189 lines
7.6 KiB
Python
189 lines
7.6 KiB
Python
import os
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import random
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from collections import OrderedDict
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from typing import List
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import numpy as np
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from PIL import Image
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from diffusers import T2IAdapter
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from torch.utils.data import DataLoader
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from diffusers import StableDiffusionXLImg2ImgPipeline
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from tqdm import tqdm
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from toolkit.config_modules import ModelConfig, GenerateImageConfig, preprocess_dataset_raw_config, DatasetConfig
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from toolkit.data_transfer_object.data_loader import FileItemDTO, DataLoaderBatchDTO
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from toolkit.sampler import get_sampler
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from toolkit.stable_diffusion_model import StableDiffusion
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import gc
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import torch
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from jobs.process import BaseExtensionProcess
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from toolkit.data_loader import get_dataloader_from_datasets
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from toolkit.train_tools import get_torch_dtype
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from controlnet_aux.midas import MidasDetector
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from diffusers.utils import load_image
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def flush():
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torch.cuda.empty_cache()
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gc.collect()
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class GenerateConfig:
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def __init__(self, **kwargs):
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self.prompts: List[str]
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self.sampler = kwargs.get('sampler', 'ddpm')
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self.neg = kwargs.get('neg', '')
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self.seed = kwargs.get('seed', -1)
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self.walk_seed = kwargs.get('walk_seed', False)
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self.guidance_scale = kwargs.get('guidance_scale', 7)
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self.sample_steps = kwargs.get('sample_steps', 20)
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self.guidance_rescale = kwargs.get('guidance_rescale', 0.0)
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self.ext = kwargs.get('ext', 'png')
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self.denoise_strength = kwargs.get('denoise_strength', 0.5)
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self.trigger_word = kwargs.get('trigger_word', None)
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class Img2ImgGenerator(BaseExtensionProcess):
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def __init__(self, process_id: int, job, config: OrderedDict):
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super().__init__(process_id, job, config)
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self.output_folder = self.get_conf('output_folder', required=True)
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self.copy_inputs_to = self.get_conf('copy_inputs_to', None)
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self.device = self.get_conf('device', 'cuda')
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self.model_config = ModelConfig(**self.get_conf('model', required=True))
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self.generate_config = GenerateConfig(**self.get_conf('generate', required=True))
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self.is_latents_cached = True
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raw_datasets = self.get_conf('datasets', None)
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if raw_datasets is not None and len(raw_datasets) > 0:
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raw_datasets = preprocess_dataset_raw_config(raw_datasets)
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self.datasets = None
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self.datasets_reg = None
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self.dtype = self.get_conf('dtype', 'float16')
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self.torch_dtype = get_torch_dtype(self.dtype)
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self.params = []
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if raw_datasets is not None and len(raw_datasets) > 0:
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for raw_dataset in raw_datasets:
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dataset = DatasetConfig(**raw_dataset)
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is_caching = dataset.cache_latents or dataset.cache_latents_to_disk
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if not is_caching:
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self.is_latents_cached = False
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if dataset.is_reg:
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if self.datasets_reg is None:
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self.datasets_reg = []
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self.datasets_reg.append(dataset)
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else:
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if self.datasets is None:
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self.datasets = []
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self.datasets.append(dataset)
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self.progress_bar = None
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self.sd = StableDiffusion(
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device=self.device,
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model_config=self.model_config,
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dtype=self.dtype,
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)
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print(f"Using device {self.device}")
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self.data_loader: DataLoader = None
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self.adapter: T2IAdapter = None
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def to_pil(self, img):
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# image comes in -1 to 1. convert to a PIL RGB image
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img = (img + 1) / 2
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img = img.clamp(0, 1)
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img = img[0].permute(1, 2, 0).cpu().numpy()
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img = (img * 255).astype(np.uint8)
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image = Image.fromarray(img)
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return image
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def run(self):
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with torch.no_grad():
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super().run()
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print("Loading model...")
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self.sd.load_model()
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device = torch.device(self.device)
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if self.model_config.is_xl:
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pipe = StableDiffusionXLImg2ImgPipeline(
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vae=self.sd.vae,
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unet=self.sd.unet,
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text_encoder=self.sd.text_encoder[0],
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text_encoder_2=self.sd.text_encoder[1],
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tokenizer=self.sd.tokenizer[0],
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tokenizer_2=self.sd.tokenizer[1],
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scheduler=get_sampler(self.generate_config.sampler),
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).to(device, dtype=self.torch_dtype)
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else:
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raise NotImplementedError("Only XL models are supported")
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pipe.set_progress_bar_config(disable=True)
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# pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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# midas_depth = torch.compile(midas_depth, mode="reduce-overhead", fullgraph=True)
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self.data_loader = get_dataloader_from_datasets(self.datasets, 1, self.sd)
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num_batches = len(self.data_loader)
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pbar = tqdm(total=num_batches, desc="Generating images")
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seed = self.generate_config.seed
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# load images from datasets, use tqdm
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for i, batch in enumerate(self.data_loader):
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batch: DataLoaderBatchDTO = batch
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file_item: FileItemDTO = batch.file_items[0]
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img_path = file_item.path
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img_filename = os.path.basename(img_path)
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img_filename_no_ext = os.path.splitext(img_filename)[0]
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img_filename = img_filename_no_ext + '.' + self.generate_config.ext
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output_path = os.path.join(self.output_folder, img_filename)
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output_caption_path = os.path.join(self.output_folder, img_filename_no_ext + '.txt')
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if self.copy_inputs_to is not None:
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output_inputs_path = os.path.join(self.copy_inputs_to, img_filename)
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output_inputs_caption_path = os.path.join(self.copy_inputs_to, img_filename_no_ext + '.txt')
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else:
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output_inputs_path = None
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output_inputs_caption_path = None
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caption = batch.get_caption_list()[0]
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if self.generate_config.trigger_word is not None:
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caption = caption.replace('[trigger]', self.generate_config.trigger_word)
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img: torch.Tensor = batch.tensor.clone()
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image = self.to_pil(img)
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# image.save(output_depth_path)
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pipe: StableDiffusionXLImg2ImgPipeline = pipe
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gen_images = pipe.__call__(
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prompt=caption,
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negative_prompt=self.generate_config.neg,
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image=image,
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num_inference_steps=self.generate_config.sample_steps,
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guidance_scale=self.generate_config.guidance_scale,
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strength=self.generate_config.denoise_strength,
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).images[0]
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os.makedirs(os.path.dirname(output_path), exist_ok=True)
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gen_images.save(output_path)
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# save caption
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with open(output_caption_path, 'w') as f:
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f.write(caption)
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if output_inputs_path is not None:
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os.makedirs(os.path.dirname(output_inputs_path), exist_ok=True)
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image.save(output_inputs_path)
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with open(output_inputs_caption_path, 'w') as f:
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f.write(caption)
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pbar.update(1)
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batch.cleanup()
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pbar.close()
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print("Done generating images")
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# cleanup
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del self.sd
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gc.collect()
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torch.cuda.empty_cache()
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