mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-11-05 12:00:15 +00:00
513 lines
22 KiB
Python
Executable File
513 lines
22 KiB
Python
Executable File
import logging
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import os
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import re
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import numpy as np
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import torch
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import torch.nn
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from einops import rearrange, repeat
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from PIL import Image, ImageDraw, ImageFilter, 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.img_utils import (
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make_gif_image,
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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.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.model_manager import get_diffusion_model
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from imaginairy.modules.midas.utils import AddMiDaS
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from imaginairy.outpaint import outpaint_arg_str_parse, prepare_image_for_outpaint
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from imaginairy.safety import SafetyMode, create_safety_score
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from imaginairy.samplers import SAMPLER_LOOKUP
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from imaginairy.samplers.base import NoiseSchedule, noise_an_image
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from imaginairy.samplers.editing import CFGEditingDenoiser
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from imaginairy.schema import ImaginePrompt, ImagineResult
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from imaginairy.utils import (
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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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randn_seeded,
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)
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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", SafetyMode.STRICT)
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if IMAGINAIRY_SAFETY_MODE in {"disabled", "classify"}:
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IMAGINAIRY_SAFETY_MODE = SafetyMode.RELAXED
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elif IMAGINAIRY_SAFETY_MODE == "filter":
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IMAGINAIRY_SAFETY_MODE = SafetyMode.STRICT
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def imagine_image_files(
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prompts,
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outdir,
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precision="autocast",
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record_step_images=False,
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output_file_extension="jpg",
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print_caption=False,
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make_comparison_gif=False,
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return_filename_type="generated",
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):
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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 = len(os.listdir(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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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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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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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.sampler_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 make_comparison_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}.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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make_gif_image(
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filepath,
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imgs=[result.images["generated"], resized_init_image],
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duration=1750,
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)
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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,
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precision="autocast",
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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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):
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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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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.info("Running in CPU mode. it's gonna be slooooooow.")
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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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model = get_diffusion_model(
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weights_location=prompt.model,
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config_path=prompt.model_config_path,
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half_mode=half_mode,
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for_inpainting=prompt.mask_image
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or prompt.mask_prompt
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or prompt.outpaint,
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)
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has_depth_channel = hasattr(model, "depth_stage_key")
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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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) 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(
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prompt.negative_prompt, model
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)
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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(
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prompt.prompts, model
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)
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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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SamplerCls = SAMPLER_LOOKUP[prompt.sampler_type.lower()]
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sampler = SamplerCls(model)
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mask = mask_image = mask_image_orig = mask_grayscale = None
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t_enc = init_latent = init_latent_noised = None
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starting_image = None
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if prompt.init_image:
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starting_image = prompt.init_image
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generation_strength = 1 - prompt.init_image_strength
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if model.cond_stage_key == "edit":
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t_enc = prompt.steps
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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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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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if mask_image is not None:
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mask_image = pillow_fit_image_within(
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mask_image,
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max_height=prompt.height,
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max_width=prompt.width,
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convert="L",
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)
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log_img(mask_image, "init mask")
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if prompt.mask_mode == ImaginePrompt.MaskMode.REPLACE:
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mask_image = ImageOps.invert(mask_image)
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log_img(
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Image.composite(init_image, mask_image, mask_image),
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"mask overlay",
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)
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mask_image_orig = mask_image
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mask_image = mask_image.resize(
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(
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mask_image.width // downsampling_factor,
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mask_image.height // downsampling_factor,
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),
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resample=Image.Resampling.LANCZOS,
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)
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log_img(mask_image, "latent_mask")
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mask = np.array(mask_image)
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mask = mask.astype(np.float32) / 255.0
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mask = mask[None, None]
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mask = torch.from_numpy(mask)
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mask = mask.to(get_device())
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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(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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shape = init_latent.shape
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log_latent(init_latent, "init_latent")
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# encode (scaled latent)
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seed_everything(prompt.seed)
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noise = randn_seeded(seed=prompt.seed, size=init_latent.size())
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noise = noise.to(get_device())
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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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batch_size = 1
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log_latent(init_latent_noised, "init_latent_noised")
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batch = {
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"txt": batch_size * [prompt.prompt_text],
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}
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c_cat = []
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c_cat_neutral = None
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depth_image_display = None
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if has_depth_channel and starting_image:
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midas_model = AddMiDaS()
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_init_image_d = np.array(starting_image.convert("RGB"))
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_init_image_d = (
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torch.from_numpy(_init_image_d).to(dtype=torch.float32) / 127.5
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- 1.0
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)
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depth_image = midas_model(_init_image_d)
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depth_image = torch.from_numpy(depth_image[None, ...])
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batch[model.depth_stage_key] = depth_image.to(device=get_device())
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_init_image_d = rearrange(_init_image_d, "h w c -> 1 c h w")
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batch["jpg"] = _init_image_d
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for ck in model.concat_keys:
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cc = batch[ck]
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cc = model.depth_model(cc)
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depth_min, depth_max = torch.amin(
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cc, dim=[1, 2, 3], keepdim=True
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), torch.amax(cc, dim=[1, 2, 3], keepdim=True)
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display_depth = (cc - depth_min) / (depth_max - depth_min)
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depth_image_display = Image.fromarray(
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(display_depth[0, 0, ...].cpu().numpy() * 255.0).astype(
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np.uint8
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)
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)
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cc = torch.nn.functional.interpolate(
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cc,
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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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depth_min, depth_max = torch.amin(
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cc, dim=[1, 2, 3], keepdim=True
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), torch.amax(cc, dim=[1, 2, 3], keepdim=True)
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cc = 2.0 * (cc - depth_min) / (depth_max - depth_min) - 1.0
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c_cat.append(cc)
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c_cat = [torch.cat(c_cat, dim=1)]
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if mask_image_orig and not has_depth_channel:
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mask_t = pillow_img_to_torch_image(
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ImageOps.invert(mask_image_orig)
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).to(get_device())
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inverted_mask = 1 - mask
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masked_image_t = init_image_t * (mask_t < 0.5)
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batch.update(
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{
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"image": repeat(
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init_image_t.to(device=get_device()),
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"1 ... -> n ...",
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n=batch_size,
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),
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"txt": batch_size * [prompt.prompt_text],
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"mask": repeat(
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inverted_mask.to(device=get_device()),
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"1 ... -> n ...",
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n=batch_size,
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),
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"masked_image": repeat(
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masked_image_t.to(device=get_device()),
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"1 ... -> n ...",
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n=batch_size,
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),
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}
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)
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for concat_key in getattr(model, "concat_keys", []):
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cc = batch[concat_key].float()
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if concat_key != model.masked_image_key:
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bchw = [batch_size, 4, shape[2], shape[3]]
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cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
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else:
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cc = model.get_first_stage_encoding(
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model.encode_first_stage(cc)
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)
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c_cat.append(cc)
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if c_cat:
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c_cat = [torch.cat(c_cat, dim=1)]
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denoiser_cls = None
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if model.cond_stage_key == "edit":
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c_cat = [model.encode_first_stage(init_image_t).mode()]
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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_neutral is None:
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c_cat_neutral = c_cat
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positive_conditioning = {
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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 = {
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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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with lc.timing("sampling"):
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samples = sampler.sample(
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num_steps=prompt.steps,
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initial_latent=init_latent_noised,
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positive_conditioning=positive_conditioning,
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neutral_conditioning=neutral_conditioning,
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guidance_scale=prompt.prompt_strength,
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t_start=t_enc,
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mask=mask,
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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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)
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# from torch.nn.functional import interpolate
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# samples = interpolate(samples, scale_factor=2, mode='nearest')
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x_samples = model.decode_first_stage(samples)
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x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
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for x_sample in x_samples:
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x_sample = x_sample.to(torch.float32)
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x_sample = 255.0 * rearrange(
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x_sample.cpu().numpy(), "c h w -> h w c"
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)
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x_sample_8_orig = x_sample.astype(np.uint8)
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img = Image.fromarray(x_sample_8_orig)
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if mask_image_orig and init_image:
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mask_final = mask_image_orig.filter(
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ImageFilter.GaussianBlur(radius=3)
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)
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log_img(mask_final, "reconstituting mask")
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mask_final = ImageOps.invert(mask_final)
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img = Image.composite(img, init_image, mask_final)
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log_img(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(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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img,
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safety_mode=IMAGINAIRY_SAFETY_MODE,
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)
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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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img = enhance_faces(
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img, fidelity=prompt.fix_faces_fidelity
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)
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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(img)
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# put the newly generated patch back into the original, full size image
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if (
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prompt.mask_modify_original
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and mask_image_orig
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and starting_image
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):
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img_to_add_back_to_original = (
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upscaled_img if upscaled_img else img
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)
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img_to_add_back_to_original = (
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img_to_add_back_to_original.resize(
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starting_image.size,
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resample=Image.Resampling.LANCZOS,
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)
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)
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mask_for_orig_size = mask_image_orig.resize(
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starting_image.size,
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resample=Image.Resampling.LANCZOS,
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)
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mask_for_orig_size = mask_for_orig_size.filter(
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ImageFilter.GaussianBlur(radius=5)
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)
|
|
log_img(mask_for_orig_size, "mask for original image size")
|
|
|
|
rebuilt_orig_img = Image.composite(
|
|
starting_image,
|
|
img_to_add_back_to_original,
|
|
mask_for_orig_size,
|
|
)
|
|
log_img(rebuilt_orig_img, "reconstituted original")
|
|
|
|
result = ImagineResult(
|
|
img=img,
|
|
prompt=prompt,
|
|
upscaled_img=upscaled_img,
|
|
is_nsfw=safety_score.is_nsfw,
|
|
safety_score=safety_score,
|
|
modified_original=rebuilt_orig_img,
|
|
mask_binary=mask_image_orig,
|
|
mask_grayscale=mask_grayscale,
|
|
depth_image=depth_image_display,
|
|
timings=lc.get_timings(),
|
|
)
|
|
logger.info(f"Image Generated. Timings: {result.timings_str()}")
|
|
yield 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 prompt_normalized(prompt):
|
|
return re.sub(r"[^a-zA-Z0-9.,\[\]-]+", "_", prompt)[:130]
|