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587 lines
20 KiB
Python
587 lines
20 KiB
Python
"""Functions for generating refined images"""
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import logging
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from typing import List, Optional
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from imaginairy.config import CONTROL_CONFIG_SHORTCUTS
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from imaginairy.schema import ControlInput, ImaginePrompt, MaskMode, WeightedPrompt
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logger = logging.getLogger(__name__)
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def generate_single_image(
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prompt: ImaginePrompt,
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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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add_caption=False,
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return_latent=False,
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dtype=None,
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half_mode=None,
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):
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import torch.nn
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from PIL import Image, 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.generate import (
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IMAGINAIRY_SAFETY_MODE,
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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.samplers import SolverName
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from imaginairy.schema import ImagineResult
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from imaginairy.utils import get_device, randn_seeded
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from imaginairy.utils.img_utils import (
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add_caption_to_image,
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combine_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.utils.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.utils.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.utils.outpaint import (
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outpaint_arg_str_parse,
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prepare_image_for_outpaint,
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)
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from imaginairy.utils.safety import create_safety_score
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if dtype is None:
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dtype = torch.float16 if half_mode else torch.float32
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get_device()
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clear_gpu_cache()
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prompt = prompt.make_concrete_copy()
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sd = get_diffusion_model_refiners(
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weights_config=prompt.model_weights,
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for_inpainting=prompt.should_use_inpainting
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and prompt.inpaint_method == "finetune",
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dtype=dtype,
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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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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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clip_text_embedding = clip_text_embedding.to(device=sd.device, dtype=sd.dtype)
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result_images: dict[str, torch.Tensor | None | Image.Image] = {}
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progress_latents: list[torch.Tensor] = []
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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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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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if control_inputs:
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control_modes = [c.mode for c in prompt.control_inputs]
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if prompt.init_image:
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starting_image = prompt.init_image
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assert prompt.init_image_strength is not None
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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 = list(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 == 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 prompt.inpaint_method == "controlnet":
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result_images["control-inpaint"] = mask_image
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control_inputs.append(
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ControlInput(mode="inpaint", image=mask_image)
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)
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seed_everything(prompt.seed)
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assert prompt.seed is not None
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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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for control_input in control_inputs:
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controlnet, control_image_t, control_image_disp = prep_control_input(
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control_input=control_input,
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sd=sd,
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init_image_t=init_image_t,
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fit_width=prompt.width,
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fit_height=prompt.height,
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)
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result_images[f"control-{control_input.mode}"] = control_image_disp
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controlnets.append((controlnet, control_image_t))
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if prompt.allow_compose_phase:
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cutoff_size = get_model_default_image_size(prompt.model_architecture)
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cutoff_size = (int(cutoff_size[0] * 1.30), int(cutoff_size[1] * 1.30))
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compose_kwargs = {
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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": cutoff_size,
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"dtype": dtype,
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}
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if prompt.init_image:
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compose_kwargs.update(
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{
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"target_height": init_image.height,
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"target_width": init_image.width,
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}
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)
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comp_image, comp_img_orig = _generate_composition_image(**compose_kwargs)
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if comp_image is not None:
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prompt.fix_faces = False # done in composition
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result_images["composition"] = comp_img_orig
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result_images["composition-upscaled"] = comp_image
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composition_strength = prompt.composition_strength
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first_step = int((prompt.steps) * composition_strength)
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noise_step = int((prompt.steps - 1) * composition_strength)
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log_img(comp_img_orig, "comp_image")
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log_img(comp_image, "comp_image_upscaled")
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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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compose_control_inputs: list[ControlInput] = [
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ControlInput(mode="details", image=comp_image, strength=1),
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]
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for control_input in compose_control_inputs:
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(
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controlnet,
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control_image_t,
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control_image_disp,
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) = prep_control_input(
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control_input=control_input,
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sd=sd,
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init_image_t=None,
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fit_width=prompt.width,
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fit_height=prompt.height,
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)
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result_images[f"control-{control_input.mode}"] = control_image_disp
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controlnets.append((controlnet, control_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.solver_type.lower() == SolverName.DPMPP:
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sd.scheduler = DPMSolver(num_inference_steps=prompt.steps)
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elif prompt.solver_type.lower() == SolverName.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 solver type: {prompt.solver_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") and mask_image is not None:
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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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# if "cuda" in str(sd.lda.device):
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# sd.lda.to("cpu")
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clear_gpu_cache()
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# print(f"moving unet to {sd.device}")
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# sd.unet.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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clear_gpu_cache()
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logger.debug("Decoding image")
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if x.device != sd.lda.device:
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sd.lda.to(x.device)
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clear_gpu_cache()
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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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# todo: do something smarter
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result_images.update(
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{
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"upscaled": upscaled_img,
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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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}
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)
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result = ImagineResult(
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img=gen_img,
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prompt=prompt,
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is_nsfw=safety_score.is_nsfw,
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safety_score=safety_score,
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result_images=result_images,
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timings=lc.get_timings(),
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progress_latents=[], # todo
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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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clear_gpu_cache()
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return result
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def _prompts_to_embeddings(prompts, text_encoder):
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import torch
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if not prompts:
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prompts = [WeightedPrompt(text="")]
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total_weight = sum(wp.weight for wp in prompts)
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if str(text_encoder.device) == "cpu":
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text_encoder = text_encoder.to(dtype=torch.float32)
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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.utils.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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def clear_gpu_cache():
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import gc
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import torch
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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def prep_control_input(
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control_input: ControlInput, sd, init_image_t, fit_width, fit_height
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):
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from PIL import ImageOps
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from imaginairy.utils import get_device
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from imaginairy.utils.img_utils import (
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pillow_fit_image_within,
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pillow_img_to_torch_image,
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)
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from imaginairy.utils.log_utils import (
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log_img,
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)
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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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else:
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raise ValueError("No control image found")
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assert control_image is not None
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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=fit_height,
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max_width=fit_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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from imaginairy.img_processors.control_modes import CONTROL_MODES
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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( # type: ignore
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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) # type: ignore
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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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log_img(control_image_disp, "control_image")
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if len(control_image_t.shape) == 3:
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raise ValueError("Control image must be 4D")
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if control_image_t.shape[1] != 3:
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raise ValueError("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
|
|
or control_image_t.max() > 1
|
|
):
|
|
msg = f"Control image must be in [0, 1] but we received {control_image_t.min()} and {control_image_t.max()}"
|
|
raise ValueError(msg)
|
|
|
|
if control_image_t.max() == control_image_t.min():
|
|
msg = f"No control signal found in control image {control_input.mode}."
|
|
raise ValueError(msg)
|
|
|
|
control_config = CONTROL_CONFIG_SHORTCUTS.get(control_input.mode, None)
|
|
if not control_config:
|
|
msg = f"Unknown control mode: {control_input.mode}"
|
|
raise ValueError(msg)
|
|
from refiners.foundationals.latent_diffusion import SD1ControlnetAdapter
|
|
|
|
controlnet = SD1ControlnetAdapter( # type: ignore
|
|
name=control_input.mode,
|
|
target=sd.unet,
|
|
weights_location=control_config.weights_location,
|
|
)
|
|
controlnet.set_scale(control_input.strength)
|
|
return controlnet, control_image_t, control_image_disp
|
|
|
|
|
|
def _generate_composition_image(
|
|
prompt,
|
|
target_height,
|
|
target_width,
|
|
cutoff: tuple[int, int] = (512, 512),
|
|
dtype=None,
|
|
):
|
|
from PIL import Image
|
|
|
|
from imaginairy.utils import default, get_default_dtype
|
|
from imaginairy.utils.img_utils import calc_scale_to_fit_within
|
|
from imaginairy.utils.named_resolutions import normalize_image_size
|
|
|
|
cutoff = normalize_image_size(cutoff)
|
|
if prompt.width <= cutoff[0] and prompt.height <= cutoff[1]:
|
|
return None, None
|
|
|
|
dtype = default(dtype, get_default_dtype)
|
|
|
|
shrink_scale = calc_scale_to_fit_within(
|
|
height=prompt.height,
|
|
width=prompt.width,
|
|
max_size=cutoff,
|
|
)
|
|
|
|
composition_prompt = prompt.full_copy(
|
|
deep=True,
|
|
update={
|
|
"size": (
|
|
int(prompt.width * shrink_scale),
|
|
int(prompt.height * shrink_scale),
|
|
),
|
|
"steps": None,
|
|
"upscale": False,
|
|
"fix_faces": False,
|
|
"mask_modify_original": False,
|
|
"allow_compose_phase": False,
|
|
"caption_text": None,
|
|
},
|
|
)
|
|
|
|
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
|
|
|
|
if prompt.fix_faces:
|
|
from imaginairy.enhancers.face_restoration_codeformer import enhance_faces
|
|
|
|
img = enhance_faces(img, fidelity=prompt.fix_faces_fidelity)
|
|
|
|
img = upscale_image(img, ultrasharp=True)
|
|
|
|
img = img.resize(
|
|
(target_width, target_height),
|
|
resample=Image.Resampling.LANCZOS,
|
|
)
|
|
|
|
return img, result.images["generated"]
|