import os import numpy as np from typing import List, Union import PIL import copy from einops import rearrange import torch import torch.utils.data import torch.utils.checkpoint from diffusers.pipeline_utils import DiffusionPipeline from tqdm.auto import tqdm from video_diffusion.common.image_util import make_grid, annotate_image from video_diffusion.common.image_util import save_gif_mp4_folder_type class P2pSampleLogger: def __init__( self, editing_prompts: List[str], clip_length: int, logdir: str, subdir: str = "sample", num_samples_per_prompt: int = 1, sample_seeds: List[int] = None, num_inference_steps: int = 20, guidance_scale: float = 7, strength: float = None, annotate: bool = False, annotate_size: int = 15, use_make_grid: bool = True, grid_column_size: int = 2, prompt2prompt_edit: bool=False, p2p_config: dict = None, use_inversion_attention: bool = True, source_prompt: str = None, traverse_p2p_config: bool = False, **args ) -> None: self.editing_prompts = editing_prompts self.clip_length = clip_length self.guidance_scale = guidance_scale self.num_inference_steps = num_inference_steps self.strength = strength if sample_seeds is None: max_num_samples_per_prompt = int(1e5) if num_samples_per_prompt > max_num_samples_per_prompt: raise ValueError sample_seeds = torch.randint(0, max_num_samples_per_prompt, (num_samples_per_prompt,)) sample_seeds = sorted(sample_seeds.numpy().tolist()) self.sample_seeds = sample_seeds self.logdir = os.path.join(logdir, subdir) os.makedirs(self.logdir) self.annotate = annotate self.annotate_size = annotate_size self.make_grid = use_make_grid self.grid_column_size = grid_column_size self.prompt2prompt_edit = prompt2prompt_edit self.p2p_config = p2p_config self.use_inversion_attention = use_inversion_attention self.source_prompt = source_prompt self.traverse_p2p_config =traverse_p2p_config def log_sample_images( self, pipeline: DiffusionPipeline, device: torch.device, step: int, image: Union[torch.FloatTensor, PIL.Image.Image] = None, latents: torch.FloatTensor = None, uncond_embeddings_list: List[torch.FloatTensor] = None, save_dir = None, ): torch.cuda.empty_cache() samples_all = [] attention_all = [] # handle input image if image is not None: input_pil_images = pipeline.numpy_to_pil(tensor_to_numpy(image))[0] if self.annotate : samples_all.append([ annotate_image(image, "input sequence", font_size=self.annotate_size) for image in input_pil_images ]) else: samples_all.append(input_pil_images) for idx, prompt in enumerate(tqdm(self.editing_prompts, desc="Generating sample images")): if self.prompt2prompt_edit: if self.traverse_p2p_config: p2p_config_now = copy.deepcopy(self.p2p_config[idx]) else: p2p_config_now = copy.deepcopy(self.p2p_config[idx]) if idx == 0 and not self.use_inversion_attention: edit_type = 'save' p2p_config_now.update({'save_self_attention': True}) print('Reflash the attention map in pipeline') else: edit_type = 'swap' p2p_config_now.update({'save_self_attention': False}) p2p_config_now.update({'use_inversion_attention': self.use_inversion_attention}) else: edit_type = None input_prompt = prompt for seed in self.sample_seeds: generator = torch.Generator(device=device) generator.manual_seed(seed) sequence_return = pipeline( prompt=input_prompt, source_prompt = self.editing_prompts[0] if self.source_prompt is None else self.source_prompt, edit_type = edit_type, image=image, # torch.Size([8, 3, 512, 512]) strength=self.strength, generator=generator, num_inference_steps=self.num_inference_steps, clip_length=self.clip_length, guidance_scale=self.guidance_scale, num_images_per_prompt=1, # used in null inversion latents = latents, uncond_embeddings_list = uncond_embeddings_list, save_path = save_dir, **p2p_config_now, ) if self.prompt2prompt_edit: sequence = sequence_return['sdimage_output'].images[0] attention_output = sequence_return['attention_output'] else: sequence = sequence_return.images[0] torch.cuda.empty_cache() if self.annotate: images = [ annotate_image(image, prompt, font_size=self.annotate_size) for image in sequence ] else: images = sequence if self.make_grid: samples_all.append(images) if self.prompt2prompt_edit: if attention_output is not None: attention_all.append(attention_output) save_path = os.path.join(self.logdir, f"step_{step}_{idx}_{seed}.gif") save_gif_mp4_folder_type(images, save_path) if self.prompt2prompt_edit: if attention_output is not None: save_gif_mp4_folder_type(attention_output, save_path.replace('.gif', 'atten.gif')) if self.make_grid: samples_all = [make_grid(images, cols=int(np.ceil(np.sqrt(len(samples_all))))) for images in zip(*samples_all)] save_path = os.path.join(self.logdir, f"step_{step}.gif") save_gif_mp4_folder_type(samples_all, save_path) if self.prompt2prompt_edit: if len(attention_all) > 0 : attention_all = [make_grid(images, cols=1) for images in zip(*attention_all)] if len(attention_all) > 0: save_gif_mp4_folder_type(attention_all, save_path.replace('.gif', 'atten.gif')) return samples_all def tensor_to_numpy(image, b=1): image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 image = image.cpu().float().numpy() image = rearrange(image, "(b f) c h w -> b f h w c", b=b) return image