Upload extensions_built_in/advanced_generator/ReferenceGenerator.py with huggingface_hub

extensions_built_in/advanced_generator/ReferenceGenerator.py

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+import os
+import random
+from collections import OrderedDict
+from typing import List
+
+import numpy as np
+from PIL import Image
+from diffusers import T2IAdapter
+from torch.utils.data import DataLoader
+from diffusers import StableDiffusionXLAdapterPipeline, StableDiffusionAdapterPipeline
+from tqdm import tqdm
+
+from toolkit.config_modules import ModelConfig, GenerateImageConfig, preprocess_dataset_raw_config, DatasetConfig
+from toolkit.data_transfer_object.data_loader import FileItemDTO, DataLoaderBatchDTO
+from toolkit.sampler import get_sampler
+from toolkit.stable_diffusion_model import StableDiffusion
+import gc
+import torch
+from jobs.process import BaseExtensionProcess
+from toolkit.data_loader import get_dataloader_from_datasets
+from toolkit.train_tools import get_torch_dtype
+from controlnet_aux.midas import MidasDetector
+from diffusers.utils import load_image
+
+
+def flush():
+    torch.cuda.empty_cache()
+    gc.collect()
+
+
+class GenerateConfig:
+
+    def __init__(self, **kwargs):
+        self.prompts: List[str]
+        self.sampler = kwargs.get('sampler', 'ddpm')
+        self.neg = kwargs.get('neg', '')
+        self.seed = kwargs.get('seed', -1)
+        self.walk_seed = kwargs.get('walk_seed', False)
+        self.t2i_adapter_path = kwargs.get('t2i_adapter_path', None)
+        self.guidance_scale = kwargs.get('guidance_scale', 7)
+        self.sample_steps = kwargs.get('sample_steps', 20)
+        self.prompt_2 = kwargs.get('prompt_2', None)
+        self.neg_2 = kwargs.get('neg_2', None)
+        self.prompts = kwargs.get('prompts', None)
+        self.guidance_rescale = kwargs.get('guidance_rescale', 0.0)
+        self.ext = kwargs.get('ext', 'png')
+        self.adapter_conditioning_scale = kwargs.get('adapter_conditioning_scale', 1.0)
+        if kwargs.get('shuffle', False):
+            # shuffle the prompts
+            random.shuffle(self.prompts)
+
+
+class ReferenceGenerator(BaseExtensionProcess):
+
+    def __init__(self, process_id: int, job, config: OrderedDict):
+        super().__init__(process_id, job, config)
+        self.output_folder = self.get_conf('output_folder', required=True)
+        self.device = self.get_conf('device', 'cuda')
+        self.model_config = ModelConfig(**self.get_conf('model', required=True))
+        self.generate_config = GenerateConfig(**self.get_conf('generate', required=True))
+        self.is_latents_cached = True
+        raw_datasets = self.get_conf('datasets', None)
+        if raw_datasets is not None and len(raw_datasets) > 0:
+            raw_datasets = preprocess_dataset_raw_config(raw_datasets)
+        self.datasets = None
+        self.datasets_reg = None
+        self.dtype = self.get_conf('dtype', 'float16')
+        self.torch_dtype = get_torch_dtype(self.dtype)
+        self.params = []
+        if raw_datasets is not None and len(raw_datasets) > 0:
+            for raw_dataset in raw_datasets:
+                dataset = DatasetConfig(**raw_dataset)
+                is_caching = dataset.cache_latents or dataset.cache_latents_to_disk
+                if not is_caching:
+                    self.is_latents_cached = False
+                if dataset.is_reg:
+                    if self.datasets_reg is None:
+                        self.datasets_reg = []
+                    self.datasets_reg.append(dataset)
+                else:
+                    if self.datasets is None:
+                        self.datasets = []
+                    self.datasets.append(dataset)
+
+        self.progress_bar = None
+        self.sd = StableDiffusion(
+            device=self.device,
+            model_config=self.model_config,
+            dtype=self.dtype,
+        )
+        print(f"Using device {self.device}")
+        self.data_loader: DataLoader = None
+        self.adapter: T2IAdapter = None
+
+    def run(self):
+        super().run()
+        print("Loading model...")
+        self.sd.load_model()
+        device = torch.device(self.device)
+
+        if self.generate_config.t2i_adapter_path is not None:
+            self.adapter = T2IAdapter.from_pretrained(
+                self.generate_config.t2i_adapter_path,
+                torch_dtype=self.torch_dtype,
+                varient="fp16"
+            ).to(device)
+
+        midas_depth = MidasDetector.from_pretrained(
+            "valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large"
+        ).to(device)
+
+        if self.model_config.is_xl:
+            pipe = StableDiffusionXLAdapterPipeline(
+                vae=self.sd.vae,
+                unet=self.sd.unet,
+                text_encoder=self.sd.text_encoder[0],
+                text_encoder_2=self.sd.text_encoder[1],
+                tokenizer=self.sd.tokenizer[0],
+                tokenizer_2=self.sd.tokenizer[1],
+                scheduler=get_sampler(self.generate_config.sampler),
+                adapter=self.adapter,
+            ).to(device, dtype=self.torch_dtype)
+        else:
+            pipe = StableDiffusionAdapterPipeline(
+                vae=self.sd.vae,
+                unet=self.sd.unet,
+                text_encoder=self.sd.text_encoder,
+                tokenizer=self.sd.tokenizer,
+                scheduler=get_sampler(self.generate_config.sampler),
+                safety_checker=None,
+                feature_extractor=None,
+                requires_safety_checker=False,
+                adapter=self.adapter,
+            ).to(device, dtype=self.torch_dtype)
+        pipe.set_progress_bar_config(disable=True)
+
+        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+        # midas_depth = torch.compile(midas_depth, mode="reduce-overhead", fullgraph=True)
+
+        self.data_loader = get_dataloader_from_datasets(self.datasets, 1, self.sd)
+
+        num_batches = len(self.data_loader)
+        pbar = tqdm(total=num_batches, desc="Generating images")
+        seed = self.generate_config.seed
+        # load images from datasets, use tqdm
+        for i, batch in enumerate(self.data_loader):
+            batch: DataLoaderBatchDTO = batch
+
+            file_item: FileItemDTO = batch.file_items[0]
+            img_path = file_item.path
+            img_filename = os.path.basename(img_path)
+            img_filename_no_ext = os.path.splitext(img_filename)[0]
+            output_path = os.path.join(self.output_folder, img_filename)
+            output_caption_path = os.path.join(self.output_folder, img_filename_no_ext + '.txt')
+            output_depth_path = os.path.join(self.output_folder, img_filename_no_ext + '.depth.png')
+
+            caption = batch.get_caption_list()[0]
+
+            img: torch.Tensor = batch.tensor.clone()
+            # image comes in -1 to 1. convert to a PIL RGB image
+            img = (img + 1) / 2
+            img = img.clamp(0, 1)
+            img = img[0].permute(1, 2, 0).cpu().numpy()
+            img = (img * 255).astype(np.uint8)
+            image = Image.fromarray(img)
+
+            width, height = image.size
+            min_res = min(width, height)
+
+            if self.generate_config.walk_seed:
+                seed = seed + 1
+
+            if self.generate_config.seed == -1:
+                # random
+                seed = random.randint(0, 1000000)
+
+            torch.manual_seed(seed)
+            torch.cuda.manual_seed(seed)
+
+            # generate depth map
+            image = midas_depth(
+                image,
+                detect_resolution=min_res,  # do 512 ?
+                image_resolution=min_res
+            )
+
+            # image.save(output_depth_path)
+
+            gen_images = pipe(
+                prompt=caption,
+                negative_prompt=self.generate_config.neg,
+                image=image,
+                num_inference_steps=self.generate_config.sample_steps,
+                adapter_conditioning_scale=self.generate_config.adapter_conditioning_scale,
+                guidance_scale=self.generate_config.guidance_scale,
+            ).images[0]
+            os.makedirs(os.path.dirname(output_path), exist_ok=True)
+            gen_images.save(output_path)
+
+            # save caption
+            with open(output_caption_path, 'w') as f:
+                f.write(caption)
+
+            pbar.update(1)
+            batch.cleanup()
+
+        pbar.close()
+        print("Done generating images")
+        # cleanup
+        del self.sd
+        gc.collect()
+        torch.cuda.empty_cache()