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811 lines
30 KiB
Python
811 lines
30 KiB
Python
"""Functions for generating and processing images"""
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import logging
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import os
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import re
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from typing import TYPE_CHECKING, Any, Callable
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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, LazyLoadingImage
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logger = logging.getLogger(__name__)
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# leave undocumented. I'd ask that no one publicize this flag. Just want a
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# slight barrier to entry. Please don't use this is any way that's gonna cause
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# the media or politicians to freak out about AI...
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IMAGINAIRY_SAFETY_MODE = os.getenv("IMAGINAIRY_SAFETY_MODE", "strict")
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if IMAGINAIRY_SAFETY_MODE in {"disabled", "classify"}:
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IMAGINAIRY_SAFETY_MODE = "relaxed"
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elif IMAGINAIRY_SAFETY_MODE == "filter":
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IMAGINAIRY_SAFETY_MODE = "strict"
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# we put this in the global scope so it can be used in the interactive shell
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_most_recent_result = None
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def imagine_image_files(
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prompts: "list[ImaginePrompt] | ImaginePrompt",
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outdir: str,
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precision: str = "autocast",
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record_step_images: bool = False,
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output_file_extension: str = "jpg",
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print_caption: bool = False,
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make_gif: bool = False,
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make_compare_gif: bool = False,
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return_filename_type: str = "generated",
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videogen: bool = False,
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):
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from PIL import ImageDraw
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from imaginairy.api.video_sample import generate_video
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from imaginairy.utils import get_next_filenumber
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from imaginairy.utils.animations import make_bounce_animation
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from imaginairy.utils.img_utils import pillow_fit_image_within
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generated_imgs_path = os.path.join(outdir, "generated")
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os.makedirs(generated_imgs_path, exist_ok=True)
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base_count = get_next_filenumber(generated_imgs_path)
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output_file_extension = output_file_extension.lower()
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if output_file_extension not in {"jpg", "png"}:
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raise ValueError("Must output a png or jpg")
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if not isinstance(prompts, list):
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prompts = [prompts]
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def _record_step(img, description, image_count, step_count, prompt):
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steps_path = os.path.join(outdir, "steps", f"{base_count:08}_S{prompt.seed}")
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os.makedirs(steps_path, exist_ok=True)
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filename = f"{base_count:08}_S{prompt.seed}_{image_count:04}_step{step_count:03}_{prompt_normalized(description)[:40]}.jpg"
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destination = os.path.join(steps_path, filename)
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draw = ImageDraw.Draw(img)
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draw.text((10, 10), str(description))
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img.save(destination)
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if make_gif:
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for p in prompts:
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p.collect_progress_latents = True
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result_filenames = []
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for result in imagine(
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prompts,
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precision=precision,
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debug_img_callback=_record_step if record_step_images else None,
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add_caption=print_caption,
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):
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prompt = result.prompt
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if prompt.is_intermediate:
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# we don't save intermediate images
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continue
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img_str = ""
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if prompt.init_image:
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img_str = f"_img2img-{prompt.init_image_strength}"
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basefilename = (
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f"{base_count:06}_{prompt.seed}_{prompt.solver_type.replace('_', '')}{prompt.steps}_"
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f"PS{prompt.prompt_strength}{img_str}_{prompt_normalized(prompt.prompt_text)}"
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)
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for image_type in result.images:
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subpath = os.path.join(outdir, image_type)
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os.makedirs(subpath, exist_ok=True)
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filepath = os.path.join(
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subpath, f"{basefilename}_[{image_type}].{output_file_extension}"
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)
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result.save(filepath, image_type=image_type)
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logger.info(f" [{image_type}] saved to: {filepath}")
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if image_type == return_filename_type:
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result_filenames.append(filepath)
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if videogen:
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try:
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generate_video(
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input_path=filepath,
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)
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except FileNotFoundError as e:
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logger.error(str(e))
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exit(1)
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if make_gif and result.progress_latents:
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subpath = os.path.join(outdir, "gif")
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os.makedirs(subpath, exist_ok=True)
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filepath = os.path.join(subpath, f"{basefilename}.gif")
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frames = [*result.progress_latents, result.images["generated"]]
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if prompt.init_image:
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resized_init_image = pillow_fit_image_within(
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prompt.init_image, prompt.width, prompt.height
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)
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frames = [resized_init_image, *frames]
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frames.reverse()
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make_bounce_animation(
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imgs=frames,
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outpath=filepath,
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start_pause_duration_ms=1500,
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end_pause_duration_ms=1000,
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)
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logger.info(f" [gif] {len(frames)} frames saved to: {filepath}")
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if make_compare_gif and prompt.init_image:
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subpath = os.path.join(outdir, "gif")
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os.makedirs(subpath, exist_ok=True)
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filepath = os.path.join(subpath, f"{basefilename}_[compare].gif")
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resized_init_image = pillow_fit_image_within(
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prompt.init_image, prompt.width, prompt.height
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)
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frames = [result.images["generated"], resized_init_image]
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make_bounce_animation(
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imgs=frames,
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outpath=filepath,
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)
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logger.info(f" [gif-comparison] saved to: {filepath}")
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base_count += 1
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del result
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return result_filenames
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def imagine(
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prompts: "list[ImaginePrompt] | str | ImaginePrompt",
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precision: str = "autocast",
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debug_img_callback: Callable | None = None,
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progress_img_callback: Callable | None = None,
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progress_img_interval_steps: int = 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: bool = False,
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unsafe_retry_count: int = 1,
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):
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import torch.nn
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from imaginairy.api.generate_refiners import _generate_single_image
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from imaginairy.schema import ImaginePrompt
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from imaginairy.utils import (
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check_torch_version,
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fix_torch_group_norm,
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fix_torch_nn_layer_norm,
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get_device,
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platform_appropriate_autocast,
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)
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check_torch_version()
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prompts = [ImaginePrompt(prompts)] if isinstance(prompts, str) else prompts
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prompts = [prompts] if isinstance(prompts, ImaginePrompt) else prompts
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try:
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num_prompts = str(len(prompts))
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except TypeError:
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num_prompts = "?"
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if get_device() == "cpu":
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logger.warning("Running in CPU mode. It's gonna be slooooooow.")
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from imaginairy.utils.torch_installer import torch_version_check
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torch_version_check()
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if half_mode is None:
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half_mode = "cuda" in get_device() or get_device() == "mps"
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with torch.no_grad(), platform_appropriate_autocast(
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precision
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), fix_torch_nn_layer_norm(), fix_torch_group_norm():
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for i, prompt in enumerate(prompts):
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logger.info(
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f"🖼 Generating {i + 1}/{num_prompts}: {prompt.prompt_description()}"
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)
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for attempt in range(unsafe_retry_count + 1):
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if attempt > 0 and isinstance(prompt.seed, int):
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prompt.seed += 100_000_000 + attempt
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result = _generate_single_image(
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prompt,
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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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half_mode=half_mode,
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add_caption=add_caption,
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dtype=torch.float16 if half_mode else torch.float32,
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)
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if not result.safety_score.is_filtered:
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break
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if attempt < unsafe_retry_count:
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logger.info(" Image was unsafe, retrying with new seed...")
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yield result
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def _generate_single_image_compvis(
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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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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 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 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.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 ControlInput, ImagineResult, MaskMode
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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=half_mode,
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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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|
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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]
|
|
if control_input.mode == "inpaint":
|
|
control_image_t = control_prep_function( # type: ignore
|
|
control_image_input_t, init_image_t
|
|
)
|
|
else:
|
|
control_image_t = control_prep_function(control_image_input_t) # type: ignore
|
|
else:
|
|
control_image_t = (control_image_input_t + 1) / 2
|
|
|
|
control_image_disp = control_image_t * 2 - 1
|
|
result_images[f"control-{control_input.mode}"] = control_image_disp
|
|
log_img(control_image_disp, "control_image")
|
|
|
|
if len(control_image_t.shape) == 3:
|
|
raise RuntimeError("Control image must be 4D")
|
|
|
|
if control_image_t.shape[1] != 3:
|
|
raise RuntimeError("Control image must have 3 channels")
|
|
|
|
if (
|
|
control_input.mode != "inpaint"
|
|
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 RuntimeError(msg)
|
|
|
|
if control_image_t.max() == control_image_t.min():
|
|
msg = f"No control signal found in control image {control_input.mode}."
|
|
raise RuntimeError(msg)
|
|
|
|
c_cat.append(control_image_t)
|
|
control_strengths.append(control_input.strength)
|
|
|
|
elif hasattr(model, "masked_image_key"):
|
|
# inpainting model
|
|
assert mask_image_orig is not None
|
|
assert mask_latent is not None
|
|
mask_t = pillow_img_to_torch_image(ImageOps.invert(mask_image_orig)).to(
|
|
get_device()
|
|
)
|
|
inverted_mask = 1 - mask_latent
|
|
masked_image_t = init_image_t * (mask_t < 0.5)
|
|
log_img(masked_image_t, "masked_image")
|
|
|
|
inverted_mask_latent = torch.nn.functional.interpolate(
|
|
inverted_mask, size=shape[-2:]
|
|
)
|
|
c_cat.append(inverted_mask_latent)
|
|
|
|
masked_image_latent = model.get_first_stage_encoding(
|
|
model.encode_first_stage(masked_image_t)
|
|
)
|
|
c_cat.append(masked_image_latent)
|
|
|
|
elif model.cond_stage_key == "edit":
|
|
# pix2pix model
|
|
c_cat = [model.encode_first_stage(init_image_t)]
|
|
assert init_latent is not None
|
|
c_cat_neutral = [torch.zeros_like(init_latent)]
|
|
denoiser_cls = CFGEditingDenoiser
|
|
if c_cat:
|
|
c_cat = [torch.cat([c], dim=1) for c in c_cat]
|
|
|
|
if c_cat_neutral is None:
|
|
c_cat_neutral = c_cat
|
|
|
|
positive_conditioning_d: dict[str, Any] = {
|
|
"c_concat": c_cat,
|
|
"c_crossattn": [positive_conditioning],
|
|
}
|
|
neutral_conditioning_d: dict[str, Any] = {
|
|
"c_concat": c_cat_neutral,
|
|
"c_crossattn": [neutral_conditioning],
|
|
}
|
|
del neutral_conditioning
|
|
del positive_conditioning
|
|
|
|
if control_strengths and is_controlnet_model:
|
|
positive_conditioning_d["control_strengths"] = torch.Tensor(
|
|
control_strengths
|
|
)
|
|
neutral_conditioning_d["control_strengths"] = torch.Tensor(
|
|
control_strengths
|
|
)
|
|
|
|
if (
|
|
prompt.allow_compose_phase
|
|
and not is_controlnet_model
|
|
and model.cond_stage_key != "edit"
|
|
):
|
|
default_size = get_model_default_image_size(
|
|
prompt.model_weights.architecture
|
|
)
|
|
if prompt.init_image:
|
|
comp_image = _generate_composition_image(
|
|
prompt=prompt,
|
|
target_height=init_image.height,
|
|
target_width=init_image.width,
|
|
cutoff=default_size,
|
|
)
|
|
else:
|
|
comp_image = _generate_composition_image(
|
|
prompt=prompt,
|
|
target_height=prompt.height,
|
|
target_width=prompt.width,
|
|
cutoff=default_size,
|
|
)
|
|
if comp_image is not None:
|
|
result_images["composition"] = comp_image
|
|
# noise = noise[:, :, : comp_image.height, : comp_image.shape[3]]
|
|
t_enc = int(prompt.steps * 0.65)
|
|
log_img(comp_image, "comp_image")
|
|
comp_image_t = pillow_img_to_torch_image(comp_image)
|
|
comp_image_t = comp_image_t.to(get_device())
|
|
init_latent = model.get_first_stage_encoding(
|
|
model.encode_first_stage(comp_image_t)
|
|
)
|
|
with lc.timing("sampling"):
|
|
samples = solver.sample(
|
|
num_steps=prompt.steps,
|
|
positive_conditioning=positive_conditioning_d,
|
|
neutral_conditioning=neutral_conditioning_d,
|
|
guidance_scale=prompt.prompt_strength,
|
|
t_start=t_enc,
|
|
mask=mask_latent,
|
|
orig_latent=init_latent,
|
|
shape=shape,
|
|
batch_size=1,
|
|
denoiser_cls=denoiser_cls,
|
|
noise=noise,
|
|
)
|
|
if return_latent:
|
|
return samples
|
|
|
|
with lc.timing("decoding"):
|
|
gen_imgs_t = model.decode_first_stage(samples)
|
|
gen_img = torch_img_to_pillow_img(gen_imgs_t)
|
|
|
|
if mask_image_orig and init_image:
|
|
mask_final = mask_image_orig.copy()
|
|
log_img(mask_final, "reconstituting mask")
|
|
mask_final = ImageOps.invert(mask_final)
|
|
gen_img = Image.composite(gen_img, init_image, mask_final)
|
|
gen_img = combine_image(
|
|
original_img=init_image,
|
|
generated_img=gen_img,
|
|
mask_img=mask_image_orig,
|
|
)
|
|
log_img(gen_img, "reconstituted image")
|
|
|
|
upscaled_img = None
|
|
rebuilt_orig_img = None
|
|
|
|
if add_caption:
|
|
caption = generate_caption(gen_img)
|
|
logger.info(f"Generated caption: {caption}")
|
|
|
|
with lc.timing("safety-filter"):
|
|
safety_score = create_safety_score(
|
|
gen_img,
|
|
safety_mode=IMAGINAIRY_SAFETY_MODE,
|
|
)
|
|
if safety_score.is_filtered:
|
|
progress_latents.clear()
|
|
if not safety_score.is_filtered:
|
|
if prompt.fix_faces:
|
|
logger.info("Fixing 😊 's in 🖼 using CodeFormer...")
|
|
with lc.timing("face enhancement"):
|
|
gen_img = enhance_faces(gen_img, fidelity=prompt.fix_faces_fidelity)
|
|
if prompt.upscale:
|
|
logger.info("Upscaling 🖼 using real-ESRGAN...")
|
|
with lc.timing("upscaling"):
|
|
upscaled_img = upscale_image(gen_img)
|
|
|
|
# put the newly generated patch back into the original, full-size image
|
|
if prompt.mask_modify_original and mask_image_orig and starting_image:
|
|
img_to_add_back_to_original = upscaled_img if upscaled_img else gen_img
|
|
rebuilt_orig_img = combine_image(
|
|
original_img=starting_image,
|
|
generated_img=img_to_add_back_to_original,
|
|
mask_img=mask_image_orig,
|
|
)
|
|
|
|
if prompt.caption_text:
|
|
caption_text = prompt.caption_text.format(prompt=prompt.prompt_text)
|
|
add_caption_to_image(gen_img, caption_text)
|
|
|
|
result_images["upscaled"] = upscaled_img
|
|
result_images["modified_original"] = rebuilt_orig_img
|
|
result_images["mask_binary"] = mask_image_orig
|
|
result_images["mask_grayscale"] = mask_grayscale
|
|
|
|
result = ImagineResult(
|
|
img=gen_img,
|
|
prompt=prompt,
|
|
is_nsfw=safety_score.is_nsfw,
|
|
safety_score=safety_score,
|
|
result_images=result_images,
|
|
timings=lc.get_timings(),
|
|
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 calc_scale_to_fit_within(height: int, width: int, max_size) -> float:
|
|
max_width, max_height = normalize_image_size(max_size)
|
|
if width <= max_width and height <= max_height:
|
|
return 1
|
|
|
|
width_ratio = max_width / width
|
|
height_ratio = max_height / height
|
|
|
|
return min(width_ratio, height_ratio)
|
|
|
|
|
|
def _scale_latent(
|
|
latent,
|
|
model,
|
|
h,
|
|
w,
|
|
):
|
|
from torch.nn import functional as F
|
|
|
|
# convert to non-latent-space first
|
|
img = model.decode_first_stage(latent)
|
|
img = F.interpolate(img, size=(h, w), mode="bicubic", align_corners=False)
|
|
latent = model.get_first_stage_encoding(model.encode_first_stage(img))
|
|
return latent
|
|
|
|
|
|
def _generate_composition_image(
|
|
prompt,
|
|
target_height,
|
|
target_width,
|
|
cutoff: tuple[int, int] = (512, 512),
|
|
dtype=None,
|
|
):
|
|
from PIL import Image
|
|
|
|
from imaginairy.api.generate_refiners import _generate_single_image
|
|
from imaginairy.utils import default, get_default_dtype
|
|
|
|
cutoff = normalize_image_size(cutoff)
|
|
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_realesrgan 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"]
|
|
|
|
|
|
def prompt_normalized(prompt, length=130):
|
|
return re.sub(r"[^a-zA-Z0-9.,\[\]-]+", "_", prompt)[:length]
|
|
|
|
|
|
def combine_image(original_img, generated_img, mask_img):
|
|
"""Combine the generated image with the original image using the mask image."""
|
|
from PIL import Image
|
|
|
|
from imaginairy.utils.log_utils import log_img
|
|
|
|
generated_img = generated_img.resize(
|
|
original_img.size,
|
|
resample=Image.Resampling.LANCZOS,
|
|
)
|
|
|
|
mask_for_orig_size = mask_img.resize(
|
|
original_img.size,
|
|
resample=Image.Resampling.LANCZOS,
|
|
)
|
|
log_img(mask_for_orig_size, "mask for original image size")
|
|
|
|
rebuilt_orig_img = Image.composite(
|
|
original_img,
|
|
generated_img,
|
|
mask_for_orig_size,
|
|
)
|
|
log_img(rebuilt_orig_img, "reconstituted original")
|
|
return rebuilt_orig_img
|