Tensor, device as Device, dtype as DType, nn -from torch.nn import functional as F - -from imaginairy.schema import WeightedPrompt -from imaginairy.utils.feather_tile import rebuild_image, tile_image +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import ( + SDXLUNet, +) from imaginairy.weight_management.conversion import cast_weights logger = logging.getLogger(__name__) @@ -375,8 +385,8 @@ class StableDiffusion_1_Inpainting(TileModeMixin, RefinerStableDiffusion_1_Inpai import torch total_weight = sum(wp.weight for wp in prompts) - if str(self.clip_text_encoder.device) == "cpu": - self.clip_text_encoder = self.clip_text_encoder.to(dtype=torch.float32) + if str(self.clip_text_encoder.device) == "cpu": # type: ignore + self.clip_text_encoder = self.clip_text_encoder.to(dtype=torch.float32) # type: ignore conditioning = sum( self.clip_text_encoder(wp.text) * (wp.weight / total_weight) for wp in prompts diff --git a/imaginairy/utils/model_manager.py b/imaginairy/utils/model_manager.py index d2036b6..3705a96 100644 --- a/imaginairy/utils/model_manager.py +++ b/imaginairy/utils/model_manager.py @@ -16,9 +16,6 @@ from huggingface_hub import ( try_to_load_from_cache, ) from omegaconf import OmegaConf -from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL -from refiners.foundationals.latent_diffusion import DoubleTextEncoder, SD1UNet, SDXLUNet -from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel from safetensors.torch import load_file from imaginairy import config as iconfig @@ -29,6 +26,17 @@ from imaginairy.utils import clear_gpu_cache, get_device, instantiate_from_confi from imaginairy.utils.model_cache import memory_managed_model from imaginairy.utils.named_resolutions import normalize_image_size from imaginairy.utils.paths import PKG_ROOT +from imaginairy.vendored.refiners.foundationals.clip.text_encoder import ( + CLIPTextEncoderL, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion import ( + DoubleTextEncoder, + SD1UNet, + SDXLUNet, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.model import ( + LatentDiffusionModel, +) from imaginairy.weight_management import translators logger = logging.getLogger(__name__) @@ -823,7 +831,7 @@ def open_weights(filepath, device=None): device = get_device() if "safetensor" in filepath.lower(): - from refiners.fluxion.utils import safe_open + from imaginairy.vendored.refiners.fluxion.utils import safe_open with safe_open(path=filepath, framework="pytorch", device=device) as tensors: state_dict = { diff --git a/imaginairy/vendored/refiners/LICENSE b/imaginairy/vendored/refiners/LICENSE new file mode 100644 index 0000000..6dc5ab1 --- /dev/null +++ b/imaginairy/vendored/refiners/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Lagon Technologies + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/imaginairy/vendored/__init__.py b/imaginairy/vendored/refiners/__init__.py similarity index 100% rename from imaginairy/vendored/__init__.py rename to imaginairy/vendored/refiners/__init__.py diff --git a/imaginairy/vendored/refiners/fluxion/__init__.py b/imaginairy/vendored/refiners/fluxion/__init__.py new file mode 100644 index 0000000..f6623be --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/__init__.py @@ -0,0 +1,3 @@ +from imaginairy.vendored.refiners.fluxion.utils import load_from_safetensors, manual_seed, norm, pad, save_to_safetensors + +__all__ = ["norm", "manual_seed", "save_to_safetensors", "load_from_safetensors", "pad"] diff --git a/imaginairy/vendored/refiners/fluxion/adapters/__init__.py b/imaginairy/vendored/refiners/fluxion/adapters/__init__.py new file mode 100644 index 0000000..5b183a0 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/adapters/__init__.py @@ -0,0 +1,3 @@ +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter + +__all__ = ["Adapter"] diff --git a/imaginairy/vendored/refiners/fluxion/adapters/adapter.py b/imaginairy/vendored/refiners/fluxion/adapters/adapter.py new file mode 100644 index 0000000..d885f90 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/adapters/adapter.py @@ -0,0 +1,101 @@ +import contextlib +from typing import Any, Generic, Iterator, TypeVar + +import imaginairy.vendored.refiners.fluxion.layers as fl + +T = TypeVar("T", bound=fl.Module) +TAdapter = TypeVar("TAdapter", bound="Adapter[Any]") # Self (see PEP 673) + + +class Adapter(Generic[T]): + # we store _target into a one element list to avoid pytorch thinking it is a submodule + _target: "list[T]" + + def __init_subclass__(cls, **kwargs: Any) -> None: + super().__init_subclass__(**kwargs) + assert issubclass(cls, fl.Chain), f"Adapter {cls.__name__} must be a Chain" + + @property + def target(self) -> T: + return self._target[0] + + @contextlib.contextmanager + def setup_adapter(self, target: T) -> Iterator[None]: + assert isinstance(self, fl.Chain) + assert (not hasattr(self, "_modules")) or ( + len(self) == 0 + ), "Call the Chain constructor in the setup_adapter context." + self._target = [target] + + if not isinstance(self.target, fl.ContextModule): + yield + return + + _old_can_refresh_parent = target._can_refresh_parent + target._can_refresh_parent = False + yield + target._can_refresh_parent = _old_can_refresh_parent + + def inject(self: TAdapter, parent: fl.Chain | None = None) -> TAdapter: + assert isinstance(self, fl.Chain) + + if (parent is None) and isinstance(self.target, fl.ContextModule): + parent = self.target.parent + if parent is not None: + assert isinstance(parent, fl.Chain), f"{self.target} has invalid parent {parent}" + + target_parent = self.find_parent(self.target) + + if parent is None: + if isinstance(self.target, fl.ContextModule): + self.target._set_parent(target_parent) # type: ignore[reportPrivateUsage] + return self + + # In general, `true_parent` is `parent`. We do this to support multiple adaptation, + # i.e. initializing two adapters before injecting them. + true_parent = parent.ensure_find_parent(self.target) + true_parent.replace( + old_module=self.target, + new_module=self, + old_module_parent=target_parent, + ) + return self + + def eject(self) -> None: + assert isinstance(self, fl.Chain) + + # In general, the "actual target" is the target. + # Here we deal with the edge case where the target + # is part of the replacement block and has been adapted by + # another adapter after this one. For instance, this is the + # case when stacking Controlnets. + actual_target = lookup_top_adapter(self, self.target) + + if (parent := self.parent) is None: + if isinstance(actual_target, fl.ContextModule): + actual_target._set_parent(None) # type: ignore[reportPrivateUsage] + else: + parent.replace(old_module=self, new_module=actual_target) + + def _pre_structural_copy(self) -> None: + if isinstance(self.target, fl.Chain): + raise RuntimeError("Chain adapters typically cannot be copied, eject them first.") + + def _post_structural_copy(self: TAdapter, source: TAdapter) -> None: + self._target = [source.target] + + +def lookup_top_adapter(top: fl.Chain, target: fl.Module) -> fl.Module: + """Lookup and return last adapter in parents tree (or target if none).""" + + target_parent = top.find_parent(target) + if (target_parent is None) or (target_parent == top): + return target + + r, p = target, target_parent + while p != top: + if isinstance(p, Adapter): + r = p + assert p.parent, f"parent tree of {top} is broken" + p = p.parent + return r diff --git a/imaginairy/vendored/refiners/fluxion/adapters/lora.py b/imaginairy/vendored/refiners/fluxion/adapters/lora.py new file mode 100644 index 0000000..3dd4c0b --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/adapters/lora.py @@ -0,0 +1,130 @@ +from typing import Any, Generic, Iterable, TypeVar + +from torch import Tensor, device as Device, dtype as DType +from torch.nn import Parameter as TorchParameter +from torch.nn.init import normal_, zeros_ + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter + +T = TypeVar("T", bound=fl.Chain) +TLoraAdapter = TypeVar("TLoraAdapter", bound="LoraAdapter[Any]") # Self (see PEP 673) + + +class Lora(fl.Chain): + def __init__( + self, + in_features: int, + out_features: int, + rank: int = 16, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.in_features = in_features + self.out_features = out_features + self.rank = rank + self.scale: float = 1.0 + + super().__init__( + fl.Linear(in_features=in_features, out_features=rank, bias=False, device=device, dtype=dtype), + fl.Linear(in_features=rank, out_features=out_features, bias=False, device=device, dtype=dtype), + fl.Lambda(func=self.scale_outputs), + ) + + normal_(tensor=self.Linear_1.weight, std=1 / self.rank) + zeros_(tensor=self.Linear_2.weight) + + def scale_outputs(self, x: Tensor) -> Tensor: + return x * self.scale + + def set_scale(self, scale: float) -> None: + self.scale = scale + + def load_weights(self, down_weight: Tensor, up_weight: Tensor) -> None: + self.Linear_1.weight = TorchParameter(down_weight.to(device=self.device, dtype=self.dtype)) + self.Linear_2.weight = TorchParameter(up_weight.to(device=self.device, dtype=self.dtype)) + + @property + def up_weight(self) -> Tensor: + return self.Linear_2.weight.data + + @property + def down_weight(self) -> Tensor: + return self.Linear_1.weight.data + + +class SingleLoraAdapter(fl.Sum, Adapter[fl.Linear]): + def __init__( + self, + target: fl.Linear, + rank: int = 16, + scale: float = 1.0, + ) -> None: + self.in_features = target.in_features + self.out_features = target.out_features + self.rank = rank + self.scale = scale + with self.setup_adapter(target): + super().__init__( + target, + Lora( + in_features=target.in_features, + out_features=target.out_features, + rank=rank, + device=target.device, + dtype=target.dtype, + ), + ) + self.Lora.set_scale(scale=scale) + + +class LoraAdapter(Generic[T], fl.Chain, Adapter[T]): + def __init__( + self, + target: T, + sub_targets: Iterable[tuple[fl.Linear, fl.Chain]], + rank: int | None = None, + scale: float = 1.0, + weights: list[Tensor] | None = None, + ) -> None: + with self.setup_adapter(target): + super().__init__(target) + + if weights is not None: + assert len(weights) % 2 == 0 + weights_rank = weights[0].shape[1] + if rank is None: + rank = weights_rank + else: + assert rank == weights_rank + + assert rank is not None, "either pass a rank or weights" + + self.sub_targets = sub_targets + self.sub_adapters: list[tuple[SingleLoraAdapter, fl.Chain]] = [] + + for linear, parent in self.sub_targets: + self.sub_adapters.append((SingleLoraAdapter(target=linear, rank=rank, scale=scale), parent)) + + if weights is not None: + assert len(self.sub_adapters) == (len(weights) // 2) + for i, (adapter, _) in enumerate(self.sub_adapters): + lora = adapter.Lora + assert ( + lora.rank == weights[i * 2].shape[1] + ), f"Rank of Lora layer {lora.rank} must match shape of weights {weights[i*2].shape[1]}" + adapter.Lora.load_weights(up_weight=weights[i * 2], down_weight=weights[i * 2 + 1]) + + def inject(self: TLoraAdapter, parent: fl.Chain | None = None) -> TLoraAdapter: + for adapter, adapter_parent in self.sub_adapters: + adapter.inject(adapter_parent) + return super().inject(parent) + + def eject(self) -> None: + for adapter, _ in self.sub_adapters: + adapter.eject() + super().eject() + + @property + def weights(self) -> list[Tensor]: + return [w for adapter, _ in self.sub_adapters for w in [adapter.Lora.up_weight, adapter.Lora.down_weight]] diff --git a/imaginairy/vendored/refiners/fluxion/context.py b/imaginairy/vendored/refiners/fluxion/context.py new file mode 100644 index 0000000..e8871e2 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/context.py @@ -0,0 +1,53 @@ +from typing import Any + +from torch import Tensor + +Context = dict[str, Any] +Contexts = dict[str, Context] + + +class ContextProvider: + def __init__(self) -> None: + self.contexts: Contexts = {} + + def set_context(self, key: str, value: Context) -> None: + self.contexts[key] = value + + def get_context(self, key: str) -> Any: + return self.contexts.get(key) + + def update_contexts(self, new_contexts: Contexts) -> None: + for key, value in new_contexts.items(): + if key not in self.contexts: + self.contexts[key] = value + else: + self.contexts[key].update(value) + + @staticmethod + def create(contexts: Contexts) -> "ContextProvider": + provider = ContextProvider() + provider.update_contexts(contexts) + return provider + + def __add__(self, other: "ContextProvider") -> "ContextProvider": + self.contexts.update(other.contexts) + return self + + def __lshift__(self, other: "ContextProvider") -> "ContextProvider": + other.contexts.update(self.contexts) + return other + + def __bool__(self) -> bool: + return bool(self.contexts) + + def _get_repr_for_value(self, value: Any) -> str: + if isinstance(value, Tensor): + return f"Tensor(shape={value.shape}, dtype={value.dtype}, device={value.device})" + return repr(value) + + def _get_repr_for_dict(self, context_dict: Context) -> dict[str, str]: + return {key: self._get_repr_for_value(value) for key, value in context_dict.items()} + + def __repr__(self) -> str: + contexts_repr = {key: self._get_repr_for_dict(value) for key, value in self.contexts.items()} + return f"{self.__class__.__name__}(contexts={contexts_repr})" diff --git a/imaginairy/vendored/refiners/fluxion/layers/__init__.py b/imaginairy/vendored/refiners/fluxion/layers/__init__.py new file mode 100644 index 0000000..0e6223b --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/__init__.py @@ -0,0 +1,110 @@ +from imaginairy.vendored.refiners.fluxion.layers.activations import GLU, ApproximateGeLU, GeLU, ReLU, Sigmoid, SiLU +from imaginairy.vendored.refiners.fluxion.layers.attentions import Attention, SelfAttention, SelfAttention2d +from imaginairy.vendored.refiners.fluxion.layers.basics import ( + Buffer, + Chunk, + Cos, + Flatten, + GetArg, + Identity, + Multiply, + Parameter, + Permute, + Reshape, + Sin, + Slicing, + Squeeze, + Transpose, + Unbind, + Unflatten, + Unsqueeze, + View, +) +from imaginairy.vendored.refiners.fluxion.layers.chain import ( + Breakpoint, + Chain, + Concatenate, + Distribute, + Lambda, + Matmul, + Parallel, + Passthrough, + Residual, + Return, + SetContext, + Sum, + UseContext, +) +from imaginairy.vendored.refiners.fluxion.layers.conv import Conv2d, ConvTranspose2d +from imaginairy.vendored.refiners.fluxion.layers.converter import Converter +from imaginairy.vendored.refiners.fluxion.layers.embedding import Embedding +from imaginairy.vendored.refiners.fluxion.layers.linear import Linear, MultiLinear +from imaginairy.vendored.refiners.fluxion.layers.maxpool import MaxPool1d, MaxPool2d +from imaginairy.vendored.refiners.fluxion.layers.module import ContextModule, Module, WeightedModule +from imaginairy.vendored.refiners.fluxion.layers.norm import GroupNorm, InstanceNorm2d, LayerNorm, LayerNorm2d +from imaginairy.vendored.refiners.fluxion.layers.padding import ReflectionPad2d +from imaginairy.vendored.refiners.fluxion.layers.pixelshuffle import PixelUnshuffle +from imaginairy.vendored.refiners.fluxion.layers.sampling import Downsample, Interpolate, Upsample + +__all__ = [ + "Embedding", + "LayerNorm", + "GroupNorm", + "LayerNorm2d", + "InstanceNorm2d", + "GeLU", + "GLU", + "SiLU", + "ReLU", + "ApproximateGeLU", + "Sigmoid", + "Attention", + "SelfAttention", + "SelfAttention2d", + "Identity", + "GetArg", + "View", + "Flatten", + "Unflatten", + "Transpose", + "Permute", + "Squeeze", + "Unsqueeze", + "Reshape", + "Slicing", + "Parameter", + "Sin", + "Cos", + "Chunk", + "Multiply", + "Unbind", + "Matmul", + "Buffer", + "Lambda", + "Return", + "Sum", + "Residual", + "Chain", + "UseContext", + "SetContext", + "Parallel", + "Distribute", + "Passthrough", + "Breakpoint", + "Concatenate", + "Conv2d", + "ConvTranspose2d", + "Linear", + "MultiLinear", + "Downsample", + "Upsample", + "Module", + "WeightedModule", + "ContextModule", + "Interpolate", + "ReflectionPad2d", + "PixelUnshuffle", + "Converter", + "MaxPool1d", + "MaxPool2d", +] diff --git a/imaginairy/vendored/refiners/fluxion/layers/activations.py b/imaginairy/vendored/refiners/fluxion/layers/activations.py new file mode 100644 index 0000000..ac9df64 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/activations.py @@ -0,0 +1,77 @@ +from torch import Tensor, sigmoid +from torch.nn.functional import ( + gelu, # type: ignore + silu, +) + +from imaginairy.vendored.refiners.fluxion.layers.module import Module + + +class Activation(Module): + def __init__(self) -> None: + super().__init__() + + +class SiLU(Activation): + def __init__(self) -> None: + super().__init__() + + def forward(self, x: Tensor) -> Tensor: + return silu(x) # type: ignore + + +class ReLU(Activation): + def __init__(self) -> None: + super().__init__() + + def forward(self, x: Tensor) -> Tensor: + return x.relu() + + +class GeLU(Activation): + def __init__(self) -> None: + super().__init__() + + def forward(self, x: Tensor) -> Tensor: + return gelu(x) # type: ignore + + +class ApproximateGeLU(Activation): + """ + The approximate form of Gaussian Error Linear Unit (GELU) + For more details, see section 2: https://arxiv.org/abs/1606.08415 + """ + + def __init__(self) -> None: + super().__init__() + + def forward(self, x: Tensor) -> Tensor: + return x * sigmoid(1.702 * x) + + +class Sigmoid(Activation): + def __init__(self) -> None: + super().__init__() + + def forward(self, x: Tensor) -> Tensor: + return x.sigmoid() + + +class GLU(Activation): + """ + Gated Linear Unit activation layer. + + See https://arxiv.org/abs/2002.05202v1 for details. + """ + + def __init__(self, activation: Activation) -> None: + super().__init__() + self.activation = activation + + def __repr__(self): + return f"{self.__class__.__name__}(activation={self.activation})" + + def forward(self, x: Tensor) -> Tensor: + assert x.shape[-1] % 2 == 0, "Non-batch input dimension must be divisible by 2" + output, gate = x.chunk(2, dim=-1) + return output * self.activation(gate) diff --git a/imaginairy/vendored/refiners/fluxion/layers/attentions.py b/imaginairy/vendored/refiners/fluxion/layers/attentions.py new file mode 100644 index 0000000..db04269 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/attentions.py @@ -0,0 +1,246 @@ +import math + +import torch +from jaxtyping import Float +from torch import Tensor, device as Device, dtype as DType +from torch.nn.functional import scaled_dot_product_attention as _scaled_dot_product_attention # type: ignore + +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.fluxion.layers.basics import Identity +from imaginairy.vendored.refiners.fluxion.layers.chain import Chain, Distribute, Lambda, Parallel +from imaginairy.vendored.refiners.fluxion.layers.linear import Linear +from imaginairy.vendored.refiners.fluxion.layers.module import Module + + +def scaled_dot_product_attention( + query: Float[Tensor, "batch source_sequence_length dim"], + key: Float[Tensor, "batch target_sequence_length dim"], + value: Float[Tensor, "batch target_sequence_length dim"], + is_causal: bool = False, +) -> Float[Tensor, "batch source_sequence_length dim"]: + return _scaled_dot_product_attention(query, key, value, is_causal=is_causal) # type: ignore + + +def sparse_dot_product_attention_non_optimized( + query: Float[Tensor, "batch source_sequence_length dim"], + key: Float[Tensor, "batch target_sequence_length dim"], + value: Float[Tensor, "batch target_sequence_length dim"], + is_causal: bool = False, +) -> Float[Tensor, "batch source_sequence_length dim"]: + if is_causal: + # TODO: implement causal attention + raise NotImplementedError("Causal attention for non_optimized attention is not yet implemented") + _, _, _, dim = query.shape + attention = query @ key.permute(0, 1, 3, 2) + attention = attention / math.sqrt(dim) + attention = torch.softmax(input=attention, dim=-1) + return attention @ value + + +class ScaledDotProductAttention(Module): + def __init__( + self, + num_heads: int = 1, + is_causal: bool | None = None, + is_optimized: bool = True, + slice_size: int | None = None, + ) -> None: + super().__init__() + self.num_heads = num_heads + self.is_causal = is_causal + self.is_optimized = is_optimized + self.slice_size = slice_size + self.dot_product = ( + scaled_dot_product_attention if self.is_optimized else sparse_dot_product_attention_non_optimized + ) + + def forward( + self, + query: Float[Tensor, "batch num_queries embedding_dim"], + key: Float[Tensor, "batch num_keys embedding_dim"], + value: Float[Tensor, "batch num_values embedding_dim"], + is_causal: bool | None = None, + ) -> Float[Tensor, "batch num_queries dim"]: + if self.slice_size is None: + return self._process_attention(query, key, value, is_causal) + + return self._sliced_attention(query, key, value, is_causal=is_causal, slice_size=self.slice_size) + + def _sliced_attention( + self, + query: Float[Tensor, "batch num_queries embedding_dim"], + key: Float[Tensor, "batch num_keys embedding_dim"], + value: Float[Tensor, "batch num_values embedding_dim"], + slice_size: int, + is_causal: bool | None = None, + ) -> Float[Tensor, "batch num_queries dim"]: + _, num_queries, _ = query.shape + output = torch.zeros_like(query) + for start_idx in range(0, num_queries, slice_size): + end_idx = min(start_idx + slice_size, num_queries) + output[:, start_idx:end_idx, :] = self._process_attention( + query[:, start_idx:end_idx, :], key, value, is_causal + ) + return output + + def _process_attention( + self, + query: Float[Tensor, "batch num_queries embedding_dim"], + key: Float[Tensor, "batch num_keys embedding_dim"], + value: Float[Tensor, "batch num_values embedding_dim"], + is_causal: bool | None = None, + ) -> Float[Tensor, "batch num_queries dim"]: + return self.merge_multi_head( + x=self.dot_product( + query=self.split_to_multi_head(query), + key=self.split_to_multi_head(key), + value=self.split_to_multi_head(value), + is_causal=( + is_causal if is_causal is not None else (self.is_causal if self.is_causal is not None else False) + ), + ) + ) + + def split_to_multi_head( + self, x: Float[Tensor, "batch_size sequence_length embedding_dim"] + ) -> Float[Tensor, "batch_size num_heads sequence_length (embedding_dim//num_heads)"]: + assert ( + len(x.shape) == 3 + ), f"Expected tensor with shape (batch_size sequence_length embedding_dim), got {x.shape}" + assert ( + x.shape[-1] % self.num_heads == 0 + ), f"Embedding dim (x.shape[-1]={x.shape[-1]}) must be divisible by num heads" + return x.reshape(x.shape[0], x.shape[1], self.num_heads, x.shape[-1] // self.num_heads).transpose(1, 2) + + def merge_multi_head( + self, x: Float[Tensor, "batch_size num_heads sequence_length heads_dim"] + ) -> Float[Tensor, "batch_size sequence_length heads_dim * num_heads"]: + return x.transpose(1, 2).reshape(x.shape[0], x.shape[2], self.num_heads * x.shape[-1]) + + +class Attention(Chain): + def __init__( + self, + embedding_dim: int, + num_heads: int = 1, + key_embedding_dim: int | None = None, + value_embedding_dim: int | None = None, + inner_dim: int | None = None, + use_bias: bool = True, + is_causal: bool | None = None, + is_optimized: bool = True, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + assert ( + embedding_dim % num_heads == 0 + ), f"embedding_dim {embedding_dim} must be divisible by num_heads {num_heads}" + self.embedding_dim = embedding_dim + self.num_heads = num_heads + self.heads_dim = embedding_dim // num_heads + self.key_embedding_dim = key_embedding_dim or embedding_dim + self.value_embedding_dim = value_embedding_dim or embedding_dim + self.inner_dim = inner_dim or embedding_dim + self.use_bias = use_bias + self.is_causal = is_causal + self.is_optimized = is_optimized + super().__init__( + Distribute( + Linear( + in_features=self.embedding_dim, + out_features=self.inner_dim, + bias=self.use_bias, + device=device, + dtype=dtype, + ), + Linear( + in_features=self.key_embedding_dim, + out_features=self.inner_dim, + bias=self.use_bias, + device=device, + dtype=dtype, + ), + Linear( + in_features=self.value_embedding_dim, + out_features=self.inner_dim, + bias=self.use_bias, + device=device, + dtype=dtype, + ), + ), + ScaledDotProductAttention(num_heads=num_heads, is_causal=is_causal, is_optimized=is_optimized), + Linear( + in_features=self.inner_dim, + out_features=self.embedding_dim, + bias=True, + device=device, + dtype=dtype, + ), + ) + + +class SelfAttention(Attention): + def __init__( + self, + embedding_dim: int, + inner_dim: int | None = None, + num_heads: int = 1, + use_bias: bool = True, + is_causal: bool | None = None, + is_optimized: bool = True, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( + embedding_dim=embedding_dim, + inner_dim=inner_dim, + num_heads=num_heads, + use_bias=use_bias, + is_causal=is_causal, + is_optimized=is_optimized, + device=device, + dtype=dtype, + ) + self.insert(0, Parallel(Identity(), Identity(), Identity())) + + +class SelfAttention2d(SelfAttention): + def __init__( + self, + channels: int, + num_heads: int = 1, + use_bias: bool = True, + is_causal: bool | None = None, + is_optimized: bool = True, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + assert channels % num_heads == 0, f"channels {channels} must be divisible by num_heads {num_heads}" + self.channels = channels + super().__init__( + embedding_dim=channels, + num_heads=num_heads, + use_bias=use_bias, + is_causal=is_causal, + is_optimized=is_optimized, + device=device, + dtype=dtype, + ) + self.insert(0, Lambda(self.tensor_2d_to_sequence)) + self.append(Lambda(self.sequence_to_tensor_2d)) + + def init_context(self) -> Contexts: + return {"reshape": {"height": None, "width": None}} + + def tensor_2d_to_sequence( + self, x: Float[Tensor, "batch channels height width"] + ) -> Float[Tensor, "batch height*width channels"]: + height, width = x.shape[-2:] + self.set_context(context="reshape", value={"height": height, "width": width}) + return x.reshape(x.shape[0], x.shape[1], height * width).transpose(1, 2) + + def sequence_to_tensor_2d( + self, x: Float[Tensor, "batch sequence_length channels"] + ) -> Float[Tensor, "batch channels height width"]: + height, width = self.use_context("reshape").values() + return x.transpose(1, 2).reshape(x.shape[0], x.shape[2], height, width) diff --git a/imaginairy/vendored/refiners/fluxion/layers/basics.py b/imaginairy/vendored/refiners/fluxion/layers/basics.py new file mode 100644 index 0000000..e18b4d1 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/basics.py @@ -0,0 +1,207 @@ +import torch +from torch import Size, Tensor, device as Device, dtype as DType, randn +from torch.nn import Parameter as TorchParameter + +from imaginairy.vendored.refiners.fluxion.layers.module import Module, WeightedModule + + +class Identity(Module): + def __init__(self) -> None: + super().__init__() + + def forward(self, x: Tensor) -> Tensor: + return x + + +class View(Module): + def __init__(self, *shape: int) -> None: + super().__init__() + self.shape = shape + + def forward(self, x: Tensor) -> Tensor: + return x.view(*self.shape) + + +class GetArg(Module): + def __init__(self, index: int) -> None: + super().__init__() + self.index = index + + def forward(self, *args: Tensor) -> Tensor: + return args[self.index] + + +class Flatten(Module): + def __init__(self, start_dim: int = 0, end_dim: int = -1) -> None: + super().__init__() + self.start_dim = start_dim + self.end_dim = end_dim + + def forward(self, x: Tensor) -> Tensor: + return x.flatten(self.start_dim, self.end_dim) + + +class Unflatten(Module): + def __init__(self, dim: int) -> None: + super().__init__() + self.dim = dim + + def forward(self, x: Tensor, sizes: Size) -> Tensor: + return x.unflatten(self.dim, sizes) # type: ignore + + +class Reshape(Module): + """ + Reshape the input tensor to the given shape. The shape must be compatible with the input tensor shape. The batch + dimension is preserved. + """ + + def __init__(self, *shape: int) -> None: + super().__init__() + self.shape = shape + + def forward(self, x: Tensor) -> Tensor: + return x.reshape(x.shape[0], *self.shape) + + +class Transpose(Module): + def __init__(self, dim0: int, dim1: int) -> None: + super().__init__() + self.dim0 = dim0 + self.dim1 = dim1 + + def forward(self, x: Tensor) -> Tensor: + return x.transpose(self.dim0, self.dim1) + + +class Permute(Module): + def __init__(self, *dims: int) -> None: + super().__init__() + self.dims = dims + + def forward(self, x: Tensor) -> Tensor: + return x.permute(*self.dims) + + +class Slicing(Module): + def __init__(self, dim: int = 0, start: int = 0, end: int | None = None, step: int = 1) -> None: + super().__init__() + self.dim = dim + self.start = start + self.end = end + self.step = step + + def forward(self, x: Tensor) -> Tensor: + dim_size = x.shape[self.dim] + start = self.start if self.start >= 0 else dim_size + self.start + end = self.end or dim_size + end = end if end >= 0 else dim_size + end + start = max(min(start, dim_size), 0) + end = max(min(end, dim_size), 0) + if start >= end: + return self.get_empty_slice(x) + indices = torch.arange(start=start, end=end, step=self.step, device=x.device) + return x.index_select(self.dim, indices) + + def get_empty_slice(self, x: Tensor) -> Tensor: + """ + Return an empty slice of the same shape as the input tensor to mimic PyTorch's slicing behavior. + """ + shape = list(x.shape) + shape[self.dim] = 0 + return torch.empty(*shape, device=x.device) + + +class Squeeze(Module): + def __init__(self, dim: int) -> None: + super().__init__() + self.dim = dim + + def forward(self, x: Tensor) -> Tensor: + return x.squeeze(self.dim) + + +class Unsqueeze(Module): + def __init__(self, dim: int) -> None: + super().__init__() + self.dim = dim + + def forward(self, x: Tensor) -> Tensor: + return x.unsqueeze(self.dim) + + +class Unbind(Module): + def __init__(self, dim: int = 0) -> None: + self.dim = dim + super().__init__() + + def forward(self, x: Tensor) -> tuple[Tensor, ...]: + return x.unbind(dim=self.dim) # type: ignore + + +class Chunk(Module): + def __init__(self, chunks: int, dim: int = 0) -> None: + self.chunks = chunks + self.dim = dim + super().__init__() + + def forward(self, x: Tensor) -> tuple[Tensor, ...]: + return x.chunk(chunks=self.chunks, dim=self.dim) # type: ignore + + +class Sin(Module): + def forward(self, x: Tensor) -> Tensor: + return torch.sin(input=x) + + +class Cos(Module): + def forward(self, x: Tensor) -> Tensor: + return torch.cos(input=x) + + +class Multiply(Module): + def __init__(self, scale: float = 1.0, bias: float = 0.0) -> None: + super().__init__() + self.scale = scale + self.bias = bias + + def forward(self, x: Tensor) -> Tensor: + return self.scale * x + self.bias + + +class Parameter(WeightedModule): + """ + A layer that wraps a tensor as a parameter. This is useful to create a parameter that is not a weight or a bias. + """ + + def __init__(self, *dims: int, device: Device | str | None = None, dtype: DType | None = None) -> None: + super().__init__() + self.dims = dims + self.weight = TorchParameter(randn(*dims, device=device, dtype=dtype)) + + def forward(self, x: Tensor) -> Tensor: + return self.weight.expand(x.shape[0], *self.dims) + + +class Buffer(WeightedModule): + """ + A layer that wraps a tensor as a buffer. This is useful to create a buffer that is not a weight or a bias. + + Buffers are not trainable. + """ + + def __init__(self, *dims: int, device: Device | str | None = None, dtype: DType | None = None) -> None: + super().__init__() + self.dims = dims + self.register_buffer("buffer", randn(*dims, device=device, dtype=dtype)) + + @property + def device(self) -> Device: + return self.buffer.device + + @property + def dtype(self) -> DType: + return self.buffer.dtype + + def forward(self, _: Tensor) -> Tensor: + return self.buffer diff --git a/imaginairy/vendored/refiners/fluxion/layers/chain.py b/imaginairy/vendored/refiners/fluxion/layers/chain.py new file mode 100644 index 0000000..6fc1f35 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/chain.py @@ -0,0 +1,586 @@ +import inspect +import re +import sys +import traceback +from collections import defaultdict +from typing import Any, Callable, Iterable, Iterator, TypeVar, cast, overload + +import torch +from torch import Tensor, cat, device as Device, dtype as DType + +from imaginairy.vendored.refiners.fluxion.context import ContextProvider, Contexts +from imaginairy.vendored.refiners.fluxion.layers.module import ContextModule, Module, ModuleTree, WeightedModule +from imaginairy.vendored.refiners.fluxion.utils import summarize_tensor + +T = TypeVar("T", bound=Module) +TChain = TypeVar("TChain", bound="Chain") # because Self (PEP 673) is not in 3.10 + + +class Lambda(Module): + """Lambda is a wrapper around a callable object that allows it to be used as a PyTorch module.""" + + def __init__(self, func: Callable[..., Any]) -> None: + super().__init__() + self.func = func + + def forward(self, *args: Any) -> Any: + return self.func(*args) + + def __str__(self) -> str: + func_name = getattr(self.func, "__name__", "partial_function") + return f"Lambda({func_name}{str(inspect.signature(self.func))})" + + +def generate_unique_names( + modules: tuple[Module, ...], +) -> dict[str, Module]: + class_counts: dict[str, int] = {} + unique_names: list[tuple[str, Module]] = [] + for module in modules: + class_name = module.__class__.__name__ + class_counts[class_name] = class_counts.get(class_name, 0) + 1 + name_counter: dict[str, int] = {} + for module in modules: + class_name = module.__class__.__name__ + name_counter[class_name] = name_counter.get(class_name, 0) + 1 + unique_name = f"{class_name}_{name_counter[class_name]}" if class_counts[class_name] > 1 else class_name + unique_names.append((unique_name, module)) + return dict(unique_names) + + +class UseContext(ContextModule): + def __init__(self, context: str, key: str) -> None: + super().__init__() + self.context = context + self.key = key + self.func: Callable[[Any], Any] = lambda x: x + + def __call__(self, *args: Any) -> Any: + context = self.use_context(self.context) + assert context, f"context {self.context} is unset" + value = context.get(self.key) + assert value is not None, f"context entry {self.context}.{self.key} is unset" + return self.func(value) + + def __repr__(self): + return f"{self.__class__.__name__}(context={repr(self.context)}, key={repr(self.key)})" + + def compose(self, func: Callable[[Any], Any]) -> "UseContext": + self.func = func + return self + + +class SetContext(ContextModule): + """A Module that sets a context value when executed. + + The context need to pre exist in the context provider. + #TODO Is there a way to create the context if it doesn't exist? + """ + + def __init__(self, context: str, key: str, callback: Callable[[Any, Any], Any] | None = None) -> None: + super().__init__() + self.context = context + self.key = key + self.callback = callback + + def __call__(self, x: Tensor) -> Tensor: + if context := self.use_context(self.context): + if not self.callback: + context.update({self.key: x}) + else: + self.callback(context[self.key], x) + + return x + + def __repr__(self): + return f"{self.__class__.__name__}(context={repr(self.context)}, key={repr(self.key)})" + + +class ReturnException(Exception): + """Exception raised when a Return module is encountered.""" + + def __init__(self, value: Tensor): + self.value = value + + +class Return(Module): + """A Module that stops the execution of a Chain when encountered.""" + + def forward(self, x: Tensor): + raise ReturnException(x) + + +def structural_copy(m: T) -> T: + return m.structural_copy() if isinstance(m, ContextModule) else m + + +class ChainError(RuntimeError): + """Exception raised when an error occurs during the execution of a Chain.""" + + def __init__(self, message: str, /) -> None: + super().__init__(message) + + +class Chain(ContextModule): + _modules: dict[str, Module] + _provider: ContextProvider + _tag = "CHAIN" + + def __init__(self, *args: Module | Iterable[Module]) -> None: + super().__init__() + self._provider = ContextProvider() + modules = cast( + tuple[Module], + ( + tuple(args[0]) + if len(args) == 1 and isinstance(args[0], Iterable) and not isinstance(args[0], Chain) + else tuple(args) + ), + ) + + for module in modules: + # Violating this would mean a ContextModule ends up in two chains, + # with a single one correctly set as its parent. + assert ( + (not isinstance(module, ContextModule)) + or (not module._can_refresh_parent) + or (module.parent is None) + or (module.parent == self) + ), f"{module.__class__.__name__} already has parent {module.parent.__class__.__name__}" + + self._regenerate_keys(modules) + self._reset_context() + + for module in self: + if isinstance(module, ContextModule) and module.parent != self: + module._set_parent(self) + + def __setattr__(self, name: str, value: Any) -> None: + if isinstance(value, torch.nn.Module): + raise ValueError( + "Chain does not support setting modules by attribute. Instead, use a mutation method like `append` or" + " wrap it within a single element list to prevent pytorch from registering it as a submodule." + ) + super().__setattr__(name, value) + + @property + def provider(self) -> ContextProvider: + return self._provider + + def init_context(self) -> Contexts: + return {} + + def _register_provider(self, context: Contexts | None = None) -> None: + if context: + self._provider.update_contexts(context) + + for module in self: + if isinstance(module, Chain): + module._register_provider(context=self._provider.contexts) + + def _reset_context(self) -> None: + self._register_provider(self.init_context()) + + def set_context(self, context: str, value: Any) -> None: + self._provider.set_context(context, value) + self._register_provider() + + def _show_error_in_tree(self, name: str, /, max_lines: int = 20) -> str: + tree = ModuleTree(module=self) + classname_counter: dict[str, int] = defaultdict(int) + first_ancestor = self.get_parents()[-1] if self.get_parents() else self + + def find_state_dict_key(module: Module, /) -> str | None: + for key, layer in module.named_modules(): + if layer == self: + return ".".join((key, name)) + return None + + for child in tree: + classname, count = name.rsplit(sep="_", maxsplit=1) if "_" in name else (name, "1") + if child["class_name"] == classname: + classname_counter[classname] += 1 + if classname_counter[classname] == int(count): + state_dict_key = find_state_dict_key(first_ancestor) + child["value"] = f">>> {child['value']} | {state_dict_key}" + break + + tree_repr = tree._generate_tree_repr(tree.root, depth=3) # type: ignore[reportPrivateUsage] + + lines = tree_repr.split(sep="\n") + error_line_idx = next((idx for idx, line in enumerate(iterable=lines) if line.startswith(">>>")), 0) + + return ModuleTree.shorten_tree_repr(tree_repr, line_index=error_line_idx, max_lines=max_lines) + + @staticmethod + def _pretty_print_args(*args: Any) -> str: + """ + Flatten nested tuples and print tensors with their shape and other informations. + """ + + def _flatten_tuple(t: Tensor | tuple[Any, ...], /) -> list[Any]: + if isinstance(t, tuple): + return [item for subtuple in t for item in _flatten_tuple(subtuple)] + else: + return [t] + + flat_args = _flatten_tuple(args) + + return "\n".join( + [ + f"{idx}: {summarize_tensor(arg) if isinstance(arg, Tensor) else arg}" + for idx, arg in enumerate(iterable=flat_args) + ] + ) + + def _filter_traceback(self, *frames: traceback.FrameSummary) -> list[traceback.FrameSummary]: + patterns_to_exclude = [ + (r"torch/nn/modules/", r"^_call_impl$"), + (r"torch/nn/functional\.py", r""), + (r"refiners/fluxion/layers/", r"^_call_layer$"), + (r"refiners/fluxion/layers/", r"^forward$"), + (r"refiners/fluxion/layers/chain\.py", r""), + (r"", r"^_"), + ] + + def should_exclude(frame: traceback.FrameSummary, /) -> bool: + for filename_pattern, name_pattern in patterns_to_exclude: + if re.search(pattern=filename_pattern, string=frame.filename) and re.search( + pattern=name_pattern, string=frame.name + ): + return True + return False + + return [frame for frame in frames if not should_exclude(frame)] + + def _call_layer(self, layer: Module, name: str, /, *args: Any) -> Any: + try: + return layer(*args) + except Exception as e: + exc_type, _, exc_traceback = sys.exc_info() + assert exc_type + tb_list = traceback.extract_tb(tb=exc_traceback) + filtered_tb_list = self._filter_traceback(*tb_list) + formatted_tb = "".join(traceback.format_list(extracted_list=filtered_tb_list)) + pretty_args = Chain._pretty_print_args(args) + error_tree = self._show_error_in_tree(name) + + exception_str = re.sub(pattern=r"\n\s*\n", repl="\n", string=str(object=e)) + message = f"{formatted_tb}\n{exception_str}\n---------------\n{error_tree}\n{pretty_args}" + if "Error" not in exception_str: + message = f"{exc_type.__name__}:\n {message}" + + raise ChainError(message) from None + + def forward(self, *args: Any) -> Any: + result: tuple[Any] | Any = None + intermediate_args: tuple[Any, ...] = args + for name, layer in self._modules.items(): + result = self._call_layer(layer, name, *intermediate_args) + intermediate_args = (result,) if not isinstance(result, tuple) else result + + self._reset_context() + return result + + def _regenerate_keys(self, modules: Iterable[Module]) -> None: + self._modules = generate_unique_names(tuple(modules)) # type: ignore + + def __add__(self, other: "Chain | Module | list[Module]") -> "Chain": + if isinstance(other, Module): + other = Chain(other) + if isinstance(other, list): + other = Chain(*other) + return Chain(*self, *other) + + @overload + def __getitem__(self, key: int) -> Module: + ... + + @overload + def __getitem__(self, key: str) -> Module: + ... + + @overload + def __getitem__(self, key: slice) -> "Chain": + ... + + def __getitem__(self, key: int | str | slice) -> Module: + if isinstance(key, slice): + copy = self.structural_copy() + copy._regenerate_keys(modules=list(copy)[key]) + return copy + elif isinstance(key, str): + return self._modules[key] + else: + return list(self)[key] + + def __iter__(self) -> Iterator[Module]: + return iter(self._modules.values()) + + def __len__(self) -> int: + return len(self._modules) + + @property + def device(self) -> Device | None: + wm = self.find(WeightedModule) + return None if wm is None else wm.device + + @property + def dtype(self) -> DType | None: + wm = self.find(WeightedModule) + return None if wm is None else wm.dtype + + def _walk( + self, predicate: Callable[[Module, "Chain"], bool] | None = None, recurse: bool = False + ) -> Iterator[tuple[Module, "Chain"]]: + if predicate is None: + predicate = lambda _m, _p: True + for module in self: + try: + p = predicate(module, self) + except StopIteration: + continue + if p: + yield (module, self) + if not recurse: + continue + if isinstance(module, Chain): + yield from module.walk(predicate, recurse) + + @overload + def walk( + self, predicate: Callable[[Module, "Chain"], bool] | None = None, recurse: bool = False + ) -> Iterator[tuple[Module, "Chain"]]: + ... + + @overload + def walk(self, predicate: type[T], recurse: bool = False) -> Iterator[tuple[T, "Chain"]]: + ... + + def walk( + self, predicate: type[T] | Callable[[Module, "Chain"], bool] | None = None, recurse: bool = False + ) -> Iterator[tuple[T, "Chain"]] | Iterator[tuple[Module, "Chain"]]: + if isinstance(predicate, type): + return self._walk(lambda m, _: isinstance(m, predicate), recurse) + else: + return self._walk(predicate, recurse) + + def layers(self, layer_type: type[T], recurse: bool = False) -> Iterator[T]: + for module, _ in self.walk(layer_type, recurse): + yield module + + def find(self, layer_type: type[T]) -> T | None: + return next(self.layers(layer_type=layer_type), None) + + def ensure_find(self, layer_type: type[T]) -> T: + r = self.find(layer_type) + assert r is not None, f"could not find {layer_type} in {self}" + return r + + def find_parent(self, module: Module) -> "Chain | None": + if module in self: # avoid DFS-crawling the whole tree + return self + for _, parent in self.walk(lambda m, _: m == module): + return parent + return None + + def ensure_find_parent(self, module: Module) -> "Chain": + r = self.find_parent(module) + assert r is not None, f"could not find {module} in {self}" + return r + + def insert(self, index: int, module: Module) -> None: + if index < 0: + index = max(0, len(self._modules) + index + 1) + modules = list(self) + modules.insert(index, module) + self._regenerate_keys(modules) + if isinstance(module, ContextModule): + module._set_parent(self) + self._register_provider() + + def insert_before_type(self, module_type: type[Module], new_module: Module) -> None: + for i, module in enumerate(self): + if isinstance(module, module_type): + self.insert(i, new_module) + return + raise ValueError(f"No module of type {module_type.__name__} found in the chain.") + + def insert_after_type(self, module_type: type[Module], new_module: Module) -> None: + for i, module in enumerate(self): + if isinstance(module, module_type): + self.insert(i + 1, new_module) + return + raise ValueError(f"No module of type {module_type.__name__} found in the chain.") + + def append(self, module: Module) -> None: + self.insert(-1, module) + + def pop(self, index: int = -1) -> Module | tuple[Module]: + modules = list(self) + if index < 0: + index = len(modules) + index + if index < 0 or index >= len(modules): + raise IndexError("Index out of range.") + removed_module = modules.pop(index) + if isinstance(removed_module, ContextModule): + removed_module._set_parent(None) + self._regenerate_keys(modules) + return removed_module + + def remove(self, module: Module) -> None: + """Remove a module from the chain.""" + modules = list(self) + try: + modules.remove(module) + except ValueError: + raise ValueError(f"{module} is not in {self}") + self._regenerate_keys(modules) + if isinstance(module, ContextModule): + module._set_parent(None) + + def replace( + self, + old_module: Module, + new_module: Module, + old_module_parent: "Chain | None" = None, + ) -> None: + """Replace a module in the chain with a new module.""" + modules = list(self) + try: + modules[modules.index(old_module)] = new_module + except ValueError: + raise ValueError(f"{old_module} is not in {self}") + self._regenerate_keys(modules) + if isinstance(new_module, ContextModule): + new_module._set_parent(self) + if isinstance(old_module, ContextModule): + old_module._set_parent(old_module_parent) + + def structural_copy(self: TChain) -> TChain: + """Copy the structure of the Chain tree. + + This method returns a recursive copy of the Chain tree where all inner nodes + (instances of Chain and its subclasses) are duplicated and all leaves + (regular Modules) are not. + + Such copies can be adapted without disrupting the base model, but do not + require extra GPU memory since the weights are in the leaves and hence not copied. + """ + if hasattr(self, "_pre_structural_copy"): + self._pre_structural_copy() + + modules = [structural_copy(m) for m in self] + clone = super().structural_copy() + clone._provider = ContextProvider.create(clone.init_context()) + + for module in modules: + clone.append(module=module) + + if hasattr(clone, "_post_structural_copy"): + clone._post_structural_copy(self) + + return clone + + def _show_only_tag(self) -> bool: + return self.__class__ == Chain + + +class Parallel(Chain): + _tag = "PAR" + + def forward(self, *args: Any) -> tuple[Tensor, ...]: + return tuple([self._call_layer(module, name, *args) for name, module in self._modules.items()]) + + def _show_only_tag(self) -> bool: + return self.__class__ == Parallel + + +class Distribute(Chain): + _tag = "DISTR" + + def forward(self, *args: Any) -> tuple[Tensor, ...]: + n, m = len(args), len(self._modules) + assert n == m, f"Number of positional arguments ({n}) must match number of sub-modules ({m})." + return tuple([self._call_layer(module, name, arg) for arg, (name, module) in zip(args, self._modules.items())]) + + def _show_only_tag(self) -> bool: + return self.__class__ == Distribute + + +class Passthrough(Chain): + _tag = "PASS" + + def forward(self, *inputs: Any) -> Any: + super().forward(*inputs) + return inputs + + def _show_only_tag(self) -> bool: + return self.__class__ == Passthrough + + +class Sum(Chain): + _tag = "SUM" + + def forward(self, *inputs: Any) -> Any: + output = None + for layer in self: + layer_output: Any = layer(*inputs) + if isinstance(layer_output, tuple): + layer_output = sum(layer_output) # type: ignore + output = layer_output if output is None else output + layer_output + return output + + def _show_only_tag(self) -> bool: + return self.__class__ == Sum + + +class Residual(Chain): + _tag = "RES" + + def forward(self, *inputs: Any) -> Any: + assert len(inputs) == 1, "Residual connection can only be used with a single input." + return super().forward(*inputs) + inputs[0] + + +class Breakpoint(ContextModule): + def __init__(self, vscode: bool = True): + super().__init__() + self.vscode = vscode + + def forward(self, *args: Any): + if self.vscode: + import debugpy # type: ignore + + debugpy.breakpoint() # type: ignore + else: + breakpoint() + return args[0] if len(args) == 1 else args + + +class Concatenate(Chain): + _tag = "CAT" + + def __init__(self, *modules: Module, dim: int = 0) -> None: + super().__init__(*modules) + self.dim = dim + + def forward(self, *args: Any) -> Tensor: + outputs = [module(*args) for module in self] + return cat([output for output in outputs if output is not None], dim=self.dim) + + def _show_only_tag(self) -> bool: + return self.__class__ == Concatenate + + +class Matmul(Chain): + _tag = "MATMUL" + + def __init__(self, input: Module, other: Module) -> None: + super().__init__( + input, + other, + ) + + def forward(self, *args: Tensor) -> Tensor: + return torch.matmul(input=self[0](*args), other=self[1](*args)) diff --git a/imaginairy/vendored/refiners/fluxion/layers/conv.py b/imaginairy/vendored/refiners/fluxion/layers/conv.py new file mode 100644 index 0000000..de7fe9f --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/conv.py @@ -0,0 +1,96 @@ +from torch import device as Device, dtype as DType, nn + +from imaginairy.vendored.refiners.fluxion.layers.module import WeightedModule + + +class Conv2d(nn.Conv2d, WeightedModule): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int | tuple[int, int], + stride: int | tuple[int, int] = (1, 1), + padding: int | tuple[int, int] | str = (0, 0), + groups: int = 1, + use_bias: bool = True, + dilation: int | tuple[int, int] = (1, 1), + padding_mode: str = "zeros", + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( # type: ignore + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + groups, + use_bias, + padding_mode, + device, + dtype, + ) + self.use_bias = use_bias + + +class Conv1d(nn.Conv1d, WeightedModule): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int | tuple[int], + stride: int | tuple[int] = 1, + padding: int | tuple[int] | str = 0, + groups: int = 1, + use_bias: bool = True, + dilation: int | tuple[int] = 1, + padding_mode: str = "zeros", + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( # type: ignore + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + groups, + use_bias, + padding_mode, + device, + dtype, + ) + + +class ConvTranspose2d(nn.ConvTranspose2d, WeightedModule): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int | tuple[int, int], + stride: int | tuple[int, int] = 1, + padding: int | tuple[int, int] = 0, + output_padding: int | tuple[int, int] = 0, + groups: int = 1, + use_bias: bool = True, + dilation: int | tuple[int, int] = 1, + padding_mode: str = "zeros", + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( # type: ignore + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + output_padding=output_padding, + dilation=dilation, + groups=groups, + bias=use_bias, + padding_mode=padding_mode, + device=device, + dtype=dtype, + ) diff --git a/imaginairy/vendored/refiners/fluxion/layers/converter.py b/imaginairy/vendored/refiners/fluxion/layers/converter.py new file mode 100644 index 0000000..35e8125 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/converter.py @@ -0,0 +1,45 @@ +from torch import Tensor + +from imaginairy.vendored.refiners.fluxion.layers.module import ContextModule + + +class Converter(ContextModule): + """ + A Converter class that adjusts tensor properties based on a parent module's settings. + + This class inherits from `ContextModule` and provides functionality to adjust + the device and dtype of input tensor(s) to match the parent module's attributes. + + Attributes: + set_device (bool): If True, matches the device of the input tensor(s) to the parent's device. + set_dtype (bool): If True, matches the dtype of the input tensor(s) to the parent's dtype. + + Note: + Ensure the parent module has `device` and `dtype` attributes if `set_device` or `set_dtype` are set to True. + """ + + def __init__(self, set_device: bool = True, set_dtype: bool = True) -> None: + super().__init__() + self.set_device = set_device + self.set_dtype = set_dtype + + def forward(self, *inputs: Tensor) -> tuple[Tensor, ...]: + parent = self.ensure_parent + converted_tensors: list[Tensor] = [] + + for x in inputs: + if self.set_device: + device = parent.device + assert device is not None, "parent has no device" + x = x.to(device=device) + if self.set_dtype: + dtype = parent.dtype + assert dtype is not None, "parent has no dtype" + x = x.to(dtype=dtype) + + converted_tensors.append(x) + + return tuple(converted_tensors) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(set_device={self.set_device}, set_dtype={self.set_dtype})" diff --git a/imaginairy/vendored/refiners/fluxion/layers/embedding.py b/imaginairy/vendored/refiners/fluxion/layers/embedding.py new file mode 100644 index 0000000..7c3cd19 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/embedding.py @@ -0,0 +1,21 @@ +from jaxtyping import Float, Int +from torch import Tensor, device as Device, dtype as DType +from torch.nn import Embedding as _Embedding + +from imaginairy.vendored.refiners.fluxion.layers.module import WeightedModule + + +class Embedding(_Embedding, WeightedModule): # type: ignore + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + device: Device | str | None = None, + dtype: DType | None = None, + ): + _Embedding.__init__( # type: ignore + self, num_embeddings=num_embeddings, embedding_dim=embedding_dim, device=device, dtype=dtype + ) + + def forward(self, x: Int[Tensor, "batch length"]) -> Float[Tensor, "batch length embedding_dim"]: # type: ignore + return super().forward(x) diff --git a/imaginairy/vendored/refiners/fluxion/layers/linear.py b/imaginairy/vendored/refiners/fluxion/layers/linear.py new file mode 100644 index 0000000..d1e5c8c --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/linear.py @@ -0,0 +1,49 @@ +from jaxtyping import Float +from torch import Tensor, device as Device, dtype as DType +from torch.nn import Linear as _Linear + +from imaginairy.vendored.refiners.fluxion.layers.activations import ReLU +from imaginairy.vendored.refiners.fluxion.layers.chain import Chain +from imaginairy.vendored.refiners.fluxion.layers.module import Module, WeightedModule + + +class Linear(_Linear, WeightedModule): + def __init__( + self, + in_features: int, + out_features: int, + bias: bool = True, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.in_features = in_features + self.out_features = out_features + super().__init__( # type: ignore + in_features=in_features, + out_features=out_features, + bias=bias, + device=device, + dtype=dtype, + ) + + def forward(self, x: Float[Tensor, "batch in_features"]) -> Float[Tensor, "batch out_features"]: # type: ignore + return super().forward(x) + + +class MultiLinear(Chain): + def __init__( + self, + input_dim: int, + output_dim: int, + inner_dim: int, + num_layers: int, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + layers: list[Module] = [] + for i in range(num_layers - 1): + layers.append(Linear(input_dim if i == 0 else inner_dim, inner_dim, device=device, dtype=dtype)) + layers.append(ReLU()) + layers.append(Linear(inner_dim, output_dim, device=device, dtype=dtype)) + + super().__init__(layers) diff --git a/imaginairy/vendored/refiners/fluxion/layers/maxpool.py b/imaginairy/vendored/refiners/fluxion/layers/maxpool.py new file mode 100644 index 0000000..8ccb711 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/maxpool.py @@ -0,0 +1,43 @@ +from torch import nn + +from imaginairy.vendored.refiners.fluxion.layers.module import Module + + +class MaxPool1d(nn.MaxPool1d, Module): + def __init__( + self, + kernel_size: int, + stride: int | None = None, + padding: int = 0, + dilation: int = 1, + return_indices: bool = False, + ceil_mode: bool = False, + ) -> None: + super().__init__( + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + return_indices=return_indices, + ceil_mode=ceil_mode, + ) + + +class MaxPool2d(nn.MaxPool2d, Module): + def __init__( + self, + kernel_size: int | tuple[int, int], + stride: int | tuple[int, int] | None = None, + padding: int | tuple[int, int] = (0, 0), + dilation: int | tuple[int, int] = (1, 1), + return_indices: bool = False, + ceil_mode: bool = False, + ) -> None: + super().__init__( + kernel_size=kernel_size, + stride=stride, + padding=padding, # type: ignore + dilation=dilation, + return_indices=return_indices, + ceil_mode=ceil_mode, + ) diff --git a/imaginairy/vendored/refiners/fluxion/layers/module.py b/imaginairy/vendored/refiners/fluxion/layers/module.py new file mode 100644 index 0000000..390a581 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/module.py @@ -0,0 +1,264 @@ +import sys +from collections import defaultdict +from inspect import Parameter, signature +from pathlib import Path +from types import ModuleType +from typing import TYPE_CHECKING, Any, DefaultDict, Generator, Sequence, TypedDict, TypeVar, cast + +from torch import device as Device, dtype as DType +from torch.nn.modules.module import Module as TorchModule + +from imaginairy.vendored.refiners.fluxion.context import Context, ContextProvider +from imaginairy.vendored.refiners.fluxion.utils import load_from_safetensors + +if TYPE_CHECKING: + from imaginairy.vendored.refiners.fluxion.layers.chain import Chain + +T = TypeVar("T", bound="Module") +TContextModule = TypeVar("TContextModule", bound="ContextModule") +BasicType = str | float | int | bool + + +class Module(TorchModule): + _parameters: dict[str, Any] + _buffers: dict[str, Any] + _tag: str = "" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, *kwargs) # type: ignore[reportUnknownMemberType] + + def __getattr__(self, name: str) -> Any: + return super().__getattr__(name=name) + + def __setattr__(self, name: str, value: Any) -> None: + return super().__setattr__(name=name, value=value) + + def load_from_safetensors(self, tensors_path: str | Path, strict: bool = True) -> "Module": + state_dict = load_from_safetensors(tensors_path) + self.load_state_dict(state_dict, strict=strict) + return self + + def named_modules(self, *args: Any, **kwargs: Any) -> "Generator[tuple[str, Module], None, None]": # type: ignore + return super().named_modules(*args) # type: ignore + + def to(self: T, device: Device | str | None = None, dtype: DType | None = None) -> T: # type: ignore + return super().to(device=device, dtype=dtype) # type: ignore + + def __str__(self) -> str: + basic_attributes_str = ", ".join( + f"{key}={value}" for key, value in self.basic_attributes(init_attrs_only=True).items() + ) + result = f"{self.__class__.__name__}({basic_attributes_str})" + return result + + def __repr__(self) -> str: + tree = ModuleTree(module=self) + return repr(tree) + + def pretty_print(self, depth: int = -1) -> None: + tree = ModuleTree(module=self) + print(tree._generate_tree_repr(tree.root, is_root=True, depth=depth)) # type: ignore[reportPrivateUsage] + + def basic_attributes(self, init_attrs_only: bool = False) -> dict[str, BasicType]: + """Return a dictionary of basic attributes of the module. + + Basic attributes are public attributes made of basic types (int, float, str, bool) or a sequence of basic types. + """ + sig = signature(obj=self.__init__) + init_params = set(sig.parameters.keys()) - {"self"} + default_values = {k: v.default for k, v in sig.parameters.items() if v.default is not Parameter.empty} + + def is_basic_attribute(key: str, value: Any) -> bool: + if key.startswith("_"): + return False + + if isinstance(value, BasicType): + return True + + if isinstance(value, Sequence) and all(isinstance(y, BasicType) for y in cast(Sequence[Any], value)): + return True + + return False + + return { + key: str(object=value) + for key, value in self.__dict__.items() + if is_basic_attribute(key=key, value=value) + and (not init_attrs_only or (key in init_params and value != default_values.get(key))) + } + + def _show_only_tag(self) -> bool: + """Whether to show only the tag when printing the module. + + This is useful to distinguish between Chain subclasses that override their forward from one another. + """ + return False + + +class ContextModule(Module): + # we store parent into a one element list to avoid pytorch thinking it's a submodule + _parent: "list[Chain]" + _can_refresh_parent: bool = True # see usage in Adapter and Chain + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, *kwargs) + self._parent = [] + + @property + def parent(self) -> "Chain | None": + return self._parent[0] if self._parent else None + + @property + def ensure_parent(self) -> "Chain": + assert self._parent, "module is not bound to a Chain" + return self._parent[0] + + def _set_parent(self, parent: "Chain | None") -> None: + if not self._can_refresh_parent: + return + if parent is None: + self._parent = [] + return + # Always insert the module in the Chain first to avoid inconsistencies. + assert self in iter(parent), f"{self} not in {parent}" + self._parent = [parent] + + @property + def provider(self) -> ContextProvider: + return self.ensure_parent.provider + + def get_parents(self) -> "list[Chain]": + return self._parent + self._parent[0].get_parents() if self._parent else [] + + def use_context(self, context_name: str) -> Context: + """Retrieve the context object from the module's context provider.""" + context = self.provider.get_context(context_name) + assert context is not None, f"Context {context_name} not found." + return context + + def structural_copy(self: TContextModule) -> TContextModule: + clone = object.__new__(self.__class__) + + not_torch_attributes = [ + key + for key, value in self.__dict__.items() + if not key.startswith("_") + and isinstance(sys.modules.get(type(value).__module__), ModuleType) + and "torch" not in sys.modules[type(value).__module__].__name__ + ] + + for k in not_torch_attributes: + setattr(clone, k, getattr(self, k)) + + ContextModule.__init__(self=clone) + + return clone + + +class WeightedModule(Module): + @property + def device(self) -> Device: + return self.weight.device + + @property + def dtype(self) -> DType: + return self.weight.dtype + + +class TreeNode(TypedDict): + value: str + class_name: str + children: list["TreeNode"] + + +class ModuleTree: + def __init__(self, module: Module) -> None: + self.root: TreeNode = self._module_to_tree(module=module) + self._fold_successive_identical(node=self.root) + + def __str__(self) -> str: + return f"{self.__class__.__name__}(root={self.root['value']})" + + def __repr__(self) -> str: + return self._generate_tree_repr(self.root, is_root=True, depth=7) + + def __iter__(self) -> Generator[TreeNode, None, None]: + for child in self.root["children"]: + yield child + + @classmethod + def shorten_tree_repr(cls, tree_repr: str, /, line_index: int = 0, max_lines: int = 20) -> str: + """Shorten the tree representation to a given number of lines around a given line index.""" + lines = tree_repr.split(sep="\n") + start_idx = max(0, line_index - max_lines // 2) + end_idx = min(len(lines), line_index + max_lines // 2 + 1) + return "\n".join(lines[start_idx:end_idx]) + + def _generate_tree_repr( + self, node: TreeNode, /, *, prefix: str = "", is_last: bool = True, is_root: bool = True, depth: int = -1 + ) -> str: + if depth == 0 and node["children"]: + return f"{prefix}{'└── ' if is_last else '├── '}{node['value']} ..." + + if depth > 0: + depth -= 1 + + tree_icon: str = "" if is_root else ("└── " if is_last else "├── ") + counts: DefaultDict[str, int] = defaultdict(int) + + for child in node["children"]: + counts[child["class_name"]] += 1 + + instance_counts: DefaultDict[str, int] = defaultdict(int) + lines = [f"{prefix}{tree_icon}{node['value']}"] + new_prefix: str = " " if is_last else "│ " + + for i, child in enumerate(iterable=node["children"]): + instance_counts[child["class_name"]] += 1 + + if counts[child["class_name"]] > 1: + child_value = f"{child['value']} #{instance_counts[child['class_name']]}" + else: + child_value = child["value"] + + child_str = self._generate_tree_repr( + {"value": child_value, "class_name": child["class_name"], "children": child["children"]}, + prefix=prefix + new_prefix, + is_last=i == len(node["children"]) - 1, + is_root=False, + depth=depth, + ) + + if child_str: + lines.append(child_str) + + return "\n".join(lines) + + def _module_to_tree(self, module: Module) -> TreeNode: + match (module._tag, module._show_only_tag()): # pyright: ignore[reportPrivateUsage] + case ("", False): + value = str(module) + case (_, True): + value = f"({module._tag})" # pyright: ignore[reportPrivateUsage] + case (_, False): + value = f"({module._tag}) {module}" # pyright: ignore[reportPrivateUsage] + + class_name = module.__class__.__name__ + + node: TreeNode = {"value": value, "class_name": class_name, "children": []} + for child in module.children(): + node["children"].append(self._module_to_tree(module=child)) # type: ignore + return node + + def _fold_successive_identical(self, node: TreeNode) -> None: + i = 0 + while i < len(node["children"]): + j = i + while j < len(node["children"]) and node["children"][i] == node["children"][j]: + j += 1 + count = j - i + if count > 1: + node["children"][i]["value"] += f" (x{count})" + del node["children"][i + 1 : j] + self._fold_successive_identical(node=node["children"][i]) + i += 1 diff --git a/imaginairy/vendored/refiners/fluxion/layers/norm.py b/imaginairy/vendored/refiners/fluxion/layers/norm.py new file mode 100644 index 0000000..9940ca3 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/norm.py @@ -0,0 +1,88 @@ +from jaxtyping import Float +from torch import Tensor, device as Device, dtype as DType, nn, ones, sqrt, zeros + +from imaginairy.vendored.refiners.fluxion.layers.module import Module, WeightedModule + + +class LayerNorm(nn.LayerNorm, WeightedModule): + def __init__( + self, + normalized_shape: int | list[int], + eps: float = 0.00001, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( # type: ignore + normalized_shape=normalized_shape, + eps=eps, + elementwise_affine=True, # otherwise not a WeightedModule + device=device, + dtype=dtype, + ) + + +class GroupNorm(nn.GroupNorm, WeightedModule): + def __init__( + self, + channels: int, + num_groups: int, + eps: float = 1e-5, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( # type: ignore + num_groups=num_groups, + num_channels=channels, + eps=eps, + affine=True, # otherwise not a WeightedModule + device=device, + dtype=dtype, + ) + self.channels = channels + self.num_groups = num_groups + self.eps = eps + + +class LayerNorm2d(WeightedModule): + """ + 2D Layer Normalization module. + + Parameters: + channels (int): Number of channels in the input tensor. + eps (float, optional): A small constant for numerical stability. Default: 1e-6. + """ + + def __init__( + self, + channels: int, + eps: float = 1e-6, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__() + self.weight = nn.Parameter(ones(channels, device=device, dtype=dtype)) + self.bias = nn.Parameter(zeros(channels, device=device, dtype=dtype)) + self.eps = eps + + def forward(self, x: Float[Tensor, "batch channels height width"]) -> Float[Tensor, "batch channels height width"]: + x_mean = x.mean(1, keepdim=True) + x_var = (x - x_mean).pow(2).mean(1, keepdim=True) + x_norm = (x - x_mean) / sqrt(x_var + self.eps) + x_out = self.weight.unsqueeze(-1).unsqueeze(-1) * x_norm + self.bias.unsqueeze(-1).unsqueeze(-1) + return x_out + + +class InstanceNorm2d(nn.InstanceNorm2d, Module): + def __init__( + self, + num_features: int, + eps: float = 1e-05, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( # type: ignore + num_features=num_features, + eps=eps, + device=device, + dtype=dtype, + ) diff --git a/imaginairy/vendored/refiners/fluxion/layers/padding.py b/imaginairy/vendored/refiners/fluxion/layers/padding.py new file mode 100644 index 0000000..2e5f888 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/padding.py @@ -0,0 +1,8 @@ +from torch import nn + +from imaginairy.vendored.refiners.fluxion.layers.module import Module + + +class ReflectionPad2d(nn.ReflectionPad2d, Module): + def __init__(self, padding: int) -> None: + super().__init__(padding=padding) diff --git a/imaginairy/vendored/refiners/fluxion/layers/pixelshuffle.py b/imaginairy/vendored/refiners/fluxion/layers/pixelshuffle.py new file mode 100644 index 0000000..aa365d2 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/pixelshuffle.py @@ -0,0 +1,8 @@ +from torch.nn import PixelUnshuffle as _PixelUnshuffle + +from imaginairy.vendored.refiners.fluxion.layers.module import Module + + +class PixelUnshuffle(_PixelUnshuffle, Module): + def __init__(self, downscale_factor: int): + _PixelUnshuffle.__init__(self, downscale_factor=downscale_factor) diff --git a/imaginairy/vendored/refiners/fluxion/layers/sampling.py b/imaginairy/vendored/refiners/fluxion/layers/sampling.py new file mode 100644 index 0000000..f95cbf3 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/layers/sampling.py @@ -0,0 +1,99 @@ +from typing import Callable + +from torch import Size, Tensor, device as Device, dtype as DType +from torch.nn.functional import pad + +from imaginairy.vendored.refiners.fluxion.layers.basics import Identity +from imaginairy.vendored.refiners.fluxion.layers.chain import Chain, Lambda, Parallel, SetContext, UseContext +from imaginairy.vendored.refiners.fluxion.layers.conv import Conv2d +from imaginairy.vendored.refiners.fluxion.layers.module import Module +from imaginairy.vendored.refiners.fluxion.utils import interpolate + + +class Downsample(Chain): + def __init__( + self, + channels: int, + scale_factor: int, + padding: int = 0, + register_shape: bool = True, + device: Device | str | None = None, + dtype: DType | None = None, + ): + """Downsamples the input by the given scale factor. + + If register_shape is True, the input shape is registered in the context. It will throw an error if the context + sampling is not set or if the context does not contain a list. + """ + self.channels = channels + self.in_channels = channels + self.out_channels = channels + self.scale_factor = scale_factor + self.padding = padding + super().__init__( + Conv2d( + in_channels=channels, + out_channels=channels, + kernel_size=3, + stride=scale_factor, + padding=padding, + device=device, + dtype=dtype, + ), + ) + if padding == 0: + zero_pad: Callable[[Tensor], Tensor] = lambda x: pad(x, (0, 1, 0, 1)) + self.insert(0, Lambda(zero_pad)) + if register_shape: + self.insert(0, SetContext(context="sampling", key="shapes", callback=self.register_shape)) + + def register_shape(self, shapes: list[Size], x: Tensor) -> None: + shapes.append(x.shape[2:]) + + +class Interpolate(Module): + def __init__(self) -> None: + super().__init__() + + def forward(self, x: Tensor, shape: Size) -> Tensor: + return interpolate(x, shape) + + +class Upsample(Chain): + def __init__( + self, + channels: int, + upsample_factor: int | None = None, + device: Device | str | None = None, + dtype: DType | None = None, + ): + """Upsamples the input by the given scale factor. + + If upsample_factor is None, the input shape is taken from the context. It will throw an error if the context + sampling is not set or if the context is empty (then you should use the dynamic version of Downsample). + """ + self.channels = channels + self.upsample_factor = upsample_factor + super().__init__( + Parallel( + Identity(), + ( + Lambda(self._get_static_shape) + if upsample_factor is not None + else UseContext(context="sampling", key="shapes").compose(lambda x: x.pop()) + ), + ), + Interpolate(), + Conv2d( + in_channels=channels, + out_channels=channels, + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + ) + + def _get_static_shape(self, x: Tensor) -> Size: + assert self.upsample_factor is not None + return Size([size * self.upsample_factor for size in x.shape[2:]]) diff --git a/imaginairy/vendored/refiners/fluxion/model_converter.py b/imaginairy/vendored/refiners/fluxion/model_converter.py new file mode 100644 index 0000000..fc0dbf9 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/model_converter.py @@ -0,0 +1,644 @@ +from collections import defaultdict +from enum import Enum, auto +from pathlib import Path +from typing import Any, DefaultDict, TypedDict + +import torch +from torch import Tensor, nn +from torch.utils.hooks import RemovableHandle + +from imaginairy.vendored.refiners.fluxion.utils import no_grad, norm, save_to_safetensors + +TORCH_BASIC_LAYERS: list[type[nn.Module]] = [ + nn.Conv1d, + nn.Conv2d, + nn.Conv3d, + nn.ConvTranspose1d, + nn.ConvTranspose2d, + nn.ConvTranspose3d, + nn.Linear, + nn.BatchNorm1d, + nn.BatchNorm2d, + nn.BatchNorm3d, + nn.LayerNorm, + nn.GroupNorm, + nn.Embedding, + nn.MaxPool2d, + nn.AvgPool2d, + nn.AdaptiveAvgPool2d, +] + + +ModelTypeShape = tuple[type[nn.Module], tuple[torch.Size, ...]] + + +class ModuleArgsDict(TypedDict): + """Represents positional and keyword arguments passed to a module. + + - `positional`: A tuple of positional arguments. + - `keyword`: A dictionary of keyword arguments. + """ + + positional: tuple[Any, ...] + keyword: dict[str, Any] + + +class ConversionStage(Enum): + """Represents the current stage of the conversion process. + + - `INIT`: The conversion process has not started. + - `BASIC_LAYERS_MATCH`: The source and target models have the same number of basic layers. + """ + + INIT = auto() + BASIC_LAYERS_MATCH = auto() + SHAPE_AND_LAYERS_MATCH = auto() + MODELS_OUTPUT_AGREE = auto() + + +class ModelConverter: + ModuleArgs = tuple[Any, ...] | dict[str, Any] | ModuleArgsDict + stage: ConversionStage = ConversionStage.INIT + _stored_mapping: dict[str, str] | None = None + + def __init__( + self, + source_model: nn.Module, + target_model: nn.Module, + source_keys_to_skip: list[str] | None = None, + target_keys_to_skip: list[str] | None = None, + custom_layer_mapping: dict[type[nn.Module], type[nn.Module]] | None = None, + threshold: float = 1e-5, + skip_output_check: bool = False, + skip_init_check: bool = False, + verbose: bool = True, + ) -> None: + """ + Create a ModelConverter. + + - `source_model`: The model to convert from. + - `target_model`: The model to convert to. + - `source_keys_to_skip`: A list of keys to skip when tracing the source model. + - `target_keys_to_skip`: A list of keys to skip when tracing the target model. + - `custom_layer_mapping`: A dictionary mapping custom layer types between the source and target models. + - `threshold`: The threshold for comparing outputs between the source and target models. + - `skip_output_check`: Whether to skip comparing the outputs of the source and target models. + - `skip_init_check`: Whether to skip checking that the source and target models have the same number of basic + layers. + - `verbose`: Whether to print messages during the conversion process. + + The conversion process consists of three stages: + + 1. Verify that the source and target models have the same number of basic layers. + 2. Find matching shapes and layers between the source and target models. + 3. Convert the source model's state_dict to match the target model's state_dict. + 4. Compare the outputs of the source and target models. + + The conversion process can be run multiple times, and will resume from the last stage. + + ### Example: + ``` + converter = ModelConverter(source_model=source, target_model=target, threshold=0.1, verbose=False) + is_converted = converter(args) + if is_converted: + converter.save_to_safetensors(path="test.pt") + ``` + """ + self.source_model = source_model + self.target_model = target_model + self.source_keys_to_skip = source_keys_to_skip or [] + self.target_keys_to_skip = target_keys_to_skip or [] + self.custom_layer_mapping = custom_layer_mapping or {} + self.threshold = threshold + self.skip_output_check = skip_output_check + self.skip_init_check = skip_init_check + self.verbose = verbose + + def __repr__(self) -> str: + return ( + f"ModelConverter(source_model={self.source_model.__class__.__name__}," + f" target_model={self.target_model.__class__.__name__}, stage={self.stage})" + ) + + def __bool__(self) -> bool: + return self.stage.value >= 2 if self.skip_output_check else self.stage.value >= 3 + + def run(self, source_args: ModuleArgs, target_args: ModuleArgs | None = None) -> bool: + """ + Run the conversion process. + + - `source_args`: The arguments to pass to the source model it can be either a tuple of positional arguments, + a dictionary of keyword arguments, or a dictionary with `positional` and `keyword` keys. If `target_args` + is not provided, these arguments will also be passed to the target model. + - `target_args`: The arguments to pass to the target model it can be either a tuple of positional arguments, + a dictionary of keyword arguments, or a dictionary with `positional` and `keyword` keys. + + ### Returns: + + - `True` if the conversion process is done and the models agree. + + The conversion process consists of three stages: + + 1. Verify that the source and target models have the same number of basic layers. + 2. Find matching shapes and layers between the source and target models. + 3. Convert the source model's state_dict to match the target model's state_dict. + 4. Compare the outputs of the source and target models. + + The conversion process can be run multiple times, and will resume from the last stage. + """ + if target_args is None: + target_args = source_args + + match self.stage: + case ConversionStage.MODELS_OUTPUT_AGREE: + self._increment_stage() + return True + + case ConversionStage.SHAPE_AND_LAYERS_MATCH if self._run_shape_and_layers_match_stage( + source_args=source_args, target_args=target_args + ): + self._increment_stage() + return True + + case ConversionStage.BASIC_LAYERS_MATCH if self._run_basic_layers_match_stage( + source_args=source_args, target_args=target_args + ): + self._increment_stage() + return self.run(source_args=source_args, target_args=target_args) + + case ConversionStage.INIT if self._run_init_stage(): + self._increment_stage() + return self.run(source_args=source_args, target_args=target_args) + + case _: + self._log(message=f"Conversion failed at stage {self.stage.value}") + return False + + def _increment_stage(self) -> None: + """Increment the stage of the conversion process.""" + match self.stage: + case ConversionStage.INIT: + self.stage = ConversionStage.BASIC_LAYERS_MATCH + self._log( + message=( + "Stage 0 -> 1 - Models have the same number of basic layers. Finding matching shapes and" + " layers..." + ) + ) + case ConversionStage.BASIC_LAYERS_MATCH: + self.stage = ConversionStage.SHAPE_AND_LAYERS_MATCH + self._log( + message=( + "Stage 1 -> 2 - Shape of both models agree. Applying state_dict to target model. Comparing" + " models..." + ) + ) + + case ConversionStage.SHAPE_AND_LAYERS_MATCH: + if self.skip_output_check: + self._log( + message=( + "Stage 2 - Nothing to do. Skipping output check. If you want to compare the outputs, set" + " `skip_output_check` to `False`" + ) + ) + else: + self.stage = ConversionStage.MODELS_OUTPUT_AGREE + self._log( + message=( + "Stage 2 -> 3 - Conversion is done and source and target models agree: you can export the" + " converted model using `save_to_safetensors`" + ) + ) + case ConversionStage.MODELS_OUTPUT_AGREE: + self._log( + message=( + "Stage 3 - Nothing to do. Conversion is done and source and target models agree: you can export" + " the converted model using `save_to_safetensors`" + ) + ) + + def get_state_dict(self) -> dict[str, Tensor]: + """Get the converted state_dict.""" + if not self: + raise ValueError("The conversion process is not done yet. Run `converter(args)` first.") + return self.target_model.state_dict() + + def get_mapping(self) -> dict[str, str]: + """Get the mapping between the source and target models' state_dicts.""" + if not self: + raise ValueError("The conversion process is not done yet. Run `converter(args)` first.") + assert self._stored_mapping is not None, "Mapping is not stored" + return self._stored_mapping + + def save_to_safetensors(self, path: Path | str, metadata: dict[str, str] | None = None, half: bool = False) -> None: + """Save the converted model to a SafeTensors file. + + This method can only be called after the conversion process is done. + + - `path`: The path to save the converted model to. + - `metadata`: Metadata to save with the converted model. + - `half`: Whether to save the converted model as half precision. + + ### Raises: + - `ValueError` if the conversion process is not done yet. Run `converter(args)` first. + """ + if not self: + raise ValueError("The conversion process is not done yet. Run `converter(args)` first.") + state_dict = self.get_state_dict() + if half: + state_dict = {key: value.half() for key, value in state_dict.items()} + save_to_safetensors(path=path, tensors=state_dict, metadata=metadata) + + def map_state_dicts( + self, + source_args: ModuleArgs, + target_args: ModuleArgs | None = None, + ) -> dict[str, str] | None: + """ + Find a mapping between the source and target models' state_dicts. + + - `source_args`: The arguments to pass to the source model it can be either a tuple of positional arguments, + a dictionary of keyword arguments, or a dictionary with `positional` and `keyword` keys. If `target_args` + is not provided, these arguments will also be passed to the target model. + - `target_args`: The arguments to pass to the target model it can be either a tuple of positional arguments, + a dictionary of keyword arguments, or a dictionary with `positional` and `keyword` keys. + + ### Returns: + - A dictionary mapping keys in the target model's state_dict to keys in the source model's state_dict. + """ + if target_args is None: + target_args = source_args + + source_order = self._trace_module_execution_order( + module=self.source_model, args=source_args, keys_to_skip=self.source_keys_to_skip + ) + target_order = self._trace_module_execution_order( + module=self.target_model, args=target_args, keys_to_skip=self.target_keys_to_skip + ) + + if not self._assert_shapes_aligned(source_order=source_order, target_order=target_order): + return None + + mapping: dict[str, str] = {} + for source_type_shape in source_order: + source_keys = source_order[source_type_shape] + target_type_shape = source_type_shape + if not self._is_torch_basic_layer(module_type=source_type_shape[0]): + for source_custom_type, target_custom_type in self.custom_layer_mapping.items(): + if source_custom_type == source_type_shape[0]: + target_type_shape = (target_custom_type, source_type_shape[1]) + break + + target_keys = target_order[target_type_shape] + mapping.update(zip(target_keys, source_keys)) + + return mapping + + def compare_models( + self, + source_args: ModuleArgs, + target_args: ModuleArgs | None = None, + threshold: float = 1e-5, + ) -> bool: + """ + Compare the outputs of the source and target models. + + - `source_args`: The arguments to pass to the source model it can be either a tuple of positional arguments, + a dictionary of keyword arguments, or a dictionary with `positional` and `keyword` keys. If `target_args` + is not provided, these arguments will also be passed to the target model. + - `target_args`: The arguments to pass to the target model it can be either a tuple of positional arguments, + a dictionary of keyword arguments, or a dictionary with `positional` and `keyword` keys. + - `threshold`: The threshold for comparing outputs between the source and target models. + """ + if target_args is None: + target_args = source_args + + source_outputs = self._collect_layers_outputs( + module=self.source_model, args=source_args, keys_to_skip=self.source_keys_to_skip + ) + target_outputs = self._collect_layers_outputs( + module=self.target_model, args=target_args, keys_to_skip=self.target_keys_to_skip + ) + + diff, prev_source_key, prev_target_key = None, None, None + for (source_key, source_output), (target_key, target_output) in zip(source_outputs, target_outputs): + diff = norm(source_output - target_output.reshape(shape=source_output.shape)).item() + if diff > threshold: + self._log( + f"Models diverged between {prev_source_key} and {source_key}, and between {prev_target_key} and" + f" {target_key}, difference in norm: {diff}" + ) + return False + prev_source_key, prev_target_key = source_key, target_key + + self._log(message=f"Models agree. Difference in norm: {diff}") + + return True + + def _run_init_stage(self) -> bool: + """Run the init stage of the conversion process.""" + if self.skip_init_check: + self._log( + message=( + "Skipping init check. If you want to check the number of basic layers, set `skip_init_check` to" + " `False`" + ) + ) + return True + + is_count_correct = self._verify_basic_layers_count() + is_not_missing_layers = self._verify_missing_basic_layers() + + return is_count_correct and is_not_missing_layers + + def _run_basic_layers_match_stage(self, source_args: ModuleArgs, target_args: ModuleArgs | None) -> bool: + """Run the basic layers match stage of the conversion process.""" + mapping = self.map_state_dicts(source_args=source_args, target_args=target_args) + self._stored_mapping = mapping + if mapping is None: + self._log(message="Models do not have matching shapes.") + return False + + source_state_dict = self.source_model.state_dict() + target_state_dict = self.target_model.state_dict() + converted_state_dict = self._convert_state_dict( + source_state_dict=source_state_dict, target_state_dict=target_state_dict, state_dict_mapping=mapping + ) + self.target_model.load_state_dict(state_dict=converted_state_dict) + + return True + + def _run_shape_and_layers_match_stage(self, source_args: ModuleArgs, target_args: ModuleArgs | None) -> bool: + """Run the shape and layers match stage of the conversion process.""" + if self.skip_output_check: + self._log( + message="Skipping output check. If you want to compare the outputs, set `skip_output_check` to `False`" + ) + return True + + try: + if self.compare_models(source_args=source_args, target_args=target_args, threshold=self.threshold): + self._log(message="Models agree. You can export the converted model using `save_to_safetensors`") + return True + else: + self._log(message="Models do not agree. Try to increase the threshold or modify the models.") + return False + except Exception as e: + self._log(message=f"An error occurred while comparing the models: {e}") + return False + + def _log(self, message: str) -> None: + """Print a message if `verbose` is `True`.""" + if self.verbose: + print(message) + + def _debug_print_shapes( + self, + shape: ModelTypeShape, + source_keys: list[str], + target_keys: list[str], + ) -> None: + """Print the shapes of the sub-modules in `source_keys` and `target_keys`.""" + self._log(message=f"{shape}") + max_len = max(len(source_keys), len(target_keys)) + for i in range(max_len): + source_key = source_keys[i] if i < len(source_keys) else "---" + target_key = target_keys[i] if i < len(target_keys) else "---" + self._log(f"\t{source_key}\t{target_key}") + + @staticmethod + def _unpack_module_args(module_args: ModuleArgs) -> tuple[tuple[Any, ...], dict[str, Any]]: + """Unpack the positional and keyword arguments passed to a module.""" + match module_args: + case tuple(positional_args): + keyword_args: dict[str, Any] = {} + case {"positional": positional_args, "keyword": keyword_args}: + pass + case _: + positional_args = () + keyword_args = dict(**module_args) + + return positional_args, keyword_args + + def _is_torch_basic_layer(self, module_type: type[nn.Module]) -> bool: + """Check if a module type is a subclass of a torch basic layer.""" + return any(issubclass(module_type, torch_basic_layer) for torch_basic_layer in TORCH_BASIC_LAYERS) + + def _infer_basic_layer_type(self, module: nn.Module) -> type[nn.Module] | None: + """Infer the type of a basic layer.""" + layer_types = ( + set(self.custom_layer_mapping.keys()) | set(self.custom_layer_mapping.values()) | set(TORCH_BASIC_LAYERS) + ) + for layer_type in layer_types: + if isinstance(module, layer_type): + return layer_type + + return None + + def get_module_signature(self, module: nn.Module) -> ModelTypeShape: + """Get the signature of a module.""" + layer_type = self._infer_basic_layer_type(module=module) + assert layer_type is not None, f"Module {module} is not a basic layer" + param_shapes = [p.shape for p in module.parameters()] + return (layer_type, tuple(param_shapes)) + + def _count_basic_layers(self, module: nn.Module) -> dict[type[nn.Module], int]: + """Count the number of basic layers in a module.""" + count: DefaultDict[type[nn.Module], int] = defaultdict(int) + for submodule in module.modules(): + layer_type = self._infer_basic_layer_type(module=submodule) + if layer_type is not None: + count[layer_type] += 1 + + return count + + def _verify_basic_layers_count(self) -> bool: + """Verify that the source and target models have the same number of basic layers.""" + source_layers = self._count_basic_layers(module=self.source_model) + target_layers = self._count_basic_layers(module=self.target_model) + + reverse_mapping = {v: k for k, v in self.custom_layer_mapping.items()} + + diff: dict[type[nn.Module], tuple[int, int]] = {} + for layer_type, source_count in source_layers.items(): + target_type = self.custom_layer_mapping.get(layer_type, layer_type) + target_count = target_layers.get(target_type, 0) + + if source_count != target_count: + diff[layer_type] = (source_count, target_count) + + for layer_type, target_count in target_layers.items(): + source_type = reverse_mapping.get(layer_type, layer_type) + source_count = source_layers.get(source_type, 0) + + if source_count != target_count: + diff[layer_type] = (source_count, target_count) + + if diff: + message = "Models do not have the same number of basic layers:\n" + for layer_type, counts in diff.items(): + message += f" {layer_type}: Source {counts[0]} - Target {counts[1]}\n" + self._log(message=message.strip()) + return False + + return True + + def _is_weighted_leaf_module(self, module: nn.Module) -> bool: + """Check if a module is a leaf module with weights.""" + return next(module.parameters(), None) is not None and next(module.children(), None) is None + + def _check_for_missing_basic_layers(self, module: nn.Module) -> list[type[nn.Module]]: + """Check if a module has weighted leaf modules that are not basic layers.""" + return [ + type(submodule) + for submodule in module.modules() + if self._is_weighted_leaf_module(module=submodule) and not self._infer_basic_layer_type(module=submodule) + ] + + def _verify_missing_basic_layers(self) -> bool: + """Verify that the source and target models do not have missing basic layers.""" + missing_source_layers = self._check_for_missing_basic_layers(module=self.source_model) + missing_target_layers = self._check_for_missing_basic_layers(module=self.target_model) + + if missing_source_layers or missing_target_layers: + self._log( + message=( + "Models might have missing basic layers. If you want to skip this check, set" + f" `skip_init_check` to `True`: {missing_source_layers}, {missing_target_layers}" + ) + ) + return False + + return True + + @no_grad() + def _trace_module_execution_order( + self, + module: nn.Module, + args: ModuleArgs, + keys_to_skip: list[str], + ) -> dict[ModelTypeShape, list[str]]: + """ + Execute a forward pass and store the order of execution of specific sub-modules. + + - `module`: The module to trace. + - `args`: The arguments to pass to the module it can be either a tuple of positional arguments, + a dictionary of keyword arguments, or a dictionary with `positional` and `keyword` keys. + - `keys_to_skip`: A list of keys to skip when tracing the module. + + ### Returns: + - A dictionary mapping the signature of each sub-module to a list of keys in the module's `named_modules` + """ + submodule_to_key: dict[nn.Module, str] = {} + execution_order: defaultdict[ModelTypeShape, list[str]] = defaultdict(list) + + def collect_execution_order_hook(layer: nn.Module, *_: Any) -> None: + layer_signature = self.get_module_signature(module=layer) + execution_order[layer_signature].append(submodule_to_key[layer]) + + hooks: list[RemovableHandle] = [] + named_modules: list[tuple[str, nn.Module]] = module.named_modules() # type: ignore + for name, submodule in named_modules: + if (self._infer_basic_layer_type(module=submodule) is not None) and name not in keys_to_skip: + submodule_to_key[submodule] = name # type: ignore + hook = submodule.register_forward_hook(hook=collect_execution_order_hook) + hooks.append(hook) + + positional_args, keyword_args = self._unpack_module_args(module_args=args) + module(*positional_args, **keyword_args) + + for hook in hooks: + hook.remove() + + return dict(execution_order) + + def _assert_shapes_aligned( + self, source_order: dict[ModelTypeShape, list[str]], target_order: dict[ModelTypeShape, list[str]] + ) -> bool: + """Assert that the shapes of the sub-modules in `source_order` and `target_order` are aligned.""" + model_type_shapes = set(source_order.keys()) | set(target_order.keys()) + + default_type_shapes = [ + type_shape for type_shape in model_type_shapes if self._is_torch_basic_layer(module_type=type_shape[0]) + ] + + shape_mismatched = False + + for model_type_shape in default_type_shapes: + source_keys = source_order.get(model_type_shape, []) + target_keys = target_order.get(model_type_shape, []) + + if len(source_keys) != len(target_keys): + shape_mismatched = True + self._debug_print_shapes(shape=model_type_shape, source_keys=source_keys, target_keys=target_keys) + + for source_custom_type in self.custom_layer_mapping.keys(): + # iterate over all type_shapes that have the same type as source_custom_type + for source_type_shape in [ + type_shape for type_shape in model_type_shapes if type_shape[0] == source_custom_type + ]: + source_keys = source_order.get(source_type_shape, []) + target_custom_type = self.custom_layer_mapping[source_custom_type] + target_type_shape = (target_custom_type, source_type_shape[1]) + target_keys = target_order.get(target_type_shape, []) + + if len(source_keys) != len(target_keys): + shape_mismatched = True + self._debug_print_shapes(shape=source_type_shape, source_keys=source_keys, target_keys=target_keys) + + return not shape_mismatched + + @staticmethod + def _convert_state_dict( + source_state_dict: dict[str, Tensor], target_state_dict: dict[str, Tensor], state_dict_mapping: dict[str, str] + ) -> dict[str, Tensor]: + """Convert the source model's state_dict to match the target model's state_dict.""" + converted_state_dict: dict[str, Tensor] = {} + for target_key in target_state_dict: + target_prefix, suffix = target_key.rsplit(sep=".", maxsplit=1) + source_prefix = state_dict_mapping[target_prefix] + source_key = ".".join([source_prefix, suffix]) + converted_state_dict[target_key] = source_state_dict[source_key] + + return converted_state_dict + + @no_grad() + def _collect_layers_outputs( + self, module: nn.Module, args: ModuleArgs, keys_to_skip: list[str] + ) -> list[tuple[str, Tensor]]: + """ + Execute a forward pass and store the output of specific sub-modules. + + - `module`: The module to trace. + - `args`: The arguments to pass to the module it can be either a tuple of positional arguments, + a dictionary of keyword arguments, or a dictionary with `positional` and `keyword` keys. + - `keys_to_skip`: A list of keys to skip when tracing the module. + + ### Returns: + - A list of tuples containing the key of each sub-module and its output. + + ### Note: + - The output of each sub-module is cloned to avoid memory leaks. + """ + submodule_to_key: dict[nn.Module, str] = {} + execution_order: list[tuple[str, Tensor]] = [] + + def collect_execution_order_hook(layer: nn.Module, _: Any, output: Tensor) -> None: + execution_order.append((submodule_to_key[layer], output.clone())) + + hooks: list[RemovableHandle] = [] + named_modules: list[tuple[str, nn.Module]] = module.named_modules() # type: ignore + for name, submodule in named_modules: + if (self._infer_basic_layer_type(module=submodule) is not None) and name not in keys_to_skip: + submodule_to_key[submodule] = name # type: ignore + hook = submodule.register_forward_hook(hook=collect_execution_order_hook) + hooks.append(hook) + + positional_args, keyword_args = self._unpack_module_args(module_args=args) + module(*positional_args, **keyword_args) + + for hook in hooks: + hook.remove() + + return execution_order diff --git a/imaginairy/vendored/refiners/fluxion/utils.py b/imaginairy/vendored/refiners/fluxion/utils.py new file mode 100644 index 0000000..deb0d46 --- /dev/null +++ b/imaginairy/vendored/refiners/fluxion/utils.py @@ -0,0 +1,206 @@ +from pathlib import Path +from typing import Any, Iterable, Literal, TypeVar + +import torch +from jaxtyping import Float +from numpy import array, float32 +from PIL import Image +from safetensors import safe_open as _safe_open # type: ignore +from safetensors.torch import save_file as _save_file # type: ignore +from torch import ( + Tensor, + device as Device, + dtype as DType, + manual_seed as _manual_seed, # type: ignore + no_grad as _no_grad, # type: ignore + norm as _norm, # type: ignore +) +from torch.nn.functional import conv2d, interpolate as _interpolate, pad as _pad # type: ignore + +T = TypeVar("T") +E = TypeVar("E") + + +def norm(x: Tensor) -> Tensor: + return _norm(x) # type: ignore + + +def manual_seed(seed: int) -> None: + _manual_seed(seed) + + +class no_grad(_no_grad): + def __new__(cls, orig_func: Any | None = None) -> "no_grad": # type: ignore + return object.__new__(cls) + + +def pad(x: Tensor, pad: Iterable[int], value: float = 0.0, mode: str = "constant") -> Tensor: + return _pad(input=x, pad=pad, value=value, mode=mode) # type: ignore + + +def interpolate(x: Tensor, factor: float | torch.Size, mode: str = "nearest") -> Tensor: + return ( + _interpolate(x, scale_factor=factor, mode=mode) + if isinstance(factor, float | int) + else _interpolate(x, size=factor, mode=mode) + ) # type: ignore + + +# Adapted from https://github.com/pytorch/vision/blob/main/torchvision/transforms/_functional_tensor.py +def normalize( + tensor: Float[Tensor, "*batch channels height width"], mean: list[float], std: list[float] +) -> Float[Tensor, "*batch channels height width"]: + assert tensor.is_floating_point() + assert tensor.ndim >= 3 + + dtype = tensor.dtype + pixel_mean = torch.tensor(mean, dtype=dtype, device=tensor.device).view(-1, 1, 1) + pixel_std = torch.tensor(std, dtype=dtype, device=tensor.device).view(-1, 1, 1) + if (pixel_std == 0).any(): + raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.") + + return (tensor - pixel_mean) / pixel_std + + +# Adapted from https://github.com/pytorch/vision/blob/main/torchvision/transforms/_functional_tensor.py +def gaussian_blur( + tensor: Float[Tensor, "*batch channels height width"], + kernel_size: int | tuple[int, int], + sigma: float | tuple[float, float] | None = None, +) -> Float[Tensor, "*batch channels height width"]: + assert torch.is_floating_point(tensor) + + def get_gaussian_kernel1d(kernel_size: int, sigma: float) -> Float[Tensor, "kernel_size"]: + ksize_half = (kernel_size - 1) * 0.5 + x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) + pdf = torch.exp(-0.5 * (x / sigma).pow(2)) + kernel1d = pdf / pdf.sum() + return kernel1d + + def get_gaussian_kernel2d( + kernel_size_x: int, kernel_size_y: int, sigma_x: float, sigma_y: float, dtype: DType, device: Device + ) -> Float[Tensor, "kernel_size_y kernel_size_x"]: + kernel1d_x = get_gaussian_kernel1d(kernel_size_x, sigma_x).to(device, dtype=dtype) + kernel1d_y = get_gaussian_kernel1d(kernel_size_y, sigma_y).to(device, dtype=dtype) + kernel2d = torch.mm(kernel1d_y[:, None], kernel1d_x[None, :]) + return kernel2d + + def default_sigma(kernel_size: int) -> float: + return kernel_size * 0.15 + 0.35 + + if isinstance(kernel_size, int): + kx, ky = kernel_size, kernel_size + else: + kx, ky = kernel_size + + if sigma is None: + sx, sy = default_sigma(kx), default_sigma(ky) + elif isinstance(sigma, float): + sx, sy = sigma, sigma + else: + assert isinstance(sigma, tuple) + sx, sy = sigma + + channels = tensor.shape[-3] + kernel = get_gaussian_kernel2d(kx, ky, sx, sy, dtype=tensor.dtype, device=tensor.device) + kernel = kernel.expand(channels, 1, kernel.shape[0], kernel.shape[1]) + + # pad = (left, right, top, bottom) + tensor = pad(tensor, pad=(kx // 2, kx // 2, ky // 2, ky // 2), mode="reflect") + tensor = conv2d(tensor, weight=kernel, groups=channels) + + return tensor + + +def image_to_tensor(image: Image.Image, device: Device | str | None = None, dtype: DType | None = None) -> Tensor: + """ + Convert a PIL Image to a Tensor. + + If the image is in mode `RGB` the tensor will have shape `[3, H, W]`, otherwise + `[1, H, W]` for mode `L` (grayscale) or `[4, H, W]` for mode `RGBA`. + + Values are clamped to the range `[0, 1]`. + """ + image_tensor = torch.tensor(array(image).astype(float32) / 255.0, device=device, dtype=dtype) + + match image.mode: + case "L": + image_tensor = image_tensor.unsqueeze(0) + case "RGBA" | "RGB": + image_tensor = image_tensor.permute(2, 0, 1) + case _: + raise ValueError(f"Unsupported image mode: {image.mode}") + + return image_tensor.unsqueeze(0) + + +def tensor_to_image(tensor: Tensor) -> Image.Image: + """ + Convert a Tensor to a PIL Image. + + The tensor must have shape `[1, channels, height, width]` where the number of + channels is either 1 (grayscale) or 3 (RGB) or 4 (RGBA). + + Expected values are in the range `[0, 1]` and are clamped to this range. + """ + assert tensor.ndim == 4 and tensor.shape[0] == 1, f"Unsupported tensor shape: {tensor.shape}" + num_channels = tensor.shape[1] + tensor = tensor.clamp(0, 1).squeeze(0) + + match num_channels: + case 1: + tensor = tensor.squeeze(0) + case 3 | 4: + tensor = tensor.permute(1, 2, 0) + case _: + raise ValueError(f"Unsupported number of channels: {num_channels}") + + return Image.fromarray((tensor.cpu().numpy() * 255).astype("uint8")) # type: ignore[reportUnknownType] + + +def safe_open( + path: Path | str, + framework: Literal["pytorch", "tensorflow", "flax", "numpy"], + device: Device | str = "cpu", +) -> dict[str, Tensor]: + framework_mapping = { + "pytorch": "pt", + "tensorflow": "tf", + "flax": "flax", + "numpy": "numpy", + } + return _safe_open(str(path), framework=framework_mapping[framework], device=str(device)) # type: ignore + + +def load_from_safetensors(path: Path | str, device: Device | str = "cpu") -> dict[str, Tensor]: + with safe_open(path=path, framework="pytorch", device=device) as tensors: # type: ignore + return {key: tensors.get_tensor(key) for key in tensors.keys()} # type: ignore + + +def load_metadata_from_safetensors(path: Path | str) -> dict[str, str] | None: + with safe_open(path=path, framework="pytorch") as tensors: # type: ignore + return tensors.metadata() # type: ignore + + +def save_to_safetensors(path: Path | str, tensors: dict[str, Tensor], metadata: dict[str, str] | None = None) -> None: + _save_file(tensors, path, metadata) # type: ignore + + +def summarize_tensor(tensor: torch.Tensor, /) -> str: + return ( + "Tensor(" + + ", ".join( + [ + f"shape=({', '.join(map(str, tensor.shape))})", + f"dtype={str(object=tensor.dtype).removeprefix('torch.')}", + f"device={tensor.device}", + f"min={tensor.min():.2f}", # type: ignore + f"max={tensor.max():.2f}", # type: ignore + f"mean={tensor.mean():.2f}", + f"std={tensor.std():.2f}", + f"norm={norm(x=tensor):.2f}", + f"grad={tensor.requires_grad}", + ] + ) + + ")" + ) diff --git a/imaginairy/vendored/refiners/foundationals/__init__.py b/imaginairy/vendored/refiners/foundationals/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/imaginairy/vendored/refiners/foundationals/clip/__init__.py b/imaginairy/vendored/refiners/foundationals/clip/__init__.py 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ztxONk0vixS8S>(hzAbw(#0>;C#ks|_ha+((8DqrW_Ad=hHlbPG7JCqoQYc=?dg}X> z=@SQQ4dhuRpq4|p(g#`?$z1JCMe_-s9ZCq=j%b8zcK3ZHG1D=BRYyW{d_f?@sE3m= z&|HmgkeI;Mhm!J^35$8OxjG^dfASN<0q3^NpF2JSh2Ov5RXTanenuCT&t8oT^u^mh zg}2{*uTwPnR^NXf-ae+l{Il@(HKu7_1qjU1RQlcj|G(kSeL{bx0Jc0m-n()VY;oHw zIzVB?O@_32+^NgTKrF&CdZi=_#;fQd&@66bKGZ$rBa(EUG?_*5Y5sg0q!@<>3)C3x zDHjQ08Yct5byJU)RZx}jpJ%P1PloKt@FZl9Bj8yPOMy-m){oxJYiEr>nJ)qwWT{Qp zj_mW)R04a=pMh1%Dv(aj1Q1nfowhih;WqMv}`9*1Gi{T$c5f9??B^HHhv{z(}TVD%q4)no;HK6^>A)q~X hSpdDrox+^4cgglrz7rRzDPZO0{{oPT@#kp3FaTBF64n3! literal 0 HcmV?d00001 diff --git a/imaginairy/vendored/refiners/foundationals/clip/common.py b/imaginairy/vendored/refiners/foundationals/clip/common.py new file mode 100644 index 0000000..f397144 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/clip/common.py @@ -0,0 +1,48 @@ +from torch import Tensor, arange, device as Device, dtype as DType + +import imaginairy.vendored.refiners.fluxion.layers as fl + + +class PositionalEncoder(fl.Chain): + def __init__( + self, + max_sequence_length: int, + embedding_dim: int, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.max_sequence_length = max_sequence_length + self.embedding_dim = embedding_dim + super().__init__( + fl.Lambda(func=self.get_position_ids), + fl.Embedding( + num_embeddings=max_sequence_length, + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + ), + ) + + @property + def position_ids(self) -> Tensor: + return arange(end=self.max_sequence_length, device=self.device).reshape(1, -1) + + def get_position_ids(self, x: Tensor) -> Tensor: + return self.position_ids[:, : x.shape[1]] + + +class FeedForward(fl.Chain): + def __init__( + self, + embedding_dim: int, + feedforward_dim: int, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.feedforward_dim = feedforward_dim + super().__init__( + fl.Linear(in_features=embedding_dim, out_features=feedforward_dim, device=device, dtype=dtype), + fl.GeLU(), + fl.Linear(in_features=feedforward_dim, out_features=embedding_dim, device=device, dtype=dtype), + ) diff --git a/imaginairy/vendored/refiners/foundationals/clip/concepts.py b/imaginairy/vendored/refiners/foundationals/clip/concepts.py new file mode 100644 index 0000000..5ea7db7 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/clip/concepts.py @@ -0,0 +1,148 @@ +import re +from typing import cast + +import torch.nn.functional as F +from torch import Tensor, cat, zeros +from torch.nn import Parameter + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter +from imaginairy.vendored.refiners.foundationals.clip.text_encoder import CLIPTextEncoder, TokenEncoder +from imaginairy.vendored.refiners.foundationals.clip.tokenizer import CLIPTokenizer + + +class EmbeddingExtender(fl.Chain, Adapter[TokenEncoder]): + old_weight: Parameter + new_weight: Parameter + + def __init__( + self, + target: TokenEncoder, + ) -> None: + with self.setup_adapter(target): + super().__init__(fl.Lambda(func=self.lookup)) + self.old_weight = cast(Parameter, target.weight) + self.new_weight = Parameter( + zeros([0, target.embedding_dim], device=target.device, dtype=target.dtype) + ) # requires_grad=True by default + + # Use F.embedding instead of nn.Embedding to make sure that gradients can only be computed for the new embeddings + def lookup(self, x: Tensor) -> Tensor: + # Concatenate old and new weights for dynamic embedding updates during training + return F.embedding(x, cat([self.old_weight, self.new_weight])) + + def add_embedding(self, embedding: Tensor) -> None: + assert embedding.shape == (self.old_weight.shape[1],) + self.new_weight = Parameter( + cat([self.new_weight, embedding.unsqueeze(0).to(self.new_weight.device, self.new_weight.dtype)]) + ) + + @property + def num_embeddings(self) -> int: + return self.old_weight.shape[0] + self.new_weight.shape[0] + + +class TokenExtender(fl.Chain, Adapter[CLIPTokenizer]): + def __init__(self, target: CLIPTokenizer) -> None: + with self.setup_adapter(target): + super().__init__( + CLIPTokenizer( + vocabulary_path=target.vocabulary_path, + sequence_length=target.sequence_length, + start_of_text_token_id=target.start_of_text_token_id, + end_of_text_token_id=target.end_of_text_token_id, + pad_token_id=target.pad_token_id, + ) + ) + + def add_token(self, token: str, token_id: int) -> None: + token = token.lower() + tokenizer = self.ensure_find(CLIPTokenizer) + assert token_id not in tokenizer.token_to_id_mapping.values() + tokenizer.token_to_id_mapping[token] = token_id + current_pattern = tokenizer.token_pattern.pattern + new_pattern = re.escape(token) + "|" + current_pattern + tokenizer.token_pattern = re.compile(new_pattern, re.IGNORECASE) + # Define the keyword as its own smallest subtoken + tokenizer.byte_pair_encoding_cache[token] = token + + +class ConceptExtender(fl.Chain, Adapter[CLIPTextEncoder]): + """ + Extends the vocabulary of a CLIPTextEncoder with one or multiple new concepts, e.g. obtained via the Textual Inversion technique. + + Example: + import torch + from imaginairy.vendored.refiners.foundationals.clip.concepts import ConceptExtender + from imaginairy.vendored.refiners.foundationals.clip.text_encoder import CLIPTextEncoderL + from imaginairy.vendored.refiners.fluxion.utils import load_from_safetensors + + encoder = CLIPTextEncoderL(device="cuda") + tensors = load_from_safetensors("CLIPTextEncoderL.safetensors") + encoder.load_state_dict(tensors) + + cat_embedding = torch.load("cat_embedding.bin")[""] + dog_embedding = torch.load("dog_embedding.bin")[""] + + extender = ConceptExtender(encoder) + extender.add_concept(token="", embedding=cat_embedding) + extender.inject() + # New concepts can be added at any time + extender.add_concept(token="", embedding=dog_embedding) + + # Now the encoder can be used with the new concepts + """ + + def __init__(self, target: CLIPTextEncoder) -> None: + with self.setup_adapter(target): + super().__init__(target) + + try: + token_encoder, token_encoder_parent = next(target.walk(TokenEncoder)) + self._token_encoder_parent = [token_encoder_parent] + + except StopIteration: + raise RuntimeError("TokenEncoder not found.") + + try: + clip_tokenizer, clip_tokenizer_parent = next(target.walk(CLIPTokenizer)) + self._clip_tokenizer_parent = [clip_tokenizer_parent] + except StopIteration: + raise RuntimeError("Tokenizer not found.") + + self._embedding_extender = [EmbeddingExtender(token_encoder)] + self._token_extender = [TokenExtender(clip_tokenizer)] + + @property + def embedding_extender(self) -> EmbeddingExtender: + assert len(self._embedding_extender) == 1, "EmbeddingExtender not found." + return self._embedding_extender[0] + + @property + def token_extender(self) -> TokenExtender: + assert len(self._token_extender) == 1, "TokenExtender not found." + return self._token_extender[0] + + @property + def token_encoder_parent(self) -> fl.Chain: + assert len(self._token_encoder_parent) == 1, "TokenEncoder parent not found." + return self._token_encoder_parent[0] + + @property + def clip_tokenizer_parent(self) -> fl.Chain: + assert len(self._clip_tokenizer_parent) == 1, "Tokenizer parent not found." + return self._clip_tokenizer_parent[0] + + def add_concept(self, token: str, embedding: Tensor) -> None: + self.embedding_extender.add_embedding(embedding) + self.token_extender.add_token(token, self.embedding_extender.num_embeddings - 1) + + def inject(self: "ConceptExtender", parent: fl.Chain | None = None) -> "ConceptExtender": + self.embedding_extender.inject(self.token_encoder_parent) + self.token_extender.inject(self.clip_tokenizer_parent) + return super().inject(parent) + + def eject(self) -> None: + self.embedding_extender.eject() + self.token_extender.eject() + super().eject() diff --git a/imaginairy/vendored/refiners/foundationals/clip/image_encoder.py b/imaginairy/vendored/refiners/foundationals/clip/image_encoder.py new file mode 100644 index 0000000..0e4a7ef --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/clip/image_encoder.py @@ -0,0 +1,179 @@ +from typing import Callable + +from torch import Tensor, device as Device, dtype as DType + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.foundationals.clip.common import FeedForward, PositionalEncoder + + +class ClassToken(fl.Chain): + def __init__(self, embedding_dim: int, device: Device | str | None = None, dtype: DType | None = None) -> None: + self.embedding_dim = embedding_dim + super().__init__(fl.Parameter(1, embedding_dim, device=device, dtype=dtype)) + + +class PatchEncoder(fl.Chain): + def __init__( + self, + in_channels: int, + out_channels: int, + patch_size: int = 16, + use_bias: bool = True, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.in_channels = in_channels + self.out_channels = out_channels + self.patch_size = patch_size + self.use_bias = use_bias + super().__init__( + fl.Conv2d( + in_channels=self.in_channels, + out_channels=self.out_channels, + kernel_size=(self.patch_size, self.patch_size), + stride=(self.patch_size, self.patch_size), + use_bias=self.use_bias, + device=device, + dtype=dtype, + ), + fl.Permute(0, 2, 3, 1), + ) + + +class TransformerLayer(fl.Chain): + def __init__( + self, + embedding_dim: int = 768, + feedforward_dim: int = 3072, + num_attention_heads: int = 12, + layer_norm_eps: float = 1e-5, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.feedforward_dim = feedforward_dim + self.num_attention_heads = num_attention_heads + self.layer_norm_eps = layer_norm_eps + super().__init__( + fl.Residual( + fl.LayerNorm(normalized_shape=embedding_dim, eps=layer_norm_eps, device=device, dtype=dtype), + fl.SelfAttention( + embedding_dim=embedding_dim, num_heads=num_attention_heads, device=device, dtype=dtype + ), + ), + fl.Residual( + fl.LayerNorm(normalized_shape=embedding_dim, eps=layer_norm_eps, device=device, dtype=dtype), + FeedForward(embedding_dim=embedding_dim, feedforward_dim=feedforward_dim, device=device, dtype=dtype), + ), + ) + + +class ViTEmbeddings(fl.Chain): + def __init__( + self, + image_size: int = 224, + embedding_dim: int = 768, + patch_size: int = 32, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.image_size = image_size + self.embedding_dim = embedding_dim + self.patch_size = patch_size + super().__init__( + fl.Concatenate( + ClassToken(embedding_dim, device=device, dtype=dtype), + fl.Chain( + PatchEncoder( + in_channels=3, + out_channels=embedding_dim, + patch_size=patch_size, + use_bias=False, + device=device, + dtype=dtype, + ), + fl.Reshape((image_size // patch_size) ** 2, embedding_dim), + ), + dim=1, + ), + fl.Residual( + PositionalEncoder( + max_sequence_length=(image_size // patch_size) ** 2 + 1, + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + ), + ), + ) + + +class CLIPImageEncoder(fl.Chain): + def __init__( + self, + image_size: int = 224, + embedding_dim: int = 768, + output_dim: int = 512, + patch_size: int = 32, + num_layers: int = 12, + num_attention_heads: int = 12, + feedforward_dim: int = 3072, + layer_norm_eps: float = 1e-5, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.image_size = image_size + self.embedding_dim = embedding_dim + self.output_dim = output_dim + self.patch_size = patch_size + self.num_layers = num_layers + self.num_attention_heads = num_attention_heads + self.feedforward_dim = feedforward_dim + cls_token_pooling: Callable[[Tensor], Tensor] = lambda x: x[:, 0, :] + super().__init__( + ViTEmbeddings( + image_size=image_size, embedding_dim=embedding_dim, patch_size=patch_size, device=device, dtype=dtype + ), + fl.LayerNorm(normalized_shape=embedding_dim, eps=layer_norm_eps, device=device, dtype=dtype), + fl.Chain( + TransformerLayer( + embedding_dim=embedding_dim, + feedforward_dim=feedforward_dim, + num_attention_heads=num_attention_heads, + layer_norm_eps=layer_norm_eps, + device=device, + dtype=dtype, + ) + for _ in range(num_layers) + ), + fl.Lambda(func=cls_token_pooling), + fl.LayerNorm(normalized_shape=embedding_dim, eps=layer_norm_eps, device=device, dtype=dtype), + fl.Linear(in_features=embedding_dim, out_features=output_dim, bias=False, device=device, dtype=dtype), + ) + + +class CLIPImageEncoderH(CLIPImageEncoder): + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + super().__init__( + embedding_dim=1280, + output_dim=1024, + patch_size=14, + num_layers=32, + num_attention_heads=16, + feedforward_dim=5120, + device=device, + dtype=dtype, + ) + + +class CLIPImageEncoderG(CLIPImageEncoder): + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + super().__init__( + embedding_dim=1664, + output_dim=1280, + patch_size=14, + num_layers=48, + num_attention_heads=16, + feedforward_dim=8192, + device=device, + dtype=dtype, + ) diff --git a/imaginairy/vendored/refiners/foundationals/clip/text_encoder.py b/imaginairy/vendored/refiners/foundationals/clip/text_encoder.py new file mode 100644 index 0000000..78ed7be --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/clip/text_encoder.py @@ -0,0 +1,195 @@ +from torch import device as Device, dtype as DType + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.foundationals.clip.common import FeedForward, PositionalEncoder +from imaginairy.vendored.refiners.foundationals.clip.tokenizer import CLIPTokenizer + + +class TokenEncoder(fl.Embedding): + def __init__( + self, + vocabulary_size: int, + embedding_dim: int, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.vocabulary_size = vocabulary_size + self.embedding_dim = embedding_dim + super().__init__( + num_embeddings=vocabulary_size, + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + ) + + +class TransformerLayer(fl.Chain): + def __init__( + self, + embedding_dim: int, + feedforward_dim: int, + num_attention_heads: int = 1, + layer_norm_eps: float = 1e-5, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.num_attention_heads = num_attention_heads + self.feedforward_dim = feedforward_dim + self.layer_norm_eps = layer_norm_eps + super().__init__( + fl.Residual( + fl.LayerNorm( + normalized_shape=embedding_dim, + eps=layer_norm_eps, + device=device, + dtype=dtype, + ), + fl.SelfAttention( + embedding_dim=embedding_dim, + num_heads=num_attention_heads, + is_causal=True, + device=device, + dtype=dtype, + ), + ), + fl.Residual( + fl.LayerNorm( + normalized_shape=embedding_dim, + eps=layer_norm_eps, + device=device, + dtype=dtype, + ), + FeedForward( + embedding_dim=embedding_dim, + feedforward_dim=feedforward_dim, + device=device, + dtype=dtype, + ), + ), + ) + + +class CLIPTextEncoder(fl.Chain): + def __init__( + self, + embedding_dim: int = 768, + max_sequence_length: int = 77, + vocabulary_size: int = 49408, + num_layers: int = 12, + num_attention_heads: int = 12, + feedforward_dim: int = 3072, + layer_norm_eps: float = 1e-5, + use_quick_gelu: bool = False, + tokenizer: CLIPTokenizer | None = None, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.max_sequence_length = max_sequence_length + self.vocabulary_size = vocabulary_size + self.num_layers = num_layers + self.num_attention_heads = num_attention_heads + self.feedforward_dim = feedforward_dim + self.layer_norm_eps = layer_norm_eps + self.use_quick_gelu = use_quick_gelu + super().__init__( + tokenizer or CLIPTokenizer(sequence_length=max_sequence_length), + fl.Converter(set_dtype=False), + fl.Sum( + TokenEncoder( + vocabulary_size=vocabulary_size, + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + ), + PositionalEncoder( + max_sequence_length=max_sequence_length, + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + ), + ), + *( + TransformerLayer( + embedding_dim=embedding_dim, + num_attention_heads=num_attention_heads, + feedforward_dim=feedforward_dim, + layer_norm_eps=layer_norm_eps, + device=device, + dtype=dtype, + ) + for _ in range(num_layers) + ), + fl.LayerNorm(normalized_shape=embedding_dim, eps=layer_norm_eps, device=device, dtype=dtype), + ) + if use_quick_gelu: + for gelu, parent in self.walk(predicate=lambda m, _: isinstance(m, fl.GeLU)): + parent.replace(old_module=gelu, new_module=fl.ApproximateGeLU()) + + +class CLIPTextEncoderL(CLIPTextEncoder): + """ + CLIPTextEncoderL is the CLIP text encoder with the following parameters: + embedding_dim=768 + num_layers=12 + num_attention_heads=12 + feedforward_dim=3072 + use_quick_gelu=True + + We replace the GeLU activation function with an approximate GeLU to comply with the original CLIP implementation + of OpenAI (https://github.com/openai/CLIP/blob/main/clip/model.py#L166) + """ + + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + super().__init__( + embedding_dim=768, + num_layers=12, + num_attention_heads=12, + feedforward_dim=3072, + use_quick_gelu=True, + device=device, + dtype=dtype, + ) + + +class CLIPTextEncoderH(CLIPTextEncoder): + """ + CLIPTextEncoderH is the CLIP text encoder with the following parameters: + embedding_dim=1024 + num_layers=23 + num_attention_heads=16 + feedforward_dim=4096 + """ + + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + super().__init__( + embedding_dim=1024, + num_layers=23, + num_attention_heads=16, + feedforward_dim=4096, + device=device, + dtype=dtype, + ) + + +class CLIPTextEncoderG(CLIPTextEncoder): + """ + CLIPTextEncoderG is the CLIP text encoder with the following parameters: + embedding_dim=1280 + num_layers=32 + num_attention_heads=16 + feedforward_dim=5120 + """ + + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + tokenizer = CLIPTokenizer(pad_token_id=0) + super().__init__( + embedding_dim=1280, + num_layers=32, + num_attention_heads=20, + feedforward_dim=5120, + tokenizer=tokenizer, + device=device, + dtype=dtype, + ) diff --git a/imaginairy/vendored/refiners/foundationals/clip/tokenizer.py b/imaginairy/vendored/refiners/foundationals/clip/tokenizer.py new file mode 100644 index 0000000..3e66659 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/clip/tokenizer.py @@ -0,0 +1,121 @@ +import gzip +import re +from functools import lru_cache +from itertools import islice +from pathlib import Path + +from torch import Tensor, tensor + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion import pad + + +class CLIPTokenizer(fl.Module): + def __init__( + self, + vocabulary_path: str | Path = Path(__file__).resolve().parent / "bpe_simple_vocab_16e6.txt.gz", + sequence_length: int = 77, + start_of_text_token_id: int = 49406, + end_of_text_token_id: int = 49407, + pad_token_id: int = 49407, + ) -> None: + super().__init__() + self.vocabulary_path = vocabulary_path + self.sequence_length = sequence_length + self.byte_to_unicode_mapping = self.get_bytes_to_unicode_mapping() + self.byte_decoder = {v: k for k, v in self.byte_to_unicode_mapping.items()} + merge_tuples = [ + tuple(merge.split()) + for merge in gzip.open(filename=vocabulary_path) + .read() + .decode(encoding="utf-8") + .split(sep="\n")[1 : 49152 - 256 - 2 + 1] + ] + vocabulary = ( + list(self.byte_to_unicode_mapping.values()) + + [v + "" for v in self.byte_to_unicode_mapping.values()] + + ["".join(merge) for merge in merge_tuples] + + ["", ""] + ) + self.token_to_id_mapping = {token: i for i, token in enumerate(iterable=vocabulary)} + self.byte_pair_encoding_ranks = {merge: i for i, merge in enumerate(iterable=merge_tuples)} + self.byte_pair_encoding_cache = {"": ""} + # Note: this regular expression does not support Unicode. It was changed so + # to get rid of the dependence on the `regex` module. Unicode support could + # potentially be added back by leveraging the `\w` character class. + self.token_pattern = re.compile( + pattern=r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[a-zA-Z]+|[0-9]|[^\s\w]+""", + flags=re.IGNORECASE, + ) + self.start_of_text_token_id: int = start_of_text_token_id + self.end_of_text_token_id: int = end_of_text_token_id + self.pad_token_id: int = pad_token_id + + def forward(self, text: str) -> Tensor: + tokens = self.encode(text=text, max_length=self.sequence_length).unsqueeze(dim=0) + assert ( + tokens.shape[1] <= self.sequence_length + ), f"Text is too long: tokens.shape[1] > sequence_length: {tokens.shape[1]} > {self.sequence_length}" + return pad(x=tokens, pad=(0, self.sequence_length - tokens.shape[1]), value=self.pad_token_id) + + @lru_cache() + def get_bytes_to_unicode_mapping(self) -> dict[int, str]: + initial_byte_values = ( + list(range(ord("!"), ord("~") + 1)) + + list(range(ord("¡"), ord("¬") + 1)) + + list(range(ord("®"), ord("ÿ") + 1)) + ) + extra_unicode_values = (byte for byte in range(2**8) if byte not in initial_byte_values) + byte_values = initial_byte_values + list(extra_unicode_values) + unicode_values = [chr(value) for value in byte_values] + return dict(zip(byte_values, unicode_values)) + + def byte_pair_encoding(self, token: str) -> str: + if token in self.byte_pair_encoding_cache: + return self.byte_pair_encoding_cache[token] + + def recursive_bpe(word: tuple[str, ...]) -> tuple[str, ...]: + if len(word) < 2: + return word + pairs = {(i, (word[i], word[i + 1])) for i in range(len(word) - 1)} + min_pair = min( + pairs, + key=lambda pair: self.byte_pair_encoding_ranks.get(pair[1], float("inf")), + ) + if min_pair[1] not in self.byte_pair_encoding_ranks: + return word + new_word: list[str] = [] + i = 0 + while i < len(word): + if i == min_pair[0]: + new_word.append(min_pair[1][0] + min_pair[1][1]) + i += 2 + else: + new_word.append(word[i]) + i += 1 + return recursive_bpe(tuple(new_word)) + + word = tuple(token[:-1]) + (token[-1] + "",) + result = " ".join(recursive_bpe(word=word)) + self.byte_pair_encoding_cache[token] = result + return result + + def encode(self, text: str, max_length: int | None = None) -> Tensor: + text = re.sub(pattern=r"\s+", repl=" ", string=text.lower()) + tokens = re.findall(pattern=self.token_pattern, string=text) + upper_bound = None + if max_length: + assert max_length >= 2 + upper_bound = max_length - 2 + encoded_tokens = islice( + ( + self.token_to_id_mapping[subtoken] + for token in tokens + for subtoken in self.byte_pair_encoding( + token="".join(self.byte_to_unicode_mapping[character] for character in token.encode("utf-8")) + ).split(sep=" ") + ), + 0, + upper_bound, + ) + return tensor(data=[self.start_of_text_token_id, *encoded_tokens, self.end_of_text_token_id]) diff --git a/imaginairy/vendored/refiners/foundationals/dinov2/__init__.py b/imaginairy/vendored/refiners/foundationals/dinov2/__init__.py new file mode 100644 index 0000000..8e802bb --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/dinov2/__init__.py @@ -0,0 +1,29 @@ +from .dinov2 import ( + DINOv2_base, + DINOv2_base_reg, + DINOv2_large, + DINOv2_large_reg, + DINOv2_small, + DINOv2_small_reg, +) +from .vit import ( + ViT, + ViT_base, + ViT_large, + ViT_small, + ViT_tiny, +) + +__all__ = [ + "DINOv2_base", + "DINOv2_base_reg", + "DINOv2_large", + "DINOv2_large_reg", + "DINOv2_small", + "DINOv2_small_reg", + "ViT", + "ViT_base", + "ViT_large", + "ViT_small", + "ViT_tiny", +] diff --git a/imaginairy/vendored/refiners/foundationals/dinov2/dinov2.py b/imaginairy/vendored/refiners/foundationals/dinov2/dinov2.py new file mode 100644 index 0000000..deb31b7 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/dinov2/dinov2.py @@ -0,0 +1,148 @@ +import torch + +from imaginairy.vendored.refiners.foundationals.dinov2.vit import ViT + +# TODO: add preprocessing logic like +# https://github.com/facebookresearch/dinov2/blob/2302b6b/dinov2/data/transforms.py#L77 + + +class DINOv2_small(ViT): + def __init__( + self, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + super().__init__( + embedding_dim=384, + patch_size=14, + image_size=518, + num_layers=12, + num_heads=6, + device=device, + dtype=dtype, + ) + + +class DINOv2_base(ViT): + def __init__( + self, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + super().__init__( + embedding_dim=768, + patch_size=14, + image_size=518, + num_layers=12, + num_heads=12, + device=device, + dtype=dtype, + ) + + +class DINOv2_large(ViT): + def __init__( + self, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + super().__init__( + embedding_dim=1024, + patch_size=14, + image_size=518, + num_layers=24, + num_heads=16, + device=device, + dtype=dtype, + ) + + +# TODO: implement SwiGLU layer +# class DINOv2_giant2(ViT): +# def __init__( +# self, +# device: torch.device | str | None = None, +# dtype: torch.dtype | None = None, +# ) -> None: +# super().__init__( +# embedding_dim=1536, +# patch_size=14, +# image_size=518, +# num_layers=40, +# num_heads=24, +# device=device, +# dtype=dtype, +# ) + + +class DINOv2_small_reg(ViT): + def __init__( + self, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + super().__init__( + embedding_dim=384, + patch_size=14, + image_size=518, + num_layers=12, + num_heads=6, + num_registers=4, + device=device, + dtype=dtype, + ) + + +class DINOv2_base_reg(ViT): + def __init__( + self, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + super().__init__( + embedding_dim=768, + patch_size=14, + image_size=518, + num_layers=12, + num_heads=12, + num_registers=4, + device=device, + dtype=dtype, + ) + + +class DINOv2_large_reg(ViT): + def __init__( + self, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + super().__init__( + embedding_dim=1024, + patch_size=14, + image_size=518, + num_layers=24, + num_heads=16, + num_registers=4, + device=device, + dtype=dtype, + ) + + +# TODO: implement SwiGLU layer +# class DINOv2_giant2_reg(ViT): +# def __init__( +# self, +# device: torch.device | str | None = None, +# dtype: torch.dtype | None = None, +# ) -> None: +# super().__init__( +# embedding_dim=1536, +# patch_size=14, +# image_size=518, +# num_layers=40, +# num_heads=24, +# num_registers=4, +# device=device, +# dtype=dtype, +# ) diff --git a/imaginairy/vendored/refiners/foundationals/dinov2/vit.py b/imaginairy/vendored/refiners/foundationals/dinov2/vit.py new file mode 100644 index 0000000..24675d5 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/dinov2/vit.py @@ -0,0 +1,373 @@ +import torch +from torch import Tensor + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.layers.activations import Activation + + +class ClassToken(fl.Chain): + """Learnable token representing the class of the input.""" + + def __init__( + self, + embedding_dim: int, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + self.embedding_dim = embedding_dim + + super().__init__( + fl.Parameter( + *(1, embedding_dim), + device=device, + dtype=dtype, + ), + ) + + +class PositionalEncoder(fl.Residual): + """Encode the position of each patch in the input.""" + + def __init__( + self, + sequence_length: int, + embedding_dim: int, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + self.num_patches = sequence_length + self.embedding_dim = embedding_dim + + super().__init__( + fl.Parameter( + *(sequence_length, embedding_dim), + device=device, + dtype=dtype, + ), + ) + + +class LayerScale(fl.WeightedModule): + """Scale the input tensor by a learnable parameter.""" + + def __init__( + self, + embedding_dim: int, + init_value: float = 1.0, + dtype: torch.dtype | None = None, + device: torch.device | str | None = None, + ) -> None: + super().__init__() + self.embedding_dim = embedding_dim + + self.register_parameter( + name="weight", + param=torch.nn.Parameter( + torch.full( + size=(embedding_dim,), + fill_value=init_value, + dtype=dtype, + device=device, + ), + ), + ) + + def forward(self, x: Tensor) -> Tensor: + return x * self.weight + + +class FeedForward(fl.Chain): + """Apply two linear transformations interleaved by an activation function.""" + + def __init__( + self, + embedding_dim: int, + feedforward_dim: int, + activation: Activation = fl.GeLU, # type: ignore + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.feedforward_dim = feedforward_dim + + super().__init__( + fl.Linear( + in_features=embedding_dim, + out_features=feedforward_dim, + device=device, + dtype=dtype, + ), + activation(), + fl.Linear( + in_features=feedforward_dim, + out_features=embedding_dim, + device=device, + dtype=dtype, + ), + ) + + +class PatchEncoder(fl.Chain): + """Encode an image into a sequence of patches.""" + + def __init__( + self, + in_channels: int, + out_channels: int, + patch_size: int, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + self.in_channels = in_channels + self.out_channels = out_channels + self.patch_size = patch_size + + super().__init__( + fl.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=patch_size, + stride=patch_size, + device=device, + dtype=dtype, + ), # (N,3,H,W) -> (N,D,P,P) + fl.Reshape(out_channels, -1), # (N,D,P,P) -> (N,D,P²) + fl.Transpose(1, 2), # (N,D,P²) -> (N,P²,D) + ) + + +class TransformerLayer(fl.Chain): + """Apply a multi-head self-attention mechanism to the input tensor.""" + + def __init__( + self, + embedding_dim: int, + num_heads: int, + norm_eps: float, + mlp_ratio: int, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.num_heads = num_heads + self.norm_eps = norm_eps + self.mlp_ratio = mlp_ratio + + super().__init__( + fl.Residual( + fl.LayerNorm( + normalized_shape=embedding_dim, + eps=norm_eps, + device=device, + dtype=dtype, + ), + fl.SelfAttention( + embedding_dim=embedding_dim, + num_heads=num_heads, + device=device, + dtype=dtype, + ), + LayerScale( + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + ), + ), + fl.Residual( + fl.LayerNorm( + normalized_shape=embedding_dim, + eps=norm_eps, + device=device, + dtype=dtype, + ), + FeedForward( + embedding_dim=embedding_dim, + feedforward_dim=embedding_dim * mlp_ratio, + device=device, + dtype=dtype, + ), + LayerScale( + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + ), + ), + ) + + +class Transformer(fl.Chain): + """Alias for a Chain of TransformerLayer.""" + + +class Registers(fl.Concatenate): + """Insert register tokens between CLS token and patches.""" + + def __init__( + self, + num_registers: int, + embedding_dim: int, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + self.num_registers = num_registers + self.embedding_dim = embedding_dim + + super().__init__( + fl.Slicing(dim=1, end=1), + fl.Parameter( + *(num_registers, embedding_dim), + device=device, + dtype=dtype, + ), + fl.Slicing(dim=1, start=1), + dim=1, + ) + + +class ViT(fl.Chain): + """Vision Transformer (ViT). + + see https://arxiv.org/abs/2010.11929v2 + """ + + def __init__( + self, + embedding_dim: int = 768, + patch_size: int = 16, + image_size: int = 224, + num_layers: int = 12, + num_heads: int = 12, + norm_eps: float = 1e-6, + mlp_ratio: int = 4, + num_registers: int = 0, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + num_patches = image_size // patch_size + self.embedding_dim = embedding_dim + self.patch_size = patch_size + self.image_size = image_size + self.num_layers = num_layers + self.num_heads = num_heads + self.norm_eps = norm_eps + self.mlp_ratio = mlp_ratio + self.num_registers = num_registers + + super().__init__( + fl.Concatenate( + ClassToken( + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + ), + PatchEncoder( + in_channels=3, + out_channels=embedding_dim, + patch_size=patch_size, + device=device, + dtype=dtype, + ), + dim=1, + ), + # TODO: support https://github.com/facebookresearch/dinov2/blob/2302b6b/dinov2/models/vision_transformer.py#L179 + PositionalEncoder( + sequence_length=num_patches**2 + 1, + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + ), + Transformer( + TransformerLayer( + embedding_dim=embedding_dim, + num_heads=num_heads, + norm_eps=norm_eps, + mlp_ratio=mlp_ratio, + device=device, + dtype=dtype, + ) + for _ in range(num_layers) + ), + fl.LayerNorm( + normalized_shape=embedding_dim, + eps=norm_eps, + device=device, + dtype=dtype, + ), + ) + + if self.num_registers > 0: + registers = Registers( + num_registers=num_registers, + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + ) + self.insert_before_type(Transformer, registers) + + +class ViT_tiny(ViT): + def __init__( + self, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + super().__init__( + embedding_dim=192, + patch_size=16, + image_size=224, + num_layers=12, + num_heads=3, + device=device, + dtype=dtype, + ) + + +class ViT_small(ViT): + def __init__( + self, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + super().__init__( + embedding_dim=384, + patch_size=16, + image_size=224, + num_layers=12, + num_heads=6, + device=device, + dtype=dtype, + ) + + +class ViT_base(ViT): + def __init__( + self, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + super().__init__( + embedding_dim=768, + patch_size=16, + image_size=224, + num_layers=12, + num_heads=12, + device=device, + dtype=dtype, + ) + + +class ViT_large(ViT): + def __init__( + self, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> None: + super().__init__( + embedding_dim=1024, + patch_size=16, + image_size=224, + num_layers=24, + num_heads=16, + device=device, + dtype=dtype, + ) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/__init__.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/__init__.py new file mode 100644 index 0000000..3699396 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/__init__.py @@ -0,0 +1,40 @@ +from imaginairy.vendored.refiners.foundationals.clip.text_encoder import ( + CLIPTextEncoderL, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.auto_encoder import ( + LatentDiffusionAutoencoder, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.freeu import SDFreeUAdapter +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers import DPMSolver, Scheduler +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1 import ( + SD1ControlnetAdapter, + SD1IPAdapter, + SD1T2IAdapter, + SD1UNet, + StableDiffusion_1, + StableDiffusion_1_Inpainting, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl import ( + DoubleTextEncoder, + SDXLIPAdapter, + SDXLT2IAdapter, + SDXLUNet, +) + +__all__ = [ + "StableDiffusion_1", + "StableDiffusion_1_Inpainting", + "SD1UNet", + "SD1ControlnetAdapter", + "SD1IPAdapter", + "SD1T2IAdapter", + "SDXLUNet", + "DoubleTextEncoder", + "SDXLIPAdapter", + "SDXLT2IAdapter", + "DPMSolver", + "Scheduler", + "CLIPTextEncoderL", + "LatentDiffusionAutoencoder", + "SDFreeUAdapter", +] diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/auto_encoder.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/auto_encoder.py new file mode 100644 index 0000000..2da2ef6 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/auto_encoder.py @@ -0,0 +1,221 @@ +from PIL import Image +from torch import Tensor, device as Device, dtype as DType + +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.fluxion.layers import ( + Chain, + Conv2d, + Downsample, + GroupNorm, + Identity, + Residual, + SelfAttention2d, + SiLU, + Slicing, + Sum, + Upsample, +) +from imaginairy.vendored.refiners.fluxion.utils import image_to_tensor, tensor_to_image + + +class Resnet(Sum): + def __init__( + self, + in_channels: int, + out_channels: int, + num_groups: int = 32, + device: Device | str | None = None, + dtype: DType | None = None, + ): + self.in_channels = in_channels + self.out_channels = out_channels + shortcut = ( + Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, device=device, dtype=dtype) + if in_channels != out_channels + else Identity() + ) + super().__init__( + shortcut, + Chain( + GroupNorm(channels=in_channels, num_groups=num_groups, device=device, dtype=dtype), + SiLU(), + Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + GroupNorm(channels=out_channels, num_groups=num_groups, device=device, dtype=dtype), + SiLU(), + Conv2d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + ), + ) + + +class Encoder(Chain): + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + resnet_sizes: list[int] = [128, 256, 512, 512, 512] + input_channels: int = 3 + latent_dim: int = 8 + resnet_layers: list[Chain] = [ + Chain( + [ + Resnet( + in_channels=resnet_sizes[i - 1] if i > 0 else resnet_sizes[0], + out_channels=resnet_sizes[i], + device=device, + dtype=dtype, + ), + Resnet(in_channels=resnet_sizes[i], out_channels=resnet_sizes[i], device=device, dtype=dtype), + ] + ) + for i in range(len(resnet_sizes)) + ] + for _, layer in zip(range(3), resnet_layers): + channels: int = layer[-1].out_channels # type: ignore + layer.append(Downsample(channels=channels, scale_factor=2, device=device, dtype=dtype)) + + attention_layer = Residual( + GroupNorm(channels=resnet_sizes[-1], num_groups=32, eps=1e-6, device=device, dtype=dtype), + SelfAttention2d(channels=resnet_sizes[-1], device=device, dtype=dtype), + ) + resnet_layers[-1].insert_after_type(Resnet, attention_layer) + super().__init__( + Conv2d( + in_channels=input_channels, + out_channels=resnet_sizes[0], + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + Chain(*resnet_layers), + Chain( + GroupNorm(channels=resnet_sizes[-1], num_groups=32, eps=1e-6, device=device, dtype=dtype), + SiLU(), + Conv2d( + in_channels=resnet_sizes[-1], + out_channels=latent_dim, + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + ), + Chain( + Conv2d(in_channels=8, out_channels=8, kernel_size=1, device=device, dtype=dtype), + Slicing(dim=1, end=4), + ), + ) + + def init_context(self) -> Contexts: + return {"sampling": {"shapes": []}} + + +class Decoder(Chain): + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + self.resnet_sizes: list[int] = [128, 256, 512, 512, 512] + self.latent_dim: int = 4 + self.output_channels: int = 3 + resnet_sizes = self.resnet_sizes[::-1] + resnet_layers: list[Chain] = [ + ( + Chain( + [ + Resnet( + in_channels=resnet_sizes[i - 1] if i > 0 else resnet_sizes[0], + out_channels=resnet_sizes[i], + device=device, + dtype=dtype, + ), + Resnet(in_channels=resnet_sizes[i], out_channels=resnet_sizes[i], device=device, dtype=dtype), + Resnet(in_channels=resnet_sizes[i], out_channels=resnet_sizes[i], device=device, dtype=dtype), + ] + ) + if i > 0 + else Chain( + [ + Resnet(in_channels=resnet_sizes[0], out_channels=resnet_sizes[i], device=device, dtype=dtype), + Resnet(in_channels=resnet_sizes[i], out_channels=resnet_sizes[i], device=device, dtype=dtype), + ] + ) + ) + for i in range(len(resnet_sizes)) + ] + attention_layer = Residual( + GroupNorm(channels=resnet_sizes[0], num_groups=32, eps=1e-6, device=device, dtype=dtype), + SelfAttention2d(channels=resnet_sizes[0], device=device, dtype=dtype), + ) + resnet_layers[0].insert(1, attention_layer) + for _, layer in zip(range(3), resnet_layers[1:]): + channels: int = layer[-1].out_channels + layer.insert(-1, Upsample(channels=channels, upsample_factor=2, device=device, dtype=dtype)) + super().__init__( + Conv2d( + in_channels=self.latent_dim, out_channels=self.latent_dim, kernel_size=1, device=device, dtype=dtype + ), + Conv2d( + in_channels=self.latent_dim, + out_channels=resnet_sizes[0], + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + Chain(*resnet_layers), + Chain( + GroupNorm(channels=resnet_sizes[-1], num_groups=32, eps=1e-6, device=device, dtype=dtype), + SiLU(), + Conv2d( + in_channels=resnet_sizes[-1], + out_channels=self.output_channels, + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + ), + ) + + +class LatentDiffusionAutoencoder(Chain): + encoder_scale = 0.18125 + + def __init__( + self, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( + Encoder(device=device, dtype=dtype), + Decoder(device=device, dtype=dtype), + ) + + def encode(self, x: Tensor) -> Tensor: + encoder = self[0] + x = self.encoder_scale * encoder(x) + return x + + def decode(self, x: Tensor) -> Tensor: + decoder = self[1] + x = decoder(x / self.encoder_scale) + return x + + def encode_image(self, image: Image.Image) -> Tensor: + x = image_to_tensor(image, device=self.device, dtype=self.dtype) + x = 2 * x - 1 + return self.encode(x) + + def decode_latents(self, x: Tensor) -> Image.Image: + x = self.decode(x) + x = (x + 1) / 2 + return tensor_to_image(x) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/cross_attention.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/cross_attention.py new file mode 100644 index 0000000..c829cd9 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/cross_attention.py @@ -0,0 +1,175 @@ +from torch import Size, Tensor, device as Device, dtype as DType + +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.fluxion.layers import ( + GLU, + Attention, + Chain, + Conv2d, + Flatten, + GeLU, + GroupNorm, + Identity, + LayerNorm, + Linear, + Parallel, + Residual, + SelfAttention, + SetContext, + Transpose, + Unflatten, + UseContext, +) + + +class CrossAttentionBlock(Chain): + def __init__( + self, + embedding_dim: int, + context_embedding_dim: int, + context_key: str, + num_heads: int = 1, + use_bias: bool = True, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.context_embedding_dim = context_embedding_dim + self.context = "cross_attention_block" + self.context_key = context_key + self.num_heads = num_heads + self.use_bias = use_bias + + super().__init__( + Residual( + LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype), + SelfAttention( + embedding_dim=embedding_dim, num_heads=num_heads, use_bias=use_bias, device=device, dtype=dtype + ), + ), + Residual( + LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype), + Parallel( + Identity(), + UseContext(context=self.context, key=context_key), + UseContext(context=self.context, key=context_key), + ), + Attention( + embedding_dim=embedding_dim, + num_heads=num_heads, + key_embedding_dim=context_embedding_dim, + value_embedding_dim=context_embedding_dim, + use_bias=use_bias, + device=device, + dtype=dtype, + ), + ), + Residual( + LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype), + Linear(in_features=embedding_dim, out_features=2 * 4 * embedding_dim, device=device, dtype=dtype), + GLU(GeLU()), + Linear(in_features=4 * embedding_dim, out_features=embedding_dim, device=device, dtype=dtype), + ), + ) + + +class StatefulFlatten(Chain): + def __init__(self, context: str, key: str, start_dim: int = 0, end_dim: int = -1) -> None: + self.start_dim = start_dim + self.end_dim = end_dim + + super().__init__( + SetContext(context=context, key=key, callback=self.push), + Flatten(start_dim=start_dim, end_dim=end_dim), + ) + + def push(self, sizes: list[Size], x: Tensor) -> None: + sizes.append( + x.shape[slice(self.start_dim, self.end_dim + 1 if self.end_dim >= 0 else x.ndim + self.end_dim + 1)] + ) + + +class CrossAttentionBlock2d(Residual): + def __init__( + self, + channels: int, + context_embedding_dim: int, + context_key: str, + num_attention_heads: int = 1, + num_attention_layers: int = 1, + num_groups: int = 32, + use_bias: bool = True, + use_linear_projection: bool = False, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + assert channels % num_attention_heads == 0, "in_channels must be divisible by num_attention_heads" + self.channels = channels + self.in_channels = channels + self.out_channels = channels + self.context_embedding_dim = context_embedding_dim + self.num_attention_heads = num_attention_heads + self.num_attention_layers = num_attention_layers + self.num_groups = num_groups + self.use_bias = use_bias + self.context_key = context_key + self.use_linear_projection = use_linear_projection + self.projection_type = "Linear" if use_linear_projection else "Conv2d" + + in_block = ( + Chain( + GroupNorm(channels=channels, num_groups=num_groups, eps=1e-6, device=device, dtype=dtype), + StatefulFlatten(context="flatten", key="sizes", start_dim=2), + Transpose(1, 2), + Linear(in_features=channels, out_features=channels, device=device, dtype=dtype), + ) + if use_linear_projection + else Chain( + GroupNorm(channels=channels, num_groups=num_groups, eps=1e-6, device=device, dtype=dtype), + Conv2d(in_channels=channels, out_channels=channels, kernel_size=1, device=device, dtype=dtype), + StatefulFlatten(context="flatten", key="sizes", start_dim=2), + Transpose(1, 2), + ) + ) + + out_block = ( + Chain( + Linear(in_features=channels, out_features=channels, device=device, dtype=dtype), + Transpose(1, 2), + Parallel( + Identity(), + UseContext(context="flatten", key="sizes").compose(lambda x: x.pop()), + ), + Unflatten(dim=2), + ) + if use_linear_projection + else Chain( + Transpose(1, 2), + Parallel( + Identity(), + UseContext(context="flatten", key="sizes").compose(lambda x: x.pop()), + ), + Unflatten(dim=2), + Conv2d(in_channels=channels, out_channels=channels, kernel_size=1, device=device, dtype=dtype), + ) + ) + + super().__init__( + in_block, + Chain( + CrossAttentionBlock( + embedding_dim=channels, + context_embedding_dim=context_embedding_dim, + context_key=context_key, + num_heads=num_attention_heads, + use_bias=use_bias, + device=device, + dtype=dtype, + ) + for _ in range(num_attention_layers) + ), + out_block, + ) + + def init_context(self) -> Contexts: + return {"flatten": {"sizes": []}} diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/freeu.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/freeu.py new file mode 100644 index 0000000..604bedc --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/freeu.py @@ -0,0 +1,94 @@ +import math +from typing import Any, Callable, Generic, TypeVar + +import torch +from torch import Tensor +from torch.fft import fftn, fftshift, ifftn, ifftshift # type: ignore + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ResidualConcatenator, SD1UNet +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet + +T = TypeVar("T", bound="SD1UNet | SDXLUNet") +TSDFreeUAdapter = TypeVar("TSDFreeUAdapter", bound="SDFreeUAdapter[Any]") # Self (see PEP 673) + + +def fourier_filter(x: Tensor, scale: float = 1, threshold: int = 1) -> Tensor: + """Fourier filter as introduced in FreeU (https://arxiv.org/abs/2309.11497). + + This version of the method comes from here: + https://github.com/ChenyangSi/FreeU/blob/main/demo/free_lunch_utils.py#L23 + """ + batch, channels, height, width = x.shape + dtype = x.dtype + device = x.device + + if not (math.log2(height).is_integer() and math.log2(width).is_integer()): + x = x.to(dtype=torch.float32) + + x_freq = fftn(x, dim=(-2, -1)) # type: ignore + x_freq = fftshift(x_freq, dim=(-2, -1)) # type: ignore + mask = torch.ones((batch, channels, height, width), device=device) # type: ignore + + center_row, center_col = height // 2, width // 2 # type: ignore + mask[..., center_row - threshold : center_row + threshold, center_col - threshold : center_col + threshold] = scale + x_freq = x_freq * mask # type: ignore + + x_freq = ifftshift(x_freq, dim=(-2, -1)) # type: ignore + x_filtered = ifftn(x_freq, dim=(-2, -1)).real # type: ignore + + return x_filtered.to(dtype=dtype) # type: ignore + + +class FreeUBackboneFeatures(fl.Module): + def __init__(self, backbone_scale: float) -> None: + super().__init__() + self.backbone_scale = backbone_scale + + def forward(self, x: Tensor) -> Tensor: + num_half_channels = x.shape[1] // 2 + x[:, :num_half_channels] = x[:, :num_half_channels] * self.backbone_scale + return x + + +class FreeUSkipFeatures(fl.Chain): + def __init__(self, n: int, skip_scale: float) -> None: + apply_filter: Callable[[Tensor], Tensor] = lambda x: fourier_filter(x, scale=skip_scale) + super().__init__( + fl.UseContext(context="unet", key="residuals").compose(lambda residuals: residuals[n]), + fl.Lambda(apply_filter), + ) + + +class FreeUResidualConcatenator(fl.Concatenate): + def __init__(self, n: int, backbone_scale: float, skip_scale: float) -> None: + super().__init__( + FreeUBackboneFeatures(backbone_scale), + FreeUSkipFeatures(n, skip_scale), + dim=1, + ) + + +class SDFreeUAdapter(Generic[T], fl.Chain, Adapter[T]): + def __init__(self, target: T, backbone_scales: list[float], skip_scales: list[float]) -> None: + assert len(backbone_scales) == len(skip_scales) + assert len(backbone_scales) <= len(target.UpBlocks) + self.backbone_scales = backbone_scales + self.skip_scales = skip_scales + with self.setup_adapter(target): + super().__init__(target) + + def inject(self: TSDFreeUAdapter, parent: fl.Chain | None = None) -> TSDFreeUAdapter: + for n, (backbone_scale, skip_scale) in enumerate(zip(self.backbone_scales, self.skip_scales)): + block = self.target.UpBlocks[n] + concat = block.ensure_find(ResidualConcatenator) + block.replace(concat, FreeUResidualConcatenator(-n - 2, backbone_scale, skip_scale)) + return super().inject(parent) + + def eject(self) -> None: + for n in range(len(self.backbone_scales)): + block = self.target.UpBlocks[n] + concat = block.ensure_find(FreeUResidualConcatenator) + block.replace(concat, ResidualConcatenator(-n - 2)) + super().eject() diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/image_prompt.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/image_prompt.py new file mode 100644 index 0000000..fe3253b --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/image_prompt.py @@ -0,0 +1,465 @@ +import math +from enum import IntEnum +from functools import partial +from typing import TYPE_CHECKING, Any, Callable, Generic, TypeVar + +from jaxtyping import Float +from PIL import Image +from torch import Tensor, cat, device as Device, dtype as DType, softmax, zeros_like + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter +from imaginairy.vendored.refiners.fluxion.adapters.lora import Lora +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.fluxion.layers.attentions import ScaledDotProductAttention +from imaginairy.vendored.refiners.fluxion.utils import image_to_tensor, normalize +from imaginairy.vendored.refiners.foundationals.clip.image_encoder import CLIPImageEncoderH + +if TYPE_CHECKING: + from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet + from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet + +T = TypeVar("T", bound="SD1UNet | SDXLUNet") +TIPAdapter = TypeVar("TIPAdapter", bound="IPAdapter[Any]") # Self (see PEP 673) + + +class ImageProjection(fl.Chain): + def __init__( + self, + clip_image_embedding_dim: int = 1024, + clip_text_embedding_dim: int = 768, + num_tokens: int = 4, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.clip_image_embedding_dim = clip_image_embedding_dim + self.clip_text_embedding_dim = clip_text_embedding_dim + self.num_tokens = num_tokens + super().__init__( + fl.Linear( + in_features=clip_image_embedding_dim, + out_features=clip_text_embedding_dim * num_tokens, + device=device, + dtype=dtype, + ), + fl.Reshape(num_tokens, clip_text_embedding_dim), + fl.LayerNorm(normalized_shape=clip_text_embedding_dim, device=device, dtype=dtype), + ) + + +class FeedForward(fl.Chain): + def __init__( + self, + embedding_dim: int, + feedforward_dim: int, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.feedforward_dim = feedforward_dim + super().__init__( + fl.Linear( + in_features=self.embedding_dim, + out_features=self.feedforward_dim, + bias=False, + device=device, + dtype=dtype, + ), + fl.GeLU(), + fl.Linear( + in_features=self.feedforward_dim, + out_features=self.embedding_dim, + bias=False, + device=device, + dtype=dtype, + ), + ) + + +# Adapted from https://github.com/tencent-ailab/IP-Adapter/blob/6212981/ip_adapter/resampler.py +# See also: +# - https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py +# - https://github.com/lucidrains/flamingo-pytorch +class PerceiverScaledDotProductAttention(fl.Module): + def __init__(self, head_dim: int, num_heads: int) -> None: + super().__init__() + self.num_heads = num_heads + # See https://github.com/tencent-ailab/IP-Adapter/blob/6212981/ip_adapter/resampler.py#L69 + # -> "More stable with f16 than dividing afterwards" + self.scale = 1 / math.sqrt(math.sqrt(head_dim)) + + def forward( + self, + key_value: Float[Tensor, "batch sequence_length 2*head_dim*num_heads"], + query: Float[Tensor, "batch num_tokens head_dim*num_heads"], + ) -> Float[Tensor, "batch num_tokens head_dim*num_heads"]: + bs, length, _ = query.shape + key, value = key_value.chunk(2, dim=-1) + + q = self.reshape_tensor(query) + k = self.reshape_tensor(key) + v = self.reshape_tensor(value) + + attention = (q * self.scale) @ (k * self.scale).transpose(-2, -1) + attention = softmax(input=attention.float(), dim=-1).type(attention.dtype) + attention = attention @ v + + return attention.permute(0, 2, 1, 3).reshape(bs, length, -1) + + def reshape_tensor( + self, x: Float[Tensor, "batch length head_dim*num_heads"] + ) -> Float[Tensor, "batch num_heads length head_dim"]: + bs, length, _ = x.shape + x = x.view(bs, length, self.num_heads, -1) + x = x.transpose(1, 2) + x = x.reshape(bs, self.num_heads, length, -1) + return x + + +class PerceiverAttention(fl.Chain): + def __init__( + self, + embedding_dim: int, + head_dim: int = 64, + num_heads: int = 8, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.head_dim = head_dim + self.inner_dim = head_dim * num_heads + super().__init__( + fl.Distribute( + fl.LayerNorm(normalized_shape=self.embedding_dim, device=device, dtype=dtype), + fl.LayerNorm(normalized_shape=self.embedding_dim, device=device, dtype=dtype), + ), + fl.Parallel( + fl.Chain( + fl.Lambda(func=self.to_kv), + fl.Linear( + in_features=self.embedding_dim, + out_features=2 * self.inner_dim, + bias=False, + device=device, + dtype=dtype, + ), # Wkv + ), + fl.Chain( + fl.GetArg(index=1), + fl.Linear( + in_features=self.embedding_dim, + out_features=self.inner_dim, + bias=False, + device=device, + dtype=dtype, + ), # Wq + ), + ), + PerceiverScaledDotProductAttention(head_dim=head_dim, num_heads=num_heads), + fl.Linear( + in_features=self.inner_dim, out_features=self.embedding_dim, bias=False, device=device, dtype=dtype + ), + ) + + def to_kv(self, x: Tensor, latents: Tensor) -> Tensor: + return cat((x, latents), dim=-2) + + +class LatentsToken(fl.Chain): + def __init__( + self, num_tokens: int, latents_dim: int, device: Device | str | None = None, dtype: DType | None = None + ) -> None: + self.num_tokens = num_tokens + self.latents_dim = latents_dim + super().__init__(fl.Parameter(num_tokens, latents_dim, device=device, dtype=dtype)) + + +class Transformer(fl.Chain): + pass + + +class TransformerLayer(fl.Chain): + pass + + +class PerceiverResampler(fl.Chain): + def __init__( + self, + latents_dim: int = 1024, + num_attention_layers: int = 8, + num_attention_heads: int = 16, + head_dim: int = 64, + num_tokens: int = 8, + input_dim: int = 768, + output_dim: int = 1024, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.latents_dim = latents_dim + self.num_attention_layers = num_attention_layers + self.head_dim = head_dim + self.num_attention_heads = num_attention_heads + self.num_tokens = num_tokens + self.input_dim = input_dim + self.output_dim = output_dim + self.feedforward_dim = 4 * self.latents_dim + super().__init__( + fl.Linear(in_features=input_dim, out_features=latents_dim, device=device, dtype=dtype), + fl.SetContext(context="perceiver_resampler", key="x"), + LatentsToken(num_tokens, latents_dim, device=device, dtype=dtype), + Transformer( + TransformerLayer( + fl.Residual( + fl.Parallel(fl.UseContext(context="perceiver_resampler", key="x"), fl.Identity()), + PerceiverAttention( + embedding_dim=latents_dim, + head_dim=head_dim, + num_heads=num_attention_heads, + device=device, + dtype=dtype, + ), + ), + fl.Residual( + fl.LayerNorm(normalized_shape=latents_dim, device=device, dtype=dtype), + FeedForward( + embedding_dim=latents_dim, feedforward_dim=self.feedforward_dim, device=device, dtype=dtype + ), + ), + ) + for _ in range(num_attention_layers) + ), + fl.Linear(in_features=latents_dim, out_features=output_dim, device=device, dtype=dtype), + fl.LayerNorm(normalized_shape=output_dim, device=device, dtype=dtype), + ) + + def init_context(self) -> Contexts: + return {"perceiver_resampler": {"x": None}} + + +class _CrossAttnIndex(IntEnum): + TXT_CROSS_ATTN = 0 # text cross-attention + IMG_CROSS_ATTN = 1 # image cross-attention + + +class InjectionPoint(fl.Chain): + pass + + +class CrossAttentionAdapter(fl.Chain, Adapter[fl.Attention]): + def __init__( + self, + target: fl.Attention, + text_sequence_length: int = 77, + image_sequence_length: int = 4, + scale: float = 1.0, + ) -> None: + self.text_sequence_length = text_sequence_length + self.image_sequence_length = image_sequence_length + self.scale = scale + + with self.setup_adapter(target): + super().__init__( + fl.Distribute( + # Note: the same query is used for image cross-attention as for text cross-attention + InjectionPoint(), # Wq + fl.Parallel( + fl.Chain( + fl.Slicing(dim=1, end=text_sequence_length), + InjectionPoint(), # Wk + ), + fl.Chain( + fl.Slicing(dim=1, start=text_sequence_length), + fl.Linear( + in_features=self.target.key_embedding_dim, + out_features=self.target.inner_dim, + bias=self.target.use_bias, + device=target.device, + dtype=target.dtype, + ), # Wk' + ), + ), + fl.Parallel( + fl.Chain( + fl.Slicing(dim=1, end=text_sequence_length), + InjectionPoint(), # Wv + ), + fl.Chain( + fl.Slicing(dim=1, start=text_sequence_length), + fl.Linear( + in_features=self.target.key_embedding_dim, + out_features=self.target.inner_dim, + bias=self.target.use_bias, + device=target.device, + dtype=target.dtype, + ), # Wv' + ), + ), + ), + fl.Sum( + fl.Chain( + fl.Lambda(func=partial(self.select_qkv, index=_CrossAttnIndex.TXT_CROSS_ATTN)), + ScaledDotProductAttention(num_heads=target.num_heads, is_causal=target.is_causal), + ), + fl.Chain( + fl.Lambda(func=partial(self.select_qkv, index=_CrossAttnIndex.IMG_CROSS_ATTN)), + ScaledDotProductAttention(num_heads=target.num_heads, is_causal=target.is_causal), + fl.Lambda(func=self.scale_outputs), + ), + ), + InjectionPoint(), # proj + ) + + def select_qkv( + self, query: Tensor, keys: tuple[Tensor, Tensor], values: tuple[Tensor, Tensor], index: _CrossAttnIndex + ) -> tuple[Tensor, Tensor, Tensor]: + return (query, keys[index.value], values[index.value]) + + def scale_outputs(self, x: Tensor) -> Tensor: + return x * self.scale + + def _predicate(self, k: type[fl.Module]) -> Callable[[fl.Module, fl.Chain], bool]: + def f(m: fl.Module, _: fl.Chain) -> bool: + if isinstance(m, Lora): # do not adapt LoRAs + raise StopIteration + return isinstance(m, k) + + return f + + def _target_linears(self) -> list[fl.Linear]: + return [m for m, _ in self.target.walk(self._predicate(fl.Linear)) if isinstance(m, fl.Linear)] + + def inject(self: "CrossAttentionAdapter", parent: fl.Chain | None = None) -> "CrossAttentionAdapter": + linears = self._target_linears() + assert len(linears) == 4 # Wq, Wk, Wv and Proj + + injection_points = list(self.layers(InjectionPoint)) + assert len(injection_points) == 4 + + for linear, ip in zip(linears, injection_points): + ip.append(linear) + assert len(ip) == 1 + + return super().inject(parent) + + def eject(self) -> None: + injection_points = list(self.layers(InjectionPoint)) + assert len(injection_points) == 4 + + for ip in injection_points: + ip.pop() + assert len(ip) == 0 + + super().eject() + + +class IPAdapter(Generic[T], fl.Chain, Adapter[T]): + # Prevent PyTorch module registration + _clip_image_encoder: list[CLIPImageEncoderH] + _grid_image_encoder: list[CLIPImageEncoderH] + _image_proj: list[fl.Module] + + def __init__( + self, + target: T, + clip_image_encoder: CLIPImageEncoderH, + image_proj: fl.Module, + scale: float = 1.0, + fine_grained: bool = False, + weights: dict[str, Tensor] | None = None, + ) -> None: + with self.setup_adapter(target): + super().__init__(target) + + self.fine_grained = fine_grained + self._clip_image_encoder = [clip_image_encoder] + if fine_grained: + self._grid_image_encoder = [self.convert_to_grid_features(clip_image_encoder)] + self._image_proj = [image_proj] + + self.sub_adapters = [ + CrossAttentionAdapter(target=cross_attn, scale=scale, image_sequence_length=self.image_proj.num_tokens) + for cross_attn in filter(lambda attn: type(attn) != fl.SelfAttention, target.layers(fl.Attention)) + ] + + if weights is not None: + image_proj_state_dict: dict[str, Tensor] = { + k.removeprefix("image_proj."): v for k, v in weights.items() if k.startswith("image_proj.") + } + self.image_proj.load_state_dict(image_proj_state_dict) + + for i, cross_attn in enumerate(self.sub_adapters): + cross_attn_state_dict: dict[str, Tensor] = {} + for k, v in weights.items(): + prefix = f"ip_adapter.{i:03d}." + if not k.startswith(prefix): + continue + cross_attn_state_dict[k.removeprefix(prefix)] = v + + cross_attn.load_state_dict(state_dict=cross_attn_state_dict) + + @property + def clip_image_encoder(self) -> CLIPImageEncoderH: + return self._clip_image_encoder[0] + + @property + def grid_image_encoder(self) -> CLIPImageEncoderH: + assert hasattr(self, "_grid_image_encoder") + return self._grid_image_encoder[0] + + @property + def image_proj(self) -> fl.Module: + return self._image_proj[0] + + def inject(self: "TIPAdapter", parent: fl.Chain | None = None) -> "TIPAdapter": + for adapter in self.sub_adapters: + adapter.inject() + return super().inject(parent) + + def eject(self) -> None: + for adapter in self.sub_adapters: + adapter.eject() + super().eject() + + def set_scale(self, scale: float) -> None: + for cross_attn in self.sub_adapters: + cross_attn.scale = scale + + # These should be concatenated to the CLIP text embedding before setting the UNet context + def compute_clip_image_embedding(self, image_prompt: Tensor) -> Tensor: + image_encoder = self.clip_image_encoder if not self.fine_grained else self.grid_image_encoder + clip_embedding = image_encoder(image_prompt) + conditional_embedding = self.image_proj(clip_embedding) + if not self.fine_grained: + negative_embedding = self.image_proj(zeros_like(clip_embedding)) + else: + # See https://github.com/tencent-ailab/IP-Adapter/blob/d580c50/tutorial_train_plus.py#L351-L352 + clip_embedding = image_encoder(zeros_like(image_prompt)) + negative_embedding = self.image_proj(clip_embedding) + return cat((negative_embedding, conditional_embedding)) + + def preprocess_image( + self, + image: Image.Image, + size: tuple[int, int] = (224, 224), + mean: list[float] | None = None, + std: list[float] | None = None, + ) -> Tensor: + # Default mean and std are parameters from https://github.com/openai/CLIP + return normalize( + image_to_tensor(image.resize(size), device=self.target.device, dtype=self.target.dtype), + mean=[0.48145466, 0.4578275, 0.40821073] if mean is None else mean, + std=[0.26862954, 0.26130258, 0.27577711] if std is None else std, + ) + + @staticmethod + def convert_to_grid_features(clip_image_encoder: CLIPImageEncoderH) -> CLIPImageEncoderH: + encoder_clone = clip_image_encoder.structural_copy() + assert isinstance(encoder_clone[-1], fl.Linear) # final proj + assert isinstance(encoder_clone[-2], fl.LayerNorm) # final normalization + assert isinstance(encoder_clone[-3], fl.Lambda) # pooling (classif token) + for _ in range(3): + encoder_clone.pop() + transfomer_layers = encoder_clone[-1] + assert isinstance(transfomer_layers, fl.Chain) and len(transfomer_layers) == 32 + transfomer_layers.pop() + return encoder_clone diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/lora.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/lora.py new file mode 100644 index 0000000..ed48270 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/lora.py @@ -0,0 +1,146 @@ +from enum import Enum +from pathlib import Path +from typing import Callable, Iterator + +from torch import Tensor + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter +from imaginairy.vendored.refiners.fluxion.adapters.lora import Lora, LoraAdapter +from imaginairy.vendored.refiners.fluxion.utils import load_from_safetensors, load_metadata_from_safetensors +from imaginairy.vendored.refiners.foundationals.clip.text_encoder import FeedForward, TransformerLayer +from imaginairy.vendored.refiners.foundationals.latent_diffusion import ( + CLIPTextEncoderL, + LatentDiffusionAutoencoder, + SD1UNet, + StableDiffusion_1, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import Controlnet + +MODELS = ["unet", "text_encoder", "lda"] + + +class LoraTarget(str, Enum): + Self = "self" + Attention = "Attention" + SelfAttention = "SelfAttention" + CrossAttention = "CrossAttentionBlock2d" + FeedForward = "FeedForward" + TransformerLayer = "TransformerLayer" + + def get_class(self) -> type[fl.Chain]: + match self: + case LoraTarget.Self: + return fl.Chain + case LoraTarget.Attention: + return fl.Attention + case LoraTarget.SelfAttention: + return fl.SelfAttention + case LoraTarget.CrossAttention: + return CrossAttentionBlock2d + case LoraTarget.FeedForward: + return FeedForward + case LoraTarget.TransformerLayer: + return TransformerLayer + + +def _predicate(k: type[fl.Module]) -> Callable[[fl.Module, fl.Chain], bool]: + def f(m: fl.Module, _: fl.Chain) -> bool: + if isinstance(m, Lora): # do not adapt other LoRAs + raise StopIteration + if isinstance(m, Controlnet): # do not adapt Controlnet linears + raise StopIteration + return isinstance(m, k) + + return f + + +def _iter_linears(module: fl.Chain) -> Iterator[tuple[fl.Linear, fl.Chain]]: + for m, p in module.walk(_predicate(fl.Linear)): + assert isinstance(m, fl.Linear) + yield (m, p) + + +def lora_targets( + module: fl.Chain, + target: LoraTarget | list[LoraTarget], +) -> Iterator[tuple[fl.Linear, fl.Chain]]: + if isinstance(target, list): + for t in target: + yield from lora_targets(module, t) + return + + if target == LoraTarget.Self: + yield from _iter_linears(module) + return + + for layer, _ in module.walk(_predicate(target.get_class())): + assert isinstance(layer, fl.Chain) + yield from _iter_linears(layer) + + +class SD1LoraAdapter(fl.Chain, Adapter[StableDiffusion_1]): + metadata: dict[str, str] | None + tensors: dict[str, Tensor] + + def __init__( + self, + target: StableDiffusion_1, + sub_targets: dict[str, list[LoraTarget]], + scale: float = 1.0, + weights: dict[str, Tensor] | None = None, + ): + with self.setup_adapter(target): + super().__init__(target) + + self.sub_adapters: list[LoraAdapter[SD1UNet | CLIPTextEncoderL | LatentDiffusionAutoencoder]] = [] + + for model_name in MODELS: + if not (model_targets := sub_targets.get(model_name, [])): + continue + model = getattr(target, "clip_text_encoder" if model_name == "text_encoder" else model_name) + + lora_weights = [weights[k] for k in sorted(weights) if k.startswith(model_name)] if weights else None + self.sub_adapters.append( + LoraAdapter[type(model)]( + model, + sub_targets=lora_targets(model, model_targets), + scale=scale, + weights=lora_weights, + ) + ) + + @classmethod + def from_safetensors( + cls, + target: StableDiffusion_1, + checkpoint_path: Path | str, + scale: float = 1.0, + ): + metadata = load_metadata_from_safetensors(checkpoint_path) + assert metadata is not None, "Invalid safetensors checkpoint: missing metadata" + tensors = load_from_safetensors(checkpoint_path, device=target.device) + + sub_targets: dict[str, list[LoraTarget]] = {} + for model_name in MODELS: + if not (v := metadata.get(f"{model_name}_targets", "")): + continue + sub_targets[model_name] = [LoraTarget(x) for x in v.split(",")] + + return cls( + target, + sub_targets, + scale=scale, + weights=tensors, + ) + + def inject(self: "SD1LoraAdapter", parent: fl.Chain | None = None) -> "SD1LoraAdapter": + for adapter in self.sub_adapters: + adapter.inject() + return super().inject(parent) + + def eject(self) -> None: + for adapter in self.sub_adapters: + adapter.eject() + super().eject() diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/model.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/model.py new file mode 100644 index 0000000..8627dc3 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/model.py @@ -0,0 +1,115 @@ +from abc import ABC, abstractmethod +from typing import TypeVar + +import torch +from PIL import Image +from torch import Tensor, device as Device, dtype as DType + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler + +T = TypeVar("T", bound="fl.Module") + + +TLatentDiffusionModel = TypeVar("TLatentDiffusionModel", bound="LatentDiffusionModel") + + +class LatentDiffusionModel(fl.Module, ABC): + def __init__( + self, + unet: fl.Module, + lda: LatentDiffusionAutoencoder, + clip_text_encoder: fl.Module, + scheduler: Scheduler, + device: Device | str = "cpu", + dtype: DType = torch.float32, + ) -> None: + super().__init__() + self.device: Device = device if isinstance(device, Device) else Device(device=device) + self.dtype = dtype + self.unet = unet.to(device=self.device, dtype=self.dtype) + self.lda = lda.to(device=self.device, dtype=self.dtype) + self.clip_text_encoder = clip_text_encoder.to(device=self.device, dtype=self.dtype) + self.scheduler = scheduler.to(device=self.device, dtype=self.dtype) + + def set_num_inference_steps(self, num_inference_steps: int) -> None: + initial_diffusion_rate = self.scheduler.initial_diffusion_rate + final_diffusion_rate = self.scheduler.final_diffusion_rate + device, dtype = self.scheduler.device, self.scheduler.dtype + self.scheduler = self.scheduler.__class__( + num_inference_steps, + initial_diffusion_rate=initial_diffusion_rate, + final_diffusion_rate=final_diffusion_rate, + ).to(device=device, dtype=dtype) + + def init_latents( + self, + size: tuple[int, int], + init_image: Image.Image | None = None, + first_step: int = 0, + noise: Tensor | None = None, + ) -> Tensor: + height, width = size + if noise is None: + noise = torch.randn(1, 4, height // 8, width // 8, device=self.device) + assert list(noise.shape[2:]) == [ + height // 8, + width // 8, + ], f"noise shape is not compatible: {noise.shape}, with size: {size}" + if init_image is None: + return noise + encoded_image = self.lda.encode_image(image=init_image.resize(size=(width, height))) + return self.scheduler.add_noise(x=encoded_image, noise=noise, step=self.steps[first_step]) + + @property + def steps(self) -> list[int]: + return self.scheduler.steps + + @abstractmethod + def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None: + ... + + @abstractmethod + def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None: + ... + + @abstractmethod + def has_self_attention_guidance(self) -> bool: + ... + + @abstractmethod + def compute_self_attention_guidance( + self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: Tensor + ) -> Tensor: + ... + + def forward( + self, x: Tensor, step: int, *, clip_text_embedding: Tensor, condition_scale: float = 7.5, **kwargs: Tensor + ) -> Tensor: + timestep = self.scheduler.timesteps[step].unsqueeze(dim=0) + self.set_unet_context(timestep=timestep, clip_text_embedding=clip_text_embedding, **kwargs) + + latents = torch.cat(tensors=(x, x)) # for classifier-free guidance + unconditional_prediction, conditional_prediction = self.unet(latents).chunk(2) + + # classifier-free guidance + noise = unconditional_prediction + condition_scale * (conditional_prediction - unconditional_prediction) + x = x.narrow(dim=1, start=0, length=4) # support > 4 channels for inpainting + + if self.has_self_attention_guidance(): + noise += self.compute_self_attention_guidance( + x=x, noise=unconditional_prediction, step=step, clip_text_embedding=clip_text_embedding, **kwargs + ) + + return self.scheduler(x, noise=noise, step=step) + + def structural_copy(self: TLatentDiffusionModel) -> TLatentDiffusionModel: + return self.__class__( + unet=self.unet.structural_copy(), + lda=self.lda.structural_copy(), + clip_text_encoder=self.clip_text_encoder.structural_copy(), + scheduler=self.scheduler, + device=self.device, + dtype=self.dtype, + ) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/multi_diffusion.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/multi_diffusion.py new file mode 100644 index 0000000..3a4913f --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/multi_diffusion.py @@ -0,0 +1,98 @@ +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import Generic, TypeVar + +import torch +from PIL import Image +from torch import Tensor, device as Device, dtype as DType + +from imaginairy.vendored.refiners.foundationals.latent_diffusion.model import LatentDiffusionModel + +MAX_STEPS = 1000 + + +@dataclass +class DiffusionTarget: + size: tuple[int, int] + offset: tuple[int, int] + clip_text_embedding: Tensor + init_latents: Tensor | None = None + mask_latent: Tensor | None = None + weight: int = 1 + condition_scale: float = 7.5 + start_step: int = 0 + end_step: int = MAX_STEPS + + def crop(self, tensor: Tensor, /) -> Tensor: + height, width = self.size + top_offset, left_offset = self.offset + return tensor[:, :, top_offset : top_offset + height, left_offset : left_offset + width] + + def paste(self, tensor: Tensor, /, crop: Tensor) -> Tensor: + height, width = self.size + top_offset, left_offset = self.offset + tensor[:, :, top_offset : top_offset + height, left_offset : left_offset + width] = crop + return tensor + + +T = TypeVar("T", bound=LatentDiffusionModel) +D = TypeVar("D", bound=DiffusionTarget) + + +@dataclass +class MultiDiffusion(Generic[T, D], ABC): + ldm: T + + def __call__(self, x: Tensor, /, noise: Tensor, step: int, targets: list[D]) -> Tensor: + num_updates = torch.zeros_like(input=x) + cumulative_values = torch.zeros_like(input=x) + + for target in targets: + match step: + case step if step == target.start_step and target.init_latents is not None: + noise_view = target.crop(noise) + view = self.ldm.scheduler.add_noise( + x=target.init_latents, + noise=noise_view, + step=step, + ) + case step if target.start_step <= step <= target.end_step: + view = target.crop(x) + case _: + continue + view = self.diffuse_target(x=view, step=step, target=target) + weight = target.weight * target.mask_latent if target.mask_latent is not None else target.weight + num_updates = target.paste(num_updates, crop=target.crop(num_updates) + weight) + cumulative_values = target.paste(cumulative_values, crop=target.crop(cumulative_values) + weight * view) + + return torch.where(condition=num_updates > 0, input=cumulative_values / num_updates, other=x) + + @abstractmethod + def diffuse_target(self, x: Tensor, step: int, target: D) -> Tensor: + ... + + @property + def steps(self) -> list[int]: + return self.ldm.steps + + @property + def device(self) -> Device: + return self.ldm.device + + @property + def dtype(self) -> DType: + return self.ldm.dtype + + def decode_latents(self, x: Tensor) -> Image.Image: + return self.ldm.lda.decode_latents(x=x) + + @staticmethod + def generate_offset_grid(size: tuple[int, int], stride: int = 8) -> list[tuple[int, int]]: + height, width = size + + return [ + (y, x) + for y in range(0, height, stride) + for x in range(0, width, stride) + if y + 64 <= height and x + 64 <= width + ] diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/preprocessors/__init__.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/preprocessors/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/preprocessors/informative_drawings.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/preprocessors/informative_drawings.py new file mode 100644 index 0000000..4408c3b --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/preprocessors/informative_drawings.py @@ -0,0 +1,107 @@ +# Adapted from https://github.com/carolineec/informative-drawings, MIT License + +from torch import device as Device, dtype as DType + +import imaginairy.vendored.refiners.fluxion.layers as fl + + +class InformativeDrawings(fl.Chain): + """Model typically used as the preprocessor for the Lineart ControlNet. + + Implements the paper "Learning to generate line drawings that convey + geometry and semantics" published in 2022 by Caroline Chan, Frédo Durand + and Phillip Isola - https://arxiv.org/abs/2203.12691 + + For use as a preprocessor it is recommended to use the weights for "Style 2". + """ + + def __init__( + self, + in_channels: int = 3, # RGB + out_channels: int = 1, # Grayscale + n_residual_blocks: int = 3, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( + fl.Chain( # Initial convolution + fl.ReflectionPad2d(3), + fl.Conv2d( + in_channels=in_channels, + out_channels=64, + kernel_size=7, + device=device, + dtype=dtype, + ), + fl.InstanceNorm2d(64, device=device, dtype=dtype), + fl.ReLU(), + ), + *( # Downsampling + fl.Chain( + fl.Conv2d( + in_channels=64 * (2**i), + out_channels=128 * (2**i), + kernel_size=3, + stride=2, + padding=1, + device=device, + dtype=dtype, + ), + fl.InstanceNorm2d(128 * (2**i), device=device, dtype=dtype), + fl.ReLU(), + ) + for i in range(2) + ), + *( # Residual blocks + fl.Residual( + fl.ReflectionPad2d(1), + fl.Conv2d( + in_channels=256, + out_channels=256, + kernel_size=3, + device=device, + dtype=dtype, + ), + fl.InstanceNorm2d(256, device=device, dtype=dtype), + fl.ReLU(), + fl.ReflectionPad2d(1), + fl.Conv2d( + in_channels=256, + out_channels=256, + kernel_size=3, + device=device, + dtype=dtype, + ), + fl.InstanceNorm2d(256, device=device, dtype=dtype), + ) + for _ in range(n_residual_blocks) + ), + *( # Upsampling + fl.Chain( + fl.ConvTranspose2d( + in_channels=128 * (2**i), + out_channels=64 * (2**i), + kernel_size=3, + stride=2, + padding=1, + output_padding=1, + device=device, + dtype=dtype, + ), + fl.InstanceNorm2d(64 * (2**i), device=device, dtype=dtype), + fl.ReLU(), + ) + for i in reversed(range(2)) + ), + fl.Chain( # Output layer + fl.ReflectionPad2d(3), + fl.Conv2d( + in_channels=64, + out_channels=out_channels, + kernel_size=7, + device=device, + dtype=dtype, + ), + fl.Sigmoid(), + ), + ) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/range_adapter.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/range_adapter.py new file mode 100644 index 0000000..50febc1 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/range_adapter.py @@ -0,0 +1,68 @@ +import math + +from jaxtyping import Float, Int +from torch import Tensor, arange, cat, cos, device as Device, dtype as DType, exp, float32, sin + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter + + +def compute_sinusoidal_embedding( + x: Int[Tensor, "*batch 1"], + embedding_dim: int, +) -> Float[Tensor, "*batch 1 embedding_dim"]: + half_dim = embedding_dim // 2 + # Note: it is important that this computation is done in float32. + # The result can be cast to lower precision later if necessary. + exponent = -math.log(10000) * arange(start=0, end=half_dim, dtype=float32, device=x.device) + exponent /= half_dim + embedding = x.unsqueeze(1).float() * exp(exponent).unsqueeze(0) + embedding = cat([cos(embedding), sin(embedding)], dim=-1) + return embedding + + +class RangeEncoder(fl.Chain): + def __init__( + self, + sinuosidal_embedding_dim: int, + embedding_dim: int, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.sinuosidal_embedding_dim = sinuosidal_embedding_dim + self.embedding_dim = embedding_dim + super().__init__( + fl.Lambda(self.compute_sinuosoidal_embedding), + fl.Converter(set_device=False, set_dtype=True), + fl.Linear(in_features=sinuosidal_embedding_dim, out_features=embedding_dim, device=device, dtype=dtype), + fl.SiLU(), + fl.Linear(in_features=embedding_dim, out_features=embedding_dim, device=device, dtype=dtype), + ) + + def compute_sinuosoidal_embedding(self, x: Int[Tensor, "*batch 1"]) -> Float[Tensor, "*batch 1 embedding_dim"]: + return compute_sinusoidal_embedding(x, embedding_dim=self.sinuosidal_embedding_dim) + + +class RangeAdapter2d(fl.Sum, Adapter[fl.Conv2d]): + def __init__( + self, + target: fl.Conv2d, + channels: int, + embedding_dim: int, + context_key: str, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.channels = channels + self.embedding_dim = embedding_dim + self.context_key = context_key + with self.setup_adapter(target): + super().__init__( + target, + fl.Chain( + fl.UseContext("range_adapter", context_key), + fl.SiLU(), + fl.Linear(in_features=embedding_dim, out_features=channels, device=device, dtype=dtype), + fl.View(-1, channels, 1, 1), + ), + ) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/reference_only_control.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/reference_only_control.py new file mode 100644 index 0000000..51e42ca --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/reference_only_control.py @@ -0,0 +1,143 @@ +from typing import Callable + +from torch import Tensor + +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter +from imaginairy.vendored.refiners.fluxion.layers import ( + Chain, + Concatenate, + Identity, + Lambda, + Parallel, + Passthrough, + SelfAttention, + SetContext, + UseContext, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet + + +class SaveLayerNormAdapter(Chain, Adapter[SelfAttention]): + def __init__(self, target: SelfAttention, context: str) -> None: + self.context = context + with self.setup_adapter(target): + super().__init__(SetContext(self.context, "norm"), target) + + +class SelfAttentionInjectionAdapter(Chain, Adapter[SelfAttention]): + def __init__( + self, + target: SelfAttention, + context: str, + style_cfg: float = 0.5, + ) -> None: + self.context = context + self.style_cfg = style_cfg + + sa_guided = target.structural_copy() + assert isinstance(sa_guided[0], Parallel) + sa_guided.replace( + sa_guided[0], + Parallel( + Identity(), + Concatenate(Identity(), UseContext(self.context, "norm"), dim=1), + Concatenate(Identity(), UseContext(self.context, "norm"), dim=1), + ), + ) + + with self.setup_adapter(target): + slice_tensor: Callable[[Tensor], Tensor] = lambda x: x[:1] + super().__init__( + Parallel(sa_guided, Chain(Lambda(slice_tensor), target)), + Lambda(self.compute_averaged_unconditioned_x), + ) + + def compute_averaged_unconditioned_x(self, x: Tensor, unguided_unconditioned_x: Tensor) -> Tensor: + x[0] = self.style_cfg * x[0] + (1.0 - self.style_cfg) * unguided_unconditioned_x + return x + + +class SelfAttentionInjectionPassthrough(Passthrough): + def __init__(self, target: SD1UNet) -> None: + guide_unet = target.structural_copy() + for i, attention_block in enumerate(guide_unet.layers(CrossAttentionBlock)): + sa = attention_block.ensure_find(SelfAttention) + assert sa.parent is not None + SaveLayerNormAdapter(sa, context=f"self_attention_context_{i}").inject() + + super().__init__( + Lambda(self._copy_diffusion_context), + UseContext("reference_only_control", "guide"), + guide_unet, + Lambda(self._restore_diffusion_context), + ) + + def _copy_diffusion_context(self, x: Tensor) -> Tensor: + # This function allows to not disrupt the accumulation of residuals in the unet (if controlnet are used) + self.set_context( + "self_attention_residuals_buffer", + {"buffer": self.use_context("unet")["residuals"]}, + ) + self.set_context( + "unet", + {"residuals": [0.0] * 13}, + ) + return x + + def _restore_diffusion_context(self, x: Tensor) -> Tensor: + self.set_context( + "unet", + { + "residuals": self.use_context("self_attention_residuals_buffer")["buffer"], + }, + ) + return x + + +class ReferenceOnlyControlAdapter(Chain, Adapter[SD1UNet]): + # TODO: Does not support batching yet. Assumes concatenated inputs for classifier-free guidance + + def __init__(self, target: SD1UNet, style_cfg: float = 0.5) -> None: + # the style_cfg is the weight of the guide in unconditionned diffusion. + # This value is recommended to be 0.5 on the sdwebui repo. + + self.sub_adapters: list[SelfAttentionInjectionAdapter] = [] + self._passthrough: list[SelfAttentionInjectionPassthrough] = [ + SelfAttentionInjectionPassthrough(target) + ] # not registered by PyTorch + + with self.setup_adapter(target): + super().__init__(target) + + for i, attention_block in enumerate(target.layers(CrossAttentionBlock)): + self.set_context(f"self_attention_context_{i}", {"norm": None}) + + sa = attention_block.ensure_find(SelfAttention) + assert sa.parent is not None + + self.sub_adapters.append( + SelfAttentionInjectionAdapter(sa, context=f"self_attention_context_{i}", style_cfg=style_cfg) + ) + + def inject(self: "ReferenceOnlyControlAdapter", parent: Chain | None = None) -> "ReferenceOnlyControlAdapter": + passthrough = self._passthrough[0] + assert passthrough not in self.target, f"{passthrough} is already injected" + for adapter in self.sub_adapters: + adapter.inject() + self.target.insert(0, passthrough) + return super().inject(parent) + + def eject(self) -> None: + passthrough = self._passthrough[0] + assert self.target[0] == passthrough, f"{passthrough} is not the first element of target UNet" + for adapter in self.sub_adapters: + adapter.eject() + self.target.pop(0) + super().eject() + + def set_controlnet_condition(self, condition: Tensor) -> None: + self.set_context("reference_only_control", {"guide": condition}) + + def structural_copy(self: "ReferenceOnlyControlAdapter") -> "ReferenceOnlyControlAdapter": + raise RuntimeError("ReferenceOnlyControlAdapter cannot be copied, eject it first.") diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/restart.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/restart.py new file mode 100644 index 0000000..e54637c --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/restart.py @@ -0,0 +1,110 @@ +from dataclasses import dataclass +from functools import cached_property +from typing import Generic, TypeVar + +import torch + +from imaginairy.vendored.refiners.foundationals.latent_diffusion.model import LatentDiffusionModel +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.ddim import DDIM +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler + +T = TypeVar("T", bound=LatentDiffusionModel) + + +def add_noise_interval( + scheduler: Scheduler, + /, + x: torch.Tensor, + noise: torch.Tensor, + initial_timestep: torch.Tensor, + target_timestep: torch.Tensor, +) -> torch.Tensor: + initial_cumulative_scale_factors = scheduler.cumulative_scale_factors[initial_timestep] + target_cumulative_scale_factors = scheduler.cumulative_scale_factors[target_timestep] + + factor = target_cumulative_scale_factors / initial_cumulative_scale_factors + noised_x = factor * x + torch.sqrt(1 - factor**2) * noise + return noised_x + + +@dataclass +class Restart(Generic[T]): + """ + Implements the restart sampling strategy from the paper "Restart Sampling for Improving Generative Processes" + (https://arxiv.org/pdf/2306.14878.pdf) + + Works only with the DDIM scheduler for now. + """ + + ldm: T + num_steps: int = 10 + num_iterations: int = 2 + start_time: float = 0.1 + end_time: float = 2 + + def __post_init__(self) -> None: + assert isinstance(self.ldm.scheduler, DDIM), "Restart sampling only works with DDIM scheduler" + + def __call__( + self, + x: torch.Tensor, + /, + clip_text_embedding: torch.Tensor, + condition_scale: float = 7.5, + **kwargs: torch.Tensor, + ) -> torch.Tensor: + original_scheduler = self.ldm.scheduler + new_scheduler = DDIM(self.ldm.scheduler.num_inference_steps, device=self.device, dtype=self.dtype) + new_scheduler.timesteps = self.timesteps + self.ldm.scheduler = new_scheduler + + for _ in range(self.num_iterations): + noise = torch.randn_like(input=x, device=self.device, dtype=self.dtype) + x = add_noise_interval( + new_scheduler, + x=x, + noise=noise, + initial_timestep=self.timesteps[-1], + target_timestep=self.timesteps[0], + ) + + for step in range(len(self.timesteps) - 1): + x = self.ldm( + x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=condition_scale, **kwargs + ) + + self.ldm.scheduler = original_scheduler + + return x + + @cached_property + def start_step(self) -> int: + sigmas = self.ldm.scheduler.noise_std / self.ldm.scheduler.cumulative_scale_factors + return int(torch.argmin(input=torch.abs(input=sigmas[self.ldm.scheduler.timesteps] - self.start_time))) + + @cached_property + def end_timestep(self) -> int: + sigmas = self.ldm.scheduler.noise_std / self.ldm.scheduler.cumulative_scale_factors + return int(torch.argmin(input=torch.abs(input=sigmas - self.end_time))) + + @cached_property + def timesteps(self) -> torch.Tensor: + return ( + torch.round( + torch.linspace( + start=int(self.ldm.scheduler.timesteps[self.start_step]), + end=self.end_timestep, + steps=self.num_steps, + ) + ) + .flip(0) + .to(device=self.device, dtype=torch.int64) + ) + + @property + def device(self) -> torch.device: + return self.ldm.device + + @property + def dtype(self) -> torch.dtype: + return self.ldm.dtype diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/__init__.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/__init__.py new file mode 100644 index 0000000..8c88eb9 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/__init__.py @@ -0,0 +1,11 @@ +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.ddim import DDIM +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.ddpm import DDPM +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.dpm_solver import DPMSolver +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler + +__all__ = [ + "Scheduler", + "DPMSolver", + "DDPM", + "DDIM", +] diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/ddim.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/ddim.py new file mode 100644 index 0000000..7ddc5c5 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/ddim.py @@ -0,0 +1,57 @@ +from torch import Tensor, arange, device as Device, dtype as Dtype, float32, sqrt, tensor + +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler + + +class DDIM(Scheduler): + def __init__( + self, + num_inference_steps: int, + num_train_timesteps: int = 1_000, + initial_diffusion_rate: float = 8.5e-4, + final_diffusion_rate: float = 1.2e-2, + noise_schedule: NoiseSchedule = NoiseSchedule.QUADRATIC, + device: Device | str = "cpu", + dtype: Dtype = float32, + ) -> None: + super().__init__( + num_inference_steps=num_inference_steps, + num_train_timesteps=num_train_timesteps, + initial_diffusion_rate=initial_diffusion_rate, + final_diffusion_rate=final_diffusion_rate, + noise_schedule=noise_schedule, + device=device, + dtype=dtype, + ) + self.timesteps = self._generate_timesteps() + + def _generate_timesteps(self) -> Tensor: + """ + Generates decreasing timesteps with 'leading' spacing and offset of 1 + similar to diffusers settings for the DDIM scheduler in Stable Diffusion 1.5 + """ + step_ratio = self.num_train_timesteps // self.num_inference_steps + timesteps = arange(start=0, end=self.num_inference_steps, step=1, device=self.device) * step_ratio + 1 + return timesteps.flip(0) + + def __call__(self, x: Tensor, noise: Tensor, step: int) -> Tensor: + timestep, previous_timestep = ( + self.timesteps[step], + ( + self.timesteps[step + 1] + if step < self.num_inference_steps - 1 + else tensor(data=[0], device=self.device, dtype=self.dtype) + ), + ) + current_scale_factor, previous_scale_factor = ( + self.cumulative_scale_factors[timestep], + ( + self.cumulative_scale_factors[previous_timestep] + if previous_timestep > 0 + else self.cumulative_scale_factors[0] + ), + ) + predicted_x = (x - sqrt(1 - current_scale_factor**2) * noise) / current_scale_factor + denoised_x = previous_scale_factor * predicted_x + sqrt(1 - previous_scale_factor**2) * noise + + return denoised_x diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/ddpm.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/ddpm.py new file mode 100644 index 0000000..7ae26d0 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/ddpm.py @@ -0,0 +1,34 @@ +from torch import Tensor, arange, device as Device + +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler + + +class DDPM(Scheduler): + """ + The Denoising Diffusion Probabilistic Models (DDPM) is a specific type of diffusion model, + which uses a specific strategy to generate the timesteps and applies the diffusion process in a specific way. + """ + + def __init__( + self, + num_inference_steps: int, + num_train_timesteps: int = 1_000, + initial_diffusion_rate: float = 8.5e-4, + final_diffusion_rate: float = 1.2e-2, + device: Device | str = "cpu", + ) -> None: + super().__init__( + num_inference_steps=num_inference_steps, + num_train_timesteps=num_train_timesteps, + initial_diffusion_rate=initial_diffusion_rate, + final_diffusion_rate=final_diffusion_rate, + device=device, + ) + + def _generate_timesteps(self) -> Tensor: + step_ratio = self.num_train_timesteps // self.num_inference_steps + timesteps = arange(start=0, end=self.num_inference_steps, step=1, device=self.device) * step_ratio + return timesteps.flip(0) + + def __call__(self, x: Tensor, noise: Tensor, step: int) -> Tensor: + raise NotImplementedError diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/dpm_solver.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/dpm_solver.py new file mode 100644 index 0000000..8ddf510 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/dpm_solver.py @@ -0,0 +1,117 @@ +from collections import deque + +import numpy as np +from torch import Tensor, device as Device, dtype as Dtype, exp, float32, tensor + +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler + + +class DPMSolver(Scheduler): + """Implements DPM-Solver++ from https://arxiv.org/abs/2211.01095 + + We only support noise prediction for now. + """ + + def __init__( + self, + num_inference_steps: int, + num_train_timesteps: int = 1_000, + initial_diffusion_rate: float = 8.5e-4, + final_diffusion_rate: float = 1.2e-2, + noise_schedule: NoiseSchedule = NoiseSchedule.QUADRATIC, + device: Device | str = "cpu", + dtype: Dtype = float32, + ): + super().__init__( + num_inference_steps=num_inference_steps, + num_train_timesteps=num_train_timesteps, + initial_diffusion_rate=initial_diffusion_rate, + final_diffusion_rate=final_diffusion_rate, + noise_schedule=noise_schedule, + device=device, + dtype=dtype, + ) + self.estimated_data = deque([tensor([])] * 2, maxlen=2) + self.initial_steps = 0 + + def _generate_timesteps(self) -> Tensor: + # We need to use numpy here because: + # numpy.linspace(0,999,31)[15] is 499.49999999999994 + # torch.linspace(0,999,31)[15] is 499.5 + # ...and we want the same result as the original codebase. + return tensor( + np.linspace(0, self.num_train_timesteps - 1, self.num_inference_steps + 1).round().astype(int)[1:], + device=self.device, + ).flip(0) + + def dpm_solver_first_order_update(self, x: Tensor, noise: Tensor, step: int) -> Tensor: + timestep, previous_timestep = ( + self.timesteps[step], + self.timesteps[step + 1 if step < len(self.timesteps) - 1 else 0], + ) + previous_ratio, current_ratio = ( + self.signal_to_noise_ratios[previous_timestep], + self.signal_to_noise_ratios[timestep], + ) + previous_scale_factor = self.cumulative_scale_factors[previous_timestep] + previous_noise_std, current_noise_std = ( + self.noise_std[previous_timestep], + self.noise_std[timestep], + ) + factor = exp(-(previous_ratio - current_ratio)) - 1.0 + denoised_x = (previous_noise_std / current_noise_std) * x - (factor * previous_scale_factor) * noise + return denoised_x + + def multistep_dpm_solver_second_order_update(self, x: Tensor, step: int) -> Tensor: + previous_timestep, current_timestep, next_timestep = ( + self.timesteps[step + 1] if step < len(self.timesteps) - 1 else tensor([0]), + self.timesteps[step], + self.timesteps[step - 1], + ) + current_data_estimation, next_data_estimation = self.estimated_data[-1], self.estimated_data[-2] + previous_ratio, current_ratio, next_ratio = ( + self.signal_to_noise_ratios[previous_timestep], + self.signal_to_noise_ratios[current_timestep], + self.signal_to_noise_ratios[next_timestep], + ) + previous_scale_factor = self.cumulative_scale_factors[previous_timestep] + previous_std, current_std = ( + self.noise_std[previous_timestep], + self.noise_std[current_timestep], + ) + estimation_delta = (current_data_estimation - next_data_estimation) / ( + (current_ratio - next_ratio) / (previous_ratio - current_ratio) + ) + factor = exp(-(previous_ratio - current_ratio)) - 1.0 + denoised_x = ( + (previous_std / current_std) * x + - (factor * previous_scale_factor) * current_data_estimation + - 0.5 * (factor * previous_scale_factor) * estimation_delta + ) + return denoised_x + + def __call__( + self, + x: Tensor, + noise: Tensor, + step: int, + ) -> Tensor: + """ + Represents one step of the backward diffusion process that iteratively denoises the input data `x`. + + This method works by estimating the denoised version of `x` and applying either a first-order or second-order + backward Euler update, which is a numerical method commonly used to solve ordinary differential equations + (ODEs). + """ + current_timestep = self.timesteps[step] + scale_factor, noise_ratio = self.cumulative_scale_factors[current_timestep], self.noise_std[current_timestep] + estimated_denoised_data = (x - noise_ratio * noise) / scale_factor + self.estimated_data.append(estimated_denoised_data) + denoised_x = ( + self.dpm_solver_first_order_update(x=x, noise=estimated_denoised_data, step=step) + if (self.initial_steps == 0) + else self.multistep_dpm_solver_second_order_update(x=x, step=step) + ) + if self.initial_steps < 2: + self.initial_steps += 1 + return denoised_x diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/scheduler.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/scheduler.py new file mode 100644 index 0000000..abf106c --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/schedulers/scheduler.py @@ -0,0 +1,128 @@ +from abc import ABC, abstractmethod +from enum import Enum +from typing import TypeVar + +from torch import Tensor, device as Device, dtype as DType, float32, linspace, log, sqrt + +T = TypeVar("T", bound="Scheduler") + + +class NoiseSchedule(str, Enum): + UNIFORM = "uniform" + QUADRATIC = "quadratic" + KARRAS = "karras" + + +class Scheduler(ABC): + """ + A base class for creating a diffusion model scheduler. + + The Scheduler creates a sequence of noise and scaling factors used in the diffusion process, + which gradually transforms the original data distribution into a Gaussian one. + + This process is described using several parameters such as initial and final diffusion rates, + and is encapsulated into a `__call__` method that applies a step of the diffusion process. + """ + + timesteps: Tensor + + def __init__( + self, + num_inference_steps: int, + num_train_timesteps: int = 1_000, + initial_diffusion_rate: float = 8.5e-4, + final_diffusion_rate: float = 1.2e-2, + noise_schedule: NoiseSchedule = NoiseSchedule.QUADRATIC, + device: Device | str = "cpu", + dtype: DType = float32, + ): + self.device: Device = Device(device) + self.dtype: DType = dtype + self.num_inference_steps = num_inference_steps + self.num_train_timesteps = num_train_timesteps + self.initial_diffusion_rate = initial_diffusion_rate + self.final_diffusion_rate = final_diffusion_rate + self.noise_schedule = noise_schedule + self.scale_factors = self.sample_noise_schedule() + self.cumulative_scale_factors = sqrt(self.scale_factors.cumprod(dim=0)) + self.noise_std = sqrt(1.0 - self.scale_factors.cumprod(dim=0)) + self.signal_to_noise_ratios = log(self.cumulative_scale_factors) - log(self.noise_std) + self.timesteps = self._generate_timesteps() + + @abstractmethod + def __call__(self, x: Tensor, noise: Tensor, step: int) -> Tensor: + """ + Applies a step of the diffusion process to the input tensor `x` using the provided `noise` and `timestep`. + + This method should be overridden by subclasses to implement the specific diffusion process. + """ + ... + + @abstractmethod + def _generate_timesteps(self) -> Tensor: + """ + Generates a tensor of timesteps. + + This method should be overridden by subclasses to provide the specific timesteps for the diffusion process. + """ + ... + + @property + def steps(self) -> list[int]: + return list(range(self.num_inference_steps)) + + def sample_power_distribution(self, power: float = 2, /) -> Tensor: + return ( + linspace( + start=self.initial_diffusion_rate ** (1 / power), + end=self.final_diffusion_rate ** (1 / power), + steps=self.num_train_timesteps, + device=self.device, + dtype=self.dtype, + ) + ** power + ) + + def sample_noise_schedule(self) -> Tensor: + match self.noise_schedule: + case "uniform": + return 1 - self.sample_power_distribution(1) + case "quadratic": + return 1 - self.sample_power_distribution(2) + case "karras": + return 1 - self.sample_power_distribution(7) + case _: + raise ValueError(f"Unknown noise schedule: {self.noise_schedule}") + + def add_noise( + self, + x: Tensor, + noise: Tensor, + step: int, + ) -> Tensor: + timestep = self.timesteps[step] + cumulative_scale_factors = self.cumulative_scale_factors[timestep] + noise_stds = self.noise_std[timestep] + noised_x = cumulative_scale_factors * x + noise_stds * noise + return noised_x + + def remove_noise(self, x: Tensor, noise: Tensor, step: int) -> Tensor: + timestep = self.timesteps[step] + cumulative_scale_factors = self.cumulative_scale_factors[timestep] + noise_stds = self.noise_std[timestep] + # See equation (15) from https://arxiv.org/pdf/2006.11239.pdf. Useful to preview progress or for guidance like + # in https://arxiv.org/pdf/2210.00939.pdf (self-attention guidance) + denoised_x = (x - noise_stds * noise) / cumulative_scale_factors + return denoised_x + + def to(self: T, device: Device | str | None = None, dtype: DType | None = None) -> T: # type: ignore + if device is not None: + self.device = Device(device) + self.timesteps = self.timesteps.to(device) + if dtype is not None: + self.dtype = dtype + self.scale_factors = self.scale_factors.to(device, dtype=dtype) + self.cumulative_scale_factors = self.cumulative_scale_factors.to(device, dtype=dtype) + self.noise_std = self.noise_std.to(device, dtype=dtype) + self.signal_to_noise_ratios = self.signal_to_noise_ratios.to(device, dtype=dtype) + return self diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/self_attention_guidance.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/self_attention_guidance.py new file mode 100644 index 0000000..24e4e20 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/self_attention_guidance.py @@ -0,0 +1,101 @@ +import math +from typing import TYPE_CHECKING, Any, Generic, TypeVar + +import torch +from jaxtyping import Float +from torch import Size, Tensor + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.fluxion.utils import gaussian_blur, interpolate +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler + +if TYPE_CHECKING: + from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet + from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet + +T = TypeVar("T", bound="SD1UNet | SDXLUNet") +TSAGAdapter = TypeVar("TSAGAdapter", bound="SAGAdapter[Any]") # Self (see PEP 673) + + +class SelfAttentionMap(fl.Passthrough): + def __init__(self, num_heads: int, context_key: str) -> None: + self.num_heads = num_heads + self.context_key = context_key + super().__init__( + fl.Lambda(func=self.compute_attention_scores), + fl.SetContext(context="self_attention_map", key=context_key), + ) + + def split_to_multi_head( + self, x: Float[Tensor, "batch_size sequence_length embedding_dim"] + ) -> Float[Tensor, "batch_size num_heads sequence_length (embedding_dim//num_heads)"]: + assert ( + len(x.shape) == 3 + ), f"Expected tensor with shape (batch_size sequence_length embedding_dim), got {x.shape}" + assert ( + x.shape[-1] % self.num_heads == 0 + ), f"Embedding dim (x.shape[-1]={x.shape[-1]}) must be divisible by num heads" + return x.reshape(x.shape[0], x.shape[1], self.num_heads, x.shape[-1] // self.num_heads).transpose(1, 2) + + def compute_attention_scores(self, query: Tensor, key: Tensor, value: Tensor) -> Tensor: + query, key = self.split_to_multi_head(query), self.split_to_multi_head(key) + _, _, _, dim = query.shape + attention = query @ key.permute(0, 1, 3, 2) + attention = attention / math.sqrt(dim) + return torch.softmax(input=attention, dim=-1) + + +class SelfAttentionShape(fl.Passthrough): + def __init__(self, context_key: str) -> None: + self.context_key = context_key + super().__init__( + fl.SetContext(context="self_attention_map", key=context_key, callback=self.register_shape), + ) + + def register_shape(self, shapes: list[Size], x: Tensor) -> None: + assert x.ndim == 4, f"Expected 4D tensor, got {x.ndim}D with shape {x.shape}" + shapes.append(x.shape[-2:]) + + +class SAGAdapter(Generic[T], fl.Chain, Adapter[T]): + def __init__(self, target: T, scale: float = 1.0, kernel_size: int = 9, sigma: float = 1.0) -> None: + self.scale = scale + self.kernel_size = kernel_size + self.sigma = sigma + with self.setup_adapter(target): + super().__init__(target) + + def inject(self: "TSAGAdapter", parent: fl.Chain | None = None) -> "TSAGAdapter": + return super().inject(parent) + + def eject(self) -> None: + super().eject() + + def compute_sag_mask( + self, latents: Float[Tensor, "batch_size channels height width"], classifier_free_guidance: bool = True + ) -> Float[Tensor, "batch_size channels height width"]: + attn_map = self.use_context("self_attention_map")["middle_block_attn_map"] + if classifier_free_guidance: + unconditional_attn, _ = attn_map.chunk(2) + attn_map = unconditional_attn + attn_shape = self.use_context("self_attention_map")["middle_block_attn_shape"].pop() + assert len(attn_shape) == 2 + b, c, h, w = latents.shape + attn_h, attn_w = attn_shape + attn_mask = attn_map.mean(dim=1, keepdim=False).sum(dim=1, keepdim=False) > 1.0 + attn_mask = attn_mask.reshape(b, attn_h, attn_w).unsqueeze(1).repeat(1, c, 1, 1).type(attn_map.dtype) + return interpolate(attn_mask, Size((h, w))) + + def compute_degraded_latents( + self, scheduler: Scheduler, latents: Tensor, noise: Tensor, step: int, classifier_free_guidance: bool = True + ) -> Tensor: + sag_mask = self.compute_sag_mask(latents=latents, classifier_free_guidance=classifier_free_guidance) + original_latents = scheduler.remove_noise(x=latents, noise=noise, step=step) + degraded_latents = gaussian_blur(original_latents, kernel_size=self.kernel_size, sigma=self.sigma) + degraded_latents = degraded_latents * sag_mask + original_latents * (1 - sag_mask) + return scheduler.add_noise(degraded_latents, noise=noise, step=step) + + def init_context(self) -> Contexts: + return {"self_attention_map": {"middle_block_attn_map": None, "middle_block_attn_shape": []}} diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/__init__.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/__init__.py new file mode 100644 index 0000000..50f5965 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/__init__.py @@ -0,0 +1,17 @@ +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import SD1ControlnetAdapter +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.image_prompt import SD1IPAdapter +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.model import ( + StableDiffusion_1, + StableDiffusion_1_Inpainting, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.t2i_adapter import SD1T2IAdapter +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet + +__all__ = [ + "StableDiffusion_1", + "StableDiffusion_1_Inpainting", + "SD1UNet", + "SD1ControlnetAdapter", + "SD1IPAdapter", + "SD1T2IAdapter", +] diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/controlnet.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/controlnet.py new file mode 100644 index 0000000..264bdb1 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/controlnet.py @@ -0,0 +1,179 @@ +from typing import Iterable, cast + +from torch import Tensor, device as Device, dtype as DType + +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.fluxion.layers import Chain, Conv2d, Lambda, Passthrough, Residual, SiLU, Slicing, UseContext +from imaginairy.vendored.refiners.foundationals.latent_diffusion.range_adapter import RangeAdapter2d +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ( + DownBlocks, + MiddleBlock, + ResidualBlock, + SD1UNet, + TimestepEncoder, +) + + +class ConditionEncoder(Chain): + """Encode an image to be used as a condition for Controlnet. + + Input is a `batch 3 width height` tensor, output is a `batch 320 width//8 height//8` tensor. + """ + + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + self.out_channels = (16, 32, 96, 256) + super().__init__( + Chain( + Conv2d( + in_channels=3, + out_channels=self.out_channels[0], + kernel_size=3, + stride=1, + padding=1, + device=device, + dtype=dtype, + ), + SiLU(), + ), + *( + Chain( + Conv2d( + in_channels=self.out_channels[i], + out_channels=self.out_channels[i], + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + SiLU(), + Conv2d( + in_channels=self.out_channels[i], + out_channels=self.out_channels[i + 1], + kernel_size=3, + stride=2, + padding=1, + device=device, + dtype=dtype, + ), + SiLU(), + ) + for i in range(len(self.out_channels) - 1) + ), + Conv2d( + in_channels=self.out_channels[-1], + out_channels=320, + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + ) + + +class Controlnet(Passthrough): + def __init__( + self, name: str, scale: float = 1.0, device: Device | str | None = None, dtype: DType | None = None + ) -> None: + """Controlnet is a Half-UNet that collects residuals from the UNet and uses them to condition the UNet. + + Input is a `batch 3 width height` tensor, output is a `batch 1280 width//8 height//8` tensor with residuals + stored in the context. + + It has to use the same context as the UNet: `unet` and `sampling`. + """ + self.name = name + self.scale = scale + super().__init__( + TimestepEncoder(context_key=f"timestep_embedding_{name}", device=device, dtype=dtype), + Slicing(dim=1, end=4), # support inpainting + DownBlocks(in_channels=4, device=device, dtype=dtype), + MiddleBlock(device=device, dtype=dtype), + ) + + # We run the condition encoder at each step. Caching the result + # is not worth it as subsequent runs take virtually no time (FG-374). + self.DownBlocks[0].append( + Residual( + UseContext("controlnet", f"condition_{name}"), + ConditionEncoder(device=device, dtype=dtype), + ), + ) + for residual_block in self.layers(ResidualBlock): + chain = residual_block.Chain + RangeAdapter2d( + target=chain.Conv2d_1, + channels=residual_block.out_channels, + embedding_dim=1280, + context_key=f"timestep_embedding_{name}", + device=device, + dtype=dtype, + ).inject(chain) + for n, block in enumerate(cast(Iterable[Chain], self.DownBlocks)): + assert hasattr(block[0], "out_channels"), ( + "The first block of every subchain in DownBlocks is expected to respond to `out_channels`," + f" {block[0]} does not." + ) + out_channels: int = block[0].out_channels + block.append( + Passthrough( + Conv2d( + in_channels=out_channels, out_channels=out_channels, kernel_size=1, device=device, dtype=dtype + ), + Lambda(self._store_nth_residual(n)), + ) + ) + self.MiddleBlock.append( + Passthrough( + Conv2d(in_channels=1280, out_channels=1280, kernel_size=1, device=device, dtype=dtype), + Lambda(self._store_nth_residual(12)), + ) + ) + + def _store_nth_residual(self, n: int): + def _store_residual(x: Tensor): + residuals = self.use_context("unet")["residuals"] + residuals[n] = residuals[n] + x * self.scale + return x + + return _store_residual + + +class SD1ControlnetAdapter(Chain, Adapter[SD1UNet]): + def __init__( + self, target: SD1UNet, name: str, scale: float = 1.0, weights: dict[str, Tensor] | None = None + ) -> None: + self.name = name + + controlnet = Controlnet(name=name, scale=scale, device=target.device, dtype=target.dtype) + if weights is not None: + controlnet.load_state_dict(weights) + self._controlnet: list[Controlnet] = [controlnet] # not registered by PyTorch + + with self.setup_adapter(target): + super().__init__(target) + + def inject(self: "SD1ControlnetAdapter", parent: Chain | None = None) -> "SD1ControlnetAdapter": + controlnet = self._controlnet[0] + target_controlnets = [x for x in self.target if isinstance(x, Controlnet)] + assert controlnet not in target_controlnets, f"{controlnet} is already injected" + for cn in target_controlnets: + assert cn.name != self.name, f"Controlnet named {self.name} is already injected" + self.target.insert(0, controlnet) + return super().inject(parent) + + def eject(self) -> None: + self.target.remove(self._controlnet[0]) + super().eject() + + def init_context(self) -> Contexts: + return {"controlnet": {f"condition_{self.name}": None}} + + def set_scale(self, scale: float) -> None: + self._controlnet[0].scale = scale + + def set_controlnet_condition(self, condition: Tensor) -> None: + self.set_context("controlnet", {f"condition_{self.name}": condition}) + + def structural_copy(self: "SD1ControlnetAdapter") -> "SD1ControlnetAdapter": + raise RuntimeError("Controlnet cannot be copied, eject it first.") diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/image_prompt.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/image_prompt.py new file mode 100644 index 0000000..d38f3d8 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/image_prompt.py @@ -0,0 +1,53 @@ +from torch import Tensor + +from imaginairy.vendored.refiners.foundationals.clip.image_encoder import CLIPImageEncoderH +from imaginairy.vendored.refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d +from imaginairy.vendored.refiners.foundationals.latent_diffusion.image_prompt import ImageProjection, IPAdapter, PerceiverResampler +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet + + +class SD1IPAdapter(IPAdapter[SD1UNet]): + def __init__( + self, + target: SD1UNet, + clip_image_encoder: CLIPImageEncoderH | None = None, + image_proj: ImageProjection | PerceiverResampler | None = None, + scale: float = 1.0, + fine_grained: bool = False, + weights: dict[str, Tensor] | None = None, + ) -> None: + clip_image_encoder = clip_image_encoder or CLIPImageEncoderH(device=target.device, dtype=target.dtype) + + if image_proj is None: + cross_attn_2d = target.ensure_find(CrossAttentionBlock2d) + image_proj = ( + ImageProjection( + clip_image_embedding_dim=clip_image_encoder.output_dim, + clip_text_embedding_dim=cross_attn_2d.context_embedding_dim, + device=target.device, + dtype=target.dtype, + ) + if not fine_grained + else PerceiverResampler( + latents_dim=cross_attn_2d.context_embedding_dim, + num_attention_layers=4, + num_attention_heads=12, + head_dim=64, + num_tokens=16, + input_dim=clip_image_encoder.embedding_dim, # = dim before final projection + output_dim=cross_attn_2d.context_embedding_dim, + device=target.device, + dtype=target.dtype, + ) + ) + elif fine_grained: + assert isinstance(image_proj, PerceiverResampler) + + super().__init__( + target=target, + clip_image_encoder=clip_image_encoder, + image_proj=image_proj, + scale=scale, + fine_grained=fine_grained, + weights=weights, + ) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py new file mode 100644 index 0000000..6d44140 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py @@ -0,0 +1,173 @@ +import numpy as np +import torch +from PIL import Image +from torch import Tensor, device as Device, dtype as DType + +from imaginairy.vendored.refiners.fluxion.utils import image_to_tensor, interpolate +from imaginairy.vendored.refiners.foundationals.clip.text_encoder import CLIPTextEncoderL +from imaginairy.vendored.refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder +from imaginairy.vendored.refiners.foundationals.latent_diffusion.model import LatentDiffusionModel +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.dpm_solver import DPMSolver +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.self_attention_guidance import SD1SAGAdapter +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet + + +class SD1Autoencoder(LatentDiffusionAutoencoder): + encoder_scale: float = 0.18215 + + +class StableDiffusion_1(LatentDiffusionModel): + unet: SD1UNet + clip_text_encoder: CLIPTextEncoderL + + def __init__( + self, + unet: SD1UNet | None = None, + lda: SD1Autoencoder | None = None, + clip_text_encoder: CLIPTextEncoderL | None = None, + scheduler: Scheduler | None = None, + device: Device | str = "cpu", + dtype: DType = torch.float32, + ) -> None: + unet = unet or SD1UNet(in_channels=4) + lda = lda or SD1Autoencoder() + clip_text_encoder = clip_text_encoder or CLIPTextEncoderL() + scheduler = scheduler or DPMSolver(num_inference_steps=30) + + super().__init__( + unet=unet, + lda=lda, + clip_text_encoder=clip_text_encoder, + scheduler=scheduler, + device=device, + dtype=dtype, + ) + + def compute_clip_text_embedding(self, text: str, negative_text: str = "") -> Tensor: + conditional_embedding = self.clip_text_encoder(text) + if text == negative_text: + return torch.cat(tensors=(conditional_embedding, conditional_embedding), dim=0) + + negative_embedding = self.clip_text_encoder(negative_text or "") + return torch.cat(tensors=(negative_embedding, conditional_embedding), dim=0) + + def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None: + self.unet.set_timestep(timestep=timestep) + self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding) + + def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None: + if enable: + if sag := self._find_sag_adapter(): + sag.scale = scale + else: + SD1SAGAdapter(target=self.unet, scale=scale).inject() + else: + if sag := self._find_sag_adapter(): + sag.eject() + + def has_self_attention_guidance(self) -> bool: + return self._find_sag_adapter() is not None + + def _find_sag_adapter(self) -> SD1SAGAdapter | None: + for p in self.unet.get_parents(): + if isinstance(p, SD1SAGAdapter): + return p + return None + + def compute_self_attention_guidance( + self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: Tensor + ) -> Tensor: + sag = self._find_sag_adapter() + assert sag is not None + + degraded_latents = sag.compute_degraded_latents( + scheduler=self.scheduler, + latents=x, + noise=noise, + step=step, + classifier_free_guidance=True, + ) + + negative_embedding, _ = clip_text_embedding.chunk(2) + timestep = self.scheduler.timesteps[step].unsqueeze(dim=0) + self.set_unet_context(timestep=timestep, clip_text_embedding=negative_embedding, **kwargs) + degraded_noise = self.unet(degraded_latents) + + return sag.scale * (noise - degraded_noise) + + +class StableDiffusion_1_Inpainting(StableDiffusion_1): + def __init__( + self, + unet: SD1UNet | None = None, + lda: SD1Autoencoder | None = None, + clip_text_encoder: CLIPTextEncoderL | None = None, + scheduler: Scheduler | None = None, + device: Device | str = "cpu", + dtype: DType = torch.float32, + ) -> None: + self.mask_latents: Tensor | None = None + self.target_image_latents: Tensor | None = None + super().__init__( + unet=unet, lda=lda, clip_text_encoder=clip_text_encoder, scheduler=scheduler, device=device, dtype=dtype + ) + + def forward( + self, x: Tensor, step: int, *, clip_text_embedding: Tensor, condition_scale: float = 7.5, **_: Tensor + ) -> Tensor: + assert self.mask_latents is not None + assert self.target_image_latents is not None + x = torch.cat(tensors=(x, self.mask_latents, self.target_image_latents), dim=1) + return super().forward( + x=x, + step=step, + clip_text_embedding=clip_text_embedding, + condition_scale=condition_scale, + ) + + def set_inpainting_conditions( + self, + target_image: Image.Image, + mask: Image.Image, + latents_size: tuple[int, int] = (64, 64), + ) -> tuple[Tensor, Tensor]: + target_image = target_image.convert(mode="RGB") + mask = mask.convert(mode="L") + + mask_tensor = torch.tensor(data=np.array(object=mask).astype(dtype=np.float32) / 255.0).to(device=self.device) + mask_tensor = (mask_tensor > 0.5).unsqueeze(dim=0).unsqueeze(dim=0).to(dtype=self.dtype) + self.mask_latents = interpolate(x=mask_tensor, factor=torch.Size(latents_size)) + + init_image_tensor = image_to_tensor(image=target_image, device=self.device, dtype=self.dtype) * 2 - 1 + masked_init_image = init_image_tensor * (1 - mask_tensor) + self.target_image_latents = self.lda.encode(x=masked_init_image) + + return self.mask_latents, self.target_image_latents + + def compute_self_attention_guidance( + self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: Tensor + ) -> Tensor: + sag = self._find_sag_adapter() + assert sag is not None + assert self.mask_latents is not None + assert self.target_image_latents is not None + + degraded_latents = sag.compute_degraded_latents( + scheduler=self.scheduler, + latents=x, + noise=noise, + step=step, + classifier_free_guidance=True, + ) + + negative_embedding, _ = clip_text_embedding.chunk(2) + timestep = self.scheduler.timesteps[step].unsqueeze(dim=0) + self.set_unet_context(timestep=timestep, clip_text_embedding=negative_embedding, **kwargs) + x = torch.cat( + tensors=(degraded_latents, self.mask_latents, self.target_image_latents), + dim=1, + ) + degraded_noise = self.unet(x) + + return sag.scale * (noise - degraded_noise) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/multi_diffusion.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/multi_diffusion.py new file mode 100644 index 0000000..620ef54 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/multi_diffusion.py @@ -0,0 +1,41 @@ +from dataclasses import dataclass, field + +from PIL import Image +from torch import Tensor + +from imaginairy.vendored.refiners.foundationals.latent_diffusion.multi_diffusion import DiffusionTarget, MultiDiffusion +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.model import ( + StableDiffusion_1, + StableDiffusion_1_Inpainting, +) + + +class SD1MultiDiffusion(MultiDiffusion[StableDiffusion_1, DiffusionTarget]): + def diffuse_target(self, x: Tensor, step: int, target: DiffusionTarget) -> Tensor: + return self.ldm( + x=x, + step=step, + clip_text_embedding=target.clip_text_embedding, + scale=target.condition_scale, + ) + + +@dataclass +class InpaintingDiffusionTarget(DiffusionTarget): + target_image: Image.Image = field(default_factory=lambda: Image.new(mode="RGB", size=(512, 512), color=255)) + mask: Image.Image = field(default_factory=lambda: Image.new(mode="L", size=(512, 512), color=255)) + + +class SD1InpaintingMultiDiffusion(MultiDiffusion[StableDiffusion_1_Inpainting, InpaintingDiffusionTarget]): + def diffuse_target(self, x: Tensor, step: int, target: InpaintingDiffusionTarget) -> Tensor: + self.ldm.set_inpainting_conditions( + target_image=target.target_image, + mask=target.mask, + ) + + return self.ldm( + x=x, + step=step, + clip_text_embedding=target.clip_text_embedding, + scale=target.condition_scale, + ) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/self_attention_guidance.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/self_attention_guidance.py new file mode 100644 index 0000000..99bccff --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/self_attention_guidance.py @@ -0,0 +1,41 @@ +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.layers.attentions import ScaledDotProductAttention +from imaginairy.vendored.refiners.foundationals.latent_diffusion.self_attention_guidance import ( + SAGAdapter, + SelfAttentionMap, + SelfAttentionShape, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import MiddleBlock, ResidualBlock, SD1UNet + + +class SD1SAGAdapter(SAGAdapter[SD1UNet]): + def __init__(self, target: SD1UNet, scale: float = 1.0, kernel_size: int = 9, sigma: float = 1.0) -> None: + super().__init__( + target=target, + scale=scale, + kernel_size=kernel_size, + sigma=sigma, + ) + + def inject(self: "SD1SAGAdapter", parent: fl.Chain | None = None) -> "SD1SAGAdapter": + middle_block = self.target.ensure_find(MiddleBlock) + middle_block.insert_after_type(ResidualBlock, SelfAttentionShape(context_key="middle_block_attn_shape")) + + # An alternative would be to replace the ScaledDotProductAttention with a version which records the attention + # scores to avoid computing these scores twice + self_attn = middle_block.ensure_find(fl.SelfAttention) + self_attn.insert_before_type( + ScaledDotProductAttention, + SelfAttentionMap(num_heads=self_attn.num_heads, context_key="middle_block_attn_map"), + ) + + return super().inject(parent) + + def eject(self) -> None: + middle_block = self.target.ensure_find(MiddleBlock) + middle_block.remove(middle_block.ensure_find(SelfAttentionShape)) + + self_attn = middle_block.ensure_find(fl.SelfAttention) + self_attn.remove(self_attn.ensure_find(SelfAttentionMap)) + + super().eject() diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/t2i_adapter.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/t2i_adapter.py new file mode 100644 index 0000000..86fd83f --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/t2i_adapter.py @@ -0,0 +1,37 @@ +from torch import Tensor + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ResidualAccumulator, SD1UNet +from imaginairy.vendored.refiners.foundationals.latent_diffusion.t2i_adapter import ConditionEncoder, T2IAdapter, T2IFeatures + + +class SD1T2IAdapter(T2IAdapter[SD1UNet]): + def __init__( + self, + target: SD1UNet, + name: str, + condition_encoder: ConditionEncoder | None = None, + scale: float = 1.0, + weights: dict[str, Tensor] | None = None, + ) -> None: + self.residual_indices = (2, 5, 8, 11) + self._features = [T2IFeatures(name=name, index=i, scale=scale) for i in range(4)] + super().__init__( + target=target, + name=name, + condition_encoder=condition_encoder or ConditionEncoder(device=target.device, dtype=target.dtype), + weights=weights, + ) + + def inject(self: "SD1T2IAdapter", parent: fl.Chain | None = None) -> "SD1T2IAdapter": + for n, feat in zip(self.residual_indices, self._features, strict=True): + block = self.target.DownBlocks[n] + for t2i_layer in block.layers(layer_type=T2IFeatures): + assert t2i_layer.name != self.name, f"T2I-Adapter named {self.name} is already injected" + block.insert_before_type(ResidualAccumulator, feat) + return super().inject(parent) + + def eject(self: "SD1T2IAdapter") -> None: + for n, feat in zip(self.residual_indices, self._features, strict=True): + self.target.DownBlocks[n].remove(feat) + super().eject() diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/unet.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/unet.py new file mode 100644 index 0000000..42e17a6 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_1/unet.py @@ -0,0 +1,288 @@ +from typing import Iterable, cast + +from torch import Tensor, device as Device, dtype as DType + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d +from imaginairy.vendored.refiners.foundationals.latent_diffusion.range_adapter import RangeAdapter2d, RangeEncoder + + +class TimestepEncoder(fl.Passthrough): + def __init__( + self, + context_key: str = "timestep_embedding", + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( + fl.UseContext("diffusion", "timestep"), + RangeEncoder(320, 1280, device=device, dtype=dtype), + fl.SetContext("range_adapter", context_key), + ) + + +class ResidualBlock(fl.Sum): + def __init__( + self, + in_channels: int, + out_channels: int, + num_groups: int = 32, + eps: float = 1e-5, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + if in_channels % num_groups != 0 or out_channels % num_groups != 0: + raise ValueError("Number of input and output channels must be divisible by num_groups.") + self.in_channels = in_channels + self.out_channels = out_channels + self.num_groups = num_groups + self.eps = eps + shortcut = ( + fl.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, device=device, dtype=dtype) + if in_channels != out_channels + else fl.Identity() + ) + super().__init__( + fl.Chain( + fl.GroupNorm(channels=in_channels, num_groups=num_groups, eps=eps, device=device, dtype=dtype), + fl.SiLU(), + fl.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + fl.GroupNorm(channels=out_channels, num_groups=num_groups, eps=eps, device=device, dtype=dtype), + fl.SiLU(), + fl.Conv2d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + ), + shortcut, + ) + + +class CLIPLCrossAttention(CrossAttentionBlock2d): + def __init__( + self, + channels: int, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( + channels=channels, + context_embedding_dim=768, + context_key="clip_text_embedding", + num_attention_heads=8, + use_bias=False, + device=device, + dtype=dtype, + ) + + +class DownBlocks(fl.Chain): + def __init__( + self, + in_channels: int, + device: Device | str | None = None, + dtype: DType | None = None, + ): + self.in_channels = in_channels + super().__init__( + fl.Chain( + fl.Conv2d( + in_channels=in_channels, out_channels=320, kernel_size=3, padding=1, device=device, dtype=dtype + ) + ), + fl.Chain( + ResidualBlock(in_channels=320, out_channels=320, device=device, dtype=dtype), + CLIPLCrossAttention(channels=320, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=320, out_channels=320, device=device, dtype=dtype), + CLIPLCrossAttention(channels=320, device=device, dtype=dtype), + ), + fl.Chain(fl.Downsample(channels=320, scale_factor=2, padding=1, device=device, dtype=dtype)), + fl.Chain( + ResidualBlock(in_channels=320, out_channels=640, device=device, dtype=dtype), + CLIPLCrossAttention(channels=640, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=640, out_channels=640, device=device, dtype=dtype), + CLIPLCrossAttention(channels=640, device=device, dtype=dtype), + ), + fl.Chain(fl.Downsample(channels=640, scale_factor=2, padding=1, device=device, dtype=dtype)), + fl.Chain( + ResidualBlock(in_channels=640, out_channels=1280, device=device, dtype=dtype), + CLIPLCrossAttention(channels=1280, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=1280, out_channels=1280, device=device, dtype=dtype), + CLIPLCrossAttention(channels=1280, device=device, dtype=dtype), + ), + fl.Chain(fl.Downsample(channels=1280, scale_factor=2, padding=1, device=device, dtype=dtype)), + fl.Chain( + ResidualBlock(in_channels=1280, out_channels=1280, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=1280, out_channels=1280, device=device, dtype=dtype), + ), + ) + + +class UpBlocks(fl.Chain): + def __init__( + self, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( + fl.Chain( + ResidualBlock(in_channels=2560, out_channels=1280, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=2560, out_channels=1280, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=2560, out_channels=1280, device=device, dtype=dtype), + fl.Upsample(channels=1280, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=2560, out_channels=1280, device=device, dtype=dtype), + CLIPLCrossAttention(channels=1280, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=2560, out_channels=1280, device=device, dtype=dtype), + CLIPLCrossAttention(channels=1280, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=1920, out_channels=1280, device=device, dtype=dtype), + CLIPLCrossAttention(channels=1280, device=device, dtype=dtype), + fl.Upsample(channels=1280, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=1920, out_channels=640, device=device, dtype=dtype), + CLIPLCrossAttention(channels=640, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=1280, out_channels=640, device=device, dtype=dtype), + CLIPLCrossAttention(channels=640, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=960, out_channels=640, device=device, dtype=dtype), + CLIPLCrossAttention(channels=640, device=device, dtype=dtype), + fl.Upsample(channels=640, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=960, out_channels=320, device=device, dtype=dtype), + CLIPLCrossAttention(channels=320, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=640, out_channels=320, device=device, dtype=dtype), + CLIPLCrossAttention(channels=320, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=640, out_channels=320, device=device, dtype=dtype), + CLIPLCrossAttention(channels=320, device=device, dtype=dtype), + ), + ) + + +class MiddleBlock(fl.Chain): + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + super().__init__( + ResidualBlock(in_channels=1280, out_channels=1280, device=device, dtype=dtype), + CLIPLCrossAttention(channels=1280, device=device, dtype=dtype), + ResidualBlock(in_channels=1280, out_channels=1280, device=device, dtype=dtype), + ) + + +class ResidualAccumulator(fl.Passthrough): + def __init__(self, n: int) -> None: + self.n = n + + super().__init__( + fl.Residual( + fl.UseContext(context="unet", key="residuals").compose(func=lambda residuals: residuals[self.n]) + ), + fl.SetContext(context="unet", key="residuals", callback=self.update), + ) + + def update(self, residuals: list[Tensor | float], x: Tensor) -> None: + residuals[self.n] = x + + +class ResidualConcatenator(fl.Chain): + def __init__(self, n: int) -> None: + self.n = n + + super().__init__( + fl.Concatenate( + fl.Identity(), + fl.UseContext(context="unet", key="residuals").compose(lambda residuals: residuals[self.n]), + dim=1, + ), + ) + + +class SD1UNet(fl.Chain): + def __init__(self, in_channels: int, device: Device | str | None = None, dtype: DType | None = None) -> None: + self.in_channels = in_channels + super().__init__( + TimestepEncoder(device=device, dtype=dtype), + DownBlocks(in_channels=in_channels, device=device, dtype=dtype), + fl.Sum( + fl.UseContext(context="unet", key="residuals").compose(lambda x: x[-1]), + MiddleBlock(device=device, dtype=dtype), + ), + UpBlocks(), + fl.Chain( + fl.GroupNorm(channels=320, num_groups=32, device=device, dtype=dtype), + fl.SiLU(), + fl.Conv2d( + in_channels=320, + out_channels=4, + kernel_size=3, + stride=1, + padding=1, + device=device, + dtype=dtype, + ), + ), + ) + for residual_block in self.layers(ResidualBlock): + chain = residual_block.Chain + RangeAdapter2d( + target=chain.Conv2d_1, + channels=residual_block.out_channels, + embedding_dim=1280, + context_key="timestep_embedding", + device=device, + dtype=dtype, + ).inject(chain) + for n, block in enumerate(cast(Iterable[fl.Chain], self.DownBlocks)): + block.append(ResidualAccumulator(n)) + for n, block in enumerate(cast(Iterable[fl.Chain], self.UpBlocks)): + block.insert(0, ResidualConcatenator(-n - 2)) + + def init_context(self) -> Contexts: + return { + "unet": {"residuals": [0.0] * 13}, + "diffusion": {"timestep": None}, + "range_adapter": {"timestep_embedding": None}, + "sampling": {"shapes": []}, + } + + def set_clip_text_embedding(self, clip_text_embedding: Tensor) -> None: + self.set_context("cross_attention_block", {"clip_text_embedding": clip_text_embedding}) + + def set_timestep(self, timestep: Tensor) -> None: + self.set_context("diffusion", {"timestep": timestep}) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/__init__.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/__init__.py new file mode 100644 index 0000000..2f8ef94 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/__init__.py @@ -0,0 +1,13 @@ +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.image_prompt import SDXLIPAdapter +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.t2i_adapter import SDXLT2IAdapter +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet + +__all__ = [ + "SDXLUNet", + "DoubleTextEncoder", + "StableDiffusion_XL", + "SDXLIPAdapter", + "SDXLT2IAdapter", +] diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/image_prompt.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/image_prompt.py new file mode 100644 index 0000000..ed5c31f --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/image_prompt.py @@ -0,0 +1,53 @@ +from torch import Tensor + +from imaginairy.vendored.refiners.foundationals.clip.image_encoder import CLIPImageEncoderH +from imaginairy.vendored.refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d +from imaginairy.vendored.refiners.foundationals.latent_diffusion.image_prompt import ImageProjection, IPAdapter, PerceiverResampler +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet + + +class SDXLIPAdapter(IPAdapter[SDXLUNet]): + def __init__( + self, + target: SDXLUNet, + clip_image_encoder: CLIPImageEncoderH | None = None, + image_proj: ImageProjection | PerceiverResampler | None = None, + scale: float = 1.0, + fine_grained: bool = False, + weights: dict[str, Tensor] | None = None, + ) -> None: + clip_image_encoder = clip_image_encoder or CLIPImageEncoderH(device=target.device, dtype=target.dtype) + + if image_proj is None: + cross_attn_2d = target.ensure_find(CrossAttentionBlock2d) + image_proj = ( + ImageProjection( + clip_image_embedding_dim=clip_image_encoder.output_dim, + clip_text_embedding_dim=cross_attn_2d.context_embedding_dim, + device=target.device, + dtype=target.dtype, + ) + if not fine_grained + else PerceiverResampler( + latents_dim=1280, # not `cross_attn_2d.context_embedding_dim` in this case + num_attention_layers=4, + num_attention_heads=20, + head_dim=64, + num_tokens=16, + input_dim=clip_image_encoder.embedding_dim, # = dim before final projection + output_dim=cross_attn_2d.context_embedding_dim, + device=target.device, + dtype=target.dtype, + ) + ) + elif fine_grained: + assert isinstance(image_proj, PerceiverResampler) + + super().__init__( + target=target, + clip_image_encoder=clip_image_encoder, + image_proj=image_proj, + scale=scale, + fine_grained=fine_grained, + weights=weights, + ) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/model.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/model.py new file mode 100644 index 0000000..7267cb4 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/model.py @@ -0,0 +1,154 @@ +import torch +from torch import Tensor, device as Device, dtype as DType + +from imaginairy.vendored.refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder +from imaginairy.vendored.refiners.foundationals.latent_diffusion.model import LatentDiffusionModel +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.ddim import DDIM +from imaginairy.vendored.refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.self_attention_guidance import SDXLSAGAdapter +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet + + +class SDXLAutoencoder(LatentDiffusionAutoencoder): + encoder_scale: float = 0.13025 + + +class StableDiffusion_XL(LatentDiffusionModel): + unet: SDXLUNet + clip_text_encoder: DoubleTextEncoder + + def __init__( + self, + unet: SDXLUNet | None = None, + lda: SDXLAutoencoder | None = None, + clip_text_encoder: DoubleTextEncoder | None = None, + scheduler: Scheduler | None = None, + device: Device | str = "cpu", + dtype: DType = torch.float32, + ) -> None: + unet = unet or SDXLUNet(in_channels=4) + lda = lda or SDXLAutoencoder() + clip_text_encoder = clip_text_encoder or DoubleTextEncoder() + scheduler = scheduler or DDIM(num_inference_steps=30) + + super().__init__( + unet=unet, + lda=lda, + clip_text_encoder=clip_text_encoder, + scheduler=scheduler, + device=device, + dtype=dtype, + ) + + def compute_clip_text_embedding(self, text: str, negative_text: str | None = None) -> tuple[Tensor, Tensor]: + conditional_embedding, conditional_pooled_embedding = self.clip_text_encoder(text) + if text == negative_text: + return torch.cat(tensors=(conditional_embedding, conditional_embedding), dim=0), torch.cat( + tensors=(conditional_pooled_embedding, conditional_pooled_embedding), dim=0 + ) + + # TODO: when negative_text is None, use zero tensor? + negative_embedding, negative_pooled_embedding = self.clip_text_encoder(negative_text or "") + + return torch.cat(tensors=(negative_embedding, conditional_embedding), dim=0), torch.cat( + tensors=(negative_pooled_embedding, conditional_pooled_embedding), dim=0 + ) + + @property + def default_time_ids(self) -> Tensor: + # [original_height, original_width, crop_top, crop_left, target_height, target_width] + # See https://arxiv.org/abs/2307.01952 > 2.2 Micro-Conditioning + time_ids = torch.tensor(data=[1024, 1024, 0, 0, 1024, 1024], device=self.device) + return time_ids.repeat(2, 1) + + def set_unet_context( + self, + *, + timestep: Tensor, + clip_text_embedding: Tensor, + pooled_text_embedding: Tensor, + time_ids: Tensor, + **_: Tensor, + ) -> None: + self.unet.set_timestep(timestep=timestep) + self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding) + self.unet.set_pooled_text_embedding(pooled_text_embedding=pooled_text_embedding) + self.unet.set_time_ids(time_ids=time_ids) + + def forward( + self, + x: Tensor, + step: int, + *, + clip_text_embedding: Tensor, + pooled_text_embedding: Tensor, + time_ids: Tensor, + condition_scale: float = 5.0, + **kwargs: Tensor, + ) -> Tensor: + return super().forward( + x=x, + step=step, + clip_text_embedding=clip_text_embedding, + pooled_text_embedding=pooled_text_embedding, + time_ids=time_ids, + condition_scale=condition_scale, + **kwargs, + ) + + def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None: + if enable: + if sag := self._find_sag_adapter(): + sag.scale = scale + else: + SDXLSAGAdapter(target=self.unet, scale=scale).inject() + else: + if sag := self._find_sag_adapter(): + sag.eject() + + def has_self_attention_guidance(self) -> bool: + return self._find_sag_adapter() is not None + + def _find_sag_adapter(self) -> SDXLSAGAdapter | None: + for p in self.unet.get_parents(): + if isinstance(p, SDXLSAGAdapter): + return p + return None + + def compute_self_attention_guidance( + self, + x: Tensor, + noise: Tensor, + step: int, + *, + clip_text_embedding: Tensor, + pooled_text_embedding: Tensor, + time_ids: Tensor, + **kwargs: Tensor, + ) -> Tensor: + sag = self._find_sag_adapter() + assert sag is not None + + degraded_latents = sag.compute_degraded_latents( + scheduler=self.scheduler, + latents=x, + noise=noise, + step=step, + classifier_free_guidance=True, + ) + + negative_embedding, _ = clip_text_embedding.chunk(2) + negative_pooled_embedding, _ = pooled_text_embedding.chunk(2) + timestep = self.scheduler.timesteps[step].unsqueeze(dim=0) + time_ids, _ = time_ids.chunk(2) + self.set_unet_context( + timestep=timestep, + clip_text_embedding=negative_embedding, + pooled_text_embedding=negative_pooled_embedding, + time_ids=time_ids, + **kwargs, + ) + degraded_noise = self.unet(degraded_latents) + + return sag.scale * (noise - degraded_noise) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/multi_diffusion.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/multi_diffusion.py new file mode 100644 index 0000000..98dee2b --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/multi_diffusion.py @@ -0,0 +1,21 @@ +from torch import Tensor + +from imaginairy.vendored.refiners.foundationals.latent_diffusion.multi_diffusion import DiffusionTarget, MultiDiffusion +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL + + +class SDXLDiffusionTarget(DiffusionTarget): + pooled_text_embedding: Tensor + time_ids: Tensor + + +class SDXLMultiDiffusion(MultiDiffusion[StableDiffusion_XL, SDXLDiffusionTarget]): + def diffuse_target(self, x: Tensor, step: int, target: SDXLDiffusionTarget) -> Tensor: + return self.ldm( + x=x, + step=step, + clip_text_embedding=target.clip_text_embedding, + pooled_text_embedding=target.pooled_text_embedding, + time_ids=target.time_ids, + condition_scale=target.condition_scale, + ) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/self_attention_guidance.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/self_attention_guidance.py new file mode 100644 index 0000000..bfbd941 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/self_attention_guidance.py @@ -0,0 +1,41 @@ +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.layers.attentions import ScaledDotProductAttention +from imaginairy.vendored.refiners.foundationals.latent_diffusion.self_attention_guidance import ( + SAGAdapter, + SelfAttentionMap, + SelfAttentionShape, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import MiddleBlock, ResidualBlock, SDXLUNet + + +class SDXLSAGAdapter(SAGAdapter[SDXLUNet]): + def __init__(self, target: SDXLUNet, scale: float = 1.0, kernel_size: int = 9, sigma: float = 1.0) -> None: + super().__init__( + target=target, + scale=scale, + kernel_size=kernel_size, + sigma=sigma, + ) + + def inject(self: "SDXLSAGAdapter", parent: fl.Chain | None = None) -> "SDXLSAGAdapter": + middle_block = self.target.ensure_find(MiddleBlock) + middle_block.insert_after_type(ResidualBlock, SelfAttentionShape(context_key="middle_block_attn_shape")) + + # An alternative would be to replace the ScaledDotProductAttention with a version which records the attention + # scores to avoid computing these scores twice + self_attn = middle_block.ensure_find(fl.SelfAttention) + self_attn.insert_before_type( + ScaledDotProductAttention, + SelfAttentionMap(num_heads=self_attn.num_heads, context_key="middle_block_attn_map"), + ) + + return super().inject(parent) + + def eject(self) -> None: + middle_block = self.target.ensure_find(MiddleBlock) + middle_block.remove(middle_block.ensure_find(SelfAttentionShape)) + + self_attn = middle_block.ensure_find(fl.SelfAttention) + self_attn.remove(self_attn.ensure_find(SelfAttentionMap)) + + super().eject() diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/t2i_adapter.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/t2i_adapter.py new file mode 100644 index 0000000..04b3cb1 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/t2i_adapter.py @@ -0,0 +1,48 @@ +from torch import Tensor + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ResidualAccumulator +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet +from imaginairy.vendored.refiners.foundationals.latent_diffusion.t2i_adapter import ConditionEncoderXL, T2IAdapter, T2IFeatures + + +class SDXLT2IAdapter(T2IAdapter[SDXLUNet]): + def __init__( + self, + target: SDXLUNet, + name: str, + condition_encoder: ConditionEncoderXL | None = None, + scale: float = 1.0, + weights: dict[str, Tensor] | None = None, + ) -> None: + self.residual_indices = (3, 5, 8) # the UNet's middle block is handled separately (see `inject` and `eject`) + self._features = [T2IFeatures(name=name, index=i, scale=scale) for i in range(4)] + super().__init__( + target=target, + name=name, + condition_encoder=condition_encoder or ConditionEncoderXL(device=target.device, dtype=target.dtype), + weights=weights, + ) + + def inject(self: "SDXLT2IAdapter", parent: fl.Chain | None = None) -> "SDXLT2IAdapter": + def sanity_check_t2i(block: fl.Chain) -> None: + for t2i_layer in block.layers(layer_type=T2IFeatures): + assert t2i_layer.name != self.name, f"T2I-Adapter named {self.name} is already injected" + + # Note: `strict=False` because `residual_indices` is shorter than `_features` due to MiddleBlock (see below) + for n, feat in zip(self.residual_indices, self._features, strict=False): + block = self.target.DownBlocks[n] + sanity_check_t2i(block) + block.insert_before_type(ResidualAccumulator, feat) + + # Special case: the MiddleBlock has no ResidualAccumulator (this is done via a subsequent layer) so just append + sanity_check_t2i(self.target.MiddleBlock) + self.target.MiddleBlock.append(self._features[-1]) + return super().inject(parent) + + def eject(self: "SDXLT2IAdapter") -> None: + # See `inject` re: `strict=False` + for n, feat in zip(self.residual_indices, self._features, strict=False): + self.target.DownBlocks[n].remove(feat) + self.target.MiddleBlock.remove(self._features[-1]) + super().eject() diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/text_encoder.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/text_encoder.py new file mode 100644 index 0000000..903e904 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/text_encoder.py @@ -0,0 +1,85 @@ +from typing import cast + +from jaxtyping import Float +from torch import Tensor, cat, device as Device, dtype as DType + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.foundationals.clip.text_encoder import CLIPTextEncoderG, CLIPTextEncoderL +from imaginairy.vendored.refiners.foundationals.clip.tokenizer import CLIPTokenizer + + +class TextEncoderWithPooling(fl.Chain, Adapter[CLIPTextEncoderG]): + def __init__( + self, + target: CLIPTextEncoderG, + projection: fl.Linear | None = None, + ) -> None: + with self.setup_adapter(target=target): + tokenizer = target.ensure_find(CLIPTokenizer) + super().__init__( + tokenizer, + fl.SetContext( + context="text_encoder_pooling", key="end_of_text_index", callback=self.set_end_of_text_index + ), + target[1:-2], + fl.Parallel( + fl.Identity(), + fl.Chain( + target[-2:], + projection + or fl.Linear( + in_features=1280, out_features=1280, bias=False, device=target.device, dtype=target.dtype + ), + fl.Lambda(func=self.pool), + ), + ), + ) + + def init_context(self) -> Contexts: + return {"text_encoder_pooling": {"end_of_text_index": []}} + + def __call__(self, text: str) -> tuple[Float[Tensor, "1 77 1280"], Float[Tensor, "1 1280"]]: + return super().__call__(text) + + @property + def tokenizer(self) -> CLIPTokenizer: + return self.ensure_find(CLIPTokenizer) + + def set_end_of_text_index(self, end_of_text_index: list[int], tokens: Tensor) -> None: + position = (tokens == self.tokenizer.end_of_text_token_id).nonzero(as_tuple=True)[1].item() + end_of_text_index.append(cast(int, position)) + + def pool(self, x: Float[Tensor, "1 77 1280"]) -> Float[Tensor, "1 1280"]: + end_of_text_index = self.use_context(context_name="text_encoder_pooling").get("end_of_text_index", []) + assert len(end_of_text_index) == 1, "End of text index not found." + return x[:, end_of_text_index[0], :] + + +class DoubleTextEncoder(fl.Chain): + def __init__( + self, + text_encoder_l: CLIPTextEncoderL | None = None, + text_encoder_g: CLIPTextEncoderG | None = None, + projection: fl.Linear | None = None, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + text_encoder_l = text_encoder_l or CLIPTextEncoderL(device=device, dtype=dtype) + text_encoder_g = text_encoder_g or CLIPTextEncoderG(device=device, dtype=dtype) + super().__init__( + fl.Parallel(text_encoder_l[:-2], text_encoder_g), + fl.Lambda(func=self.concatenate_embeddings), + ) + TextEncoderWithPooling(target=text_encoder_g, projection=projection).inject(parent=self.Parallel) + + def __call__(self, text: str) -> tuple[Float[Tensor, "1 77 2048"], Float[Tensor, "1 1280"]]: + return super().__call__(text) + + def concatenate_embeddings( + self, text_embedding_l: Tensor, text_embedding_with_pooling: tuple[Tensor, Tensor] + ) -> tuple[Tensor, Tensor]: + text_embedding_g, pooled_text_embedding = text_embedding_with_pooling + text_embedding = cat(tensors=[text_embedding_l, text_embedding_g], dim=-1) + return text_embedding, pooled_text_embedding diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/unet.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/unet.py new file mode 100644 index 0000000..03de722 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/stable_diffusion_xl/unet.py @@ -0,0 +1,285 @@ +from typing import cast + +from torch import Tensor, device as Device, dtype as DType + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d +from imaginairy.vendored.refiners.foundationals.latent_diffusion.range_adapter import ( + RangeAdapter2d, + RangeEncoder, + compute_sinusoidal_embedding, +) +from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ( + ResidualAccumulator, + ResidualBlock, + ResidualConcatenator, +) + + +class TextTimeEmbedding(fl.Chain): + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + self.timestep_embedding_dim = 1280 + self.time_ids_embedding_dim = 256 + self.text_time_embedding_dim = 2816 + super().__init__( + fl.Concatenate( + fl.UseContext(context="diffusion", key="pooled_text_embedding"), + fl.Chain( + fl.UseContext(context="diffusion", key="time_ids"), + fl.Unsqueeze(dim=-1), + fl.Lambda(func=self.compute_sinuosoidal_embedding), + fl.Reshape(-1), + ), + dim=1, + ), + fl.Converter(set_device=False, set_dtype=True), + fl.Linear( + in_features=self.text_time_embedding_dim, + out_features=self.timestep_embedding_dim, + device=device, + dtype=dtype, + ), + fl.SiLU(), + fl.Linear( + in_features=self.timestep_embedding_dim, + out_features=self.timestep_embedding_dim, + device=device, + dtype=dtype, + ), + ) + + def compute_sinuosoidal_embedding(self, x: Tensor) -> Tensor: + return compute_sinusoidal_embedding(x=x, embedding_dim=self.time_ids_embedding_dim) + + +class TimestepEncoder(fl.Passthrough): + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + self.timestep_embedding_dim = 1280 + super().__init__( + fl.Sum( + fl.Chain( + fl.UseContext(context="diffusion", key="timestep"), + RangeEncoder( + sinuosidal_embedding_dim=320, + embedding_dim=self.timestep_embedding_dim, + device=device, + dtype=dtype, + ), + ), + TextTimeEmbedding(device=device, dtype=dtype), + ), + fl.SetContext(context="range_adapter", key="timestep_embedding"), + ) + + +class SDXLCrossAttention(CrossAttentionBlock2d): + def __init__( + self, + channels: int, + num_attention_layers: int = 1, + num_attention_heads: int = 10, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( + channels=channels, + context_embedding_dim=2048, + context_key="clip_text_embedding", + num_attention_layers=num_attention_layers, + num_attention_heads=num_attention_heads, + use_bias=False, + use_linear_projection=True, + device=device, + dtype=dtype, + ) + + +class DownBlocks(fl.Chain): + def __init__(self, in_channels: int, device: Device | str | None = None, dtype: DType | None = None) -> None: + self.in_channels = in_channels + + in_block = fl.Chain( + fl.Conv2d(in_channels=in_channels, out_channels=320, kernel_size=3, padding=1, device=device, dtype=dtype) + ) + first_blocks = [ + fl.Chain( + ResidualBlock(in_channels=320, out_channels=320, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=320, out_channels=320, device=device, dtype=dtype), + ), + fl.Chain( + fl.Downsample(channels=320, scale_factor=2, padding=1, device=device, dtype=dtype), + ), + ] + second_blocks = [ + fl.Chain( + ResidualBlock(in_channels=320, out_channels=640, device=device, dtype=dtype), + SDXLCrossAttention( + channels=640, num_attention_layers=2, num_attention_heads=10, device=device, dtype=dtype + ), + ), + fl.Chain( + ResidualBlock(in_channels=640, out_channels=640, device=device, dtype=dtype), + SDXLCrossAttention( + channels=640, num_attention_layers=2, num_attention_heads=10, device=device, dtype=dtype + ), + ), + fl.Chain( + fl.Downsample(channels=640, scale_factor=2, padding=1, device=device, dtype=dtype), + ), + ] + third_blocks = [ + fl.Chain( + ResidualBlock(in_channels=640, out_channels=1280, device=device, dtype=dtype), + SDXLCrossAttention( + channels=1280, num_attention_layers=10, num_attention_heads=20, device=device, dtype=dtype + ), + ), + fl.Chain( + ResidualBlock(in_channels=1280, out_channels=1280, device=device, dtype=dtype), + SDXLCrossAttention( + channels=1280, num_attention_layers=10, num_attention_heads=20, device=device, dtype=dtype + ), + ), + ] + + super().__init__( + in_block, + *first_blocks, + *second_blocks, + *third_blocks, + ) + + +class UpBlocks(fl.Chain): + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + first_blocks = [ + fl.Chain( + ResidualBlock(in_channels=2560, out_channels=1280, device=device, dtype=dtype), + SDXLCrossAttention( + channels=1280, num_attention_layers=10, num_attention_heads=20, device=device, dtype=dtype + ), + ), + fl.Chain( + ResidualBlock(in_channels=2560, out_channels=1280, device=device, dtype=dtype), + SDXLCrossAttention( + channels=1280, num_attention_layers=10, num_attention_heads=20, device=device, dtype=dtype + ), + ), + fl.Chain( + ResidualBlock(in_channels=1920, out_channels=1280, device=device, dtype=dtype), + SDXLCrossAttention( + channels=1280, num_attention_layers=10, num_attention_heads=20, device=device, dtype=dtype + ), + fl.Upsample(channels=1280, device=device, dtype=dtype), + ), + ] + + second_blocks = [ + fl.Chain( + ResidualBlock(in_channels=1920, out_channels=640, device=device, dtype=dtype), + SDXLCrossAttention( + channels=640, num_attention_layers=2, num_attention_heads=10, device=device, dtype=dtype + ), + ), + fl.Chain( + ResidualBlock(in_channels=1280, out_channels=640, device=device, dtype=dtype), + SDXLCrossAttention( + channels=640, num_attention_layers=2, num_attention_heads=10, device=device, dtype=dtype + ), + ), + fl.Chain( + ResidualBlock(in_channels=960, out_channels=640, device=device, dtype=dtype), + SDXLCrossAttention( + channels=640, num_attention_layers=2, num_attention_heads=10, device=device, dtype=dtype + ), + fl.Upsample(channels=640, device=device, dtype=dtype), + ), + ] + + third_blocks = [ + fl.Chain( + ResidualBlock(in_channels=960, out_channels=320, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=640, out_channels=320, device=device, dtype=dtype), + ), + fl.Chain( + ResidualBlock(in_channels=640, out_channels=320, device=device, dtype=dtype), + ), + ] + + super().__init__( + *first_blocks, + *second_blocks, + *third_blocks, + ) + + +class MiddleBlock(fl.Chain): + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + super().__init__( + ResidualBlock(in_channels=1280, out_channels=1280, device=device, dtype=dtype), + SDXLCrossAttention( + channels=1280, num_attention_layers=10, num_attention_heads=20, device=device, dtype=dtype + ), + ResidualBlock(in_channels=1280, out_channels=1280, device=device, dtype=dtype), + ) + + +class OutputBlock(fl.Chain): + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + super().__init__( + fl.GroupNorm(channels=320, num_groups=32, device=device, dtype=dtype), + fl.SiLU(), + fl.Conv2d(in_channels=320, out_channels=4, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype), + ) + + +class SDXLUNet(fl.Chain): + def __init__(self, in_channels: int, device: Device | str | None = None, dtype: DType | None = None) -> None: + self.in_channels = in_channels + super().__init__( + TimestepEncoder(device=device, dtype=dtype), + DownBlocks(in_channels=in_channels, device=device, dtype=dtype), + MiddleBlock(device=device, dtype=dtype), + fl.Residual(fl.UseContext(context="unet", key="residuals").compose(lambda x: x[-1])), + UpBlocks(device=device, dtype=dtype), + OutputBlock(device=device, dtype=dtype), + ) + for residual_block in self.layers(ResidualBlock): + chain = residual_block.Chain + RangeAdapter2d( + target=chain.Conv2d_1, + channels=residual_block.out_channels, + embedding_dim=1280, + context_key="timestep_embedding", + device=device, + dtype=dtype, + ).inject(chain) + for n, block in enumerate(iterable=cast(list[fl.Chain], self.DownBlocks)): + block.append(module=ResidualAccumulator(n=n)) + for n, block in enumerate(iterable=cast(list[fl.Chain], self.UpBlocks)): + block.insert(index=0, module=ResidualConcatenator(n=-n - 2)) + + def init_context(self) -> Contexts: + return { + "unet": {"residuals": [0.0] * 10}, + "diffusion": {"timestep": None, "time_ids": None, "pooled_text_embedding": None}, + "range_adapter": {"timestep_embedding": None}, + "sampling": {"shapes": []}, + } + + def set_clip_text_embedding(self, clip_text_embedding: Tensor) -> None: + self.set_context(context="cross_attention_block", value={"clip_text_embedding": clip_text_embedding}) + + def set_timestep(self, timestep: Tensor) -> None: + self.set_context(context="diffusion", value={"timestep": timestep}) + + def set_time_ids(self, time_ids: Tensor) -> None: + self.set_context(context="diffusion", value={"time_ids": time_ids}) + + def set_pooled_text_embedding(self, pooled_text_embedding: Tensor) -> None: + self.set_context(context="diffusion", value={"pooled_text_embedding": pooled_text_embedding}) diff --git a/imaginairy/vendored/refiners/foundationals/latent_diffusion/t2i_adapter.py b/imaginairy/vendored/refiners/foundationals/latent_diffusion/t2i_adapter.py new file mode 100644 index 0000000..bcf593b --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/latent_diffusion/t2i_adapter.py @@ -0,0 +1,215 @@ +from typing import TYPE_CHECKING, Any, Generic, TypeVar + +from torch import Tensor, device as Device, dtype as DType +from torch.nn import AvgPool2d as _AvgPool2d + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.adapters.adapter import Adapter +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.fluxion.layers.module import Module + +if TYPE_CHECKING: + from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet + from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet + +T = TypeVar("T", bound="SD1UNet | SDXLUNet") +TT2IAdapter = TypeVar("TT2IAdapter", bound="T2IAdapter[Any]") # Self (see PEP 673) + + +class Downsample2d(_AvgPool2d, Module): + def __init__(self, scale_factor: int) -> None: + _AvgPool2d.__init__(self, kernel_size=scale_factor, stride=scale_factor) + + +class ResidualBlock(fl.Residual): + def __init__( + self, + channels: int, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( + fl.Conv2d( + in_channels=channels, out_channels=channels, kernel_size=3, padding=1, device=device, dtype=dtype + ), + fl.ReLU(), + fl.Conv2d(in_channels=channels, out_channels=channels, kernel_size=1, device=device, dtype=dtype), + ) + + +class ResidualBlocks(fl.Chain): + def __init__( + self, + in_channels: int, + out_channels: int, + num_residual_blocks: int = 2, + downsample: bool = False, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + preproc = Downsample2d(scale_factor=2) if downsample else fl.Identity() + shortcut = ( + fl.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, device=device, dtype=dtype) + if in_channels != out_channels + else fl.Identity() + ) + super().__init__( + preproc, + shortcut, + fl.Chain( + ResidualBlock(channels=out_channels, device=device, dtype=dtype) for _ in range(num_residual_blocks) + ), + ) + + +class StatefulResidualBlocks(fl.Chain): + def __init__( + self, + in_channels: int, + out_channels: int, + num_residual_blocks: int = 2, + downsample: bool = False, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( + ResidualBlocks( + in_channels=in_channels, + out_channels=out_channels, + num_residual_blocks=num_residual_blocks, + downsample=downsample, + device=device, + dtype=dtype, + ), + fl.SetContext(context="t2iadapter", key="features", callback=self.push), + ) + + def push(self, features: list[Tensor], x: Tensor) -> None: + features.append(x) + + +class ConditionEncoder(fl.Chain): + def __init__( + self, + in_channels: int = 3, + channels: tuple[int, int, int, int] = (320, 640, 1280, 1280), + num_residual_blocks: int = 2, + downscale_factor: int = 8, + scale: float = 1.0, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.scale = scale + super().__init__( + fl.PixelUnshuffle(downscale_factor=downscale_factor), + fl.Conv2d( + in_channels=in_channels * downscale_factor**2, + out_channels=channels[0], + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + StatefulResidualBlocks(channels[0], channels[0], num_residual_blocks, device=device, dtype=dtype), + *( + StatefulResidualBlocks( + channels[i - 1], channels[i], num_residual_blocks, downsample=True, device=device, dtype=dtype + ) + for i in range(1, len(channels)) + ), + fl.UseContext(context="t2iadapter", key="features"), + ) + + def init_context(self) -> Contexts: + return {"t2iadapter": {"features": []}} + + +class ConditionEncoderXL(ConditionEncoder, fl.Chain): + def __init__( + self, + in_channels: int = 3, + channels: tuple[int, int, int, int] = (320, 640, 1280, 1280), + num_residual_blocks: int = 2, + downscale_factor: int = 16, + scale: float = 1.0, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.scale = scale + fl.Chain.__init__( + self, + fl.PixelUnshuffle(downscale_factor=downscale_factor), + fl.Conv2d( + in_channels=in_channels * downscale_factor**2, + out_channels=channels[0], + kernel_size=3, + padding=1, + device=device, + dtype=dtype, + ), + StatefulResidualBlocks(channels[0], channels[0], num_residual_blocks, device=device, dtype=dtype), + StatefulResidualBlocks(channels[0], channels[1], num_residual_blocks, device=device, dtype=dtype), + StatefulResidualBlocks( + channels[1], channels[2], num_residual_blocks, downsample=True, device=device, dtype=dtype + ), + StatefulResidualBlocks(channels[2], channels[3], num_residual_blocks, device=device, dtype=dtype), + fl.UseContext(context="t2iadapter", key="features"), + ) + + +class T2IFeatures(fl.Residual): + def __init__(self, name: str, index: int, scale: float = 1.0) -> None: + self.name = name + self.index = index + self.scale = scale + super().__init__( + fl.UseContext(context="t2iadapter", key=f"condition_features_{self.name}").compose( + func=lambda features: self.scale * features[self.index] + ) + ) + + +class T2IAdapter(Generic[T], fl.Chain, Adapter[T]): + _condition_encoder: list[ConditionEncoder] # prevent PyTorch module registration + _features: list[T2IFeatures] = [] + + def __init__( + self, + target: T, + name: str, + condition_encoder: ConditionEncoder, + weights: dict[str, Tensor] | None = None, + ) -> None: + self.name = name + if weights is not None: + condition_encoder.load_state_dict(weights) + self._condition_encoder = [condition_encoder] + + with self.setup_adapter(target): + super().__init__(target) + + def inject(self: TT2IAdapter, parent: fl.Chain | None = None) -> TT2IAdapter: + return super().inject(parent) + + def eject(self) -> None: + super().eject() + + @property + def condition_encoder(self) -> ConditionEncoder: + return self._condition_encoder[0] + + def compute_condition_features(self, condition: Tensor) -> tuple[Tensor, ...]: + return self.condition_encoder(condition) + + def set_condition_features(self, features: tuple[Tensor, ...]) -> None: + self.set_context("t2iadapter", {f"condition_features_{self.name}": features}) + + def set_scale(self, scale: float) -> None: + for f in self._features: + f.scale = scale + + def init_context(self) -> Contexts: + return {"t2iadapter": {f"condition_features_{self.name}": None}} + + def structural_copy(self: "TT2IAdapter") -> "TT2IAdapter": + raise RuntimeError("T2I-Adapter cannot be copied, eject it first.") diff --git a/imaginairy/vendored/refiners/foundationals/segment_anything/__init__.py b/imaginairy/vendored/refiners/foundationals/segment_anything/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/imaginairy/vendored/refiners/foundationals/segment_anything/image_encoder.py b/imaginairy/vendored/refiners/foundationals/segment_anything/image_encoder.py new file mode 100644 index 0000000..28e9f9a --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/segment_anything/image_encoder.py @@ -0,0 +1,368 @@ +import torch +from torch import Tensor, device as Device, dtype as DType, nn + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.fluxion.utils import pad + + +class PatchEncoder(fl.Chain): + def __init__( + self, + in_channels: int, + out_channels: int, + patch_size: int = 16, + use_bias: bool = True, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.in_channels = in_channels + self.out_channels = out_channels + self.patch_size = patch_size + self.use_bias = use_bias + super().__init__( + fl.Conv2d( + in_channels=self.in_channels, + out_channels=self.out_channels, + kernel_size=(self.patch_size, self.patch_size), + stride=(self.patch_size, self.patch_size), + use_bias=self.use_bias, + device=device, + dtype=dtype, + ), + fl.Permute(0, 2, 3, 1), + ) + + +class PositionalEncoder(fl.Residual): + def __init__( + self, + embedding_dim: int, + image_embedding_size: tuple[int, int], + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.image_embedding_size = image_embedding_size + super().__init__( + fl.Parameter( + image_embedding_size[0], + image_embedding_size[1], + embedding_dim, + device=device, + dtype=dtype, + ), + ) + + +class RelativePositionAttention(fl.WeightedModule): + def __init__( + self, + embedding_dim: int, + num_heads: int, + spatial_size: tuple[int, int], + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__() + self.embedding_dim = embedding_dim + self.num_heads = num_heads + self.head_dim = embedding_dim // num_heads + self.spatial_size = spatial_size + self.horizontal_embedding = nn.Parameter( + data=torch.zeros(2 * spatial_size[0] - 1, self.head_dim, device=device, dtype=dtype) + ) + self.vertical_embedding = nn.Parameter( + data=torch.zeros(2 * spatial_size[1] - 1, self.head_dim, device=device, dtype=dtype) + ) + + @property + def device(self) -> Device: + return self.horizontal_embedding.device + + @property + def dtype(self) -> DType: + return self.horizontal_embedding.dtype + + def forward(self, x: Tensor) -> Tensor: + batch, height, width, _ = x.shape + x = ( + x.reshape(batch, width * height, 3, self.num_heads, -1) + .permute(2, 0, 3, 1, 4) + .reshape(3, batch * self.num_heads, width * height, -1) + ) + query, key, value = x.unbind(dim=0) + horizontal_relative_embedding, vertical_relative_embedding = self.compute_relative_embedding(x=query) + attention = (query * self.head_dim**-0.5) @ key.transpose(dim0=-2, dim1=-1) + # Order of operations is important here + attention = ( + (attention.reshape(-1, height, width, height, width) + vertical_relative_embedding) + + horizontal_relative_embedding + ).reshape(attention.shape) + attention = attention.softmax(dim=-1) + attention = attention @ value + attention = ( + attention.reshape(batch, self.num_heads, height, width, -1) + .permute(0, 2, 3, 1, 4) + .reshape(batch, height, width, -1) + ) + return attention + + def compute_relative_coords(self, size: int) -> Tensor: + x, y = torch.meshgrid(torch.arange(end=size), torch.arange(end=size), indexing="ij") + return x - y + size - 1 + + def compute_relative_embedding(self, x: Tensor) -> tuple[Tensor, Tensor]: + width, height = self.spatial_size + horizontal_coords = self.compute_relative_coords(size=width) + vertical_coords = self.compute_relative_coords(size=height) + horizontal_positional_embedding = self.horizontal_embedding[horizontal_coords] + vertical_positional_embedding = self.vertical_embedding[vertical_coords] + x = x.reshape(x.shape[0], width, height, -1) + horizontal_relative_embedding = torch.einsum("bhwc,wkc->bhwk", x, horizontal_positional_embedding).unsqueeze( + dim=-2 + ) + vertical_relative_embedding = torch.einsum("bhwc,hkc->bhwk", x, vertical_positional_embedding).unsqueeze(dim=-1) + + return horizontal_relative_embedding, vertical_relative_embedding + + +class FusedSelfAttention(fl.Chain): + def __init__( + self, + embedding_dim: int = 768, + spatial_size: tuple[int, int] = (64, 64), + num_heads: int = 1, + use_bias: bool = True, + is_causal: bool = False, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + assert ( + embedding_dim % num_heads == 0 + ), f"Embedding dim (embedding_dim={embedding_dim}) must be divisible by num heads (num_heads={num_heads})" + self.embedding_dim = embedding_dim + self.num_heads = num_heads + self.use_bias = use_bias + self.is_causal = is_causal + super().__init__( + fl.Linear( + in_features=self.embedding_dim, + out_features=3 * self.embedding_dim, + bias=self.use_bias, + device=device, + dtype=dtype, + ), + RelativePositionAttention( + embedding_dim=self.embedding_dim, + num_heads=self.num_heads, + spatial_size=spatial_size, + device=device, + dtype=dtype, + ), + fl.Linear( + in_features=self.embedding_dim, + out_features=self.embedding_dim, + bias=True, + device=device, + dtype=dtype, + ), + ) + + +class FeedForward(fl.Chain): + def __init__( + self, + embedding_dim: int, + feedforward_dim: int, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.feedforward_dim = feedforward_dim + super().__init__( + fl.Linear( + in_features=self.embedding_dim, + out_features=self.feedforward_dim, + bias=True, + device=device, + dtype=dtype, + ), + fl.GeLU(), + fl.Linear( + in_features=self.feedforward_dim, + out_features=self.embedding_dim, + bias=True, + device=device, + dtype=dtype, + ), + ) + + +class WindowPartition(fl.ContextModule): + def __init__(self) -> None: + super().__init__() + + def forward(self, x: Tensor) -> Tensor: + batch, height, width, channels = x.shape + context = self.use_context(context_name="window_partition") + context.update({"original_height": height, "original_width": width}) + window_size = context["window_size"] + padding_height = (window_size - height % window_size) % window_size + padding_width = (window_size - width % window_size) % window_size + if padding_height > 0 or padding_width > 0: + x = pad(x=x, pad=(0, 0, 0, padding_width, 0, padding_height)) + padded_height, padded_width = height + padding_height, width + padding_width + context.update({"padded_height": padded_height, "padded_width": padded_width}) + x = x.view(batch, padded_height // window_size, window_size, padded_width // window_size, window_size, channels) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, channels) + return windows + + +class WindowMerge(fl.ContextModule): + def __init__(self) -> None: + super().__init__() + + def forward(self, x: Tensor) -> Tensor: + context = self.use_context(context_name="window_partition") + window_size = context["window_size"] + padded_height, padded_width = context["padded_height"], context["padded_width"] + original_height, original_width = context["original_height"], context["original_width"] + batch_size = x.shape[0] // (padded_height * padded_width // window_size // window_size) + x = x.view(batch_size, padded_height // window_size, padded_width // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(batch_size, padded_height, padded_width, -1) + if padded_height > original_height or padded_width > original_width: + x = x[:, :original_height, :original_width, :].contiguous() + return x + + +class TransformerLayer(fl.Chain): + def __init__( + self, + embedding_dim: int, + num_heads: int, + feedforward_dim: int, + image_embedding_size: tuple[int, int], + window_size: int | None = None, + layer_norm_eps: float = 1e-6, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.num_heads = num_heads + self.feedforward_dim = feedforward_dim + self.window_size = window_size + self.layer_norm_eps = layer_norm_eps + self.image_embedding_size = image_embedding_size + attention_spatial_size = (window_size, window_size) if window_size is not None else image_embedding_size + reshape_or_merge = ( + WindowMerge() + if self.window_size is not None + else fl.Reshape(self.image_embedding_size[0], self.image_embedding_size[1], embedding_dim) + ) + super().__init__( + fl.Residual( + fl.LayerNorm(normalized_shape=embedding_dim, eps=self.layer_norm_eps, device=device, dtype=dtype), + WindowPartition() if self.window_size is not None else fl.Identity(), + FusedSelfAttention( + embedding_dim=embedding_dim, + num_heads=num_heads, + spatial_size=attention_spatial_size, + device=device, + dtype=dtype, + ), + reshape_or_merge, + ), + fl.Residual( + fl.LayerNorm(normalized_shape=embedding_dim, eps=self.layer_norm_eps, device=device, dtype=dtype), + FeedForward(embedding_dim=embedding_dim, feedforward_dim=feedforward_dim, device=device, dtype=dtype), + ), + ) + + def init_context(self) -> Contexts: + return {"window_partition": {"window_size": self.window_size}} + + +class Neck(fl.Chain): + def __init__(self, in_channels: int = 768, device: Device | str | None = None, dtype: DType | None = None) -> None: + self.in_channels = in_channels + super().__init__( + fl.Permute(0, 3, 1, 2), + fl.Conv2d( + in_channels=self.in_channels, + out_channels=256, + kernel_size=1, + use_bias=False, + device=device, + dtype=dtype, + ), + fl.LayerNorm2d(channels=256, device=device, dtype=dtype), + fl.Conv2d( + in_channels=256, + out_channels=256, + kernel_size=3, + padding=1, + use_bias=False, + device=device, + dtype=dtype, + ), + fl.LayerNorm2d(channels=256, device=device, dtype=dtype), + ) + + +class Transformer(fl.Chain): + pass + + +class SAMViT(fl.Chain): + def __init__( + self, + embedding_dim: int, + num_layers: int, + num_heads: int, + global_attention_indices: tuple[int, ...] | None = None, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.num_layers = num_layers + self.num_heads = num_heads + self.image_size = (1024, 1024) + self.patch_size = 16 + self.window_size = 14 + self.image_embedding_size = (self.image_size[0] // self.patch_size, self.image_size[1] // self.patch_size) + self.feed_forward_dim = 4 * self.embedding_dim + self.global_attention_indices = global_attention_indices or tuple() + super().__init__( + PatchEncoder( + in_channels=3, out_channels=embedding_dim, patch_size=self.patch_size, device=device, dtype=dtype + ), + PositionalEncoder( + embedding_dim=embedding_dim, image_embedding_size=self.image_embedding_size, device=device, dtype=dtype + ), + Transformer( + TransformerLayer( + embedding_dim=embedding_dim, + num_heads=num_heads, + feedforward_dim=self.feed_forward_dim, + window_size=self.window_size if i not in self.global_attention_indices else None, + image_embedding_size=self.image_embedding_size, + device=device, + dtype=dtype, + ) + for i in range(num_layers) + ), + Neck(in_channels=embedding_dim, device=device, dtype=dtype), + ) + + +class SAMViTH(SAMViT): + def __init__(self, device: Device | str | None = None, dtype: DType | None = None) -> None: + super().__init__( + embedding_dim=1280, + num_layers=32, + num_heads=16, + global_attention_indices=(7, 15, 23, 31), + device=device, + dtype=dtype, + ) diff --git a/imaginairy/vendored/refiners/foundationals/segment_anything/mask_decoder.py b/imaginairy/vendored/refiners/foundationals/segment_anything/mask_decoder.py new file mode 100644 index 0000000..e91b594 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/segment_anything/mask_decoder.py @@ -0,0 +1,264 @@ +import torch +from torch import Tensor, device as Device, dtype as DType, nn + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.context import Contexts +from imaginairy.vendored.refiners.foundationals.segment_anything.transformer import ( + SparseCrossDenseAttention, + TwoWayTranformerLayer, +) + + +class EmbeddingsAggregator(fl.ContextModule): + def __init__(self, num_output_mask: int = 3) -> None: + super().__init__() + self.num_mask_tokens = num_output_mask + + def forward(self, iou_mask_tokens: Tensor) -> Tensor: + mask_decoder = self.ensure_parent + mask_decoder_context = mask_decoder.use_context(context_name="mask_decoder") + image_embedding = mask_decoder_context["image_embedding"] + point_embedding = mask_decoder_context["point_embedding"] + mask_embedding = mask_decoder_context["mask_embedding"] + dense_positional_embedding = mask_decoder_context["dense_positional_embedding"] + + sparse_embedding = torch.cat(tensors=(iou_mask_tokens, point_embedding), dim=1) + dense_embedding = (image_embedding + mask_embedding).flatten(start_dim=2).transpose(1, 2) + if dense_positional_embedding.shape != dense_embedding.shape: + dense_positional_embedding = dense_positional_embedding.flatten(start_dim=2).transpose(1, 2) + + mask_decoder_context.update( + { + "dense_embedding": dense_embedding, + "dense_positional_embedding": dense_positional_embedding, + "sparse_embedding": sparse_embedding, + } + ) + mask_decoder.set_context(context="mask_decoder", value=mask_decoder_context) + + return sparse_embedding + + +class Transformer(fl.Chain): + pass + + +class Hypernetworks(fl.Concatenate): + def __init__( + self, + embedding_dim: int = 256, + num_layers: int = 3, + num_mask_tokens: int = 3, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__() + self.embedding_dim = embedding_dim + self.num_layers = num_layers + self.num_mask_tokens = num_mask_tokens + + super().__init__( + *[ + fl.Chain( + fl.Slicing(dim=1, start=i + 1, end=i + 2), + fl.MultiLinear( + input_dim=embedding_dim, + output_dim=embedding_dim // 8, + inner_dim=embedding_dim, + num_layers=num_layers, + device=device, + dtype=dtype, + ), + ) + for i in range(num_mask_tokens + 1) + ], + dim=1, + ) + + +class DenseEmbeddingUpscaling(fl.Chain): + def __init__( + self, + embedding_dim: int = 256, + dense_embedding_side_dim: int = 64, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__() + self.embedding_dim = embedding_dim + self.dense_embedding_side_dim = dense_embedding_side_dim + + super().__init__( + fl.UseContext(context="mask_decoder", key="dense_embedding"), + fl.Transpose(dim0=1, dim1=2), + fl.Reshape(embedding_dim, dense_embedding_side_dim, dense_embedding_side_dim), + fl.ConvTranspose2d( + in_channels=embedding_dim, + out_channels=embedding_dim // 4, + kernel_size=2, + stride=2, + device=device, + dtype=dtype, + ), + fl.LayerNorm2d(channels=embedding_dim // 4, device=device, dtype=dtype), + fl.GeLU(), + fl.ConvTranspose2d( + in_channels=embedding_dim // 4, + out_channels=embedding_dim // 8, + kernel_size=2, + stride=2, + device=device, + dtype=dtype, + ), + fl.GeLU(), + fl.Flatten(start_dim=2), + ) + + +class IOUMaskEncoder(fl.WeightedModule): + def __init__( + self, + embedding_dim: int = 256, + num_mask_tokens: int = 4, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__() + self.embedding_dim = embedding_dim + self.num_mask_tokens = num_mask_tokens + # aka prompt tokens + output token (for IoU scores prediction) + self.weight = nn.Parameter(data=torch.randn(num_mask_tokens + 1, embedding_dim, device=device, dtype=dtype)) + + def forward(self) -> Tensor: + return self.weight.unsqueeze(dim=0) + + +class MaskPrediction(fl.Chain): + def __init__( + self, + embedding_dim: int, + num_mask_tokens: int, + num_layers: int = 3, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.num_mask_tokens = num_mask_tokens + self.num_layers = num_layers + super().__init__( + fl.Matmul( + input=Hypernetworks( + embedding_dim=embedding_dim, + num_layers=num_layers, + num_mask_tokens=num_mask_tokens, + device=device, + dtype=dtype, + ), + other=DenseEmbeddingUpscaling(embedding_dim=embedding_dim, device=device, dtype=dtype), + ), + fl.Slicing(dim=1, start=1), + fl.Reshape(num_mask_tokens, embedding_dim, embedding_dim), + ) + + +class IOUPrediction(fl.Chain): + def __init__( + self, + embedding_dim: int, + num_layers: int, + num_mask_tokens: int, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.num_layers = num_layers + super().__init__( + fl.Slicing(dim=1, start=0, end=1), + fl.Squeeze(dim=0), + fl.MultiLinear( + input_dim=embedding_dim, + output_dim=num_mask_tokens + 1, + inner_dim=embedding_dim, + num_layers=num_layers, + device=device, + dtype=dtype, + ), + fl.Slicing(dim=-1, start=1), + ) + + +class MaskDecoder(fl.Chain): + def __init__( + self, + embedding_dim: int = 256, + feed_forward_dim: int = 2048, + num_layers: int = 2, + num_output_mask: int = 3, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__() + self.embedding_dim = embedding_dim + self.num_mask_tokens = num_output_mask + self.feed_forward_dim = feed_forward_dim + self.num_layers = num_layers + + super().__init__( + IOUMaskEncoder( + embedding_dim=embedding_dim, num_mask_tokens=num_output_mask + 1, device=device, dtype=dtype + ), + EmbeddingsAggregator(num_output_mask=num_output_mask), + Transformer( + *( + TwoWayTranformerLayer( + embedding_dim=embedding_dim, + num_heads=8, + feed_forward_dim=feed_forward_dim, + use_residual_self_attention=i > 0, + device=device, + dtype=dtype, + ) + for i in range(num_layers) + ), + SparseCrossDenseAttention(embedding_dim=embedding_dim, device=device, dtype=dtype), + fl.LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype), + ), + fl.Parallel( + MaskPrediction( + embedding_dim=embedding_dim, num_mask_tokens=num_output_mask, device=device, dtype=dtype + ), + IOUPrediction( + embedding_dim=embedding_dim, + num_layers=3, + num_mask_tokens=num_output_mask, + device=device, + dtype=dtype, + ), + ), + ) + + def init_context(self) -> Contexts: + return { + "mask_decoder": { + "image_embedding": None, + "point_embedding": None, + "mask_embedding": None, + "dense_positional_embedding": None, + } + } + + def set_image_embedding(self, image_embedding: Tensor) -> None: + mask_decoder_context = self.use_context(context_name="mask_decoder") + mask_decoder_context["image_embedding"] = image_embedding + + def set_point_embedding(self, point_embedding: Tensor) -> None: + mask_decoder_context = self.use_context(context_name="mask_decoder") + mask_decoder_context["point_embedding"] = point_embedding + + def set_mask_embedding(self, mask_embedding: Tensor) -> None: + mask_decoder_context = self.use_context(context_name="mask_decoder") + mask_decoder_context["mask_embedding"] = mask_embedding + + def set_dense_positional_embedding(self, dense_positional_embedding: Tensor) -> None: + mask_decoder_context = self.use_context(context_name="mask_decoder") + mask_decoder_context["dense_positional_embedding"] = dense_positional_embedding diff --git a/imaginairy/vendored/refiners/foundationals/segment_anything/model.py b/imaginairy/vendored/refiners/foundationals/segment_anything/model.py new file mode 100644 index 0000000..d9dfcc8 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/segment_anything/model.py @@ -0,0 +1,170 @@ +from dataclasses import dataclass +from typing import Sequence + +import numpy as np +import torch +from PIL import Image +from torch import Tensor, device as Device, dtype as DType + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.utils import image_to_tensor, interpolate, no_grad, normalize, pad +from imaginairy.vendored.refiners.foundationals.segment_anything.image_encoder import SAMViT, SAMViTH +from imaginairy.vendored.refiners.foundationals.segment_anything.mask_decoder import MaskDecoder +from imaginairy.vendored.refiners.foundationals.segment_anything.prompt_encoder import MaskEncoder, PointEncoder + + +@dataclass +class ImageEmbedding: + features: Tensor + original_image_size: tuple[int, int] # (height, width) + + +class SegmentAnything(fl.Module): + mask_threshold: float = 0.0 + + def __init__( + self, + image_encoder: SAMViT, + point_encoder: PointEncoder, + mask_encoder: MaskEncoder, + mask_decoder: MaskDecoder, + device: Device | str = "cpu", + dtype: DType = torch.float32, + ) -> None: + super().__init__() + self.device: Device = device if isinstance(device, Device) else Device(device=device) + self.dtype = dtype + self.image_encoder = image_encoder.to(device=self.device, dtype=self.dtype) + self.point_encoder = point_encoder.to(device=self.device, dtype=self.dtype) + self.mask_encoder = mask_encoder.to(device=self.device, dtype=self.dtype) + self.mask_decoder = mask_decoder.to(device=self.device, dtype=self.dtype) + + @no_grad() + def compute_image_embedding(self, image: Image.Image) -> ImageEmbedding: + original_size = (image.height, image.width) + target_size = self.compute_target_size(original_size) + return ImageEmbedding( + features=self.image_encoder(self.preprocess_image(image=image, target_size=target_size)), + original_image_size=original_size, + ) + + @no_grad() + def predict( + self, + input: Image.Image | ImageEmbedding, + foreground_points: Sequence[tuple[float, float]] | None = None, + background_points: Sequence[tuple[float, float]] | None = None, + box_points: Sequence[Sequence[tuple[float, float]]] | None = None, + masks: Sequence[Image.Image] | None = None, + binarize: bool = True, + ) -> tuple[Tensor, Tensor, Tensor]: + if isinstance(input, ImageEmbedding): + original_size = input.original_image_size + target_size = self.compute_target_size(original_size) + image_embedding = input.features + else: + original_size = (input.height, input.width) + target_size = self.compute_target_size(original_size) + image_embedding = self.image_encoder(self.preprocess_image(image=input, target_size=target_size)) + + coordinates, type_mask = self.point_encoder.points_to_tensor( + foreground_points=foreground_points, + background_points=background_points, + box_points=box_points, + ) + self.point_encoder.set_type_mask(type_mask=type_mask) + + if masks is not None: + mask_tensor = torch.stack( + tensors=[image_to_tensor(image=mask, device=self.device, dtype=self.dtype) for mask in masks] + ) + mask_embedding = self.mask_encoder(mask_tensor) + else: + mask_embedding = self.mask_encoder.get_no_mask_dense_embedding( + image_embedding_size=self.image_encoder.image_embedding_size + ) + point_embedding = self.point_encoder( + self.normalize(coordinates, target_size=target_size, original_size=original_size) + ) + dense_positional_embedding = self.point_encoder.get_dense_positional_embedding( + image_embedding_size=self.image_encoder.image_embedding_size + ) + + self.mask_decoder.set_image_embedding(image_embedding=image_embedding) + self.mask_decoder.set_mask_embedding(mask_embedding=mask_embedding) + self.mask_decoder.set_point_embedding(point_embedding=point_embedding) + self.mask_decoder.set_dense_positional_embedding(dense_positional_embedding=dense_positional_embedding) + + low_res_masks, iou_predictions = self.mask_decoder() + + high_res_masks = self.postprocess_masks( + masks=low_res_masks, target_size=target_size, original_size=original_size + ) + + if binarize: + high_res_masks = high_res_masks > self.mask_threshold + + return high_res_masks, iou_predictions, low_res_masks + + @property + def image_size(self) -> int: + w, h = self.image_encoder.image_size + assert w == h + return w + + def compute_target_size(self, size: tuple[int, int]) -> tuple[int, int]: + oldh, oldw = size + scale = self.image_size * 1.0 / max(oldh, oldw) + newh, neww = oldh * scale, oldw * scale + neww = int(neww + 0.5) + newh = int(newh + 0.5) + return (newh, neww) + + def preprocess_image(self, image: Image.Image, target_size: tuple[int, int]) -> Tensor: + h, w = target_size + padh = self.image_size - h + padw = self.image_size - w + image_tensor = torch.tensor( + np.array(image.resize((w, h), resample=Image.Resampling.BILINEAR)).astype(np.float32).transpose(2, 0, 1), + device=self.device, + dtype=self.dtype, + ).unsqueeze(0) + return pad( + normalize(image_tensor, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]), (0, padw, 0, padh) + ) + + def normalize(self, coordinates: Tensor, target_size: tuple[int, int], original_size: tuple[int, int]) -> Tensor: + coordinates[:, :, 0] = ((coordinates[:, :, 0] * (target_size[1] / original_size[1])) + 0.5) / self.image_size + coordinates[:, :, 1] = ((coordinates[:, :, 1] * (target_size[0] / original_size[0])) + 0.5) / self.image_size + return coordinates + + def postprocess_masks(self, masks: Tensor, target_size: tuple[int, int], original_size: tuple[int, int]) -> Tensor: + masks = interpolate(masks, factor=torch.Size((self.image_size, self.image_size)), mode="bilinear") + masks = masks[..., : target_size[0], : target_size[1]] # remove padding added at `preprocess_image` time + masks = interpolate(masks, factor=torch.Size(original_size), mode="bilinear") + return masks + + +class SegmentAnythingH(SegmentAnything): + def __init__( + self, + image_encoder: SAMViTH | None = None, + point_encoder: PointEncoder | None = None, + mask_encoder: MaskEncoder | None = None, + mask_decoder: MaskDecoder | None = None, + device: Device | str = "cpu", + dtype: DType = torch.float32, + ) -> None: + image_encoder = image_encoder or SAMViTH() + point_encoder = point_encoder or PointEncoder() + mask_encoder = mask_encoder or MaskEncoder() + mask_decoder = mask_decoder or MaskDecoder() + + super().__init__( + image_encoder=image_encoder, + point_encoder=point_encoder, + mask_encoder=mask_encoder, + mask_decoder=mask_decoder, + device=device, + dtype=dtype, + ) diff --git a/imaginairy/vendored/refiners/foundationals/segment_anything/prompt_encoder.py b/imaginairy/vendored/refiners/foundationals/segment_anything/prompt_encoder.py new file mode 100644 index 0000000..5e30a82 --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/segment_anything/prompt_encoder.py @@ -0,0 +1,192 @@ +from collections.abc import Sequence +from enum import Enum, auto + +import torch +from jaxtyping import Float, Int +from torch import Tensor, device as Device, dtype as DType, nn + +import imaginairy.vendored.refiners.fluxion.layers as fl +from imaginairy.vendored.refiners.fluxion.context import Contexts + + +class CoordinateEncoder(fl.Chain): + def __init__( + self, + num_positional_features: int = 64, + scale: float = 1, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.num_positional_features = num_positional_features + self.scale = scale + + super().__init__( + fl.Multiply(scale=2, bias=-1), + fl.Linear(in_features=2, out_features=num_positional_features, bias=False, device=device, dtype=dtype), + fl.Multiply(scale=2 * torch.pi * self.scale), + fl.Concatenate(fl.Sin(), fl.Cos(), dim=-1), + ) + + +class PointType(Enum): + BACKGROUND = auto() + FOREGROUND = auto() + BOX_TOP_LEFT = auto() + BOX_BOTTOM_RIGHT = auto() + NOT_A_POINT = auto() + + +class PointTypeEmbedding(fl.WeightedModule, fl.ContextModule): + def __init__(self, embedding_dim: int, device: Device | str | None = None, dtype: DType | None = None) -> None: + super().__init__() + self.embedding_dim = embedding_dim + self.weight = nn.Parameter(data=torch.randn(len(PointType), self.embedding_dim, device=device, dtype=dtype)) + + def forward(self, type_mask: Int[Tensor, "1 num_points"]) -> Float[Tensor, "1 num_points embedding_dim"]: + assert isinstance(type_mask, Tensor), "type_mask must be a Tensor." + + embeddings = torch.zeros(*type_mask.shape, self.embedding_dim).to(device=type_mask.device) + for type_id in PointType: + mask = type_mask == type_id.value + embeddings[mask] = self.weight[type_id.value - 1] + + return embeddings + + +class PointEncoder(fl.Chain): + def __init__( + self, embedding_dim: int = 256, scale: float = 1, device: Device | str | None = None, dtype: DType | None = None + ) -> None: + assert embedding_dim % 2 == 0, "embedding_dim must be divisible by 2." + self.embedding_dim = embedding_dim + self.scale = scale + + super().__init__( + CoordinateEncoder(num_positional_features=embedding_dim // 2, scale=scale, device=device, dtype=dtype), + fl.Lambda(func=self.pad), + fl.Residual( + fl.UseContext(context="point_encoder", key="type_mask"), + PointTypeEmbedding(embedding_dim=embedding_dim, device=device, dtype=dtype), + ), + ) + + def pad(self, x: Tensor) -> Tensor: + type_mask: Tensor = self.use_context("point_encoder")["type_mask"] + if torch.any((type_mask == PointType.BOX_TOP_LEFT.value) | (type_mask == PointType.BOX_BOTTOM_RIGHT.value)): + # Some boxes have been passed: no need to pad in this case + return x + type_mask = torch.cat( + [type_mask, torch.full((type_mask.shape[0], 1), PointType.NOT_A_POINT.value, device=type_mask.device)], + dim=1, + ) + self.set_context(context="point_encoder", value={"type_mask": type_mask}) + return torch.cat([x, torch.zeros((x.shape[0], 1, x.shape[-1]), device=x.device)], dim=1) + + def init_context(self) -> Contexts: + return { + "point_encoder": { + "type_mask": None, + } + } + + def set_type_mask(self, type_mask: Int[Tensor, "1 num_points"]) -> None: + self.set_context(context="point_encoder", value={"type_mask": type_mask}) + + def get_dense_positional_embedding( + self, image_embedding_size: tuple[int, int] + ) -> Float[Tensor, "num_positional_features height width"]: + coordinate_encoder = self.ensure_find(layer_type=CoordinateEncoder) + height, width = image_embedding_size + grid = torch.ones((height, width), device=self.device, dtype=torch.float32) + y_embedding = grid.cumsum(dim=0) - 0.5 + x_embedding = grid.cumsum(dim=1) - 0.5 + y_embedding = y_embedding / height + x_embedding = x_embedding / width + positional_embedding = ( + coordinate_encoder(torch.stack(tensors=[x_embedding, y_embedding], dim=-1)) + .permute(2, 0, 1) + .unsqueeze(dim=0) + ) + return positional_embedding + + def points_to_tensor( + self, + foreground_points: Sequence[tuple[float, float]] | None = None, + background_points: Sequence[tuple[float, float]] | None = None, + not_a_points: Sequence[tuple[float, float]] | None = None, + box_points: Sequence[Sequence[tuple[float, float]]] | None = None, + ) -> tuple[Float[Tensor, "1 num_points 2"], Int[Tensor, "1 num_points"]]: + foreground_points = foreground_points or [] + background_points = background_points or [] + not_a_points = not_a_points or [] + box_points = box_points or [] + top_left_points = [box[0] for box in box_points] + bottom_right_points = [box[1] for box in box_points] + coordinates: list[Tensor] = [] + type_ids: list[Tensor] = [] + + # Must be in sync with PointType enum + for type_id, coords_seq in zip( + PointType, [background_points, foreground_points, top_left_points, bottom_right_points, not_a_points] + ): + if len(coords_seq) > 0: + coords_tensor = torch.tensor(data=list(coords_seq), dtype=torch.float, device=self.device) + coordinates.append(coords_tensor) + point_ids = torch.tensor(data=[type_id.value] * len(coords_seq), dtype=torch.int, device=self.device) + type_ids.append(point_ids) + + all_coordinates = torch.cat(tensors=coordinates, dim=0).unsqueeze(dim=0) + type_mask = torch.cat(tensors=type_ids, dim=0).unsqueeze(dim=0) + + return all_coordinates, type_mask + + +class MaskEncoder(fl.Chain): + def __init__( + self, + embedding_dim: int = 256, + intermediate_channels: int = 16, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.intermediate_channels = intermediate_channels + super().__init__( + fl.Conv2d( + in_channels=1, + out_channels=self.intermediate_channels // 4, + kernel_size=2, + stride=2, + device=device, + dtype=dtype, + ), + fl.LayerNorm2d(channels=self.intermediate_channels // 4, device=device, dtype=dtype), + fl.GeLU(), + fl.Conv2d( + in_channels=self.intermediate_channels // 4, + out_channels=self.intermediate_channels, + kernel_size=2, + stride=2, + device=device, + dtype=dtype, + ), + fl.LayerNorm2d(channels=self.intermediate_channels, device=device, dtype=dtype), + fl.GeLU(), + fl.Conv2d( + in_channels=self.intermediate_channels, + out_channels=self.embedding_dim, + kernel_size=1, + device=device, + dtype=dtype, + ), + ) + self.register_parameter( + "no_mask_embedding", nn.Parameter(torch.randn(1, embedding_dim, device=device, dtype=dtype)) + ) + + def get_no_mask_dense_embedding( + self, image_embedding_size: tuple[int, int], batch_size: int = 1 + ) -> Float[Tensor, "batch embedding_dim image_embedding_height image_embedding_width"]: + return self.no_mask_embedding.reshape(1, -1, 1, 1).expand( + batch_size, -1, image_embedding_size[0], image_embedding_size[1] + ) diff --git a/imaginairy/vendored/refiners/foundationals/segment_anything/transformer.py b/imaginairy/vendored/refiners/foundationals/segment_anything/transformer.py new file mode 100644 index 0000000..dba509f --- /dev/null +++ b/imaginairy/vendored/refiners/foundationals/segment_anything/transformer.py @@ -0,0 +1,158 @@ +from torch import device as Device, dtype as DType + +import imaginairy.vendored.refiners.fluxion.layers as fl + + +class CrossAttention(fl.Attention): + def __init__( + self, + embedding_dim: int, + cross_embedding_dim: int | None = None, + num_heads: int = 1, + inner_dim: int | None = None, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + super().__init__( + embedding_dim=embedding_dim, + key_embedding_dim=cross_embedding_dim, + num_heads=num_heads, + inner_dim=inner_dim, + is_optimized=False, + device=device, + dtype=dtype, + ) + self.cross_embedding_dim = cross_embedding_dim or embedding_dim + self.insert(index=0, module=fl.Parallel(fl.GetArg(index=0), fl.GetArg(index=1), fl.GetArg(index=1))) + + +class FeedForward(fl.Residual): + def __init__( + self, embedding_dim: int, feed_forward_dim: int, device: Device | str | None = None, dtype: DType | None = None + ) -> None: + self.embedding_dim = embedding_dim + self.feed_forward_dim = feed_forward_dim + super().__init__( + fl.Linear(in_features=embedding_dim, out_features=feed_forward_dim, device=device, dtype=dtype), + fl.ReLU(), + fl.Linear(in_features=feed_forward_dim, out_features=embedding_dim, device=device, dtype=dtype), + ) + + +class SparseSelfAttention(fl.Residual): + def __init__( + self, + embedding_dim: int, + inner_dim: int | None = None, + num_heads: int = 1, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + add_sparse_embedding = fl.Residual(fl.UseContext(context="mask_decoder", key="sparse_embedding")) + super().__init__( + fl.Parallel(add_sparse_embedding, add_sparse_embedding, fl.Identity()), + fl.Attention( + embedding_dim=embedding_dim, + inner_dim=inner_dim, + num_heads=num_heads, + is_optimized=False, + device=device, + dtype=dtype, + ), + ) + + +class SparseCrossDenseAttention(fl.Residual): + def __init__( + self, embedding_dim: int, num_heads: int = 8, device: Device | str | None = None, dtype: DType | None = None + ) -> None: + self.embedding_dim = embedding_dim + self.num_heads = num_heads + super().__init__( + fl.Parallel( + fl.Residual( + fl.UseContext(context="mask_decoder", key="sparse_embedding"), + ), + fl.Sum( + fl.UseContext(context="mask_decoder", key="dense_embedding"), + fl.UseContext(context="mask_decoder", key="dense_positional_embedding"), + ), + fl.UseContext(context="mask_decoder", key="dense_embedding"), + ), + fl.Attention( + embedding_dim=embedding_dim, + inner_dim=embedding_dim // 2, + num_heads=num_heads, + is_optimized=False, + device=device, + dtype=dtype, + ), + ) + + +class DenseCrossSparseAttention(fl.Chain): + def __init__( + self, embedding_dim: int, num_heads: int = 8, device: Device | str | None = None, dtype: DType | None = None + ) -> None: + super().__init__( + fl.Parallel( + fl.Sum( + fl.UseContext(context="mask_decoder", key="dense_embedding"), + fl.UseContext(context="mask_decoder", key="dense_positional_embedding"), + ), + fl.Residual( + fl.UseContext(context="mask_decoder", key="sparse_embedding"), + ), + fl.Identity(), + ), + fl.Attention( + embedding_dim=embedding_dim, + inner_dim=embedding_dim // 2, + num_heads=num_heads, + is_optimized=False, + device=device, + dtype=dtype, + ), + ) + + +class TwoWayTranformerLayer(fl.Chain): + def __init__( + self, + embedding_dim: int, + num_heads: int = 8, + feed_forward_dim: int = 2048, + use_residual_self_attention: bool = True, + device: Device | str | None = None, + dtype: DType | None = None, + ) -> None: + self.embedding_dim = embedding_dim + self.num_heads = num_heads + self.feed_forward_dim = feed_forward_dim + + self_attention = ( + SparseSelfAttention(embedding_dim=embedding_dim, num_heads=num_heads, device=device, dtype=dtype) + if use_residual_self_attention + else fl.SelfAttention( + embedding_dim=embedding_dim, num_heads=num_heads, is_optimized=False, device=device, dtype=dtype + ) + ) + + super().__init__( + self_attention, + fl.LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype), + SparseCrossDenseAttention(embedding_dim=embedding_dim, num_heads=num_heads, device=device, dtype=dtype), + fl.LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype), + FeedForward(embedding_dim=embedding_dim, feed_forward_dim=feed_forward_dim, device=device, dtype=dtype), + fl.LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype), + fl.Passthrough( + fl.Sum( + fl.UseContext(context="mask_decoder", key="dense_embedding"), + DenseCrossSparseAttention( + embedding_dim=embedding_dim, num_heads=num_heads, device=device, dtype=dtype + ), + ), + fl.LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype), + fl.SetContext(context="mask_decoder", key="dense_embedding"), + ), + ) diff --git a/imaginairy/vendored/refiners/py.typed b/imaginairy/vendored/refiners/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/imaginairy/vendored/refiners/readme.txt b/imaginairy/vendored/refiners/readme.txt new file mode 100644 index 0000000..a1bb296 --- /dev/null +++ b/imaginairy/vendored/refiners/readme.txt @@ -0,0 +1 @@ +vendored from git@github.com:finegrain-ai/refiners.git @ 20c229903f53d05dc1c44659ec97603660ef964c diff --git a/requirements-dev.txt b/requirements-dev.txt index 5fe97fd..182f449 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -94,7 +94,7 @@ importlib-metadata==7.0.1 iniconfig==2.0.0 # via pytest jaxtyping==0.2.25 - # via refiners + # via imaginAIry (setup.py) jinja2==3.1.2 # via torch kiwisolver==1.4.5 @@ -133,7 +133,6 @@ numpy==1.24.4 # matplotlib # numba # opencv-python - # refiners # scipy # torchvision # transformers @@ -160,7 +159,6 @@ pillow==10.2.0 # imageio # imaginAIry (setup.py) # matplotlib - # refiners # torchvision pluggy==1.3.0 # via pytest @@ -199,8 +197,6 @@ pyyaml==6.0.1 # responses # timm # transformers -refiners==0.2.0 - # via imaginAIry (setup.py) regex==2023.12.25 # via # diffusers @@ -216,13 +212,12 @@ requests==2.31.0 # transformers responses==0.24.1 # via -r requirements-dev.in -ruff==0.1.9 +ruff==0.1.11 # via -r requirements-dev.in -safetensors==0.3.3 +safetensors==0.4.1 # via # diffusers # imaginAIry (setup.py) - # refiners # timm # transformers scipy==1.10.1 @@ -262,7 +257,6 @@ torch==2.1.2 # imaginAIry (setup.py) # kornia # open-clip-torch - # refiners # timm # torchdiffeq # torchvision diff --git a/setup.py b/setup.py index a96ae31..4d655fa 100644 --- a/setup.py +++ b/setup.py @@ -95,9 +95,10 @@ setup( # need to migration to 2.0 "pydantic>=2.3.0", "requests>=2.28.1", - "refiners>=0.2.0", + # "refiners>=0.2.0", + "jaxtyping>=0.2.23", # refiners dependency "einops>=0.3.0", - "safetensors>=0.2.1", + "safetensors>=0.4.0", # scipy is a sub dependency but v1.11 doesn't support python 3.8. https://docs.scipy.org/doc/scipy/dev/toolchain.html#numpy "scipy<1.11", "timm>=0.4.12,!=0.9.0,!=0.9.1", # for vendored blip

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