mirror of
https://github.com/brycedrennan/imaginAIry
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440 lines
16 KiB
Python
440 lines
16 KiB
Python
import logging
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from typing import List, Optional
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from imaginairy import WeightedPrompt
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from imaginairy.config import CONTROLNET_CONFIG_SHORTCUTS
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from imaginairy.model_manager import load_controlnet_adapter
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logger = logging.getLogger(__name__)
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def _generate_single_image(
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prompt,
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debug_img_callback=None,
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progress_img_callback=None,
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progress_img_interval_steps=3,
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progress_img_interval_min_s=0.1,
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half_mode=None,
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add_caption=False,
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# controlnet, finetune, naive, auto
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inpaint_method="finetune",
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return_latent=False,
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):
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import gc
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import torch.nn
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from PIL import ImageOps
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from pytorch_lightning import seed_everything
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from refiners.foundationals.latent_diffusion.schedulers import DDIM, DPMSolver
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from tqdm import tqdm
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from imaginairy.api import (
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IMAGINAIRY_SAFETY_MODE,
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_generate_composition_image,
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combine_image,
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)
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from imaginairy.enhancers.clip_masking import get_img_mask
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from imaginairy.enhancers.describe_image_blip import generate_caption
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from imaginairy.enhancers.face_restoration_codeformer import enhance_faces
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from imaginairy.enhancers.upscale_realesrgan import upscale_image
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from imaginairy.img_utils import (
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add_caption_to_image,
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pillow_fit_image_within,
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pillow_img_to_torch_image,
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pillow_mask_to_latent_mask,
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)
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from imaginairy.log_utils import (
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ImageLoggingContext,
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log_img,
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log_latent,
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)
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from imaginairy.model_manager import (
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get_diffusion_model_refiners,
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get_model_default_image_size,
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)
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from imaginairy.outpaint import outpaint_arg_str_parse, prepare_image_for_outpaint
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from imaginairy.safety import create_safety_score
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from imaginairy.samplers import SamplerName
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from imaginairy.schema import ImaginePrompt, ImagineResult
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from imaginairy.utils import get_device, randn_seeded
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get_device()
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gc.collect()
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torch.cuda.empty_cache()
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prompt = prompt.make_concrete_copy()
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control_modes = []
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control_inputs = prompt.control_inputs or []
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control_inputs = control_inputs.copy()
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for_inpainting = bool(prompt.mask_image or prompt.mask_prompt or prompt.outpaint)
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if control_inputs:
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control_modes = [c.mode for c in prompt.control_inputs]
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sd = get_diffusion_model_refiners(
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weights_location=prompt.model,
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config_path=prompt.model_config_path,
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control_weights_locations=tuple(control_modes),
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half_mode=half_mode,
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for_inpainting=for_inpainting and inpaint_method == "finetune",
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)
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seed_everything(prompt.seed)
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downsampling_factor = 8
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latent_channels = 4
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batch_size = 1
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mask_image = None
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mask_image_orig = None
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prompt = prompt.make_concrete_copy()
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def latent_logger(latents):
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progress_latents.append(latents)
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with ImageLoggingContext(
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prompt=prompt,
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model=sd,
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debug_img_callback=debug_img_callback,
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progress_img_callback=progress_img_callback,
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progress_img_interval_steps=progress_img_interval_steps,
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progress_img_interval_min_s=progress_img_interval_min_s,
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progress_latent_callback=latent_logger
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if prompt.collect_progress_latents
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else None,
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) as lc:
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sd.set_tile_mode(prompt.tile_mode)
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clip_text_embedding = _calc_conditioning(
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positive_prompts=prompt.prompts,
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negative_prompts=prompt.negative_prompt,
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positive_conditioning=prompt.conditioning,
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text_encoder=sd.clip_text_encoder,
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)
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result_images = {}
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progress_latents = []
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first_step = 0
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mask_grayscale = None
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shape = [
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batch_size,
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latent_channels,
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prompt.height // downsampling_factor,
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prompt.width // downsampling_factor,
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]
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init_latent = None
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noise_step = None
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if prompt.init_image:
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starting_image = prompt.init_image
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first_step = int((prompt.steps) * prompt.init_image_strength)
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# noise_step = int((prompt.steps - 1) * prompt.init_image_strength)
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if prompt.mask_prompt:
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mask_image, mask_grayscale = get_img_mask(
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starting_image, prompt.mask_prompt, threshold=0.1
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)
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elif prompt.mask_image:
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mask_image = prompt.mask_image.convert("L")
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if prompt.outpaint:
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outpaint_kwargs = outpaint_arg_str_parse(prompt.outpaint)
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starting_image, mask_image = prepare_image_for_outpaint(
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starting_image, mask_image, **outpaint_kwargs
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)
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init_image = pillow_fit_image_within(
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starting_image,
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max_height=prompt.height,
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max_width=prompt.width,
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)
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init_image_t = pillow_img_to_torch_image(init_image)
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init_image_t = init_image_t.to(device=sd.device, dtype=sd.dtype)
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init_latent = sd.lda.encode(init_image_t)
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shape = init_latent.shape
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log_latent(init_latent, "init_latent")
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if mask_image is not None:
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mask_image = pillow_fit_image_within(
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mask_image,
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max_height=prompt.height,
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max_width=prompt.width,
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convert="L",
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)
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log_img(mask_image, "init mask")
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if prompt.mask_mode == ImaginePrompt.MaskMode.REPLACE:
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mask_image = ImageOps.invert(mask_image)
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mask_image_orig = mask_image
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log_img(mask_image, "latent_mask")
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pillow_mask_to_latent_mask(
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mask_image, downsampling_factor=downsampling_factor
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).to(get_device())
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# if inpaint_method == "controlnet":
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# result_images["control-inpaint"] = mask_image
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# control_inputs.append(
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# ControlNetInput(mode="inpaint", image=mask_image)
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# )
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seed_everything(prompt.seed)
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noise = randn_seeded(seed=prompt.seed, size=shape).to(
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get_device(), dtype=sd.dtype
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)
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noised_latent = noise
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controlnets = []
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if control_modes:
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control_strengths = []
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from imaginairy.img_processors.control_modes import CONTROL_MODES
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for control_input in control_inputs:
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if control_input.image_raw is not None:
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control_image = control_input.image_raw
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elif control_input.image is not None:
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control_image = control_input.image
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control_image = control_image.convert("RGB")
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log_img(control_image, "control_image_input")
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control_image_input = pillow_fit_image_within(
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control_image,
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max_height=prompt.height,
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max_width=prompt.width,
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)
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if control_input.mode == "inpaint":
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control_image_input = ImageOps.invert(control_image_input)
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control_image_input_t = pillow_img_to_torch_image(control_image_input)
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control_image_input_t = control_image_input_t.to(get_device())
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if control_input.image_raw is None:
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control_prep_function = CONTROL_MODES[control_input.mode]
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if control_input.mode == "inpaint":
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control_image_t = control_prep_function(
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control_image_input_t, init_image_t
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)
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else:
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control_image_t = control_prep_function(control_image_input_t)
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else:
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control_image_t = (control_image_input_t + 1) / 2
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control_image_disp = control_image_t * 2 - 1
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result_images[f"control-{control_input.mode}"] = control_image_disp
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log_img(control_image_disp, "control_image")
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if len(control_image_t.shape) == 3:
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raise RuntimeError("Control image must be 4D")
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if control_image_t.shape[1] != 3:
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raise RuntimeError("Control image must have 3 channels")
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if (
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control_input.mode != "inpaint"
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and control_image_t.min() < 0
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or control_image_t.max() > 1
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):
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msg = f"Control image must be in [0, 1] but we received {control_image_t.min()} and {control_image_t.max()}"
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raise RuntimeError(msg)
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if control_image_t.max() == control_image_t.min():
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msg = f"No control signal found in control image {control_input.mode}."
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raise RuntimeError(msg)
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control_strengths.append(control_input.strength)
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control_weights_path = CONTROLNET_CONFIG_SHORTCUTS.get(
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control_input.mode, None
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).weights_url
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controlnet = load_controlnet_adapter(
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name=control_input.mode,
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control_weights_location=control_weights_path,
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target_unet=sd.unet,
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scale=control_input.strength,
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)
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controlnets.append((controlnet, control_image_t))
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if prompt.allow_compose_phase:
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if prompt.init_image:
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comp_image, comp_img_orig = _generate_composition_image(
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prompt=prompt,
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target_height=init_image.height,
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target_width=init_image.width,
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cutoff=get_model_default_image_size(prompt.model),
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)
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else:
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comp_image, comp_img_orig = _generate_composition_image(
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prompt=prompt,
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target_height=prompt.height,
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target_width=prompt.width,
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cutoff=get_model_default_image_size(prompt.model),
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)
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if comp_image is not None:
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result_images["composition"] = comp_img_orig
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result_images["composition-upscaled"] = comp_image
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# noise = noise[:, :, : comp_image.height, : comp_image.shape[3]]
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comp_cutoff = 0.60
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first_step = int((prompt.steps) * comp_cutoff)
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noise_step = int((prompt.steps - 1) * comp_cutoff)
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# noise_step = int(prompt.steps * max(comp_cutoff - 0.05, 0))
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# noise_step = max(noise_step, 0)
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# noise_step = min(noise_step, prompt.steps - 1)
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log_img(comp_image, "comp_image")
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comp_image_t = pillow_img_to_torch_image(comp_image)
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comp_image_t = comp_image_t.to(sd.device, dtype=sd.dtype)
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init_latent = sd.lda.encode(comp_image_t)
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for controlnet, control_image_t in controlnets:
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controlnet.set_controlnet_condition(
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control_image_t.to(device=sd.device, dtype=sd.dtype)
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)
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controlnet.inject()
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if prompt.sampler_type.lower() == SamplerName.K_DPMPP_2M:
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sd.scheduler = DPMSolver(num_inference_steps=prompt.steps)
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elif prompt.sampler_type.lower() == SamplerName.DDIM:
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sd.scheduler = DDIM(num_inference_steps=prompt.steps)
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else:
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msg = f"Unknown sampler type: {prompt.sampler_type}"
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raise ValueError(msg)
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sd.scheduler.to(device=sd.device, dtype=sd.dtype)
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sd.set_num_inference_steps(prompt.steps)
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if hasattr(sd, "mask_latents"):
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sd.set_inpainting_conditions(
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target_image=init_image,
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mask=ImageOps.invert(mask_image),
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latents_size=shape[-2:],
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)
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if init_latent is not None:
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noise_step = noise_step if noise_step is not None else first_step
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if first_step >= len(sd.steps):
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noised_latent = init_latent
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else:
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noised_latent = sd.scheduler.add_noise(
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x=init_latent, noise=noise, step=sd.steps[noise_step]
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)
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x = noised_latent
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x = x.to(device=sd.device, dtype=sd.dtype)
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for step in tqdm(sd.steps[first_step:], bar_format=" {l_bar}{bar}{r_bar}"):
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log_latent(x, "noisy_latent")
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x = sd(
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x,
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step=step,
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clip_text_embedding=clip_text_embedding,
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condition_scale=prompt.prompt_strength,
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)
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logger.debug("Decoding image")
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gen_img = sd.lda.decode_latents(x)
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if mask_image_orig and init_image:
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result_images["pre-reconstitution"] = gen_img
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mask_final = mask_image_orig.copy()
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# mask_final = ImageOps.invert(mask_final)
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log_img(mask_final, "reconstituting mask")
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# gen_img = Image.composite(gen_img, init_image, mask_final)
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gen_img = combine_image(
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original_img=init_image,
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generated_img=gen_img,
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mask_img=mask_final,
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)
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log_img(gen_img, "reconstituted image")
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upscaled_img = None
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rebuilt_orig_img = None
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if add_caption:
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caption = generate_caption(gen_img)
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logger.info(f"Generated caption: {caption}")
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with lc.timing("safety-filter"):
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safety_score = create_safety_score(
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gen_img,
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safety_mode=IMAGINAIRY_SAFETY_MODE,
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)
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if safety_score.is_filtered:
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progress_latents.clear()
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if not safety_score.is_filtered:
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if prompt.fix_faces:
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logger.info("Fixing 😊 's in 🖼 using CodeFormer...")
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with lc.timing("face enhancement"):
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gen_img = enhance_faces(gen_img, fidelity=prompt.fix_faces_fidelity)
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if prompt.upscale:
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logger.info("Upscaling 🖼 using real-ESRGAN...")
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with lc.timing("upscaling"):
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upscaled_img = upscale_image(gen_img)
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# put the newly generated patch back into the original, full-size image
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if prompt.mask_modify_original and mask_image_orig and starting_image:
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logger.info("Combining inpainting with original image...")
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img_to_add_back_to_original = upscaled_img if upscaled_img else gen_img
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rebuilt_orig_img = combine_image(
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original_img=starting_image,
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generated_img=img_to_add_back_to_original,
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mask_img=mask_image_orig,
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)
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if prompt.caption_text:
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caption_text = prompt.caption_text.format(prompt=prompt.prompt_text)
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add_caption_to_image(gen_img, caption_text)
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result = ImagineResult(
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img=gen_img,
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prompt=prompt,
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upscaled_img=upscaled_img,
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is_nsfw=safety_score.is_nsfw,
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safety_score=safety_score,
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modified_original=rebuilt_orig_img,
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mask_binary=mask_image_orig,
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mask_grayscale=mask_grayscale,
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result_images=result_images,
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timings={},
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progress_latents=[],
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)
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_most_recent_result = result
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if result.timings:
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logger.info(f"Image Generated. Timings: {result.timings_str()}")
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for controlnet, _ in controlnets:
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controlnet.eject()
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gc.collect()
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torch.cuda.empty_cache()
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return result
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def _prompts_to_embeddings(prompts, text_encoder):
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total_weight = sum(wp.weight for wp in prompts)
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conditioning = sum(
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text_encoder(wp.text) * (wp.weight / total_weight) for wp in prompts
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)
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return conditioning
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def _calc_conditioning(
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positive_prompts: Optional[List[WeightedPrompt]],
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negative_prompts: Optional[List[WeightedPrompt]],
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positive_conditioning,
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text_encoder,
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):
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import torch
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from imaginairy.log_utils import log_conditioning
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# need to expand if doing batches
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neutral_conditioning = _prompts_to_embeddings(negative_prompts, text_encoder)
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log_conditioning(neutral_conditioning, "neutral conditioning")
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if positive_conditioning is None:
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positive_conditioning = _prompts_to_embeddings(positive_prompts, text_encoder)
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log_conditioning(positive_conditioning, "positive conditioning")
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clip_text_embedding = torch.cat(
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tensors=(neutral_conditioning, positive_conditioning), dim=0
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)
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return clip_text_embedding
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