| | 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 = [] |
| | |
| | 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, |
| | 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, |
| | |
| | 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) |
| | |
| |
|
| | image = image.cpu().float().numpy() |
| | image = rearrange(image, "(b f) c h w -> b f h w c", b=b) |
| | return image |
| |
|
