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553 lines
21 KiB
Python
553 lines
21 KiB
Python
import logging
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from typing import TYPE_CHECKING, Any
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from imaginairy.api.generate import IMAGINAIRY_SAFETY_MODE
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from imaginairy.utils import seed_everything
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from imaginairy.utils.img_utils import calc_scale_to_fit_within, combine_image
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from imaginairy.utils.named_resolutions import normalize_image_size
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if TYPE_CHECKING:
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from imaginairy.schema import ImaginePrompt
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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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# controlnet, finetune, naive, auto
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inpaint_method="finetune",
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return_latent=False,
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dtype=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 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 import upscale_image
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from imaginairy.modules.midas.api import torch_image_to_depth_map
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from imaginairy.samplers import SOLVER_LOOKUP
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from imaginairy.samplers.editing import CFGEditingDenoiser
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from imaginairy.schema import (
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ControlInput,
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ImagineResult,
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LazyLoadingImage,
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MaskMode,
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)
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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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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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torch_img_to_pillow_img,
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)
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from imaginairy.utils.log_utils import (
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ImageLoggingContext,
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log_conditioning,
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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,
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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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latent_channels = 4
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downsampling_factor = 8
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batch_size = 1
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# global _most_recent_result
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# handle prompt pulling in previous values
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# if isinstance(prompt.init_image, str) and prompt.init_image.startswith("*prev"):
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# _, img_type = prompt.init_image.strip("*").split(".")
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# prompt.init_image = _most_recent_result.images[img_type]
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# if isinstance(prompt.mask_image, str) and prompt.mask_image.startswith("*prev"):
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# _, img_type = prompt.mask_image.strip("*").split(".")
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# prompt.mask_image = _most_recent_result.images[img_type]
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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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if inpaint_method == "auto":
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if prompt.model_weights in {"SD-1.5"}:
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inpaint_method = "finetune"
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else:
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inpaint_method = "controlnet"
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if for_inpainting and inpaint_method == "controlnet":
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control_modes.append("inpaint")
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model = get_diffusion_model(
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weights_location=prompt.model_weights,
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config_path=prompt.model_architecture,
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control_weights_locations=control_modes,
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half_mode=dtype == torch.float16,
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for_inpainting=for_inpainting and inpaint_method == "finetune",
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)
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is_controlnet_model = hasattr(model, "control_key")
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progress_latents = []
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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=model,
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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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seed_everything(prompt.seed)
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model.tile_mode(prompt.tile_mode)
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with lc.timing("conditioning"):
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# need to expand if doing batches
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neutral_conditioning = _prompts_to_embeddings(prompt.negative_prompt, model)
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_prompts_to_embeddings("", model)
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log_conditioning(neutral_conditioning, "neutral conditioning")
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if prompt.conditioning is not None:
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positive_conditioning = prompt.conditioning
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else:
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positive_conditioning = _prompts_to_embeddings(prompt.prompts, model)
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log_conditioning(positive_conditioning, "positive conditioning")
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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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SolverCls = SOLVER_LOOKUP[prompt.solver_type.lower()]
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solver = SolverCls(model)
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mask_image: Image.Image | LazyLoadingImage | None = None
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mask_latent = mask_image_orig = mask_grayscale = None
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init_latent: torch.Tensor | None = None
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t_enc = None
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starting_image = None
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denoiser_cls = None
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c_cat = []
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c_cat_neutral = None
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result_images: dict[str, torch.Tensor | Image.Image | None] = {}
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assert prompt.seed is not None
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seed_everything(prompt.seed)
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noise = randn_seeded(seed=prompt.seed, size=shape).to(get_device())
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control_strengths = []
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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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generation_strength = 1 - prompt.init_image_strength
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if model.cond_stage_key == "edit" or generation_strength >= 1:
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t_enc = None
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else:
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t_enc = int(prompt.steps * generation_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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assert starting_image is not None
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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).to(get_device())
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init_latent = model.get_first_stage_encoding(
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model.encode_first_stage(init_image_t)
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)
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assert init_latent is not None
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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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mask_latent = 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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ControlInput(mode="inpaint", image=mask_image)
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)
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assert prompt.seed is not None
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seed_everything(prompt.seed)
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noise = randn_seeded(seed=prompt.seed, size=list(init_latent.shape)).to(
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get_device()
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)
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# noise = noise[:, :, : init_latent.shape[2], : init_latent.shape[3]]
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# schedule = NoiseSchedule(
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# model_num_timesteps=model.num_timesteps,
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# ddim_num_steps=prompt.steps,
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# model_alphas_cumprod=model.alphas_cumprod,
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# ddim_discretize="uniform",
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# )
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# if generation_strength >= 1:
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# # prompt strength gets converted to time encodings,
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# # which means you can't get to true 0 without this hack
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# # (or setting steps=1000)
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# init_latent_noised = noise
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# else:
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# init_latent_noised = noise_an_image(
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# init_latent,
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# torch.tensor([t_enc - 1]).to(get_device()),
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# schedule=schedule,
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# noise=noise,
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# )
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if hasattr(model, "depth_stage_key"):
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# depth model
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depth_t = torch_image_to_depth_map(init_image_t)
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depth_latent = torch.nn.functional.interpolate(
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depth_t,
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size=shape[2:],
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mode="bicubic",
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align_corners=False,
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)
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result_images["depth_image"] = depth_t
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c_cat.append(depth_latent)
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elif is_controlnet_model:
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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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else:
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raise RuntimeError("Control image must be provided")
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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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assert control_image is not None
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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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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( # 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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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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c_cat.append(control_image_t)
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control_strengths.append(control_input.strength)
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elif hasattr(model, "masked_image_key"):
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# inpainting model
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assert mask_image_orig is not None
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assert mask_latent is not None
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mask_t = pillow_img_to_torch_image(ImageOps.invert(mask_image_orig)).to(
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get_device()
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)
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inverted_mask = 1 - mask_latent
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masked_image_t = init_image_t * (mask_t < 0.5)
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log_img(masked_image_t, "masked_image")
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inverted_mask_latent = torch.nn.functional.interpolate(
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inverted_mask, size=shape[-2:]
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)
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c_cat.append(inverted_mask_latent)
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masked_image_latent = model.get_first_stage_encoding(
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model.encode_first_stage(masked_image_t)
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)
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c_cat.append(masked_image_latent)
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elif model.cond_stage_key == "edit":
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# pix2pix model
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c_cat = [model.encode_first_stage(init_image_t)]
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assert init_latent is not None
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c_cat_neutral = [torch.zeros_like(init_latent)]
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denoiser_cls = CFGEditingDenoiser
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if c_cat:
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c_cat = [torch.cat([c], dim=1) for c in c_cat]
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if c_cat_neutral is None:
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c_cat_neutral = c_cat
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positive_conditioning_d: dict[str, Any] = {
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"c_concat": c_cat,
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"c_crossattn": [positive_conditioning],
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}
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neutral_conditioning_d: dict[str, Any] = {
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"c_concat": c_cat_neutral,
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"c_crossattn": [neutral_conditioning],
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}
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del neutral_conditioning
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del positive_conditioning
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if control_strengths and is_controlnet_model:
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positive_conditioning_d["control_strengths"] = torch.Tensor(
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control_strengths
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)
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neutral_conditioning_d["control_strengths"] = torch.Tensor(
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control_strengths
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)
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if (
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prompt.allow_compose_phase
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and not is_controlnet_model
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and model.cond_stage_key != "edit"
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):
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default_size = get_model_default_image_size(
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prompt.model_weights.architecture
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)
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if prompt.init_image:
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comp_image = _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=default_size,
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)
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else:
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comp_image = _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=default_size,
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)
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if comp_image is not None:
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result_images["composition"] = comp_image
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# noise = noise[:, :, : comp_image.height, : comp_image.shape[3]]
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t_enc = int(prompt.steps * 0.65)
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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(get_device())
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init_latent = model.get_first_stage_encoding(
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model.encode_first_stage(comp_image_t)
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)
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with lc.timing("sampling"):
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samples = solver.sample(
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num_steps=prompt.steps,
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positive_conditioning=positive_conditioning_d,
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neutral_conditioning=neutral_conditioning_d,
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guidance_scale=prompt.prompt_strength,
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t_start=t_enc,
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mask=mask_latent,
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orig_latent=init_latent,
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shape=shape,
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batch_size=1,
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denoiser_cls=denoiser_cls,
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noise=noise,
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)
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if return_latent:
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return samples
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with lc.timing("decoding"):
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gen_imgs_t = model.decode_first_stage(samples)
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gen_img = torch_img_to_pillow_img(gen_imgs_t)
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if mask_image_orig and init_image:
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mask_final = mask_image_orig.copy()
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log_img(mask_final, "reconstituting mask")
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mask_final = ImageOps.invert(mask_final)
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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_image_orig,
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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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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_images["upscaled"] = upscaled_img
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result_images["modified_original"] = rebuilt_orig_img
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result_images["mask_binary"] = mask_image_orig
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result_images["mask_grayscale"] = mask_grayscale
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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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performance_stats=lc.get_performance_stats(),
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progress_latents=progress_latents.copy(),
|
|
)
|
|
|
|
# _most_recent_result = result
|
|
logger.info(f"Image Generated. Timings: {result.timings_str()}")
|
|
return result
|
|
|
|
|
|
def _prompts_to_embeddings(prompts, model):
|
|
total_weight = sum(wp.weight for wp in prompts)
|
|
conditioning = sum(
|
|
model.get_learned_conditioning(wp.text) * (wp.weight / total_weight)
|
|
for wp in prompts
|
|
)
|
|
return conditioning
|
|
|
|
|
|
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
|
|
|
|
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,
|
|
},
|
|
)
|
|
|
|
result = _generate_single_image(composition_prompt, dtype=dtype)
|
|
img = result.images["generated"]
|
|
while img.width < target_width:
|
|
from imaginairy.enhancers.upscale import upscale_image
|
|
|
|
img = upscale_image(img)
|
|
|
|
# samples = generate_single_image(composition_prompt, return_latent=True)
|
|
# while samples.shape[-1] * 8 < target_width:
|
|
# samples = upscale_latent(samples)
|
|
#
|
|
# img = model_latent_to_pillow_img(samples)
|
|
|
|
img = img.resize(
|
|
(target_width, target_height),
|
|
resample=Image.Resampling.LANCZOS,
|
|
)
|
|
|
|
return img, result.images["generated"]
|