diff --git a/dataset_code/spatialvid/offoload_features_hv_official.py b/dataset_code/spatialvid/offoload_features_hv_official.py new file mode 100644 index 0000000000000000000000000000000000000000..bc14c005306cbd07193c1ebc8bda05ee853ec462 --- /dev/null +++ b/dataset_code/spatialvid/offoload_features_hv_official.py @@ -0,0 +1,307 @@ +import argparse +import os +from tqdm import tqdm +from diffusers import AutoencoderKLHunyuanVideo +from transformers import ( + CLIPTextModel, + CLIPTokenizer, + LlamaModel, + LlamaTokenizerFast, + SiglipImageProcessor, + SiglipVisionModel, +) +from diffusers.video_processor import VideoProcessor +from diffusers.utils import export_to_video, load_image + +from dummy_dataloader_official import BucketedFeatureDataset, BucketedSampler, collate_fn +from torch.utils.data import DataLoader + +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data.distributed import DistributedSampler +from torch.utils.data import Subset +import torchvision.transforms as transforms +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.animation import FuncAnimation +from IPython.display import HTML, display +from IPython.display import clear_output + +from accelerate import Accelerator, DistributedType +from accelerate.logging import get_logger +from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed +from diffusers.training_utils import free_memory + +from accelerate import Accelerator +from utils_framepack import encode_image, encode_prompt + +def setup_distributed_env(): + dist.init_process_group(backend="nccl") + torch.cuda.set_device(int(os.environ["LOCAL_RANK"])) + +def cleanup_distributed_env(): + dist.destroy_process_group() + +def main(rank, world_size, global_rank, stride, batch_size, dataloader_num_workers, csv_file, video_folder, output_latent_folder, pretrained_model_name_or_path, siglip_model_name_or_path): + weight_dtype = torch.bfloat16 + device = rank + seed = 42 + + # Load the tokenizers + tokenizer_one = LlamaTokenizerFast.from_pretrained( + pretrained_model_name_or_path, + subfolder="tokenizer", + ) + tokenizer_two = CLIPTokenizer.from_pretrained( + pretrained_model_name_or_path, + subfolder="tokenizer_2", + ) + feature_extractor = SiglipImageProcessor.from_pretrained( + siglip_model_name_or_path, + subfolder="feature_extractor", + + ) + + vae = AutoencoderKLHunyuanVideo.from_pretrained( + pretrained_model_name_or_path, + subfolder="vae", + torch_dtype=torch.float32, + ) + vae_scale_factor_spatial = vae.spatial_compression_ratio + video_processor = VideoProcessor(vae_scale_factor=vae_scale_factor_spatial) + + text_encoder_one = LlamaModel.from_pretrained( + pretrained_model_name_or_path, + subfolder="text_encoder", + torch_dtype=weight_dtype, + ) + text_encoder_two = CLIPTextModel.from_pretrained( + pretrained_model_name_or_path, + subfolder="text_encoder_2", + torch_dtype=weight_dtype, + ) + image_encoder = SiglipVisionModel.from_pretrained( + siglip_model_name_or_path, + subfolder="image_encoder", + torch_dtype=weight_dtype, + ) + + vae.requires_grad_(False) + text_encoder_one.requires_grad_(False) + text_encoder_two.requires_grad_(False) + image_encoder.requires_grad_(False) + vae.eval() + text_encoder_one.eval() + text_encoder_two.eval() + image_encoder.eval() + + vae = vae.to(device) + text_encoder_one = text_encoder_one.to(device) + text_encoder_two = text_encoder_two.to(device) + image_encoder = image_encoder.to(device) + + # dist.barrier() + dataset = BucketedFeatureDataset(csv_file=csv_file, video_folder=video_folder, stride=stride, force_rebuild=True) + sampler = BucketedSampler(dataset, batch_size=batch_size, drop_last=True, shuffle=True, seed=seed) + dataloader = DataLoader( + dataset, + batch_sampler=sampler, + collate_fn=collate_fn, + num_workers=dataloader_num_workers, + # pin_memory=True, + prefetch_factor=2 if dataloader_num_workers != 0 else None, + # persistent_workers=True if dataloader_num_workers > 0 else False, + ) + + print(len(dataset), len(dataloader)) + accelerator = Accelerator() + dataloader = accelerator.prepare(dataloader) + print(f"Dataset size: {len(dataset)}, Dataloader batches: {len(dataloader)}") + print(f"Process index: {accelerator.process_index}, World size: {accelerator.num_processes}") + + sampler.set_epoch(0) + if rank==0: + pbar = tqdm(total=len(dataloader), desc="Processing") + # dist.barrier() + for idx, batch in enumerate(dataloader): + free_memory() + + valid_indices = [] + valid_uttids = [] + valid_num_frames = [] + valid_heights = [] + valid_widths = [] + valid_videos = [] + valid_prompts = [] + valid_first_frames_images = [] + + for i, (uttid, num_frame, height, width) in enumerate(zip(batch["uttid"], batch["video_metadata"]["num_frames"], batch["video_metadata"]["height"], batch["video_metadata"]["width"])): + os.makedirs(output_latent_folder, exist_ok=True) + output_path = os.path.join(output_latent_folder, f"{uttid}_{num_frame}_{height}_{width}.pt") + if not os.path.exists(output_path): + valid_indices.append(i) + valid_uttids.append(uttid) + valid_num_frames.append(num_frame) + valid_heights.append(height) + valid_widths.append(width) + valid_videos.append(batch["videos"][i]) + valid_prompts.append(batch["prompts"][i]) + valid_first_frames_images.append(batch["first_frames_images"][i]) + else: + print(f"skipping {uttid}") + + if not valid_indices: + print("skipping entire batch!") + if rank==0: + pbar.update(1) + pbar.set_postfix({"batch": idx}) + continue + + batch = None + del batch + free_memory() + + batch = { + "uttid": valid_uttids, + "video_metadata": { + "num_frames": valid_num_frames, + "height": valid_heights, + "width": valid_widths + }, + "videos": torch.stack(valid_videos), + "prompts": valid_prompts, + "first_frames_images": torch.stack(valid_first_frames_images), + } + + if len(batch["uttid"]) == 0: + print("All samples in this batch are already processed, skipping!") + continue + + with torch.no_grad(): + # Get Vae feature 1 + pixel_values = batch["videos"].permute(0, 2, 1, 3, 4).to(dtype=vae.dtype, device=device) + vae_latents = vae.encode(pixel_values).latent_dist.sample() + vae_latents = vae_latents * vae.config.scaling_factor + + # Encode prompts + prompts = batch["prompts"] + prompt_embeds, pooled_prompt_embeds, prompt_attention_mask = encode_prompt( + tokenizer=tokenizer_one, + text_encoder=text_encoder_one, + tokenizer_2=tokenizer_two, + text_encoder_2=text_encoder_two, + prompt=prompts, + device=device, + ) + + # Prepare images + image_tensor = batch["first_frames_images"] + images = [transforms.ToPILImage()(x.to(torch.uint8)) for x in image_tensor] + image = video_processor.preprocess(image=images, height=batch["videos"].shape[-2], width=batch["videos"].shape[-1]) + image_embeds = encode_image( + feature_extractor, + image_encoder, + image, + device=device, + dtype=weight_dtype, + ) + + for uttid, num_frame, height, width, cur_vae_latent, cur_prompt_embed, cur_pooled_prompt_embed, cur_prompt_attention_mask, cur_image_embed in zip(batch["uttid"], batch["video_metadata"]["num_frames"], batch["video_metadata"]["height"], batch["video_metadata"]["width"], vae_latents, prompt_embeds, pooled_prompt_embeds, prompt_attention_mask, image_embeds): + output_path = os.path.join(output_latent_folder, f"{uttid}_{num_frame}_{height}_{width}.pt") + temp_to_save = { + "vae_latent": cur_vae_latent.cpu().detach(), + "prompt_embed": cur_prompt_embed.cpu().detach(), + "pooled_prompt_embeds": cur_pooled_prompt_embed.cpu().detach(), + "prompt_attention_mask": cur_prompt_attention_mask.cpu().detach(), + "image_embeds": cur_image_embed.cpu().detach(), + } + torch.save( + temp_to_save, + output_path + ) + print(f"save latent to: {output_path}") + + if rank==0: + pbar.update(1) + pbar.set_postfix({"batch": idx}) + + + pixel_values = None + prompts = None + image_tensor = None + images = None + vae_latents = None + vae_latents_2 = None + image_embeds = None + prompt_embeds = None + pooled_prompt_embeds = None + prompt_attention_mask = None + batch = None + valid_indices = None + valid_uttids = None + valid_num_frames = None + valid_heights = None + valid_widths = None + valid_videos = None + valid_prompts = None + valid_first_frames_images = None + temp_to_save = None + + del pixel_values + del prompts + del image_tensor + del images + del vae_latents + del vae_latents_2 + del image_embeds + del batch + del valid_indices + del valid_uttids + del valid_num_frames + del valid_heights + del valid_widths + del valid_videos + del valid_prompts + del valid_first_frames_images + del temp_to_save + + free_memory() + # dist.barrier() + # dist.barrier() + dist.destroy_process_group() + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Script for running model training and data processing.") + parser.add_argument("--stride", type=int, default=2, help="Batch size for processing") + parser.add_argument("--batch_size", type=int, default=1, help="Batch size for processing") + parser.add_argument("--dataloader_num_workers", type=int, default=0, help="Number of workers for data loading") + parser.add_argument("--csv_file", type=str, default="/mnt/bn/yufan-dev-my/ysh/Ckpts/Lixsp11/Sekai-Project/train/sekai-game-drone_updated.csv", help="Path to the config file") + parser.add_argument("--video_folder", type=str, default="/mnt/bn/yufan-dev-my/ysh/Ckpts/Lixsp11/Sekai-Project/sekai-game-drone", help="Path to the config file") + parser.add_argument("--output_latent_folder", type=str, default="/mnt/bn/yufan-dev-my/ysh/Ckpts/Lixsp11/Sekai-Project/sekai-game-drone/latents", help="Folder to store output latents") + parser.add_argument("--pretrained_model_name_or_path", type=str, default="/mnt/bn/yufan-dev-my/ysh/Ckpts/hunyuanvideo-community/HunyuanVideo", help="Pretrained model path") + parser.add_argument("--siglip_model_name_or_path", type=str, default="/mnt/bn/yufan-dev-my/ysh/Ckpts/lllyasviel/flux_redux_bfl", help="Siglip model path") + args = parser.parse_args() + + + setup_distributed_env() + + global_rank = dist.get_rank() + local_rank = int(os.environ["LOCAL_RANK"]) + device = torch.cuda.current_device() + world_size = dist.get_world_size() + + main( + rank=device, + world_size=world_size, + global_rank=global_rank, + stride=args.stride, + batch_size=args.batch_size, + dataloader_num_workers=args.dataloader_num_workers, + csv_file=args.csv_file, + video_folder=args.video_folder, + output_latent_folder=args.output_latent_folder, + pretrained_model_name_or_path=args.pretrained_model_name_or_path, + siglip_model_name_or_path=args.siglip_model_name_or_path, + ) \ No newline at end of file diff --git a/dataset_code/spatialvid/utils_framepack.py b/dataset_code/spatialvid/utils_framepack.py new file mode 100644 index 0000000000000000000000000000000000000000..007bc8e9f648ab4d0816a5d76d25eaf8995f4fe8 --- /dev/null +++ b/dataset_code/spatialvid/utils_framepack.py @@ -0,0 +1,1229 @@ +import math +import random +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +from einops import rearrange, repeat + +from diffusers.training_utils import compute_density_for_timestep_sampling + + +DEFAULT_PROMPT_TEMPLATE = { + "template": ( + "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: " + "1. The main content and theme of the video." + "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." + "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." + "4. background environment, light, style and atmosphere." + "5. camera angles, movements, and transitions used in the video:<|eot_id|>" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" + ), + "crop_start": 95, +} + +def get_config_value(args, name): + if hasattr(args, name): + return getattr(args, name) + elif hasattr(args, 'training_config') and hasattr(args.training_config, name): + return getattr(args.training_config, name) + else: + raise AttributeError(f"Neither args nor args.training_config has attribute '{name}'") + +# Copied from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video.HunyuanVideoPipeline._get_llama_prompt_embeds +def _get_llama_prompt_embeds( + tokenizer, + text_encoder, + prompt: Union[str, List[str]], + prompt_template: Dict[str, Any], + num_videos_per_prompt: int = 1, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + max_sequence_length: int = 256, + num_hidden_layers_to_skip: int = 2, +) -> Tuple[torch.Tensor, torch.Tensor]: + device = device + dtype = dtype + + prompt = [prompt] if isinstance(prompt, str) else prompt + batch_size = len(prompt) + + prompt = [prompt_template["template"].format(p) for p in prompt] + + crop_start = prompt_template.get("crop_start", None) + if crop_start is None: + prompt_template_input = tokenizer( + prompt_template["template"], + padding="max_length", + return_tensors="pt", + return_length=False, + return_overflowing_tokens=False, + return_attention_mask=False, + ) + crop_start = prompt_template_input["input_ids"].shape[-1] + # Remove <|eot_id|> token and placeholder {} + crop_start -= 2 + + max_sequence_length += crop_start + text_inputs = tokenizer( + prompt, + max_length=max_sequence_length, + padding="max_length", + truncation=True, + return_tensors="pt", + return_length=False, + return_overflowing_tokens=False, + return_attention_mask=True, + ) + text_input_ids = text_inputs.input_ids.to(device=device) + prompt_attention_mask = text_inputs.attention_mask.to(device=device) + + prompt_embeds = text_encoder( + input_ids=text_input_ids, + attention_mask=prompt_attention_mask, + output_hidden_states=True, + ).hidden_states[-(num_hidden_layers_to_skip + 1)] + prompt_embeds = prompt_embeds.to(dtype=dtype) + + if crop_start is not None and crop_start > 0: + prompt_embeds = prompt_embeds[:, crop_start:] + prompt_attention_mask = prompt_attention_mask[:, crop_start:] + + # duplicate text embeddings for each generation per prompt, using mps friendly method + _, seq_len, _ = prompt_embeds.shape + prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) + prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) + prompt_attention_mask = prompt_attention_mask.repeat(1, num_videos_per_prompt) + prompt_attention_mask = prompt_attention_mask.view(batch_size * num_videos_per_prompt, seq_len) + + return prompt_embeds, prompt_attention_mask + + +# Copied from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video.HunyuanVideoPipeline._get_clip_prompt_embeds +def _get_clip_prompt_embeds( + tokenizer_2, + text_encoder_2, + prompt: Union[str, List[str]], + num_videos_per_prompt: int = 1, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + max_sequence_length: int = 77, +) -> torch.Tensor: + device = device + dtype = dtype + + prompt = [prompt] if isinstance(prompt, str) else prompt + batch_size = len(prompt) + + text_inputs = tokenizer_2( + prompt, + padding="max_length", + max_length=max_sequence_length, + truncation=True, + return_tensors="pt", + ) + + text_input_ids = text_inputs.input_ids + untruncated_ids = tokenizer_2(prompt, padding="longest", return_tensors="pt").input_ids + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): + _ = tokenizer_2.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1]) + + prompt_embeds = text_encoder_2(text_input_ids.to(device), output_hidden_states=False).pooler_output + + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt) + prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, -1) + + return prompt_embeds + + +# Copied from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video.HunyuanVideoPipeline.encode_prompt +def encode_prompt( + tokenizer, + text_encoder, + tokenizer_2, + text_encoder_2, + prompt: Union[str, List[str]], + prompt_2: Union[str, List[str]] = None, + prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE, + num_videos_per_prompt: int = 1, + prompt_embeds: Optional[torch.Tensor] = None, + pooled_prompt_embeds: Optional[torch.Tensor] = None, + prompt_attention_mask: Optional[torch.Tensor] = None, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + max_sequence_length: int = 256, +): + if prompt_embeds is None: + prompt_embeds, prompt_attention_mask = _get_llama_prompt_embeds( + tokenizer, + text_encoder, + prompt, + prompt_template, + num_videos_per_prompt, + device=device, + dtype=dtype, + max_sequence_length=max_sequence_length, + ) + + if pooled_prompt_embeds is None: + if prompt_2 is None: + prompt_2 = prompt + pooled_prompt_embeds = _get_clip_prompt_embeds( + tokenizer_2, + text_encoder_2, + prompt, + num_videos_per_prompt, + device=device, + dtype=dtype, + max_sequence_length=77, + ) + + return prompt_embeds, pooled_prompt_embeds, prompt_attention_mask + + +def encode_image( + feature_extractor, + image_encoder, + image: torch.Tensor, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, +): + device = device + image = (image + 1) / 2.0 # [-1, 1] -> [0, 1] + image = feature_extractor(images=image, return_tensors="pt", do_rescale=False).to( + device=device, dtype=image_encoder.dtype + ) + image_embeds = image_encoder(**image).last_hidden_state + return image_embeds.to(dtype=dtype) + + +def get_framepack_input_t2v( + vae, + pixel_values, # [-1, 1], (B, C, F, H, W) + latent_window_size: int = 9, + vanilla_sampling: bool = False, + dtype: Optional[torch.dtype] = None, + is_keep_x0=False, +): + # calculate latent frame count from original frame count (4n+1) + latent_f = (pixel_values.shape[2] - 1) // 4 + 1 + # assert latent_f % latent_window_size == 0 + + # calculate the total number of sections (excluding the first frame, divided by window size) + total_latent_sections = math.floor(latent_f / latent_window_size) # 2.0 + if total_latent_sections < 1: + min_frames_needed = latent_window_size * 4 + 1 + raise ValueError( + f"Not enough frames for FramePack: {pixel_values.shape[2]} frames ({latent_f} latent frames), minimum required: {min_frames_needed} frames ({latent_window_size + 1} latent frames)" + ) + + # actual latent frame count (aligned to section boundaries) + latent_f_aligned = total_latent_sections * latent_window_size + + # actual video frame count + frame_count_aligned = (latent_f_aligned - 1) * 4 + 1 # 73 + if frame_count_aligned != pixel_values.shape[2]: # 73 != 89 + print( + f"Frame count mismatch: required={frame_count_aligned} != actual={pixel_values.shape[2]}, trimming to {frame_count_aligned}" + ) + pixel_values = pixel_values[ + :, :, :frame_count_aligned, :, : + ] # torch.Size([1, 3, 89, 480, 832]) -> torch.Size([1, 3, 73, 480, 832]) + + latent_f = latent_f_aligned # Update to the aligned value + + # VAE encode + pixel_values = pixel_values.to(device=vae.device, dtype=vae.dtype) + latents = vae.encode(pixel_values).latent_dist.sample() + latents = latents * vae.config.scaling_factor + latents = latents.to(dtype=dtype) + + all_target_latents = [] + all_target_latent_indices = [] + all_clean_latents = [] + all_clean_latent_indices = [] + all_clean_latents_2x = [] + all_clean_latent_2x_indices = [] + all_clean_latents_4x = [] + all_clean_latent_4x_indices = [] + section_to_video_idx = [] + + if vanilla_sampling: + # Vanilla Sampling Logic + if is_keep_x0: + for b in range(latents.shape[0]): + video_lat = latents[b : b + 1] # Keep batch dim: 1, C, F_aligned, H, W + + for section_index in range(total_latent_sections): + target_start_f = section_index * latent_window_size + target_end_f = target_start_f + latent_window_size + start_latent = video_lat[:, :, 0:1, :, :] + target_latents = video_lat[:, :, target_start_f:target_end_f, :, :] + + # Clean latents preparation (Vanilla) + if section_index == 0: + clean_latents_total_count = 2 + 2 + 16 + else: + clean_latents_total_count = 1 + 2 + 16 + history_latents = torch.zeros( + size=( + 1, + 16, + clean_latents_total_count, + video_lat.shape[-2], + video_lat.shape[-1], + ), + device=video_lat.device, + dtype=video_lat.dtype, + ) + + history_start_f = 0 + video_start_f = target_start_f - clean_latents_total_count + copy_count = clean_latents_total_count + + if video_start_f < 0: + history_start_f = -video_start_f + copy_count = clean_latents_total_count - history_start_f + video_start_f = 0 + if copy_count > 0: + history_latents[:, :, history_start_f:] = video_lat[ + :, :, video_start_f : video_start_f + copy_count, :, : + ] + + # indices generation (Vanilla): copy from FramePack-F1 + if section_index == 0: + indices = torch.arange(0, sum([16, 2, 2, latent_window_size])).unsqueeze(0) + ( + clean_latent_4x_indices, + clean_latent_2x_indices, + clean_latent_indices, + latent_indices, + ) = indices.split([16, 2, 2, latent_window_size], dim=1) + clean_latents_4x, clean_latents_2x, clean_latents = history_latents.split([16, 2, 2], dim=2) + else: + indices = torch.arange(0, sum([1, 16, 2, 1, latent_window_size])).unsqueeze(0) + ( + clean_latent_indices_start, + clean_latent_4x_indices, + clean_latent_2x_indices, + clean_latent_1x_indices, + latent_indices, + ) = indices.split([1, 16, 2, 1, latent_window_size], dim=1) + clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1) + + clean_latents_4x, clean_latents_2x, clean_latents_1x = history_latents.split([16, 2, 1], dim=2) + clean_latents = torch.cat([start_latent, clean_latents_1x], dim=2) + + all_target_latents.append(target_latents) + all_target_latent_indices.append(latent_indices) + all_clean_latents.append(clean_latents) + all_clean_latent_indices.append(clean_latent_indices) + all_clean_latents_2x.append(clean_latents_2x) + all_clean_latent_2x_indices.append(clean_latent_2x_indices) + all_clean_latents_4x.append(clean_latents_4x) + all_clean_latent_4x_indices.append(clean_latent_4x_indices) + section_to_video_idx.append(b) + else: + for b in range(latents.shape[0]): + video_lat = latents[b : b + 1] # Keep batch dim: 1, C, F_aligned, H, W + + for section_index in range(total_latent_sections): + target_start_f = section_index * latent_window_size + target_end_f = target_start_f + latent_window_size + target_latents = video_lat[:, :, target_start_f:target_end_f, :, :] + + # Clean latents preparation (Vanilla) + clean_latents_total_count = 2 + 2 + 16 + history_latents = torch.zeros( + size=( + 1, + 16, + clean_latents_total_count, + video_lat.shape[-2], + video_lat.shape[-1], + ), + device=video_lat.device, + dtype=video_lat.dtype, + ) + + history_start_f = 0 + video_start_f = target_start_f - clean_latents_total_count + copy_count = clean_latents_total_count + + if video_start_f < 0: + history_start_f = -video_start_f + copy_count = clean_latents_total_count - history_start_f + video_start_f = 0 + if copy_count > 0: + history_latents[:, :, history_start_f:] = video_lat[ + :, :, video_start_f : video_start_f + copy_count, :, : + ] + + # indices generation (Vanilla): copy from FramePack-F1 + indices = torch.arange(0, sum([16, 2, 2, latent_window_size])).unsqueeze(0) + ( + clean_latent_4x_indices, + clean_latent_2x_indices, + clean_latent_indices, + latent_indices, + ) = indices.split([16, 2, 2, latent_window_size], dim=1) + clean_latents_4x, clean_latents_2x, clean_latents = history_latents.split([16, 2, 2], dim=2) + + all_target_latents.append(target_latents) + all_target_latent_indices.append(latent_indices) + all_clean_latents.append(clean_latents) + all_clean_latent_indices.append(clean_latent_indices) + all_clean_latents_2x.append(clean_latents_2x) + all_clean_latent_2x_indices.append(clean_latent_2x_indices) + all_clean_latents_4x.append(clean_latents_4x) + all_clean_latent_4x_indices.append(clean_latent_4x_indices) + section_to_video_idx.append(b) + else: + pass + + # Stack all sections into batches + batched_target_latents = torch.cat(all_target_latents, dim=0) + batched_target_latent_indices = torch.cat(all_target_latent_indices, dim=0) + batched_clean_latents = torch.cat(all_clean_latents, dim=0) + batched_clean_latent_indices = torch.cat(all_clean_latent_indices, dim=0) + batched_clean_latents_2x = torch.cat(all_clean_latents_2x, dim=0) + batched_clean_latent_2x_indices = torch.cat(all_clean_latent_2x_indices, dim=0) + batched_clean_latents_4x = torch.cat(all_clean_latents_4x, dim=0) + batched_clean_latent_4x_indices = torch.cat(all_clean_latent_4x_indices, dim=0) + + return ( + batched_target_latents, + batched_target_latent_indices, + batched_clean_latents, + batched_clean_latent_indices, + batched_clean_latents_2x, + batched_clean_latent_2x_indices, + batched_clean_latents_4x, + batched_clean_latent_4x_indices, + section_to_video_idx, + ) + + +def get_framepack_input_i2v( + vae, + pixel_values, # [-1, 1], (B, C, F, H, W) + latent_window_size: int = 9, + vanilla_sampling: bool = False, + dtype: Optional[torch.dtype] = None, +): + # calculate latent frame count from original frame count (4n+1) + latent_f = (pixel_values.shape[2] - 1) // 4 + 1 + + # calculate the total number of sections (excluding the first frame, divided by window size) + total_latent_sections = math.floor((latent_f - 1) / latent_window_size) # 2.0 + if total_latent_sections < 1: + min_frames_needed = latent_window_size * 4 + 1 + raise ValueError( + f"Not enough frames for FramePack: {pixel_values.shape[2]} frames ({latent_f} latent frames), minimum required: {min_frames_needed} frames ({latent_window_size + 1} latent frames)" + ) + + # actual latent frame count (aligned to section boundaries) + latent_f_aligned = total_latent_sections * latent_window_size + 1 + + # actual video frame count + frame_count_aligned = (latent_f_aligned - 1) * 4 + 1 # 73 + if frame_count_aligned != pixel_values.shape[2]: # 73 != 89 + print( + f"Frame count mismatch: required={frame_count_aligned} != actual={pixel_values.shape[2]}, trimming to {frame_count_aligned}" + ) + pixel_values = pixel_values[ + :, :, :frame_count_aligned, :, : + ] # torch.Size([1, 3, 89, 480, 832]) -> torch.Size([1, 3, 73, 480, 832]) + + latent_f = latent_f_aligned # Update to the aligned value + + # VAE encode + pixel_values = pixel_values.to(device=vae.device, dtype=vae.dtype) + latents = vae.encode(pixel_values).latent_dist.sample() + latents = latents * vae.config.scaling_factor + latents = latents.to(dtype=dtype) + + all_target_latents = [] + all_target_latent_indices = [] + all_clean_latents = [] + all_clean_latent_indices = [] + all_clean_latents_2x = [] + all_clean_latent_2x_indices = [] + all_clean_latents_4x = [] + all_clean_latent_4x_indices = [] + section_to_video_idx = [] + + if vanilla_sampling: + # Vanilla Sampling Logic + for b in range(latents.shape[0]): + video_lat = latents[b : b + 1] # Keep batch dim: 1, C, F_aligned, H, W + + for section_index in range(total_latent_sections): + target_start_f = section_index * latent_window_size + 1 + target_end_f = target_start_f + latent_window_size + target_latents = video_lat[:, :, target_start_f:target_end_f, :, :] + start_latent = video_lat[:, :, 0:1, :, :] + + # Clean latents preparation (Vanilla) + clean_latents_total_count = 1 + 2 + 16 + history_latents = torch.zeros( + size=( + 1, + 16, + clean_latents_total_count, + video_lat.shape[-2], + video_lat.shape[-1], + ), + device=video_lat.device, + dtype=video_lat.dtype, + ) + + history_start_f = 0 + video_start_f = target_start_f - clean_latents_total_count + copy_count = clean_latents_total_count + + if video_start_f < 0: + history_start_f = -video_start_f + copy_count = clean_latents_total_count - history_start_f + video_start_f = 0 + if copy_count > 0: + history_latents[:, :, history_start_f:] = video_lat[ + :, :, video_start_f : video_start_f + copy_count, :, : + ] + + # indices generation (Vanilla): copy from FramePack-F1 + indices = torch.arange(0, sum([1, 16, 2, 1, latent_window_size])).unsqueeze(0) + ( + clean_latent_indices_start, + clean_latent_4x_indices, + clean_latent_2x_indices, + clean_latent_1x_indices, + latent_indices, + ) = indices.split([1, 16, 2, 1, latent_window_size], dim=1) + clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1) + + clean_latents_4x, clean_latents_2x, clean_latents_1x = history_latents.split([16, 2, 1], dim=2) + clean_latents = torch.cat([start_latent, clean_latents_1x], dim=2) + + all_target_latents.append(target_latents) + all_target_latent_indices.append(latent_indices) + all_clean_latents.append(clean_latents) + all_clean_latent_indices.append(clean_latent_indices) + all_clean_latents_2x.append(clean_latents_2x) + all_clean_latent_2x_indices.append(clean_latent_2x_indices) + all_clean_latents_4x.append(clean_latents_4x) + all_clean_latent_4x_indices.append(clean_latent_4x_indices) + section_to_video_idx.append(b) + else: + # padding is reversed for inference (future to past) + latent_paddings = list(reversed(range(total_latent_sections))) # [1, 0] + # Note: The padding trick for inference. See the paper for details. + if total_latent_sections > 4: + latent_paddings = [3] + [2] * (total_latent_sections - 3) + [1, 0] + + for b in range(latents.shape[0]): + video_lat = latents[ + b : b + 1 + ] # keep batch dim, (1, C, F, H, W) # torch.Size([1, 16, 19, 60, 104]) + + # emulate inference step (history latents) + # Note: In inference, history_latents stores *generated* future latents. + # Here, for caching, we just need its shape and type for clean_* tensors. + # The actual content doesn't matter much as clean_* will be overwritten. + history_latents = torch.zeros( + ( + 1, + video_lat.shape[1], + 1 + 2 + 16, + video_lat.shape[3], + video_lat.shape[4], + ), + dtype=video_lat.dtype, + ).to(video_lat.device) # torch.Size([1, 16, 19, 60, 104]) + + latent_f_index = latent_f - latent_window_size # Start from the last section # 19 - 9 = 10 + section_index = total_latent_sections - 1 # 2 - 1 = 1 + + for latent_padding in latent_paddings: + is_last_section = ( + section_index == 0 + ) # the last section in inference order == the first section in time + latent_padding_size = latent_padding * latent_window_size + if is_last_section: + assert latent_f_index == 1, "Last section should be starting from frame 1" + + # indices generation (same as inference) + indices = torch.arange(0, sum([1, latent_padding_size, latent_window_size, 1, 2, 16])).unsqueeze(0) + ( + clean_latent_indices_pre, # Index for start_latent + blank_indices, # Indices for padding (future context in inference) + latent_indices, # Indices for the target latents to predict + clean_latent_indices_post, # Index for the most recent history frame + clean_latent_2x_indices, # Indices for the next 2 history frames + clean_latent_4x_indices, # Indices for the next 16 history frames + ) = indices.split([1, latent_padding_size, latent_window_size, 1, 2, 16], dim=1) + + # Indices for clean_latents (start + recent history) + clean_latent_indices = torch.cat([clean_latent_indices_pre, clean_latent_indices_post], dim=1) + + # clean latents preparation (emulating inference) + clean_latents_pre = video_lat[:, :, 0:1, :, :] # Always the first frame (start_latent) + clean_latents_post, clean_latents_2x, clean_latents_4x = history_latents[ + :, :, : 1 + 2 + 16, :, : + ].split([1, 2, 16], dim=2) + clean_latents = torch.cat( + [clean_latents_pre, clean_latents_post], dim=2 + ) # Combine start frame + placeholder + + # Target latents for this section (ground truth) + target_latents = video_lat[:, :, latent_f_index : latent_f_index + latent_window_size, :, :] + + all_target_latents.append(target_latents) + all_target_latent_indices.append(latent_indices) + all_clean_latents.append(clean_latents) + all_clean_latent_indices.append(clean_latent_indices) + all_clean_latents_2x.append(clean_latents_2x) + all_clean_latent_2x_indices.append(clean_latent_2x_indices) + all_clean_latents_4x.append(clean_latents_4x) + all_clean_latent_4x_indices.append(clean_latent_4x_indices) + section_to_video_idx.append(b) + + if is_last_section: # If this was the first section generated in inference (time=0) + # History gets the start frame + the generated first section + generated_latents_for_history = video_lat[:, :, : latent_window_size + 1, :, :] + else: + # History gets the generated current section + generated_latents_for_history = target_latents # Use true latents as stand-in for generated + + history_latents = torch.cat([generated_latents_for_history, history_latents], dim=2) + + section_index -= 1 + latent_f_index -= latent_window_size + + # Stack all sections into batches + batched_target_latents = torch.cat(all_target_latents, dim=0) + batched_target_latent_indices = torch.cat(all_target_latent_indices, dim=0) + batched_clean_latents = torch.cat(all_clean_latents, dim=0) + batched_clean_latent_indices = torch.cat(all_clean_latent_indices, dim=0) + batched_clean_latents_2x = torch.cat(all_clean_latents_2x, dim=0) + batched_clean_latent_2x_indices = torch.cat(all_clean_latent_2x_indices, dim=0) + batched_clean_latents_4x = torch.cat(all_clean_latents_4x, dim=0) + batched_clean_latent_4x_indices = torch.cat(all_clean_latent_4x_indices, dim=0) + + return ( + batched_target_latents, + batched_target_latent_indices, + batched_clean_latents, + batched_clean_latent_indices, + batched_clean_latents_2x, + batched_clean_latent_2x_indices, + batched_clean_latents_4x, + batched_clean_latent_4x_indices, + section_to_video_idx, + ) + + +def get_pyramid_input( + args, + scheduler, + latents, # [b c t h w] + pyramid_stage_num=3, + pyramid_sample_ratios=[1, 2, 1], + pyramid_sample_mode="efficient", # ["efficient", "full", "diffusion_forcing", "stream_sample"] + pyramid_stream_inference_steps=[10, 10, 10], + stream_chunk_size=5, +): + assert pyramid_stage_num == len(pyramid_sample_ratios) + if pyramid_sample_mode not in ["efficient", "full", "diffusion_forcing", "stream_sample"]: + raise ValueError( + f"Invalid pyramid_sample_mode: {pyramid_sample_mode}. Must be one of ['efficient', 'full', 'diffusion_forcing', 'dance_forcing']." + ) + + # Get clen pyramid latent list + pyramid_latent_list = [] + pyramid_latent_list.append(latents) + num_frames, height, width = latents.shape[-3], latents.shape[-2], latents.shape[-1] + for _ in range(pyramid_stage_num - 1): + height //= 2 + width //= 2 + latents = rearrange(latents, "b c t h w -> (b t) c h w") + latents = torch.nn.functional.interpolate(latents, size=(height, width), mode="bilinear") + latents = rearrange(latents, "(b t) c h w -> b c t h w", t=num_frames) + pyramid_latent_list.append(latents) + pyramid_latent_list = list(reversed(pyramid_latent_list)) + + # Get pyramid noise list + noise = torch.randn_like(pyramid_latent_list[-1]) + device = noise.device + dtype = pyramid_latent_list[-1].dtype + latent_frame_num = noise.shape[2] + input_video_num = noise.shape[0] + + height, width = noise.shape[-2], noise.shape[-1] + noise_list = [noise] + cur_noise = noise + for i_s in range(pyramid_stage_num - 1): + height //= 2 + width //= 2 + cur_noise = rearrange(cur_noise, "b c t h w -> (b t) c h w") + cur_noise = F.interpolate(cur_noise, size=(height, width), mode="bilinear") * 2 + cur_noise = rearrange(cur_noise, "(b t) c h w -> b c t h w", t=latent_frame_num) + noise_list.append(cur_noise) + noise_list = list(reversed(noise_list)) # make sure from low res to high res + + # Get pyramid target list + if pyramid_sample_mode == "efficient": + assert input_video_num % (int(sum(pyramid_sample_ratios))) == 0 + # To calculate the padding batchsize and column size + bsz = input_video_num // int(sum(pyramid_sample_ratios)) + column_size = int(sum(pyramid_sample_ratios)) + column_to_stage = {} + i_sum = 0 + for i_s, column_num in enumerate(pyramid_sample_ratios): + for index in range(i_sum, i_sum + column_num): + column_to_stage[index] = i_s + i_sum += column_num + + # from low resolution to high resolution + noisy_latents_list = [] + sigmas_list = [] + targets_list = [] + timesteps_list = [] + training_steps = scheduler.config.num_train_timesteps + for index in range(column_size): + i_s = column_to_stage[index] + clean_latent = pyramid_latent_list[i_s][index::column_size] # [bs, c, t, h, w] + last_clean_latent = None if i_s == 0 else pyramid_latent_list[i_s - 1][index::column_size] + start_sigma = scheduler.start_sigmas[i_s] + end_sigma = scheduler.end_sigmas[i_s] + + if i_s == 0: + start_point = noise_list[i_s][index::column_size] + else: + # Get the upsampled latent + last_clean_latent = rearrange(last_clean_latent, "b c t h w -> (b t) c h w") + last_clean_latent = F.interpolate( + last_clean_latent, + size=( + last_clean_latent.shape[-2] * 2, + last_clean_latent.shape[-1] * 2, + ), + mode="nearest", + ) + last_clean_latent = rearrange(last_clean_latent, "(b t) c h w -> b c t h w", t=latent_frame_num) + start_point = start_sigma * noise_list[i_s][index::column_size] + (1 - start_sigma) * last_clean_latent + + if i_s == pyramid_stage_num - 1: + end_point = clean_latent + else: + end_point = end_sigma * noise_list[i_s][index::column_size] + (1 - end_sigma) * clean_latent + + # Sample a random timestep for each image + # for weighting schemes where we sample timesteps non-uniformly + u = compute_density_for_timestep_sampling( + weighting_scheme=get_config_value(args, 'weighting_scheme'), + batch_size=bsz, + logit_mean=get_config_value(args, 'logit_mean'), + logit_std=get_config_value(args, 'logit_std'), + mode_scale=get_config_value(args, 'mode_scale'), + ) + indices = (u * training_steps).long() # Totally 1000 training steps per stage + indices = indices.clamp(0, training_steps - 1) + timesteps = scheduler.timesteps_per_stage[i_s][indices].to(device=device) + + # Add noise according to flow matching. + # zt = (1 - texp) * x + texp * z1 + sigmas = scheduler.sigmas_per_stage[i_s][indices].to(device=device) + while len(sigmas.shape) < start_point.ndim: + sigmas = sigmas.unsqueeze(-1) + + noisy_latents = sigmas * start_point + (1 - sigmas) * end_point + + # [stage1_latent, stage2_latent, ..., stagen_latent], which will be concat after patching + noisy_latents_list.append([noisy_latents.to(dtype)]) + sigmas_list.append(sigmas.to(dtype)) + timesteps_list.append(timesteps.to(dtype)) + targets_list.append(start_point - end_point) # The standard rectified flow matching objective + elif pyramid_sample_mode == "full": + # To calculate the batchsize + bsz = input_video_num + + # from low resolution to high resolution + noisy_latents_list = [] + sigmas_list = [] + targets_list = [] + timesteps_list = [] + training_steps = scheduler.config.num_train_timesteps + for i_s, cur_sample_ratio in zip(range(pyramid_stage_num), pyramid_sample_ratios): + clean_latent = pyramid_latent_list[i_s] # [bs, c, t, h, w] + last_clean_latent = None if i_s == 0 else pyramid_latent_list[i_s - 1] + start_sigma = scheduler.start_sigmas[i_s] + end_sigma = scheduler.end_sigmas[i_s] + + if i_s == 0: + start_point = noise_list[i_s] + else: + # Get the upsampled latent + last_clean_latent = rearrange(last_clean_latent, "b c t h w -> (b t) c h w") + last_clean_latent = F.interpolate( + last_clean_latent, + size=( + last_clean_latent.shape[-2] * 2, + last_clean_latent.shape[-1] * 2, + ), + mode="nearest", + ) + last_clean_latent = rearrange(last_clean_latent, "(b t) c h w -> b c t h w", t=latent_frame_num) + start_point = start_sigma * noise_list[i_s] + (1 - start_sigma) * last_clean_latent + + if i_s == pyramid_stage_num - 1: + end_point = clean_latent + else: + end_point = end_sigma * noise_list[i_s] + (1 - end_sigma) * clean_latent + + for _ in range(cur_sample_ratio): + # Sample a random timestep for each image + # for weighting schemes where we sample timesteps non-uniformly + u = compute_density_for_timestep_sampling( + weighting_scheme=get_config_value(args, 'weighting_scheme'), + batch_size=bsz, + logit_mean=get_config_value(args, 'logit_mean'), + logit_std=get_config_value(args, 'logit_std'), + mode_scale=get_config_value(args, 'mode_scale'), + ) + indices = (u * training_steps).long() # Totally 1000 training steps per stage + indices = indices.clamp(0, training_steps - 1) + timesteps = scheduler.timesteps_per_stage[i_s][indices].to(device=device) + + # Add noise according to flow matching. + # zt = (1 - texp) * x + texp * z1 + sigmas = scheduler.sigmas_per_stage[i_s][indices].to(device=device) + while len(sigmas.shape) < start_point.ndim: + sigmas = sigmas.unsqueeze(-1) + + noisy_latents = sigmas * start_point + (1 - sigmas) * end_point + + # [stage1_latent, stage2_latent, ..., stagen_latent] + noisy_latents_list.append(noisy_latents.to(dtype)) + sigmas_list.append(sigmas.to(dtype)) + timesteps_list.append(timesteps.to(dtype)) + targets_list.append(start_point - end_point) # The standard rectified flow matching objective + elif pyramid_sample_mode == "diffusion_forcing": + # To calculate the batchsize + bsz = input_video_num + latent_chunk_num = latent_frame_num // stream_chunk_size + assert latent_frame_num % stream_chunk_size == 0 + + # from low resolution to high resolution + noisy_latents_list = [] + sigmas_list = [] + targets_list = [] + timesteps_list = [] + training_steps = scheduler.config.num_train_timesteps + for i_s, cur_sample_ratio in zip(range(pyramid_stage_num), pyramid_sample_ratios): + clean_latent = pyramid_latent_list[i_s] # [bs, c, t, h, w] + last_clean_latent = None if i_s == 0 else pyramid_latent_list[i_s - 1] + start_sigma = scheduler.start_sigmas[i_s] + end_sigma = scheduler.end_sigmas[i_s] + + if i_s == 0: + start_point = noise_list[i_s] + else: + # Get the upsampled latent + last_clean_latent = rearrange(last_clean_latent, "b c t h w -> (b t) c h w") + last_clean_latent = F.interpolate( + last_clean_latent, + size=( + last_clean_latent.shape[-2] * 2, + last_clean_latent.shape[-1] * 2, + ), + mode="nearest", + ) + last_clean_latent = rearrange(last_clean_latent, "(b t) c h w -> b c t h w", t=latent_frame_num) + start_point = start_sigma * noise_list[i_s] + (1 - start_sigma) * last_clean_latent + + if i_s == pyramid_stage_num - 1: + end_point = clean_latent + else: + end_point = end_sigma * noise_list[i_s] + (1 - end_sigma) * clean_latent + + for _ in range(cur_sample_ratio): + # Sample a random timestep for each image + # for weighting schemes where we sample timesteps non-uniformly + u = compute_density_for_timestep_sampling( + weighting_scheme=get_config_value(args, 'weighting_scheme'), + batch_size=bsz * latent_chunk_num, + logit_mean=get_config_value(args, 'logit_mean'), + logit_std=get_config_value(args, 'logit_std'), + mode_scale=get_config_value(args, 'mode_scale'), + ) + indices = (u * training_steps).long() # Totally 1000 training steps per stage + indices = indices.clamp(0, training_steps - 1) + + timesteps = scheduler.timesteps_per_stage[i_s][indices].to(device=device) + timesteps = timesteps.view(bsz, latent_chunk_num) # [bsz, latent_chunk_num] + sigmas = scheduler.sigmas_per_stage[i_s][indices].to(device=device) + sigmas = sigmas.view(bsz, latent_chunk_num) # [bsz, latent_chunk_num] + + chunk_index = ( + torch.arange(latent_frame_num, device=device).unsqueeze(0).expand(bsz, -1) // stream_chunk_size + ) + chunk_index = chunk_index.clamp(max=latent_chunk_num - 1) + sigmas = torch.gather(sigmas, 1, chunk_index) # [bsz, t] + timesteps = torch.gather(timesteps, 1, chunk_index) + + # Add noise according to flow matching. + # zt = (1 - texp) * x + texp * z1 + sigmas = ( + sigmas.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) + ) # reshape to [bsz, 1, t, 1, 1] for broadcasting + noisy_latents = sigmas * start_point + (1 - sigmas) * end_point + + # [stage1_latent, stage2_latent, ..., stagen_latent] + noisy_latents_list.append(noisy_latents.to(dtype)) # torch.Size([2, 16, 10, 12, 20]) + sigmas_list.append(sigmas.to(dtype)) # torch.Size([2, 1, 10, 1, 1]) + timesteps_list.append(timesteps.to(dtype)) # torch.Size([2, 10]) + targets_list.append(start_point - end_point) # The standard rectified flow matching objective + elif pyramid_sample_mode == "stream_sample": + # training_all_progressive_timesteps + # skip 0. (1, max_inference_steps):[1.3850, 44.1200, 86.8550, 129.5900, 172.3250, + # 215.0600, 257.7950, 300.5300, 343.2650, 386.0000, + # 386.3580, 426.0960, 465.8340, 505.5720, 545.3100, + # 585.0480, 624.7860, 664.5240, 704.2620, 744.0000, + # 744.2560, 772.6720, 801.0880, 829.5040, 857.9200, + # 886.3360, 914.7520, 943.1680, 971.5840, 1000.0000] + + # progressive_timesteps_stages + # stream_chunk_size=3: + # [ 386., 386., 386., 744., 744., 744., 1000., 1000., 1000.] high, mid, low + # [343.2650, 343.2650, 343.2650, 704.2620, 704.2620, 704.2620, 971.5840, 971.5840, 971.5840] high, mid, low + # [300.5300, 300.5300, 300.5300, 664.5240, 664.5240, 664.5240, 943.1680, 943.1680, 943.1680] high, mid, low + # [257.7950, 257.7950, 257.7950, 624.7860, 624.7860, 624.7860, 914.7520, 914.7520, 914.7520] high, mid, low + # [215.0600, 215.0600, 215.0600, 585.0480, 585.0480, 585.0480, 886.3360, 886.3360, 886.3360] high, mid, low + # [172.3250, 172.3250, 172.3250, 545.3100, 545.3100, 545.3100, 857.9200, 857.9200, 857.9200] high, mid, low + # [129.5900, 129.5900, 129.5900, 505.5720, 505.5720, 505.5720, 829.5040, 829.5040, 829.5040] high, mid, low + # [ 86.8550, 86.8550, 86.8550, 465.8340, 465.8340, 465.8340, 801.0880, 801.0880, 801.0880] high, mid, low + # [ 44.1200, 44.1200, 44.1200, 426.0960, 426.0960, 426.0960, 772.6720, 772.6720, 772.6720] high, mid, low + # [ 1.3850, 1.3850, 1.3850, 386.3580, 386.3580, 386.3580, 744.2560, 744.2560, 744.2560] high, mid, low + + # stream_chunk_size=5, shape = (training_num_steps_to_be_saved, latent_frame_num): + # [545.3100, 545.3100, 545.3100, 545.3100, 545.3100, 1000.0000, 1000.0000, 1000.0000, 1000.0000, 1000.0000] mid, low + # [505.5720, 505.5720, 505.5720, 505.5720, 505.5720, 971.5840, 971.5840, 971.5840, 971.5840, 971.5840] mid, low + # [465.8340, 465.8340, 465.8340, 465.8340, 465.8340, 943.1680, 943.1680, 943.1680, 943.1680, 943.1680] mid, low + # [426.0960, 426.0960, 426.0960, 426.0960, 426.0960, 914.7520, 914.7520, 914.7520, 914.7520, 914.7520] mid, low + # [386.3580, 386.3580, 386.3580, 386.3580, 386.3580, 886.3360, 886.3360, 886.3360, 886.3360, 886.3360] mid, low + # [386.0000, 386.0000, 386.0000, 386.0000, 386.0000, 857.9200, 857.9200, 857.9200, 857.9200, 857.9200] high, low + # [343.2650, 343.2650, 343.2650, 343.2650, 343.2650, 829.5040, 829.5040, 829.5040, 829.5040, 829.5040] high, low + # [300.5300, 300.5300, 300.5300, 300.5300, 300.5300, 801.0880, 801.0880, 801.0880, 801.0880, 801.0880] high, low + # [257.7950, 257.7950, 257.7950, 257.7950, 257.7950, 772.6720, 772.6720, 772.6720, 772.6720, 772.6720] high, low + # [215.0600, 215.0600, 215.0600, 215.0600, 215.0600, 744.2560, 744.2560, 744.2560, 744.2560, 744.2560] high, low + # [172.3250, 172.3250, 172.3250, 172.3250, 172.3250, 744.0000, 744.0000, 744.0000, 744.0000, 744.0000] high, mid + # [129.5900, 129.5900, 129.5900, 129.5900, 129.5900, 704.2620, 704.2620, 704.2620, 704.2620, 704.2620] high, mid + # [ 86.8550, 86.8550, 86.8550, 86.8550, 86.8550, 664.5240, 664.5240, 664.5240, 664.5240, 664.5240] high, mid + # [ 44.1200, 44.1200, 44.1200, 44.1200, 44.1200, 624.7860, 624.7860, 624.7860, 624.7860, 624.7860] high, mid + # [ 1.3850, 1.3850, 1.3850, 1.3850, 1.3850, 585.0480, 585.0480, 585.0480, 585.0480, 585.0480] high, mid + + # To calculate the batchsize + bsz = input_video_num + + # Get multi stage timesteps for streamgen + ( + training_num_steps_to_be_saved, + training_all_timesteps_stage_ids, + training_all_progressive_timesteps, + progressive_timesteps_stages, + ) = get_stream_sample( + scheduler=scheduler, + max_latent_frame_num=latent_frame_num, + stream_chunk_size=stream_chunk_size, + pyramid_stage_num=pyramid_stage_num, + pyramid_stream_inference_steps=pyramid_stream_inference_steps, + ) + timestep_to_stage = { + float(t.item()): int(stage.item()) + for t, stage in zip(training_all_progressive_timesteps[0], training_all_timesteps_stage_ids[0]) + } + + while True: + initialization = random.choice([True, False]) + termination = random.choice([True, False]) + if not (initialization and termination): # Make sure not both are True + break + + stage_i = random.randint(0, training_num_steps_to_be_saved - 1) + timesteps = progressive_timesteps_stages[stage_i].clone().repeat(bsz, 1) # (b, f) + if initialization: # get the ending timesteps, [999]x5 from [91, 192, ..., 999]x5 + timesteps = timesteps[:, -latent_frame_num:] + elif termination: # get the starting timesteps, [91]x5 from [91, ..., 999]x5 + timesteps = timesteps[:, :latent_frame_num] + + # For stage mapping / Get sigmas + sigmas, stage_latent_mapping = get_sigmas_from_pyramid_timesteps(scheduler, timesteps, timestep_to_stage) + + # To device + timesteps = timesteps.to(device) + sigmas = sigmas.to(device) + + # Get pyramid stage points + stage_point_list = [] + for i_s in range(pyramid_stage_num): + clean_latent = pyramid_latent_list[i_s] # [bs, c, t, h, w] + last_clean_latent = None if i_s == 0 else pyramid_latent_list[i_s - 1] + start_sigma = scheduler.start_sigmas[i_s] + end_sigma = scheduler.end_sigmas[i_s] + + if i_s == 0: + start_point = noise_list[i_s] + else: + # Get the upsampled latent + last_clean_latent = rearrange(last_clean_latent, "b c t h w -> (b t) c h w") + last_clean_latent = F.interpolate( + last_clean_latent, + size=( + last_clean_latent.shape[-2] * 2, + last_clean_latent.shape[-1] * 2, + ), + mode="nearest", + ) + last_clean_latent = rearrange(last_clean_latent, "(b t) c h w -> b c t h w", t=latent_frame_num) + start_point = start_sigma * noise_list[i_s] + (1 - start_sigma) * last_clean_latent + + if i_s == pyramid_stage_num - 1: + end_point = clean_latent + else: + end_point = end_sigma * noise_list[i_s] + (1 - end_sigma) * clean_latent + + stage_point_list.append((start_point, end_point)) + + noisy_latents_list = [] # torch.Size([2, 16, 10, 12, 20]) + targets_list = [] # torch.Size([2, 16, 10, 12, 20]) + sigmas_list = [] # torch.Size([2, 1, 10, 1, 1]) + timesteps_list = [] # torch.Size([2, 10]) + temp_noisy_latents_list = [] + temp_targets_list = [] + + unique_elements = list(map(int, torch.unique(stage_latent_mapping))) + for cur_stage in reversed(unique_elements): + stage_indices = torch.nonzero(stage_latent_mapping == cur_stage, as_tuple=True) + start_index = stage_indices[1][0].item() + end_index = start_index + stream_chunk_size + + start_point, end_point = stage_point_list[cur_stage] + start_point_slice = start_point[:, :, start_index:end_index, :, :] + end_point_slice = end_point[:, :, start_index:end_index, :, :] + + sigmas_slice = sigmas[:, :, start_index:end_index, :, :] + noisy_latents = sigmas_slice * start_point_slice + (1 - sigmas_slice) * end_point_slice + target = start_point_slice - end_point_slice + + temp_noisy_latents_list.append(noisy_latents.to(dtype)) + temp_targets_list.append(target) + + noisy_latents_list.append(temp_noisy_latents_list) + targets_list.append(temp_targets_list) + sigmas_list.append(sigmas.to(dtype)) + timesteps_list.append(timesteps.to(dtype=dtype)) + + return noisy_latents_list, sigmas_list, timesteps_list, targets_list + + +def get_sigmas_from_pyramid_timesteps(scheduler, timesteps, timestep_to_stage): + # For stage mapping + flat_timesteps = timesteps.flatten() + stage_latent_mapping = torch.tensor( + [timestep_to_stage.get(float(t.item()), -1) for t in flat_timesteps], + device=timesteps.device, + ).view(timesteps.shape) + + # Get sigmas + sigmas = torch.full_like(timesteps, -1.0) + for i in range(timesteps.shape[0]): + for j in range(timesteps.shape[1]): + temp_stage_mapping = int(stage_latent_mapping[i, j]) + target_value = timesteps[i, j] + temp_indice = ( + ( + torch.isclose( + scheduler.timesteps_per_stage[temp_stage_mapping], + target_value.clone().detach().to(scheduler.timesteps_per_stage[temp_stage_mapping].dtype), + ) + ) + .nonzero(as_tuple=True)[0] + .item() + ) + sigmas[i, j] = scheduler.sigmas_per_stage[temp_stage_mapping][temp_indice] + sigmas = sigmas.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) + + return sigmas, stage_latent_mapping + + +def get_stream_sample( + scheduler, + max_latent_frame_num, + stream_chunk_size, + pyramid_stage_num=3, + pyramid_stream_inference_steps=[10, 10, 10], +): + max_inference_steps = sum(pyramid_stream_inference_steps) + + # Set training all progressive timesteps and stage mapping + all_progressive_timesteps_list = [] + timestep_stage_list = [] + for stage_idx in range(pyramid_stage_num): + scheduler.set_timesteps(pyramid_stream_inference_steps[stage_idx], stage_idx) + temp_timesteps = scheduler.timesteps # shape: (n_i,) + all_progressive_timesteps_list.append(temp_timesteps) + timestep_stage_list.append( + torch.full_like(temp_timesteps, fill_value=stage_idx) + ) # same shape, filled with stage_idx + all_progressive_timesteps = torch.cat(all_progressive_timesteps_list).unsqueeze(0).flip(1) # (1, T) + all_timesteps_stage_ids = torch.cat(timestep_stage_list).unsqueeze(0).flip(1) + + # Set training progressive timesteps stages + # every stream_chunk_size frames is treated as one, using the same noise level. f' = f / c + assert max_latent_frame_num % stream_chunk_size == 0, ( + f"num_frames should be multiple of stream_chunk_size, {max_latent_frame_num} % {stream_chunk_size} != 0" + ) + assert max_inference_steps % (max_latent_frame_num // stream_chunk_size) == 0, ( + f"max_inference_steps should be multiple of max_latent_frame_num // stream_chunk_size, {max_inference_steps} % {max_latent_frame_num // stream_chunk_size} != 0" + ) + num_steps_to_be_saved = max_inference_steps // ( + max_latent_frame_num // stream_chunk_size + ) # every m steps, save stream_chunk_size frames. m = t / f' = t / (f / c) = c * (t / f) + + # (b, t) -> [(b, t / m) in reverse range(m)] -> [(b, f) in reverse range(m)] + progressive_timesteps_stages = [ + repeat( + all_progressive_timesteps[:, (num_steps_to_be_saved - 1) - s :: num_steps_to_be_saved], + "b f -> b f c", + c=stream_chunk_size, + ).flatten(1, 2) + for s in range(num_steps_to_be_saved) + ] + + return num_steps_to_be_saved, all_timesteps_stage_ids, all_progressive_timesteps, progressive_timesteps_stages + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--weighting_scheme", + type=str, + default="logit_normal", + choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"], + help=('We default to the "none" weighting scheme for uniform sampling and uniform loss'), + ) + parser.add_argument( + "--logit_mean", + type=float, + default=0.0, + help="mean to use when using the `'logit_normal'` weighting scheme.", + ) + parser.add_argument( + "--logit_std", + type=float, + default=1.0, + help="std to use when using the `'logit_normal'` weighting scheme.", + ) + parser.add_argument( + "--mode_scale", + type=float, + default=1.29, + help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.", + ) + args = parser.parse_args() + + device = "cuda" + + import sys + + sys.path.append("../") + from scheduler.scheduling_flow_matching_pyramid import PyramidFlowMatchEulerDiscreteScheduler + + stages = [1, 2, 4] + timestep_shift = 1.0 + stage_range = [0, 1 / 3, 2 / 3, 1] + scheduler_gamma = 1 / 3 + scheduler = PyramidFlowMatchEulerDiscreteScheduler( + shift=timestep_shift, + stages=len(stages), + stage_range=stage_range, + gamma=scheduler_gamma, + ) + print( + f"The start sigmas and end sigmas of each stage is Start: {scheduler.start_sigmas}, End: {scheduler.end_sigmas}, Ori_start: {scheduler.ori_start_sigmas}" + ) + + # Test get_framepack_input + from diffusers import AutoencoderKLHunyuanVideo + + # 5: (21, 41, 61, 81, 101) + # 6: (25, 49, 73, 97, 121) + # 7: (29, 57, 85, 113, 141) + # 8: (33, 65, 97, 129, 161) + # 9: (37, 73, 109, 145, 181) + # 10: (41, 81, 121, 161, 201) + # 11: (45, 89, 133, 177, 221) + # 12: (49, 97, 145, 193, 241) + + pixel_values = torch.randn([2, 3, 241, 384, 640], device=device).clamp(-1, 1) + pixel_values = pixel_values.to(torch.bfloat16) + vae = AutoencoderKLHunyuanVideo.from_pretrained( + "/mnt/workspace/checkpoints/hunyuanvideo-community/HunyuanVideo/", + subfolder="vae", + weight_dtype=torch.bfloat16, + ).to(device) + vae.requires_grad_(False) + vae.eval() + + ( + model_input, # torch.Size([2, 16, 9, 60, 104]) + indices_latents, # torch.Size([2, 9]) + latents_clean, # torch.Size([2, 16, 2, 60, 104]) + indices_clean_latents, # torch.Size([2, 2]) + latents_history_2x, # torch.Size([2, 16, 2, 60, 104]) + indices_latents_history_2x, # torch.Size([2, 2]) + latents_history_4x, # torch.Size([2, 16, 16, 60, 104]) + indices_latents_history_4x, # torch.Size([2, 16]) + section_to_video_idx, + ) = get_framepack_input_i2v( + vae=vae, + pixel_values=pixel_values, # torch.Size([1, 3, 73, 480, 832]) + latent_window_size=12, + vanilla_sampling=False, + dtype=torch.bfloat16, + ) + + print(indices_latents, "\n", indices_clean_latents, "\n", indices_latents_history_2x, "\n", indices_latents_history_4x) + + # print( + # indices_latents, + # "\n", + # indices_clean_latents, + # "\n", + # indices_latents_history_2x, + # "\n", + # indices_latents_history_4x, + # ) + + # Test get_pyramid_input + # model_input = torch.randn([2, 16, 10, 48, 80], device=device) + # noisy_model_input_list, sigmas_list, timesteps_list, targets_list = get_pyramid_input( + # args=args, + # scheduler=scheduler, + # latents=model_input, + # pyramid_stage_num=3, + # pyramid_sample_ratios=[1, 2, 1], + # pyramid_sample_mode="stream_sample", + # stream_chunk_size=3, + # pyramid_stream_inference_steps=[10, 10, 10], + # ) + + # if isinstance(noisy_model_input_list[0], list): + # total_sample_count = sum(y.shape[0] for x in noisy_model_input_list for y in x) + # else: + # total_sample_count = sum(x.shape[0] for x in noisy_model_input_list) + # batch_size = model_input.shape[0] diff --git a/exp_code/1_benchmark/1.py b/exp_code/1_benchmark/1.py new file mode 100644 index 0000000000000000000000000000000000000000..d343e8bff5684b62c2179d5bf0ed74847e1cfc62 --- /dev/null +++ b/exp_code/1_benchmark/1.py @@ -0,0 +1,748 @@ +from causvid.models.wan.wan_base.modules.attention import attention +from causvid.models.wan.wan_base.modules.model import ( + WanRMSNorm, + rope_apply, + WanLayerNorm, + WAN_CROSSATTENTION_CLASSES, + Head, + rope_params, + MLPProj, + sinusoidal_embedding_1d +) +from torch.nn.attention.flex_attention import create_block_mask, flex_attention +from diffusers.configuration_utils import ConfigMixin, register_to_config +from torch.nn.attention.flex_attention import BlockMask +from diffusers.models.modeling_utils import ModelMixin +import torch.nn as nn +import torch +import math + +# wan 1.3B model has a weird channel / head configurations and require max-autotune to work with flexattention +# see https://github.com/pytorch/pytorch/issues/133254 +# change to default for other models +flex_attention = torch.compile( + flex_attention, dynamic=False, mode="max-autotune") + + +def causal_rope_apply(x, grid_sizes, freqs, start_frame=0): + n, c = x.size(2), x.size(3) // 2 + + # split freqs + freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) + + # loop over samples + output = [] + + for i, (f, h, w) in enumerate(grid_sizes.tolist()): + seq_len = f * h * w + + # precompute multipliers + x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape( + seq_len, n, -1, 2)) + freqs_i = torch.cat([ + freqs[0][start_frame:start_frame + f].view(f, 1, 1, -1).expand(f, h, w, -1), + freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), + freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) + ], + dim=-1).reshape(seq_len, 1, -1) + + # apply rotary embedding + x_i = torch.view_as_real(x_i * freqs_i).flatten(2) + x_i = torch.cat([x_i, x[i, seq_len:]]) + + # append to collection + output.append(x_i) + return torch.stack(output).type_as(x) + + +class CausalWanSelfAttention(nn.Module): + + def __init__(self, + dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + eps=1e-6): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.window_size = window_size + self.qk_norm = qk_norm + self.eps = eps + + # layers + self.q = nn.Linear(dim, dim) + self.k = nn.Linear(dim, dim) + self.v = nn.Linear(dim, dim) + self.o = nn.Linear(dim, dim) + self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() + self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() + + def forward(self, x, seq_lens, grid_sizes, freqs, block_mask, kv_cache=None, current_start=0, current_end=0): + r""" + Args: + x(Tensor): Shape [B, L, num_heads, C / num_heads] + seq_lens(Tensor): Shape [B] + grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + block_mask (BlockMask) + """ + b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim + + # query, key, value function + def qkv_fn(x): + q = self.norm_q(self.q(x)).view(b, s, n, d) + k = self.norm_k(self.k(x)).view(b, s, n, d) + v = self.v(x).view(b, s, n, d) + return q, k, v + + q, k, v = qkv_fn(x) + + if kv_cache is None: + roped_query = rope_apply(q, grid_sizes, freqs).type_as(v) + roped_key = rope_apply(k, grid_sizes, freqs).type_as(v) + + padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1] + padded_roped_query = torch.cat( + [roped_query, + torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]], + device=q.device, dtype=v.dtype)], + dim=1 + ) + + padded_roped_key = torch.cat( + [roped_key, torch.zeros([k.shape[0], padded_length, k.shape[2], k.shape[3]], + device=k.device, dtype=v.dtype)], + dim=1 + ) + + padded_v = torch.cat( + [v, torch.zeros([v.shape[0], padded_length, v.shape[2], v.shape[3]], + device=v.device, dtype=v.dtype)], + dim=1 + ) + + # print(q.shape, k.shape, v.shape, padded_roped_query.shape, padded_roped_key.shape, padded_v.shape) + x = flex_attention( + query=padded_roped_query.transpose(2, 1), + key=padded_roped_key.transpose(2, 1), + value=padded_v.transpose(2, 1), + block_mask=block_mask + )[:, :, :-padded_length].transpose(2, 1) + else: + roped_query = causal_rope_apply( + q, grid_sizes, freqs, start_frame=current_start // math.prod(grid_sizes[0][1:]).item()).type_as(v) + roped_key = causal_rope_apply( + k, grid_sizes, freqs, start_frame=current_start // math.prod(grid_sizes[0][1:]).item()).type_as(v) + + kv_cache["k"][:, current_start:current_end] = roped_key + kv_cache["v"][:, current_start:current_end] = v + + x = attention(roped_query, kv_cache["k"][:, :current_end], kv_cache["v"][:, :current_end]) + + # print(x.shape, q.shape, k.shape, v.shape, roped_query.shape, roped_key.shape, kv_cache["k"][:, :current_end].shape, kv_cache["v"][:, :current_end].shape) + + # output + x = x.flatten(2) + x = self.o(x) + return x + + +class CausalWanAttentionBlock(nn.Module): + + def __init__(self, + cross_attn_type, + dim, + ffn_dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=False, + eps=1e-6): + super().__init__() + self.dim = dim + self.ffn_dim = ffn_dim + self.num_heads = num_heads + self.window_size = window_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + + # layers + self.norm1 = WanLayerNorm(dim, eps) + self.self_attn = CausalWanSelfAttention(dim, num_heads, window_size, qk_norm, + eps) + self.norm3 = WanLayerNorm( + dim, eps, + elementwise_affine=True) if cross_attn_norm else nn.Identity() + self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim, + num_heads, + (-1, -1), + qk_norm, + eps) + self.norm2 = WanLayerNorm(dim, eps) + self.ffn = nn.Sequential( + nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), + nn.Linear(ffn_dim, dim)) + + # modulation + self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) + + def forward( + self, + x, + e, + seq_lens, + grid_sizes, + freqs, + context, + context_lens, + block_mask, + kv_cache=None, + crossattn_cache=None, + current_start=0, + current_end=0 + ): + r""" + Args: + x(Tensor): Shape [B, L, C] + e(Tensor): Shape [B, F, 6, C] + seq_lens(Tensor): Shape [B], length of each sequence in batch + grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1] + # assert e.dtype == torch.float32 + # with amp.autocast(dtype=torch.float32): + e = (self.modulation.unsqueeze(1) + e).chunk(6, dim=2) + # assert e[0].dtype == torch.float32 + + # self-attention + y = self.self_attn( + (self.norm1(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) + * (1 + e[1]) + e[0]).flatten(1, 2), + seq_lens, grid_sizes, + freqs, block_mask, kv_cache, current_start, current_end) + + # with amp.autocast(dtype=torch.float32): + x = x + (y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) + * e[2]).flatten(1, 2) + + # cross-attention & ffn function + def cross_attn_ffn(x, context, context_lens, e, crossattn_cache=None): + x = x + self.cross_attn(self.norm3(x), context, + context_lens, crossattn_cache=crossattn_cache) + y = self.ffn( + (self.norm2(x).unflatten(dim=1, sizes=(num_frames, + frame_seqlen)) * (1 + e[4]) + e[3]).flatten(1, 2) + ) + # with amp.autocast(dtype=torch.float32): + x = x + (y.unflatten(dim=1, sizes=(num_frames, + frame_seqlen)) * e[5]).flatten(1, 2) + return x + + x = cross_attn_ffn(x, context, context_lens, e, crossattn_cache) + return x + + +class CausalHead(nn.Module): + + def __init__(self, dim, out_dim, patch_size, eps=1e-6): + super().__init__() + self.dim = dim + self.out_dim = out_dim + self.patch_size = patch_size + self.eps = eps + + # layers + out_dim = math.prod(patch_size) * out_dim + self.norm = WanLayerNorm(dim, eps) + self.head = nn.Linear(dim, out_dim) + + # modulation + self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5) + + def forward(self, x, e): + r""" + Args: + x(Tensor): Shape [B, L1, C] + e(Tensor): Shape [B, F, 1, C] + """ + # assert e.dtype == torch.float32 + # with amp.autocast(dtype=torch.float32): + num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1] + e = (self.modulation.unsqueeze(1) + e).chunk(2, dim=2) + x = (self.head( + self.norm(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * + (1 + e[1]) + e[0])) + return x + + +class CausalWanModel(ModelMixin, ConfigMixin): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + ignore_for_config = [ + 'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim', 'window_size' + ] + _no_split_modules = ['WanAttentionBlock'] + _supports_gradient_checkpointing = True + + @register_to_config + def __init__(self, + model_type='t2v', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6): + r""" + Initialize the diffusion model backbone. + + Args: + model_type (`str`, *optional*, defaults to 't2v'): + Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) + patch_size (`tuple`, *optional*, defaults to (1, 2, 2)): + 3D patch dimensions for video embedding (t_patch, h_patch, w_patch) + text_len (`int`, *optional*, defaults to 512): + Fixed length for text embeddings + in_dim (`int`, *optional*, defaults to 16): + Input video channels (C_in) + dim (`int`, *optional*, defaults to 2048): + Hidden dimension of the transformer + ffn_dim (`int`, *optional*, defaults to 8192): + Intermediate dimension in feed-forward network + freq_dim (`int`, *optional*, defaults to 256): + Dimension for sinusoidal time embeddings + text_dim (`int`, *optional*, defaults to 4096): + Input dimension for text embeddings + out_dim (`int`, *optional*, defaults to 16): + Output video channels (C_out) + num_heads (`int`, *optional*, defaults to 16): + Number of attention heads + num_layers (`int`, *optional*, defaults to 32): + Number of transformer blocks + window_size (`tuple`, *optional*, defaults to (-1, -1)): + Window size for local attention (-1 indicates global attention) + qk_norm (`bool`, *optional*, defaults to True): + Enable query/key normalization + cross_attn_norm (`bool`, *optional*, defaults to False): + Enable cross-attention normalization + eps (`float`, *optional*, defaults to 1e-6): + Epsilon value for normalization layers + """ + + super().__init__() + + assert model_type in ['t2v', 'i2v'] + self.model_type = model_type + + self.patch_size = patch_size + self.text_len = text_len + self.in_dim = in_dim + self.dim = dim + self.ffn_dim = ffn_dim + self.freq_dim = freq_dim + self.text_dim = text_dim + self.out_dim = out_dim + self.num_heads = num_heads + self.num_layers = num_layers + self.window_size = window_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + + # embeddings + self.patch_embedding = nn.Conv3d( + in_dim, dim, kernel_size=patch_size, stride=patch_size) + self.text_embedding = nn.Sequential( + nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), + nn.Linear(dim, dim)) + + self.time_embedding = nn.Sequential( + nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) + self.time_projection = nn.Sequential( + nn.SiLU(), nn.Linear(dim, dim * 6)) + + # blocks + cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn' + self.blocks = nn.ModuleList([ + CausalWanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads, + window_size, qk_norm, cross_attn_norm, eps) + for _ in range(num_layers) + ]) + + # head + self.head = CausalHead(dim, out_dim, patch_size, eps) + + # buffers (don't use register_buffer otherwise dtype will be changed in to()) + assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0 + d = dim // num_heads + self.freqs = torch.cat([ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)) + ], + dim=1) + + if model_type == 'i2v': + self.img_emb = MLPProj(1280, dim) + + # initialize weights + self.init_weights() + + self.gradient_checkpointing = False + + self.block_mask = None + + self.num_frame_per_block = 1 + + def _set_gradient_checkpointing(self, module, value=False): + self.gradient_checkpointing = value + + @staticmethod + def _prepare_blockwise_causal_attn_mask( + device: torch.device | str, num_frames: int = 21, + frame_seqlen: int = 1560, num_frame_per_block=1 + ) -> BlockMask: + """ + we will divide the token sequence into the following format + [1 latent frame] [1 latent frame] ... [1 latent frame] + We use flexattention to construct the attention mask + """ + total_length = num_frames * frame_seqlen + + # we do right padding to get to a multiple of 128 + padded_length = math.ceil(total_length / 128) * 128 - total_length + + ends = torch.zeros(total_length + padded_length, + device=device, dtype=torch.long) + + # Block-wise causal mask will attend to all elements that are before the end of the current chunk + frame_indices = torch.arange( + start=0, + end=total_length, + step=frame_seqlen * num_frame_per_block, + device=device + ) + + for tmp in frame_indices: + ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + \ + frame_seqlen * num_frame_per_block + + def attention_mask(b, h, q_idx, kv_idx): + return (kv_idx < ends[q_idx]) | (q_idx == kv_idx) + # return ((kv_idx < total_length) & (q_idx < total_length)) | (q_idx == kv_idx) # bidirectional mask + + block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length, + KV_LEN=total_length + padded_length, _compile=False, device=device) + + import torch.distributed as dist + if not dist.is_initialized() or dist.get_rank() == 0: + print( + f" cache a block wise causal mask with block size of {num_frame_per_block} frames") + print(block_mask) + + return block_mask + + def _forward_inference( + self, + x, + t, + context, + seq_len, + clip_fea=None, + y=None, + kv_cache: dict = None, + crossattn_cache: dict = None, + current_start: int = 0, + current_end: int = 0 + ): + r""" + Run the diffusion model with kv caching. + See Algorithm 2 of CausVid paper https://arxiv.org/abs/2412.07772 for details. + This function will be run for num_frame times. + Process the latent frames one by one (1560 tokens each) + + Args: + x (List[Tensor]): + List of input video tensors, each with shape [C_in, F, H, W] + t (Tensor): + Diffusion timesteps tensor of shape [B] + context (List[Tensor]): + List of text embeddings each with shape [L, C] + seq_len (`int`): + Maximum sequence length for positional encoding + clip_fea (Tensor, *optional*): + CLIP image features for image-to-video mode + y (List[Tensor], *optional*): + Conditional video inputs for image-to-video mode, same shape as x + + Returns: + List[Tensor]: + List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] + """ + if self.model_type == 'i2v': + assert clip_fea is not None and y is not None + # params + device = self.patch_embedding.weight.device + if self.freqs.device != device: + self.freqs = self.freqs.to(device) + + if y is not None: + x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] + + # embeddings + x = [self.patch_embedding(u.unsqueeze(0)) for u in x] + grid_sizes = torch.stack( + [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) + x = [u.flatten(2).transpose(1, 2) for u in x] + seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) + assert seq_lens.max() <= seq_len + x = torch.cat(x) + """ + torch.cat([ + torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], + dim=1) for u in x + ]) + """ + + # time embeddings + # with amp.autocast(dtype=torch.float32): + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x)) + e0 = self.time_projection(e).unflatten( + 1, (6, self.dim)).unflatten(dim=0, sizes=t.shape) + # assert e.dtype == torch.float32 and e0.dtype == torch.float32 + + # context + context_lens = None + context = self.text_embedding( + torch.stack([ + torch.cat( + [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) + for u in context + ])) + + if clip_fea is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + + # arguments + kwargs = dict( + e=e0, + seq_lens=seq_lens, + grid_sizes=grid_sizes, + freqs=self.freqs, + context=context, + context_lens=context_lens, + block_mask=self.block_mask + ) + + def create_custom_forward(module): + def custom_forward(*inputs, **kwargs): + return module(*inputs, **kwargs) + return custom_forward + + for block_index, block in enumerate(self.blocks): + if torch.is_grad_enabled() and self.gradient_checkpointing: + assert False + else: + kwargs.update( + { + "kv_cache": kv_cache[block_index], + "crossattn_cache": crossattn_cache[block_index], + "current_start": current_start, + "current_end": current_end + } + ) + x = block(x, **kwargs) + + # head + x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2)) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return torch.stack(x) + + def _forward_train( + self, + x, + t, + context, + seq_len, + clip_fea=None, + y=None, + ): + r""" + Forward pass through the diffusion model + + Args: + x (List[Tensor]): + List of input video tensors, each with shape [C_in, F, H, W] + t (Tensor): + Diffusion timesteps tensor of shape [B] + context (List[Tensor]): + List of text embeddings each with shape [L, C] + seq_len (`int`): + Maximum sequence length for positional encoding + clip_fea (Tensor, *optional*): + CLIP image features for image-to-video mode + y (List[Tensor], *optional*): + Conditional video inputs for image-to-video mode, same shape as x + + Returns: + List[Tensor]: + List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] + """ + if self.model_type == 'i2v': + assert clip_fea is not None and y is not None + # params + device = self.patch_embedding.weight.device + if self.freqs.device != device: + self.freqs = self.freqs.to(device) + + # Construct blockwise causal attn mask + if self.block_mask is None: + self.block_mask = self._prepare_blockwise_causal_attn_mask( + device, num_frames=x.shape[2], + frame_seqlen=x.shape[-2] * + x.shape[-1] // (self.patch_size[1] * self.patch_size[2]), + num_frame_per_block=self.num_frame_per_block + ) + + if y is not None: + x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] + + # embeddings + x = [self.patch_embedding(u.unsqueeze(0)) for u in x] + grid_sizes = torch.stack( + [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) + x = [u.flatten(2).transpose(1, 2) for u in x] + seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) + assert seq_lens.max() <= seq_len + x = torch.cat([torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1) for u in x]) + + # time embeddings + # with amp.autocast(dtype=torch.float32): + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x)) + e0 = self.time_projection(e).unflatten( + 1, (6, self.dim)).unflatten(dim=0, sizes=t.shape) + # assert e.dtype == torch.float32 and e0.dtype == torch.float32 + + # context + context_lens = None + context = self.text_embedding( + torch.stack([ + torch.cat( + [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) + for u in context + ])) + + if clip_fea is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + + # arguments + kwargs = dict( + e=e0, + seq_lens=seq_lens, + grid_sizes=grid_sizes, + freqs=self.freqs, + context=context, + context_lens=context_lens, + block_mask=self.block_mask) + + def create_custom_forward(module): + def custom_forward(*inputs, **kwargs): + return module(*inputs, **kwargs) + return custom_forward + + for block in self.blocks: + if torch.is_grad_enabled() and self.gradient_checkpointing: + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, **kwargs, + use_reentrant=False, + ) + else: + x = block(x, **kwargs) + + # head + x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2)) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return torch.stack(x) + + def forward( + self, + *args, + **kwargs + ): + if kwargs.get('kv_cache', None) is not None: + return self._forward_inference(*args, **kwargs) + else: + return self._forward_train(*args, **kwargs) + + def unpatchify(self, x, grid_sizes): + r""" + Reconstruct video tensors from patch embeddings. + + Args: + x (List[Tensor]): + List of patchified features, each with shape [L, C_out * prod(patch_size)] + grid_sizes (Tensor): + Original spatial-temporal grid dimensions before patching, + shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) + + Returns: + List[Tensor]: + Reconstructed video tensors with shape [C_out, F, H / 8, W / 8] + """ + + c = self.out_dim + out = [] + for u, v in zip(x, grid_sizes.tolist()): + u = u[:math.prod(v)].view(*v, *self.patch_size, c) + u = torch.einsum('fhwpqrc->cfphqwr', u) + u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)]) + out.append(u) + return out + + def init_weights(self): + r""" + Initialize model parameters using Xavier initialization. + """ + + # basic init + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + nn.init.zeros_(m.bias) + + # init embeddings + nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1)) + for m in self.text_embedding.modules(): + if isinstance(m, nn.Linear): + nn.init.normal_(m.weight, std=.02) + for m in self.time_embedding.modules(): + if isinstance(m, nn.Linear): + nn.init.normal_(m.weight, std=.02) + + # init output layer + nn.init.zeros_(self.head.head.weight) diff --git a/exp_code/1_benchmark/2.py b/exp_code/1_benchmark/2.py new file mode 100644 index 0000000000000000000000000000000000000000..21686a6a9dc9567b4d9660bb2a634e84bea2aebf --- /dev/null +++ b/exp_code/1_benchmark/2.py @@ -0,0 +1,1059 @@ +from wan.modules.attention import attention +from wan.modules.model import ( + WanRMSNorm, + rope_apply, + WanLayerNorm, + WAN_CROSSATTENTION_CLASSES, + rope_params, + MLPProj, + sinusoidal_embedding_1d +) +from torch.nn.attention.flex_attention import create_block_mask, flex_attention +from diffusers.configuration_utils import ConfigMixin, register_to_config +from torch.nn.attention.flex_attention import BlockMask +from diffusers.models.modeling_utils import ModelMixin +import torch.nn as nn +import torch +import math +import torch.distributed as dist + +# wan 1.3B model has a weird channel / head configurations and require max-autotune to work with flexattention +# see https://github.com/pytorch/pytorch/issues/133254 +# change to default for other models +flex_attention = torch.compile( + flex_attention, dynamic=False, mode="max-autotune-no-cudagraphs") + + +def causal_rope_apply(x, grid_sizes, freqs, start_frame=0): + n, c = x.size(2), x.size(3) // 2 + + # split freqs + freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) + + # loop over samples + output = [] + + for i, (f, h, w) in enumerate(grid_sizes.tolist()): + seq_len = f * h * w + + # precompute multipliers + x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape( + seq_len, n, -1, 2)) + freqs_i = torch.cat([ + freqs[0][start_frame:start_frame + f].view(f, 1, 1, -1).expand(f, h, w, -1), + freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), + freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) + ], + dim=-1).reshape(seq_len, 1, -1) + + # apply rotary embedding + x_i = torch.view_as_real(x_i * freqs_i).flatten(2) + x_i = torch.cat([x_i, x[i, seq_len:]]) + + # append to collection + output.append(x_i) + return torch.stack(output).type_as(x) + + +class CausalWanSelfAttention(nn.Module): + + def __init__(self, + dim, + num_heads, + local_attn_size=-1, + sink_size=0, + qk_norm=True, + eps=1e-6): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.local_attn_size = local_attn_size + self.sink_size = sink_size + self.qk_norm = qk_norm + self.eps = eps + self.max_attention_size = 32760 if local_attn_size == -1 else local_attn_size * 1560 + + # layers + self.q = nn.Linear(dim, dim) + self.k = nn.Linear(dim, dim) + self.v = nn.Linear(dim, dim) + self.o = nn.Linear(dim, dim) + self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() + self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() + + def forward( + self, + x, + seq_lens, + grid_sizes, + freqs, + block_mask, + kv_cache=None, + current_start=0, + cache_start=None + ): + r""" + Args: + x(Tensor): Shape [B, L, num_heads, C / num_heads] + seq_lens(Tensor): Shape [B] + grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + block_mask (BlockMask) + """ + b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim + if cache_start is None: + cache_start = current_start + + # query, key, value function + def qkv_fn(x): + q = self.norm_q(self.q(x)).view(b, s, n, d) + k = self.norm_k(self.k(x)).view(b, s, n, d) + v = self.v(x).view(b, s, n, d) + return q, k, v + + q, k, v = qkv_fn(x) + + if kv_cache is None: + # if it is teacher forcing training? + is_tf = (s == seq_lens[0].item() * 2) + if is_tf: + q_chunk = torch.chunk(q, 2, dim=1) + k_chunk = torch.chunk(k, 2, dim=1) + roped_query = [] + roped_key = [] + # rope should be same for clean and noisy parts + for ii in range(2): + rq = rope_apply(q_chunk[ii], grid_sizes, freqs).type_as(v) + rk = rope_apply(k_chunk[ii], grid_sizes, freqs).type_as(v) + roped_query.append(rq) + roped_key.append(rk) + + roped_query = torch.cat(roped_query, dim=1) + roped_key = torch.cat(roped_key, dim=1) + + padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1] + padded_roped_query = torch.cat( + [roped_query, + torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]], + device=q.device, dtype=v.dtype)], + dim=1 + ) + + padded_roped_key = torch.cat( + [roped_key, torch.zeros([k.shape[0], padded_length, k.shape[2], k.shape[3]], + device=k.device, dtype=v.dtype)], + dim=1 + ) + + padded_v = torch.cat( + [v, torch.zeros([v.shape[0], padded_length, v.shape[2], v.shape[3]], + device=v.device, dtype=v.dtype)], + dim=1 + ) + + x = flex_attention( + query=padded_roped_query.transpose(2, 1), + key=padded_roped_key.transpose(2, 1), + value=padded_v.transpose(2, 1), + block_mask=block_mask + )[:, :, :-padded_length].transpose(2, 1) + + else: + roped_query = rope_apply(q, grid_sizes, freqs).type_as(v) + roped_key = rope_apply(k, grid_sizes, freqs).type_as(v) + + padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1] + padded_roped_query = torch.cat( + [roped_query, + torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]], + device=q.device, dtype=v.dtype)], + dim=1 + ) + + padded_roped_key = torch.cat( + [roped_key, torch.zeros([k.shape[0], padded_length, k.shape[2], k.shape[3]], + device=k.device, dtype=v.dtype)], + dim=1 + ) + + padded_v = torch.cat( + [v, torch.zeros([v.shape[0], padded_length, v.shape[2], v.shape[3]], + device=v.device, dtype=v.dtype)], + dim=1 + ) + + x = flex_attention( + query=padded_roped_query.transpose(2, 1), + key=padded_roped_key.transpose(2, 1), + value=padded_v.transpose(2, 1), + block_mask=block_mask + )[:, :, :-padded_length].transpose(2, 1) + else: + frame_seqlen = math.prod(grid_sizes[0][1:]).item() + current_start_frame = current_start // frame_seqlen + roped_query = causal_rope_apply( + q, grid_sizes, freqs, start_frame=current_start_frame).type_as(v) + roped_key = causal_rope_apply( + k, grid_sizes, freqs, start_frame=current_start_frame).type_as(v) + + current_end = current_start + roped_query.shape[1] + sink_tokens = self.sink_size * frame_seqlen + # If we are using local attention and the current KV cache size is larger than the local attention size, we need to truncate the KV cache + kv_cache_size = kv_cache["k"].shape[1] + num_new_tokens = roped_query.shape[1] + if self.local_attn_size != -1 and (current_end > kv_cache["global_end_index"].item()) and ( + num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size): + # Calculate the number of new tokens added in this step + # Shift existing cache content left to discard oldest tokens + # Clone the source slice to avoid overlapping memory error + num_evicted_tokens = num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size + num_rolled_tokens = kv_cache["local_end_index"].item() - num_evicted_tokens - sink_tokens + kv_cache["k"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \ + kv_cache["k"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone() + kv_cache["v"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \ + kv_cache["v"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone() + # Insert the new keys/values at the end + local_end_index = kv_cache["local_end_index"].item() + current_end - \ + kv_cache["global_end_index"].item() - num_evicted_tokens + local_start_index = local_end_index - num_new_tokens + kv_cache["k"][:, local_start_index:local_end_index] = roped_key + kv_cache["v"][:, local_start_index:local_end_index] = v + else: + # Assign new keys/values directly up to current_end + local_end_index = kv_cache["local_end_index"].item() + current_end - kv_cache["global_end_index"].item() + local_start_index = local_end_index - num_new_tokens + kv_cache["k"][:, local_start_index:local_end_index] = roped_key + kv_cache["v"][:, local_start_index:local_end_index] = v + + x = attention( + roped_query, + kv_cache["k"][:, max(0, local_end_index - self.max_attention_size):local_end_index], + kv_cache["v"][:, max(0, local_end_index - self.max_attention_size):local_end_index] + ) + kv_cache["global_end_index"].fill_(current_end) + kv_cache["local_end_index"].fill_(local_end_index) + + # output + x = x.flatten(2) + x = self.o(x) + return x + + +class CausalWanAttentionBlock(nn.Module): + + def __init__(self, + cross_attn_type, + dim, + ffn_dim, + num_heads, + local_attn_size=-1, + sink_size=0, + qk_norm=True, + cross_attn_norm=False, + eps=1e-6): + super().__init__() + self.dim = dim + self.ffn_dim = ffn_dim + self.num_heads = num_heads + self.local_attn_size = local_attn_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + + # layers + self.norm1 = WanLayerNorm(dim, eps) + self.self_attn = CausalWanSelfAttention(dim, num_heads, local_attn_size, sink_size, qk_norm, eps) + self.norm3 = WanLayerNorm( + dim, eps, + elementwise_affine=True) if cross_attn_norm else nn.Identity() + self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim, + num_heads, + (-1, -1), + qk_norm, + eps) + self.norm2 = WanLayerNorm(dim, eps) + self.ffn = nn.Sequential( + nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), + nn.Linear(ffn_dim, dim)) + + # modulation + self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) + + def forward( + self, + x, + e, + seq_lens, + grid_sizes, + freqs, + context, + context_lens, + block_mask, + kv_cache=None, + crossattn_cache=None, + current_start=0, + cache_start=None + ): + r""" + Args: + x(Tensor): Shape [B, L, C] + e(Tensor): Shape [B, F, 6, C] + seq_lens(Tensor): Shape [B], length of each sequence in batch + grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1] + # assert e.dtype == torch.float32 + # with amp.autocast(dtype=torch.float32): + e = (self.modulation.unsqueeze(1) + e).chunk(6, dim=2) + # assert e[0].dtype == torch.float32 + + # self-attention + y = self.self_attn( + (self.norm1(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[1]) + e[0]).flatten(1, 2), + seq_lens, grid_sizes, + freqs, block_mask, kv_cache, current_start, cache_start) + + # with amp.autocast(dtype=torch.float32): + x = x + (y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[2]).flatten(1, 2) + + # cross-attention & ffn function + def cross_attn_ffn(x, context, context_lens, e, crossattn_cache=None): + x = x + self.cross_attn(self.norm3(x), context, + context_lens, crossattn_cache=crossattn_cache) + y = self.ffn( + (self.norm2(x).unflatten(dim=1, sizes=(num_frames, + frame_seqlen)) * (1 + e[4]) + e[3]).flatten(1, 2) + ) + # with amp.autocast(dtype=torch.float32): + x = x + (y.unflatten(dim=1, sizes=(num_frames, + frame_seqlen)) * e[5]).flatten(1, 2) + return x + + x = cross_attn_ffn(x, context, context_lens, e, crossattn_cache) + return x + + +class CausalHead(nn.Module): + + def __init__(self, dim, out_dim, patch_size, eps=1e-6): + super().__init__() + self.dim = dim + self.out_dim = out_dim + self.patch_size = patch_size + self.eps = eps + + # layers + out_dim = math.prod(patch_size) * out_dim + self.norm = WanLayerNorm(dim, eps) + self.head = nn.Linear(dim, out_dim) + + # modulation + self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5) + + def forward(self, x, e): + r""" + Args: + x(Tensor): Shape [B, L1, C] + e(Tensor): Shape [B, F, 1, C] + """ + # assert e.dtype == torch.float32 + # with amp.autocast(dtype=torch.float32): + num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1] + e = (self.modulation.unsqueeze(1) + e).chunk(2, dim=2) + x = (self.head(self.norm(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[1]) + e[0])) + return x + + +class CausalWanModel(ModelMixin, ConfigMixin): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + ignore_for_config = [ + 'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim' + ] + _no_split_modules = ['WanAttentionBlock'] + _supports_gradient_checkpointing = True + + @register_to_config + def __init__(self, + model_type='t2v', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + local_attn_size=-1, + sink_size=0, + qk_norm=True, + cross_attn_norm=True, + eps=1e-6): + r""" + Initialize the diffusion model backbone. + + Args: + model_type (`str`, *optional*, defaults to 't2v'): + Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) + patch_size (`tuple`, *optional*, defaults to (1, 2, 2)): + 3D patch dimensions for video embedding (t_patch, h_patch, w_patch) + text_len (`int`, *optional*, defaults to 512): + Fixed length for text embeddings + in_dim (`int`, *optional*, defaults to 16): + Input video channels (C_in) + dim (`int`, *optional*, defaults to 2048): + Hidden dimension of the transformer + ffn_dim (`int`, *optional*, defaults to 8192): + Intermediate dimension in feed-forward network + freq_dim (`int`, *optional*, defaults to 256): + Dimension for sinusoidal time embeddings + text_dim (`int`, *optional*, defaults to 4096): + Input dimension for text embeddings + out_dim (`int`, *optional*, defaults to 16): + Output video channels (C_out) + num_heads (`int`, *optional*, defaults to 16): + Number of attention heads + num_layers (`int`, *optional*, defaults to 32): + Number of transformer blocks + local_attn_size (`int`, *optional*, defaults to -1): + Window size for temporal local attention (-1 indicates global attention) + sink_size (`int`, *optional*, defaults to 0): + Size of the attention sink, we keep the first `sink_size` frames unchanged when rolling the KV cache + qk_norm (`bool`, *optional*, defaults to True): + Enable query/key normalization + cross_attn_norm (`bool`, *optional*, defaults to False): + Enable cross-attention normalization + eps (`float`, *optional*, defaults to 1e-6): + Epsilon value for normalization layers + """ + + super().__init__() + + assert model_type in ['t2v', 'i2v'] + self.model_type = model_type + + self.patch_size = patch_size + self.text_len = text_len + self.in_dim = in_dim + self.dim = dim + self.ffn_dim = ffn_dim + self.freq_dim = freq_dim + self.text_dim = text_dim + self.out_dim = out_dim + self.num_heads = num_heads + self.num_layers = num_layers + self.local_attn_size = local_attn_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + + # embeddings + self.patch_embedding = nn.Conv3d( + in_dim, dim, kernel_size=patch_size, stride=patch_size) + self.text_embedding = nn.Sequential( + nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), + nn.Linear(dim, dim)) + + self.time_embedding = nn.Sequential( + nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) + self.time_projection = nn.Sequential( + nn.SiLU(), nn.Linear(dim, dim * 6)) + + # blocks + cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn' + self.blocks = nn.ModuleList([ + CausalWanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads, + local_attn_size, sink_size, qk_norm, cross_attn_norm, eps) + for _ in range(num_layers) + ]) + + # head + self.head = CausalHead(dim, out_dim, patch_size, eps) + + # buffers (don't use register_buffer otherwise dtype will be changed in to()) + assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0 + d = dim // num_heads + self.freqs = torch.cat([ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)) + ], + dim=1) + + if model_type == 'i2v': + self.img_emb = MLPProj(1280, dim) + + # initialize weights + self.init_weights() + + self.gradient_checkpointing = False + + self.block_mask = None + + self.num_frame_per_block = 1 + self.independent_first_frame = False + + def _set_gradient_checkpointing(self, module, value=False): + self.gradient_checkpointing = value + + @staticmethod + def _prepare_blockwise_causal_attn_mask( + device: torch.device | str, num_frames: int = 21, + frame_seqlen: int = 1560, num_frame_per_block=1, local_attn_size=-1 + ) -> BlockMask: + """ + we will divide the token sequence into the following format + [1 latent frame] [1 latent frame] ... [1 latent frame] + We use flexattention to construct the attention mask + """ + total_length = num_frames * frame_seqlen + + # we do right padding to get to a multiple of 128 + padded_length = math.ceil(total_length / 128) * 128 - total_length + + ends = torch.zeros(total_length + padded_length, + device=device, dtype=torch.long) + + # Block-wise causal mask will attend to all elements that are before the end of the current chunk + frame_indices = torch.arange( + start=0, + end=total_length, + step=frame_seqlen * num_frame_per_block, + device=device + ) + + for tmp in frame_indices: + ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + \ + frame_seqlen * num_frame_per_block + + def attention_mask(b, h, q_idx, kv_idx): + if local_attn_size == -1: + return (kv_idx < ends[q_idx]) | (q_idx == kv_idx) + else: + return ((kv_idx < ends[q_idx]) & (kv_idx >= (ends[q_idx] - local_attn_size * frame_seqlen))) | (q_idx == kv_idx) + # return ((kv_idx < total_length) & (q_idx < total_length)) | (q_idx == kv_idx) # bidirectional mask + + block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length, + KV_LEN=total_length + padded_length, _compile=False, device=device) + + import torch.distributed as dist + if not dist.is_initialized() or dist.get_rank() == 0: + print( + f" cache a block wise causal mask with block size of {num_frame_per_block} frames") + print(block_mask) + + # import imageio + # import numpy as np + # from torch.nn.attention.flex_attention import create_mask + + # mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length + + # padded_length, KV_LEN=total_length + padded_length, device=device) + # import cv2 + # mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024)) + # imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask)) + + return block_mask + + @staticmethod + def _prepare_teacher_forcing_mask( + device: torch.device | str, num_frames: int = 21, + frame_seqlen: int = 1560, num_frame_per_block=1 + ) -> BlockMask: + """ + we will divide the token sequence into the following format + [1 latent frame] [1 latent frame] ... [1 latent frame] + We use flexattention to construct the attention mask + """ + # debug + DEBUG = False + if DEBUG: + num_frames = 9 + frame_seqlen = 256 + + total_length = num_frames * frame_seqlen * 2 + + # we do right padding to get to a multiple of 128 + padded_length = math.ceil(total_length / 128) * 128 - total_length + + clean_ends = num_frames * frame_seqlen + # for clean context frames, we can construct their flex attention mask based on a [start, end] interval + context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long) + # for noisy frames, we need two intervals to construct the flex attention mask [context_start, context_end] [noisy_start, noisy_end] + noise_context_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long) + noise_context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long) + noise_noise_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long) + noise_noise_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long) + + # Block-wise causal mask will attend to all elements that are before the end of the current chunk + attention_block_size = frame_seqlen * num_frame_per_block + frame_indices = torch.arange( + start=0, + end=num_frames * frame_seqlen, + step=attention_block_size, + device=device, dtype=torch.long + ) + + # attention for clean context frames + for start in frame_indices: + context_ends[start:start + attention_block_size] = start + attention_block_size + + noisy_image_start_list = torch.arange( + num_frames * frame_seqlen, total_length, + step=attention_block_size, + device=device, dtype=torch.long + ) + noisy_image_end_list = noisy_image_start_list + attention_block_size + + # attention for noisy frames + for block_index, (start, end) in enumerate(zip(noisy_image_start_list, noisy_image_end_list)): + # attend to noisy tokens within the same block + noise_noise_starts[start:end] = start + noise_noise_ends[start:end] = end + # attend to context tokens in previous blocks + # noise_context_starts[start:end] = 0 + noise_context_ends[start:end] = block_index * attention_block_size + + def attention_mask(b, h, q_idx, kv_idx): + # first design the mask for clean frames + clean_mask = (q_idx < clean_ends) & (kv_idx < context_ends[q_idx]) + # then design the mask for noisy frames + # noisy frames will attend to all clean preceeding clean frames + itself + C1 = (kv_idx < noise_noise_ends[q_idx]) & (kv_idx >= noise_noise_starts[q_idx]) + C2 = (kv_idx < noise_context_ends[q_idx]) & (kv_idx >= noise_context_starts[q_idx]) + noise_mask = (q_idx >= clean_ends) & (C1 | C2) + + eye_mask = q_idx == kv_idx + return eye_mask | clean_mask | noise_mask + + block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length, + KV_LEN=total_length + padded_length, _compile=False, device=device) + + if DEBUG: + print(block_mask) + import imageio + import numpy as np + from torch.nn.attention.flex_attention import create_mask + + mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length + + padded_length, KV_LEN=total_length + padded_length, device=device) + import cv2 + mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024)) + imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask)) + + return block_mask + + @staticmethod + def _prepare_blockwise_causal_attn_mask_i2v( + device: torch.device | str, num_frames: int = 21, + frame_seqlen: int = 1560, num_frame_per_block=4, local_attn_size=-1 + ) -> BlockMask: + """ + we will divide the token sequence into the following format + [1 latent frame] [N latent frame] ... [N latent frame] + The first frame is separated out to support I2V generation + We use flexattention to construct the attention mask + """ + total_length = num_frames * frame_seqlen + + # we do right padding to get to a multiple of 128 + padded_length = math.ceil(total_length / 128) * 128 - total_length + + ends = torch.zeros(total_length + padded_length, + device=device, dtype=torch.long) + + # special handling for the first frame + ends[:frame_seqlen] = frame_seqlen + + # Block-wise causal mask will attend to all elements that are before the end of the current chunk + frame_indices = torch.arange( + start=frame_seqlen, + end=total_length, + step=frame_seqlen * num_frame_per_block, + device=device + ) + + for idx, tmp in enumerate(frame_indices): + ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + \ + frame_seqlen * num_frame_per_block + + def attention_mask(b, h, q_idx, kv_idx): + if local_attn_size == -1: + return (kv_idx < ends[q_idx]) | (q_idx == kv_idx) + else: + return ((kv_idx < ends[q_idx]) & (kv_idx >= (ends[q_idx] - local_attn_size * frame_seqlen))) | \ + (q_idx == kv_idx) + + block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length, + KV_LEN=total_length + padded_length, _compile=False, device=device) + + if not dist.is_initialized() or dist.get_rank() == 0: + print( + f" cache a block wise causal mask with block size of {num_frame_per_block} frames") + print(block_mask) + + # import imageio + # import numpy as np + # from torch.nn.attention.flex_attention import create_mask + + # mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length + + # padded_length, KV_LEN=total_length + padded_length, device=device) + # import cv2 + # mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024)) + # imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask)) + + return block_mask + + def _forward_inference( + self, + x, + t, + context, + seq_len, + clip_fea=None, + y=None, + kv_cache: dict = None, + crossattn_cache: dict = None, + current_start: int = 0, + cache_start: int = 0 + ): + r""" + Run the diffusion model with kv caching. + See Algorithm 2 of CausVid paper https://arxiv.org/abs/2412.07772 for details. + This function will be run for num_frame times. + Process the latent frames one by one (1560 tokens each) + + Args: + x (List[Tensor]): + List of input video tensors, each with shape [C_in, F, H, W] + t (Tensor): + Diffusion timesteps tensor of shape [B] + context (List[Tensor]): + List of text embeddings each with shape [L, C] + seq_len (`int`): + Maximum sequence length for positional encoding + clip_fea (Tensor, *optional*): + CLIP image features for image-to-video mode + y (List[Tensor], *optional*): + Conditional video inputs for image-to-video mode, same shape as x + + Returns: + List[Tensor]: + List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] + """ + + if self.model_type == 'i2v': + assert clip_fea is not None and y is not None + # params + device = self.patch_embedding.weight.device + if self.freqs.device != device: + self.freqs = self.freqs.to(device) + + if y is not None: + x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] + + # embeddings + x = [self.patch_embedding(u.unsqueeze(0)) for u in x] + grid_sizes = torch.stack( + [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) + x = [u.flatten(2).transpose(1, 2) for u in x] + seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) + assert seq_lens.max() <= seq_len + x = torch.cat(x) + """ + torch.cat([ + torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], + dim=1) for u in x + ]) + """ + + # time embeddings + # with amp.autocast(dtype=torch.float32): + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x)) + e0 = self.time_projection(e).unflatten( + 1, (6, self.dim)).unflatten(dim=0, sizes=t.shape) + # assert e.dtype == torch.float32 and e0.dtype == torch.float32 + + # context + context_lens = None + context = self.text_embedding( + torch.stack([ + torch.cat( + [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) + for u in context + ])) + + if clip_fea is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + + # arguments + kwargs = dict( + e=e0, + seq_lens=seq_lens, + grid_sizes=grid_sizes, + freqs=self.freqs, + context=context, + context_lens=context_lens, + block_mask=self.block_mask + ) + + def create_custom_forward(module): + def custom_forward(*inputs, **kwargs): + return module(*inputs, **kwargs) + return custom_forward + + for block_index, block in enumerate(self.blocks): + if torch.is_grad_enabled() and self.gradient_checkpointing: + kwargs.update( + { + "kv_cache": kv_cache[block_index], + "current_start": current_start, + "cache_start": cache_start + } + ) + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, **kwargs, + use_reentrant=False, + ) + else: + kwargs.update( + { + "kv_cache": kv_cache[block_index], + "crossattn_cache": crossattn_cache[block_index], + "current_start": current_start, + "cache_start": cache_start + } + ) + x = block(x, **kwargs) + + # head + x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2)) + # unpatchify + x = self.unpatchify(x, grid_sizes) + return torch.stack(x) + + def _forward_train( + self, + x, + t, + context, + seq_len, + clean_x=None, + aug_t=None, + clip_fea=None, + y=None, + ): + r""" + Forward pass through the diffusion model + + Args: + x (List[Tensor]): + List of input video tensors, each with shape [C_in, F, H, W] + t (Tensor): + Diffusion timesteps tensor of shape [B] + context (List[Tensor]): + List of text embeddings each with shape [L, C] + seq_len (`int`): + Maximum sequence length for positional encoding + clip_fea (Tensor, *optional*): + CLIP image features for image-to-video mode + y (List[Tensor], *optional*): + Conditional video inputs for image-to-video mode, same shape as x + + Returns: + List[Tensor]: + List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] + """ + if self.model_type == 'i2v': + assert clip_fea is not None and y is not None + # params + device = self.patch_embedding.weight.device + if self.freqs.device != device: + self.freqs = self.freqs.to(device) + + # Construct blockwise causal attn mask + if self.block_mask is None: + if clean_x is not None: + if self.independent_first_frame: + raise NotImplementedError() + else: + self.block_mask = self._prepare_teacher_forcing_mask( + device, num_frames=x.shape[2], + frame_seqlen=x.shape[-2] * x.shape[-1] // (self.patch_size[1] * self.patch_size[2]), + num_frame_per_block=self.num_frame_per_block + ) + else: + if self.independent_first_frame: + self.block_mask = self._prepare_blockwise_causal_attn_mask_i2v( + device, num_frames=x.shape[2], + frame_seqlen=x.shape[-2] * x.shape[-1] // (self.patch_size[1] * self.patch_size[2]), + num_frame_per_block=self.num_frame_per_block, + local_attn_size=self.local_attn_size + ) + else: + self.block_mask = self._prepare_blockwise_causal_attn_mask( + device, num_frames=x.shape[2], + frame_seqlen=x.shape[-2] * x.shape[-1] // (self.patch_size[1] * self.patch_size[2]), + num_frame_per_block=self.num_frame_per_block, + local_attn_size=self.local_attn_size + ) + + if y is not None: + x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] + + # embeddings + x = [self.patch_embedding(u.unsqueeze(0)) for u in x] + + grid_sizes = torch.stack( + [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) + x = [u.flatten(2).transpose(1, 2) for u in x] + + seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) + assert seq_lens.max() <= seq_len + x = torch.cat([ + torch.cat([u, u.new_zeros(1, seq_lens[0] - u.size(1), u.size(2))], + dim=1) for u in x + ]) + + # time embeddings + # with amp.autocast(dtype=torch.float32): + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x)) + e0 = self.time_projection(e).unflatten( + 1, (6, self.dim)).unflatten(dim=0, sizes=t.shape) + # assert e.dtype == torch.float32 and e0.dtype == torch.float32 + + # context + context_lens = None + context = self.text_embedding( + torch.stack([ + torch.cat( + [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) + for u in context + ])) + + if clip_fea is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + + if clean_x is not None: + clean_x = [self.patch_embedding(u.unsqueeze(0)) for u in clean_x] + clean_x = [u.flatten(2).transpose(1, 2) for u in clean_x] + + seq_lens_clean = torch.tensor([u.size(1) for u in clean_x], dtype=torch.long) + assert seq_lens_clean.max() <= seq_len + clean_x = torch.cat([ + torch.cat([u, u.new_zeros(1, seq_lens_clean[0] - u.size(1), u.size(2))], dim=1) for u in clean_x + ]) + + x = torch.cat([clean_x, x], dim=1) + if aug_t is None: + aug_t = torch.zeros_like(t) + e_clean = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, aug_t.flatten()).type_as(x)) + e0_clean = self.time_projection(e_clean).unflatten( + 1, (6, self.dim)).unflatten(dim=0, sizes=t.shape) + e0 = torch.cat([e0_clean, e0], dim=1) + + # arguments + kwargs = dict( + e=e0, + seq_lens=seq_lens, + grid_sizes=grid_sizes, + freqs=self.freqs, + context=context, + context_lens=context_lens, + block_mask=self.block_mask) + + def create_custom_forward(module): + def custom_forward(*inputs, **kwargs): + return module(*inputs, **kwargs) + return custom_forward + + for block in self.blocks: + if torch.is_grad_enabled() and self.gradient_checkpointing: + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, **kwargs, + use_reentrant=False, + ) + else: + x = block(x, **kwargs) + + if clean_x is not None: + x = x[:, x.shape[1] // 2:] + + # head + x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2)) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return torch.stack(x) + + def forward( + self, + *args, + **kwargs + ): + if kwargs.get('kv_cache', None) is not None: + return self._forward_inference(*args, **kwargs) + else: + return self._forward_train(*args, **kwargs) + + def unpatchify(self, x, grid_sizes): + r""" + Reconstruct video tensors from patch embeddings. + + Args: + x (List[Tensor]): + List of patchified features, each with shape [L, C_out * prod(patch_size)] + grid_sizes (Tensor): + Original spatial-temporal grid dimensions before patching, + shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) + + Returns: + List[Tensor]: + Reconstructed video tensors with shape [C_out, F, H / 8, W / 8] + """ + + c = self.out_dim + out = [] + for u, v in zip(x, grid_sizes.tolist()): + u = u[:math.prod(v)].view(*v, *self.patch_size, c) + u = torch.einsum('fhwpqrc->cfphqwr', u) + u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)]) + out.append(u) + return out + + def init_weights(self): + r""" + Initialize model parameters using Xavier initialization. + """ + + # basic init + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + nn.init.zeros_(m.bias) + + # init embeddings + nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1)) + for m in self.text_embedding.modules(): + if isinstance(m, nn.Linear): + nn.init.normal_(m.weight, std=.02) + for m in self.time_embedding.modules(): + if isinstance(m, nn.Linear): + nn.init.normal_(m.weight, std=.02) + + # init output layer + nn.init.zeros_(self.head.head.weight) diff --git a/exp_code/1_benchmark/ALG/.gitignore b/exp_code/1_benchmark/ALG/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..0bbb00a9964101e5c4a1abccd4f7d8a346bf4134 --- /dev/null +++ b/exp_code/1_benchmark/ALG/.gitignore @@ -0,0 +1,2 @@ +*.DS_Store +.vscode/ \ No newline at end of file diff --git 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dtype: "bfloat16" + +generation: + height: null + width: null + num_frames: 49 + num_inference_steps: 50 + guidance_scale: 6.0 + +alg: + use_low_pass_guidance: True + + lp_filter_type: "down_up" + lp_filter_in_latent: True + + lp_blur_sigma: null + lp_blur_kernel_size: null + lp_resize_factor: 0.25 + + lp_strength_schedule_type: "interval" + schedule_blur_kernel_size: False + + schedule_interval_start_time: 0.0 + schedule_interval_end_time: 0.04 + + schedule_linear_start_weight: null + schedule_linear_end_weight: null + schedule_linear_end_time: null + +video: + fps: 12 \ No newline at end of file diff --git a/exp_code/1_benchmark/ALG/configs/cogvideox_default.yaml b/exp_code/1_benchmark/ALG/configs/cogvideox_default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d4350991d0cd41a97ef96529ef43503cc858559c --- /dev/null +++ b/exp_code/1_benchmark/ALG/configs/cogvideox_default.yaml @@ -0,0 +1,16 @@ +model: + path: "THUDM/CogVideoX-5b-I2V" + dtype: "bfloat16" + +generation: + height: null + width: null + num_frames: 49 + num_inference_steps: 50 + guidance_scale: 6.0 + +alg: + use_low_pass_guidance: False + +video: + fps: 12 \ No newline at end of file diff --git a/exp_code/1_benchmark/ALG/configs/hunyuan_video_alg.yaml b/exp_code/1_benchmark/ALG/configs/hunyuan_video_alg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6ea1e95f918083cd0b5dbfcfcd7607c1061a335c --- /dev/null +++ b/exp_code/1_benchmark/ALG/configs/hunyuan_video_alg.yaml @@ -0,0 +1,36 @@ +model: + path: "/mnt/bn/yufan-dev-my/ysh/Ckpts/hunyuanvideo-community/HunyuanVideo-I2V" + dtype: "bfloat16" + flow_shift: 7.0 #7.0 if i2v_stable else 17.0 + flow_reverse: false + +generation: + num_frames: 129 + num_inference_steps: 20 + guidance_scale: 6.0 + i2v_stable: true + true_cfg_scale: 1.0 + +alg: + use_low_pass_guidance: True + + lp_filter_type: "down_up" + lp_filter_in_latent: True + + lp_blur_sigma: null + lp_blur_kernel_size: null + lp_resize_factor: 0.625 + + lp_strength_schedule_type: "interval" + schedule_blur_kernel_size: False + + schedule_interval_start_time: 0.0 + schedule_interval_end_time: 0.04 + + schedule_linear_start_weight: null + schedule_linear_end_weight: null + schedule_linear_end_time: null + +video: + resolution: 360p + fps: 30 \ No newline at end of file diff --git a/exp_code/1_benchmark/ALG/configs/hunyuan_video_default.yaml b/exp_code/1_benchmark/ALG/configs/hunyuan_video_default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bd8de7916b477f6cc1c9d5ed4a433517876c1d7a --- /dev/null +++ b/exp_code/1_benchmark/ALG/configs/hunyuan_video_default.yaml @@ -0,0 +1,19 @@ +model: + path: "/mnt/bn/yufan-dev-my/ysh/Ckpts/hunyuanvideo-community/HunyuanVideo-I2V" + dtype: "bfloat16" + flow_shift: 7.0 #7.0 if i2v_stable else 17.0 + flow_reverse: false + +generation: + num_frames: 129 + num_inference_steps: 50 + guidance_scale: 6.0 + i2v_stable: true + true_cfg_scale: 1.0 + +alg: + use_low_pass_guidance: True + +video: + resolution: 360p + fps: 30 \ No newline at end of file diff --git a/exp_code/1_benchmark/ALG/configs/wan_alg.yaml b/exp_code/1_benchmark/ALG/configs/wan_alg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..82dfa563380f7d691748bb8b600a9f3131557007 --- /dev/null +++ b/exp_code/1_benchmark/ALG/configs/wan_alg.yaml @@ -0,0 +1,33 @@ +model: + path: "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers" + dtype: "bfloat16" + +generation: + num_frames: 81 + num_inference_steps: 50 + guidance_scale: 5.0 + height: 480 + width: 832 + +alg: + use_low_pass_guidance: True + + lp_filter_type: "down_up" + lp_filter_in_latent: True + + lp_blur_sigma: null + lp_blur_kernel_size: null + lp_resize_factor: 0.4 + + lp_strength_schedule_type: "interval" + schedule_blur_kernel_size: False + + schedule_interval_start_time: 0.0 + schedule_interval_end_time: 0.20 + + schedule_linear_start_weight: null + schedule_linear_end_weight: null + schedule_linear_end_time: null + +video: + fps: 16 \ No newline at end of file diff --git a/exp_code/1_benchmark/ALG/configs/wan_default.yaml b/exp_code/1_benchmark/ALG/configs/wan_default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..067a19778d6bb016f351cc0a9dc54ea15d02e9e8 --- /dev/null +++ b/exp_code/1_benchmark/ALG/configs/wan_default.yaml @@ -0,0 +1,16 @@ +model: + path: "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers" + dtype: "bfloat16" + +generation: + num_frames: 81 + num_inference_steps: 50 + guidance_scale: 5.0 + height: 480 + width: 832 + +alg: + use_low_pass_guidance: False + +video: + fps: 16 \ No newline at end of file diff --git a/exp_code/1_benchmark/ALG/lp_utils.py b/exp_code/1_benchmark/ALG/lp_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..626721942ad5361d589d951617f13d1731758166 --- /dev/null +++ b/exp_code/1_benchmark/ALG/lp_utils.py @@ -0,0 +1,189 @@ +import math +import torch +import torch.nn.functional as F +import torchvision.transforms.functional as tvF +import numpy as np + + +def apply_low_pass_filter( + tensor: torch.Tensor, + filter_type: str, + # Gaussian Blur Params + blur_sigma: float, + blur_kernel_size: float, # Can be float (relative) or int (absolute) + # Down/Up Sampling Params + resize_factor: float, +): + """ + Applies the specified low-pass filtering operation to the input tensor. + Handles 4D ([B, C, H, W]) and 5D ([B, C, F, H, W]) tensors by temporarily + reshaping 5D tensors for spatial filtering. + """ + # --- Early Exits for No-Op Cases --- + if filter_type == "none": + return tensor + if filter_type == "down_up" and resize_factor == 1.0: + return tensor + if filter_type == "gaussian_blur" and blur_sigma == 0: + return tensor + + # --- Reshape 5D tensor for spatial filtering --- + is_5d = tensor.ndim == 5 + if is_5d: + B, C, K, H, W = tensor.shape + # Flatten frames into batch dimension using view + tensor = tensor.view(B * K, C, H, W) + else: + B, C, H, W = tensor.shape + + # --- Apply Selected Filter --- + if filter_type == "gaussian_blur": + if isinstance(blur_kernel_size, float): + kernel_val = max(int(blur_kernel_size * H), 1) + else: + kernel_val = int(blur_kernel_size) + if kernel_val % 2 == 0: + kernel_val += 1 + tensor = tvF.gaussian_blur(tensor, kernel_size=[kernel_val, kernel_val], sigma=[blur_sigma, blur_sigma]) + + elif filter_type == "down_up": + h0, w0 = tensor.shape[-2:] + h1 = max(1, int(round(h0 * resize_factor))) + w1 = max(1, int(round(w0 * resize_factor))) + tensor = F.interpolate(tensor, size=(h1, w1), mode="bilinear", align_corners=False, antialias=True) + tensor = F.interpolate(tensor, size=(h0, w0), mode="bilinear", align_corners=False, antialias=True) + + # --- Restore original 5D shape if necessary --- + if is_5d: + tensor = tensor.view(B, C, K, H, W) + + return tensor + + +def get_lp_strength( + step_index: int, + total_steps: int, + lp_strength_schedule_type: str, + # Interval params + schedule_interval_start_time: float, + schedule_interval_end_time: float, + # Linear params + schedule_linear_start_weight: float, + schedule_linear_end_weight: float, + schedule_linear_end_time: float, + # Exponential params + schedule_exp_decay_rate: float, +) -> float: + """ + Calculates the low-pass guidance strength multiplier for the current timestep + based on the specified schedule. + """ + step_norm = step_index / max(total_steps - 1, 1) + + if lp_strength_schedule_type == "linear": + schedule_duration_fraction = schedule_linear_end_time + if schedule_duration_fraction <= 0: + return schedule_linear_start_weight + if step_norm >= schedule_duration_fraction: + current_strength = schedule_linear_end_weight + else: + progress = step_norm / schedule_duration_fraction + current_strength = schedule_linear_start_weight * (1 - progress) + schedule_linear_end_weight * progress + return current_strength + + elif lp_strength_schedule_type == "interval": + if schedule_interval_start_time <= step_norm <= schedule_interval_end_time: + return 1.0 + else: + return 0.0 + + elif lp_strength_schedule_type == "exponential": + decay_rate = schedule_exp_decay_rate + if decay_rate < 0: + print(f"Warning: Negative exponential_decay_rate ({decay_rate}) is unusual. Using abs value.") + decay_rate = abs(decay_rate) + return math.exp(-decay_rate * step_norm) + + elif lp_strength_schedule_type == "none": + return 1.0 + else: + print(f"Warning: Unknown lp_strength_schedule_type '{lp_strength_schedule_type}'. Using constant strength 1.0.") + return 1.0 + +def _generate_crop_size_list(base_size=256, patch_size=32, max_ratio=4.0): + """generate crop size list (HunyuanVideo) + + Args: + base_size (int, optional): the base size for generate bucket. Defaults to 256. + patch_size (int, optional): the stride to generate bucket. Defaults to 32. + max_ratio (float, optional): th max ratio for h or w based on base_size . Defaults to 4.0. + + Returns: + list: generate crop size list + """ + num_patches = round((base_size / patch_size) ** 2) + assert max_ratio >= 1.0 + crop_size_list = [] + wp, hp = num_patches, 1 + while wp > 0: + if max(wp, hp) / min(wp, hp) <= max_ratio: + crop_size_list.append((wp * patch_size, hp * patch_size)) + if (hp + 1) * wp <= num_patches: + hp += 1 + else: + wp -= 1 + return crop_size_list + +def _get_closest_ratio(height: float, width: float, ratios: list, buckets: list): + """get the closest ratio in the buckets (HunyuanVideo) + + Args: + height (float): video height + width (float): video width + ratios (list): video aspect ratio + buckets (list): buckets generate by `generate_crop_size_list` + + Returns: + the closest ratio in the buckets and the corresponding ratio + """ + aspect_ratio = float(height) / float(width) + diff_ratios = ratios - aspect_ratio + + if aspect_ratio >= 1: + indices = [(index, x) for index, x in enumerate(diff_ratios) if x <= 0] + else: + indices = [(index, x) for index, x in enumerate(diff_ratios) if x > 0] + + closest_ratio_id = min(indices, key=lambda pair: abs(pair[1]))[0] + closest_size = buckets[closest_ratio_id] + closest_ratio = ratios[closest_ratio_id] + + return closest_size, closest_ratio + +def get_hunyuan_video_size(i2v_resolution, input_image): + """ + Map to target height and width based on resolution for HunyuanVideo + + Args: + height (float): video height + width (float): video width + ratios (list): video aspect ratio + buckets (list): buckets generate by `generate_crop_size_list` + + Returns: + the closest ratio in the buckets and the corresponding ratio + """ + if i2v_resolution == "720p": + bucket_hw_base_size = 960 + elif i2v_resolution == "540p": + bucket_hw_base_size = 720 + elif i2v_resolution == "360p": + bucket_hw_base_size = 480 + + origin_size = input_image.size + + crop_size_list = _generate_crop_size_list(bucket_hw_base_size, 32) + aspect_ratios = np.array([round(float(h)/float(w), 5) for h, w in crop_size_list]) + closest_size, _ = _get_closest_ratio(origin_size[1], origin_size[0], aspect_ratios, crop_size_list) + target_height, target_width = closest_size + return target_height, target_width \ No newline at end of file diff --git a/exp_code/1_benchmark/ALG/pipeline_cogvideox_image2video_lowpass.py b/exp_code/1_benchmark/ALG/pipeline_cogvideox_image2video_lowpass.py new file mode 100644 index 0000000000000000000000000000000000000000..2f02a0a42a01d732007a032681885600ed92f47f --- /dev/null +++ b/exp_code/1_benchmark/ALG/pipeline_cogvideox_image2video_lowpass.py @@ -0,0 +1,1158 @@ +# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +import math +from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Set + +import PIL +import torch +import torch.nn.functional as F +import torchvision.transforms.functional as tvF +from transformers import T5EncoderModel, T5Tokenizer + +from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback +from diffusers.image_processor import PipelineImageInput +from diffusers.loaders import CogVideoXLoraLoaderMixin +from diffusers.models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel +from diffusers.models.embeddings import get_3d_rotary_pos_embed +from diffusers.pipelines.pipeline_utils import DiffusionPipeline +from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler +from diffusers.utils import ( + is_torch_xla_available, + logging, + replace_example_docstring, +) +from diffusers.utils.torch_utils import randn_tensor +from diffusers.video_processor import VideoProcessor + +from diffusers.pipelines.cogvideo.pipeline_output import CogVideoXPipelineOutput + +import lp_utils + +if is_torch_xla_available(): + import torch_xla.core.xla_model as xm + + XLA_AVAILABLE = True +else: + XLA_AVAILABLE = False + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> from diffusers import CogVideoXImageToVideoPipeline + >>> from diffusers.utils import export_to_video, load_image + + >>> pipe = CogVideoXImageToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b-I2V", torch_dtype=torch.bfloat16) + >>> pipe.to("cuda") + + >>> prompt = "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot." + >>> image = load_image( + ... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg" + ... ) + >>> video = pipe(image, prompt, use_dynamic_cfg=True) + >>> export_to_video(video.frames[0], "output.mp4", fps=8) + ``` +""" + + +# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid +def get_resize_crop_region_for_grid(src, tgt_width, tgt_height): + tw = tgt_width + th = tgt_height + h, w = src + r = h / w + if r > (th / tw): + resize_height = th + resize_width = int(round(th / h * w)) + else: + resize_width = tw + resize_height = int(round(tw / w * h)) + + crop_top = int(round((th - resize_height) / 2.0)) + crop_left = int(round((tw - resize_width) / 2.0)) + + return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +): + r""" + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") + if timesteps is not None: + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents +def retrieve_latents( + encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" +): + if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": + return encoder_output.latent_dist.sample(generator) + elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": + return encoder_output.latent_dist.mode() + elif hasattr(encoder_output, "latents"): + return encoder_output.latents + else: + raise AttributeError("Could not access latents of provided encoder_output") + + +class CogVideoXImageToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin): + r""" + Pipeline for image-to-video generation using CogVideoX. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations. + text_encoder ([`T5EncoderModel`]): + Frozen text-encoder. CogVideoX uses + [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the + [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant. + tokenizer (`T5Tokenizer`): + Tokenizer of class + [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). + transformer ([`CogVideoXTransformer3DModel`]): + A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `transformer` to denoise the encoded video latents. + """ + + _optional_components = [] + model_cpu_offload_seq = "text_encoder->transformer->vae" + + _callback_tensor_inputs = [ + "latents", + "prompt_embeds", + "negative_prompt_embeds", + ] + + def __init__( + self, + tokenizer: T5Tokenizer, + text_encoder: T5EncoderModel, + vae: AutoencoderKLCogVideoX, + transformer: CogVideoXTransformer3DModel, + scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler], + ): + super().__init__() + + self.register_modules( + tokenizer=tokenizer, + text_encoder=text_encoder, + vae=vae, + transformer=transformer, + scheduler=scheduler, + ) + self.vae_scale_factor_spatial = ( + 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8 + ) + self.vae_scale_factor_temporal = ( + self.vae.config.temporal_compression_ratio if getattr(self, "vae", None) else 4 + ) + self.vae_scaling_factor_image = self.vae.config.scaling_factor if getattr(self, "vae", None) else 0.7 + + self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) + + # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds + def _get_t5_prompt_embeds( + self, + prompt: Union[str, List[str]] = None, + num_videos_per_prompt: int = 1, + max_sequence_length: int = 226, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + device = device or self._execution_device + dtype = dtype or self.text_encoder.dtype + + prompt = [prompt] if isinstance(prompt, str) else prompt + batch_size = len(prompt) + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=max_sequence_length, + truncation=True, + add_special_tokens=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because `max_sequence_length` is set to " + f" {max_sequence_length} tokens: {removed_text}" + ) + + prompt_embeds = self.text_encoder(text_input_ids.to(device))[0] + prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) + + # duplicate text embeddings for each generation per prompt, using mps friendly method + _, seq_len, _ = prompt_embeds.shape + prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) + prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) + + return prompt_embeds + + # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.encode_prompt + def encode_prompt( + self, + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + do_classifier_free_guidance: bool = True, + num_videos_per_prompt: int = 1, + prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + max_sequence_length: int = 226, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): + Whether to use classifier free guidance or not. + num_videos_per_prompt (`int`, *optional*, defaults to 1): + Number of videos that should be generated per prompt. torch device to place the resulting embeddings on + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + device: (`torch.device`, *optional*): + torch device + dtype: (`torch.dtype`, *optional*): + torch dtype + """ + device = device or self._execution_device + + prompt = [prompt] if isinstance(prompt, str) else prompt + if prompt is not None: + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + prompt_embeds = self._get_t5_prompt_embeds( + prompt=prompt, + num_videos_per_prompt=num_videos_per_prompt, + max_sequence_length=max_sequence_length, + device=device, + dtype=dtype, + ) + + if do_classifier_free_guidance and negative_prompt_embeds is None: + negative_prompt = negative_prompt or "" + negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt + + if prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + + negative_prompt_embeds = self._get_t5_prompt_embeds( + prompt=negative_prompt, + num_videos_per_prompt=num_videos_per_prompt, + max_sequence_length=max_sequence_length, + device=device, + dtype=dtype, + ) + + return prompt_embeds, negative_prompt_embeds + + def prepare_latents( + self, + image: torch.Tensor, + batch_size: int = 1, + num_channels_latents: int = 16, + num_frames: int = 13, + height: int = 60, + width: int = 90, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + generator: Optional[torch.Generator] = None, + latents: Optional[torch.Tensor] = None, + ): + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + num_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 + shape = ( + batch_size, + num_frames, + num_channels_latents, + height // self.vae_scale_factor_spatial, + width // self.vae_scale_factor_spatial, + ) + + # For CogVideoX1.5, the latent should add 1 for padding (Not use) + if self.transformer.config.patch_size_t is not None: + shape = shape[:1] + (shape[1] + shape[1] % self.transformer.config.patch_size_t,) + shape[2:] + + image = image.unsqueeze(2) # [B, C, F, H, W] + + if isinstance(generator, list): + image_latents = [ + retrieve_latents(self.vae.encode(image[i].unsqueeze(0)), generator[i]) for i in range(batch_size) + ] + else: + image_latents = [retrieve_latents(self.vae.encode(img.unsqueeze(0)), generator) for img in image] + + image_latents = torch.cat(image_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4) # [B, F, C, H, W] + + if not self.vae.config.invert_scale_latents: + image_latents = self.vae_scaling_factor_image * image_latents + else: + # This is awkward but required because the CogVideoX team forgot to multiply the + # scaling factor during training :) + image_latents = 1 / self.vae_scaling_factor_image * image_latents + + padding_shape = ( + batch_size, + num_frames - 1, + num_channels_latents, + height // self.vae_scale_factor_spatial, + width // self.vae_scale_factor_spatial, + ) + + latent_padding = torch.zeros(padding_shape, device=device, dtype=dtype) + image_latents = torch.cat([image_latents, latent_padding], dim=1) + + # Select the first frame along the second dimension + if self.transformer.config.patch_size_t is not None: + first_frame = image_latents[:, : image_latents.size(1) % self.transformer.config.patch_size_t, ...] + image_latents = torch.cat([first_frame, image_latents], dim=1) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents, image_latents + + # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.decode_latents + def decode_latents(self, latents: torch.Tensor) -> torch.Tensor: + latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width] + latents = 1 / self.vae_scaling_factor_image * latents + + frames = self.vae.decode(latents).sample + return frames + + # Copied from diffusers.pipelines.animatediff.pipeline_animatediff_video2video.AnimateDiffVideoToVideoPipeline.get_timesteps + def get_timesteps(self, num_inference_steps, timesteps, strength, device): + # get the original timestep using init_timestep + init_timestep = min(int(num_inference_steps * strength), num_inference_steps) + + t_start = max(num_inference_steps - init_timestep, 0) + timesteps = timesteps[t_start * self.scheduler.order :] + + return timesteps, num_inference_steps - t_start + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://huggingface.co/papers/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + image, + prompt, + height, + width, + negative_prompt, + callback_on_step_end_tensor_inputs, + latents=None, + prompt_embeds=None, + negative_prompt_embeds=None, + ): + if ( + not isinstance(image, torch.Tensor) + and not isinstance(image, PIL.Image.Image) + and not isinstance(image, list) + ): + raise ValueError( + "`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" + f" {type(image)}" + ) + + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.fuse_qkv_projections + def fuse_qkv_projections(self) -> None: + r"""Enables fused QKV projections.""" + self.fusing_transformer = True + self.transformer.fuse_qkv_projections() + + # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.unfuse_qkv_projections + def unfuse_qkv_projections(self) -> None: + r"""Disable QKV projection fusion if enabled.""" + if not self.fusing_transformer: + logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.") + else: + self.transformer.unfuse_qkv_projections() + self.fusing_transformer = False + + # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._prepare_rotary_positional_embeddings + def _prepare_rotary_positional_embeddings( + self, + height: int, + width: int, + num_frames: int, + device: torch.device, + ) -> Tuple[torch.Tensor, torch.Tensor]: + grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) + grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) + + p = self.transformer.config.patch_size + p_t = self.transformer.config.patch_size_t + + base_size_width = self.transformer.config.sample_width // p + base_size_height = self.transformer.config.sample_height // p + + if p_t is None: + # CogVideoX 1.0 + grid_crops_coords = get_resize_crop_region_for_grid( + (grid_height, grid_width), base_size_width, base_size_height + ) + freqs_cos, freqs_sin = get_3d_rotary_pos_embed( + embed_dim=self.transformer.config.attention_head_dim, + crops_coords=grid_crops_coords, + grid_size=(grid_height, grid_width), + temporal_size=num_frames, + device=device, + ) + else: + # CogVideoX 1.5 + base_num_frames = (num_frames + p_t - 1) // p_t + + freqs_cos, freqs_sin = get_3d_rotary_pos_embed( + embed_dim=self.transformer.config.attention_head_dim, + crops_coords=None, + grid_size=(grid_height, grid_width), + temporal_size=base_num_frames, + grid_type="slice", + max_size=(base_size_height, base_size_width), + device=device, + ) + + return freqs_cos, freqs_sin + + def prepare_lp( + self, + # --- Filter Selection & Strength --- + lp_filter_type: str, + lp_blur_sigma: float, + lp_blur_kernel_size: float, + lp_resize_factor: float, + # --- Contextual Info --- + generator: torch.Generator, + num_frames: int, + use_low_pass_guidance: bool, + lp_filter_in_latent: bool, + # --- Inputs to filter --- + orig_image_latents: torch.Tensor, # Shape [B, F_padded, C, H, W] + orig_image_tensor: torch.Tensor # Shape [B, C, H_orig, W_orig] (preprocessed RGB) + ) -> torch.Tensor | None: + """ + Prepares a low-pass filtered version of the initial image condition for guidance. (CogVideoX) + The resulting low-pass filtered latents are padded to match the required number of frames and temporal + patch size for the transformer model. + + Args: + lp_filter_type (`str`): The type of low-pass filter to apply, e.g., 'gaussian_blur', 'down_up'. + lp_blur_sigma (`float`): The sigma value for the Gaussian blur filter. + lp_blur_kernel_size (`float`): The kernel size for the Gaussian blur filter. + lp_resize_factor (`float`): The resizing factor for the 'down_up' filter. + generator (`torch.Generator`): A random generator, used for VAE sampling when filtering in image space. + num_frames (`int`): The target number of frames for the final video, used to determine padding. + use_low_pass_guidance (`bool`): If `False`, the function returns `None` immediately. + lp_filter_in_latent (`bool`): If `True`, filtering is applied in latent space. Otherwise, in image space. + orig_image_latents (`torch.Tensor`): The VAE-encoded latents of the original image. Used when + `lp_filter_in_latent` is `True`. Shape: `(batch_size, num_frames_padded, channels, height, width)`. + orig_image_tensor (`torch.Tensor`): The preprocessed original image tensor (RGB). Used when + `lp_filter_in_latent` is `False`. Shape: `(batch_size, channels, height, width)`. + + Returns: + `Optional[torch.Tensor]`: A tensor containing the low-pass filtered image latents, correctly shaped and + padded for the transformer, or `None` if `use_low_pass_guidance` is `False`. + """ + if not use_low_pass_guidance: + return None + + if not lp_filter_in_latent: + # --- Filter in Image (RGB) Space --- + + # 1. Apply the filter to the original 4D RGB tensor. + image_lp = lp_utils.apply_low_pass_filter( + orig_image_tensor, # Should be [B, C, H, W] + filter_type=lp_filter_type, + blur_sigma=lp_blur_sigma, + blur_kernel_size=lp_blur_kernel_size, + resize_factor=lp_resize_factor, + ) + # image_lp: [B, C, H, W] + + # 2. Add the frame dimension BEFORE encoding + image_lp_vae_input = image_lp.unsqueeze(2) # Shape: [B, C, 1, H, W] + + # 3. Encode the 5D tensor + encoded_lp = self.vae.encode(image_lp_vae_input).latent_dist.sample(generator=generator) + + if not self.vae.config.invert_scale_latents: + encoded_lp = self.vae_scaling_factor_image * encoded_lp + else: + encoded_lp = 1 / self.vae_scaling_factor_image * encoded_lp + + encoded_lp = encoded_lp.permute(0, 2, 1, 3, 4) + + # Calculate required latent frames based on output num_frames + padded_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 + + # Pad with zeros if needed + current_frames = encoded_lp.shape[1] # Should be 1 here + if padded_frames > current_frames: + batch_size, _, latent_channels, latent_height, latent_width = encoded_lp.shape + padding_shape = ( + batch_size, + padded_frames - current_frames, + latent_channels, + latent_height, + latent_width, + ) + lp_padding = torch.zeros(padding_shape, device=encoded_lp.device, dtype=encoded_lp.dtype) + lp_image_latents = torch.cat([encoded_lp, lp_padding], dim=1) + else: + lp_image_latents = encoded_lp[:, :padded_frames, ...] + + if self.transformer.config.patch_size_t is not None: + remainder = lp_image_latents.size(1) % self.transformer.config.patch_size_t + if remainder != 0: + num_to_prepend = self.transformer.config.patch_size_t - remainder + # Ensure num_to_prepend doesn't exceed available frames if F=1 initially + num_to_prepend = min(num_to_prepend, lp_image_latents.shape[1]) + first_frames_to_prepend = lp_image_latents[:, :num_to_prepend, ...] + lp_image_latents = torch.cat([first_frames_to_prepend, lp_image_latents], dim=1) + + else: + # --- Filter in Latent Space --- + orig_image_latents_perm = orig_image_latents.permute(0, 2, 1, 3, 4).contiguous() + lp_image_latents = lp_utils.apply_low_pass_filter( + orig_image_latents_perm, # Input has shape [B, C, F_padded, H, W] + filter_type=lp_filter_type, + blur_sigma=lp_blur_sigma, + blur_kernel_size=lp_blur_kernel_size, + resize_factor=lp_resize_factor, + ) + lp_image_latents = lp_image_latents.permute(0, 2, 1, 3, 4).contiguous() + if self.transformer.config.patch_size_t is not None: + remainder = lp_image_latents.size(1) % self.transformer.config.patch_size_t + if remainder != 0: + num_to_prepend = self.transformer.config.patch_size_t - remainder + num_to_prepend = min(num_to_prepend, lp_image_latents.shape[1]) + first_frames_to_prepend = lp_image_latents[:, :num_to_prepend, ...] + lp_image_latents = torch.cat([first_frames_to_prepend, lp_image_latents], dim=1) + + lp_image_latents = lp_image_latents.to(dtype=orig_image_latents.dtype) + + return lp_image_latents + + @property + def guidance_scale(self): + return self._guidance_scale + + @property + def num_timesteps(self): + return self._num_timesteps + + @property + def attention_kwargs(self): + return self._attention_kwargs + + @property + def current_timestep(self): + return self._current_timestep + + @property + def interrupt(self): + return self._interrupt + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + image: PipelineImageInput, + prompt: Optional[Union[str, List[str]]] = None, + negative_prompt: Optional[Union[str, List[str]]] = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_frames: int = 49, + num_inference_steps: int = 50, + timesteps: Optional[List[int]] = None, + guidance_scale: float = 6.0, + use_dynamic_cfg: bool = False, + num_videos_per_prompt: int = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: str = "pil", + return_dict: bool = True, + attention_kwargs: Optional[Dict[str, Any]] = None, + callback_on_step_end: Optional[ + Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] + ] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + max_sequence_length: int = 226, + use_low_pass_guidance: bool = False, + lp_filter_type: str = "none", # {'gaussian_blur', 'down_up'} + lp_filter_in_latent: bool = False, # When set to True, low-pass filter is done after encoder. If False, low-pass filter is applied to image directly before encoder. + lp_blur_sigma: float = 15.0, # Used with 'gaussian_blur'. Gaussian filter sigma value. + lp_blur_kernel_size: float = 0.02734375, # Used with 'gaussian_blur'. Gaussian filter size. When set to int, used directly as kernel size. When set to float, H * `lp_blur_kernel_size` is used as kernel size. + lp_resize_factor: float = 0.25, # Used with 'down_up'. Image is bilinearly downsized to (`lp_resize_factor` * WIDTH, `lp_resize_factor` * HEIGHT) and then back to original. + + lp_strength_schedule_type: str = "none", # Scheduling type for low-pass filtering strength. Options: {"none", "linear", "interval", "exponential"} + schedule_blur_kernel_size: bool = False, # If True, schedule blur kernel size as well. Otherwise, fix to initial value. + + # --- Constant Interval Scheduling Params for LP Strength --- + schedule_interval_start_time: float = 0.0, # Starting timestep for interval scheduling + schedule_interval_end_time: float = 0.05, # Ending timestep for interval scheduling + + # --- Linear Scheduling Params for LP Strength --- + schedule_linear_start_weight: float = 1.0, # Starting LP weight for linear scheduling at t=T (step 0) + schedule_linear_end_weight: float = 0.0, # Ending LP weight for linear scheduling at t=T * schedule_linear_end_time + schedule_linear_end_time: float = 0.5, # Timestep fraction at which schedule_linear_end is reached + + # --- Exponential Scheduling Params for LP Strength --- + schedule_exp_decay_rate: float = 10.0, # Decay rate for 'exponential' schedule. Higher values decay faster. Strength = exp(-rate * time_fraction). + ) -> Union[CogVideoXPipelineOutput, Tuple]: + """ + Function invoked when calling the pipeline for generation. + + Args: + image (`PipelineImageInput`): + The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`. + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + height (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial): + The height in pixels of the generated image. This is set to 480 by default for the best results. + width (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial): + The width in pixels of the generated image. This is set to 720 by default for the best results. + num_frames (`int`, defaults to `48`): + Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will + contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where + num_seconds is 6 and fps is 8. However, since videos can be saved at any fps, the only condition that + needs to be satisfied is that of divisibility mentioned above. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + timesteps (`List[int]`, *optional*): + Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument + in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is + passed will be used. Must be in descending order. + guidance_scale (`float`, *optional*, defaults to 7.0): + Guidance scale as defined in [Classifier-Free Diffusion + Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. + of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting + `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to + the text `prompt`, usually at the expense of lower image quality. + num_videos_per_prompt (`int`, *optional*, defaults to 1): + The number of videos to generate per prompt. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead + of a plain tuple. + attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, + callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by + `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeline class. + max_sequence_length (`int`, defaults to `226`): + Maximum sequence length in encoded prompt. Must be consistent with + `self.transformer.config.max_text_seq_length` otherwise may lead to poor results. + use_low_pass_guidance (`bool`, *optional*, defaults to `False`): + Whether to use low-pass guidance. This can help to improve the temporal consistency of the generated + video. + lp_filter_type (`str`, *optional*, defaults to `"none"`): + The type of low-pass filter to apply. Can be one of `gaussian_blur` or `down_up`. + lp_filter_in_latent (`bool`, *optional*, defaults to `False`): + If `True`, the low-pass filter is applied to the latent representation of the image. If `False`, it is + applied to the image in pixel space before encoding. + lp_blur_sigma (`float`, *optional*, defaults to `15.0`): + The sigma value for the Gaussian blur filter. Only used if `lp_filter_type` is `gaussian_blur`. + lp_blur_kernel_size (`float`, *optional*, defaults to `0.02734375`): + The kernel size for the Gaussian blur filter. If an `int`, it's used directly. If a `float`, the kernel + size is calculated as `height * lp_blur_kernel_size`. Only used if `lp_filter_type` is `gaussian_blur`. + lp_resize_factor (`float`, *optional*, defaults to `0.25`): + The resize factor for the down-sampling and up-sampling filter. Only used if `lp_filter_type` is + `down_up`. + lp_strength_schedule_type (`str`, *optional*, defaults to `"none"`): + The scheduling type for the low-pass filter strength. Can be one of `none`, `linear`, `interval`, or + `exponential`. + schedule_blur_kernel_size (`bool`, *optional*, defaults to `False`): + If `True`, the blur kernel size is also scheduled along with the strength. Otherwise, it remains fixed. + schedule_interval_start_time (`float`, *optional*, defaults to `0.0`): + The starting timestep fraction for interval scheduling. Only used if `lp_strength_schedule_type` is + `interval`. + schedule_interval_end_time (`float`, *optional*, defaults to `0.05`): + The ending timestep fraction for interval scheduling. Only used if `lp_strength_schedule_type` is + `interval`. + schedule_linear_start_weight (`float`, *optional*, defaults to `1.0`): + The starting weight for the low-pass filter strength in a linear schedule. Corresponds to the first + timestep. Only used if `lp_strength_schedule_type` is `linear`. + schedule_linear_end_weight (`float`, *optional*, defaults to `0.0`): + The ending weight for the low-pass filter strength in a linear schedule. Only used if + `lp_strength_schedule_type` is `linear`. + schedule_linear_end_time (`float`, *optional*, defaults to `0.5`): + The timestep fraction at which `schedule_linear_end_weight` is reached in a linear schedule. Only used + if `lp_strength_schedule_type` is `linear`. + schedule_exp_decay_rate (`float`, *optional*, defaults to `10.0`): + The decay rate for the exponential schedule. Higher values lead to faster decay. Only used if + `lp_strength_schedule_type` is `exponential`. + + Examples: + + Returns: + [`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] or `tuple`: + [`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a + `tuple`. When returning a tuple, the first element is a list with the generated images. + """ + + if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + + height = height or self.transformer.config.sample_height * self.vae_scale_factor_spatial + width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial + num_frames = num_frames or self.transformer.config.sample_frames + + num_videos_per_prompt = 1 + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + image=image, + prompt=prompt, + height=height, + width=width, + negative_prompt=negative_prompt, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + latents=latents, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + ) + self._guidance_scale = guidance_scale + self._current_timestep = None + self._attention_kwargs = attention_kwargs + self._interrupt = False + + # 2. Default call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + prompt_embeds, negative_prompt_embeds = self.encode_prompt( + prompt=prompt, + negative_prompt=negative_prompt, + do_classifier_free_guidance=do_classifier_free_guidance, + num_videos_per_prompt=num_videos_per_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + max_sequence_length=max_sequence_length, + device=device, + ) + if do_classifier_free_guidance and use_low_pass_guidance: + prompt_embeds_orig = prompt_embeds + prompt_embeds = torch.cat([negative_prompt_embeds, negative_prompt_embeds, prompt_embeds_orig], dim=0) + prompt_embeds_init = torch.cat([negative_prompt_embeds, prompt_embeds_orig], dim=0) + elif do_classifier_free_guidance: + prompt_embeds_orig = prompt_embeds + prompt_embeds_init = torch.cat([negative_prompt_embeds, prompt_embeds_orig], dim=0) + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds_orig], dim=0) + + # 4. Prepare timesteps + timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) + self._num_timesteps = len(timesteps) + + # 5. Prepare latents + latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 + # For CogVideoX 1.5, the latent frames should be padded to make it divisible by patch_size_t + patch_size_t = self.transformer.config.patch_size_t + additional_frames = 0 + if patch_size_t is not None and latent_frames % patch_size_t != 0: + additional_frames = patch_size_t - latent_frames % patch_size_t + num_frames += additional_frames * self.vae_scale_factor_temporal + image_tensor = self.video_processor.preprocess(image, height=height, width=width).to( + device, dtype=prompt_embeds.dtype + ) + + latent_channels = self.transformer.config.in_channels // 2 + latents, image_latents = self.prepare_latents( + image_tensor, + batch_size * num_videos_per_prompt, + latent_channels, + num_frames, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 7. Create rotary embeds if required + image_rotary_emb = ( + self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device) + if self.transformer.config.use_rotary_positional_embeddings + else None + ) + + # 8. Create ofs embeds if required + ofs_emb = None if self.transformer.config.ofs_embed_dim is None else latents.new_full((1,), fill_value=2.0) + + # 9. Denoising loop + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + + with self.progress_bar(total=num_inference_steps) as progress_bar: + old_pred_original_sample = None + for i, t in enumerate(timesteps): + if self.interrupt: + continue + + self._current_timestep = t + + if not use_low_pass_guidance: + two_pass = True + + # Low-pass version input + if do_classifier_free_guidance and use_low_pass_guidance: + # Timestep scheduled low-pass filter strength ([0, 1] range) + lp_strength = lp_utils.get_lp_strength( + step_index=i, + total_steps=num_inference_steps, + lp_strength_schedule_type=lp_strength_schedule_type, + schedule_interval_start_time=schedule_interval_start_time, + schedule_interval_end_time=schedule_interval_end_time, + schedule_linear_start_weight=schedule_linear_start_weight, + schedule_linear_end_weight=schedule_linear_end_weight, + schedule_linear_end_time=schedule_linear_end_time, + schedule_exp_decay_rate=schedule_exp_decay_rate, + ) + + two_pass = (lp_strength == 0 or not use_low_pass_guidance) + + if lp_strength_schedule_type == 'exponential' and lp_strength < 0.1: # Rounding for exponential (for performance) + two_pass = True + + modulated_lp_blur_sigma = lp_blur_sigma * lp_strength + if schedule_blur_kernel_size: + modulated_lp_blur_kernel_size = lp_blur_kernel_size * lp_strength # Kernel size also scales down + else: + modulated_lp_blur_kernel_size = lp_blur_kernel_size + + modulated_lp_resize_factor = 1.0 - (1.0 - lp_resize_factor) * lp_strength + + # low-pass filter + lp_image_latents = self.prepare_lp( + # --- Filter Selection & Strength (Modulated) --- + lp_filter_type=lp_filter_type, + lp_blur_sigma=modulated_lp_blur_sigma, + lp_blur_kernel_size=modulated_lp_blur_kernel_size, + lp_resize_factor=modulated_lp_resize_factor, + # --- Contextual Info --- + generator=generator, + num_frames=num_frames, + use_low_pass_guidance=use_low_pass_guidance, + lp_filter_in_latent=lp_filter_in_latent, + # --- Inputs to filter --- + orig_image_latents=image_latents, + orig_image_tensor=image_tensor + ) + + # latent_model_input = torch.cat([latents] * 2) + if two_pass: + latent_model_input = torch.cat([latents] * 2) + else: + latent_model_input = torch.cat([latents] * 3) + + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + # latent_model_input = torch.cat([latent_model_input, torch.cat([lp_image_latents] * 2, dim=0)], dim=2) + if two_pass: + latent_model_input = torch.cat([latent_model_input, torch.cat([lp_image_latents] * 2, dim=0)], dim=2) + else: + latent_model_input = torch.cat([latent_model_input, torch.cat([image_latents,lp_image_latents,lp_image_latents], dim=0)], dim=2) + + elif do_classifier_free_guidance: + latent_model_input = torch.cat([latents] * 2) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + latent_model_input = torch.cat([latent_model_input, torch.cat([image_latents] * 2, dim=0)], dim=2) + else: + latent_model_input = latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + latent_model_input = torch.cat([latent_model_input, image_latents], dim=2) + + timestep = t.expand(latent_model_input.shape[0]) + noise_pred = self.transformer( + hidden_states=latent_model_input, + encoder_hidden_states=prompt_embeds_init if two_pass else prompt_embeds, + timestep=timestep, + ofs=ofs_emb, + image_rotary_emb=image_rotary_emb, + attention_kwargs=attention_kwargs, + return_dict=False, + )[0] + noise_pred = noise_pred.float() + + # 12. Combine noise predictions with scheduled weights (triple pass) + if use_low_pass_guidance and do_classifier_free_guidance: + if two_pass: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + else: + noise_pred_uncond_init, noise_pred_uncond, noise_pred_text = noise_pred.chunk(3) + noise_pred = ( + noise_pred_uncond_init + guidance_scale * (noise_pred_text - noise_pred_uncond) + ) + elif do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + if use_dynamic_cfg: + self._guidance_scale = 1 + guidance_scale * ( + (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2 + ) + noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) + # compute the previous noisy sample x_t -> x_t-1 + if not isinstance(self.scheduler, CogVideoXDPMScheduler): + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + else: + latents, old_pred_original_sample = self.scheduler.step( + noise_pred, + old_pred_original_sample, + t, + timesteps[i - 1] if i > 0 else None, + latents, + **extra_step_kwargs, + return_dict=False, + ) + latents = latents.to(prompt_embeds.dtype) + + # call the callback, if provided + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) + + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + + if XLA_AVAILABLE: + xm.mark_step() + + self._current_timestep = None + + if not output_type == "latent": + # Discard any padding frames that were added for CogVideoX 1.5 + latents = latents[:, additional_frames:] + video = self.decode_latents(latents) + video = self.video_processor.postprocess_video(video=video, output_type=output_type) + else: + video = latents + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (video,) + + return CogVideoXPipelineOutput(frames=video) diff --git a/exp_code/1_benchmark/ALG/pipeline_hunyuan_video_image2video_lowpass.py b/exp_code/1_benchmark/ALG/pipeline_hunyuan_video_image2video_lowpass.py new file mode 100644 index 0000000000000000000000000000000000000000..79501a0febed8511fe3b1dae0516e086b4d6bb00 --- /dev/null +++ b/exp_code/1_benchmark/ALG/pipeline_hunyuan_video_image2video_lowpass.py @@ -0,0 +1,1308 @@ +# Copyright 2024 The HunyuanVideo Team and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import numpy as np +import PIL.Image +import torch +from transformers import ( + CLIPImageProcessor, + CLIPTextModel, + CLIPTokenizer, + LlamaTokenizerFast, + LlavaForConditionalGeneration, +) + +from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback +from diffusers.loaders import HunyuanVideoLoraLoaderMixin +from diffusers.models import AutoencoderKLHunyuanVideo, HunyuanVideoTransformer3DModel +from diffusers.schedulers import FlowMatchEulerDiscreteScheduler +from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring +from diffusers.utils.torch_utils import randn_tensor +from diffusers.video_processor import VideoProcessor +from diffusers.pipelines.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.hunyuan_video.pipeline_output import HunyuanVideoPipelineOutput +import math +import torchvision.transforms.functional as tvF +import torch.nn.functional as F + +import lp_utils + +if is_torch_xla_available(): + import torch_xla.core.xla_model as xm + + XLA_AVAILABLE = True +else: + XLA_AVAILABLE = False + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +EXAMPLE_DOC_STRING = """ + Examples: + ```python + >>> import torch + >>> from diffusers import HunyuanVideoImageToVideoPipeline, HunyuanVideoTransformer3DModel + >>> from diffusers.utils import load_image, export_to_video + + >>> # Available checkpoints: hunyuanvideo-community/HunyuanVideo-I2V, hunyuanvideo-community/HunyuanVideo-I2V-33ch + >>> model_id = "hunyuanvideo-community/HunyuanVideo-I2V" + >>> transformer = HunyuanVideoTransformer3DModel.from_pretrained( + ... model_id, subfolder="transformer", torch_dtype=torch.bfloat16 + ... ) + >>> pipe = HunyuanVideoImageToVideoPipeline.from_pretrained( + ... model_id, transformer=transformer, torch_dtype=torch.float16 + ... ) + >>> pipe.vae.enable_tiling() + >>> pipe.to("cuda") + + >>> prompt = "A man with short gray hair plays a red electric guitar." + >>> image = load_image( + ... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" + ... ) + + >>> # If using hunyuanvideo-community/HunyuanVideo-I2V + >>> output = pipe(image=image, prompt=prompt, guidance_scale=6.0).frames[0] + + >>> # If using hunyuanvideo-community/HunyuanVideo-I2V-33ch + >>> output = pipe(image=image, prompt=prompt, guidance_scale=1.0, true_cfg_scale=1.0).frames[0] + + >>> export_to_video(output, "output.mp4", fps=15) + ``` +""" + + +DEFAULT_PROMPT_TEMPLATE = { + "template": ( + "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the video by detailing the following aspects according to the reference image: " + "1. The main content and theme of the video." + "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." + "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." + "4. background environment, light, style and atmosphere." + "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" + "<|start_header_id|>assistant<|end_header_id|>\n\n" + ), + "crop_start": 103, + "image_emb_start": 5, + "image_emb_end": 581, + "image_emb_len": 576, + "double_return_token_id": 271, +} + + +def _expand_input_ids_with_image_tokens( + text_input_ids, + prompt_attention_mask, + max_sequence_length, + image_token_index, + image_emb_len, + image_emb_start, + image_emb_end, + pad_token_id, +): + special_image_token_mask = text_input_ids == image_token_index + num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1) + batch_indices, non_image_indices = torch.where(text_input_ids != image_token_index) + + max_expanded_length = max_sequence_length + (num_special_image_tokens.max() * (image_emb_len - 1)) + new_token_positions = torch.cumsum((special_image_token_mask * (image_emb_len - 1) + 1), -1) - 1 + text_to_overwrite = new_token_positions[batch_indices, non_image_indices] + + expanded_input_ids = torch.full( + (text_input_ids.shape[0], max_expanded_length), + pad_token_id, + dtype=text_input_ids.dtype, + device=text_input_ids.device, + ) + expanded_input_ids[batch_indices, text_to_overwrite] = text_input_ids[batch_indices, non_image_indices] + expanded_input_ids[batch_indices, image_emb_start:image_emb_end] = image_token_index + + expanded_attention_mask = torch.zeros( + (text_input_ids.shape[0], max_expanded_length), + dtype=prompt_attention_mask.dtype, + device=prompt_attention_mask.device, + ) + attn_batch_indices, attention_indices = torch.where(expanded_input_ids != pad_token_id) + expanded_attention_mask[attn_batch_indices, attention_indices] = 1.0 + expanded_attention_mask = expanded_attention_mask.to(prompt_attention_mask.dtype) + position_ids = (expanded_attention_mask.cumsum(-1) - 1).masked_fill_((expanded_attention_mask == 0), 1) + + return { + "input_ids": expanded_input_ids, + "attention_mask": expanded_attention_mask, + "position_ids": position_ids, + } + + + +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +): + r""" + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") + if timesteps is not None: + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +def retrieve_latents( + encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" +): + if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": + return encoder_output.latent_dist.sample(generator) + elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": + return encoder_output.latent_dist.mode() + elif hasattr(encoder_output, "latents"): + return encoder_output.latents + else: + raise AttributeError("Could not access latents of provided encoder_output") + + +class HunyuanVideoImageToVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): + r""" + Pipeline for image-to-video generation using HunyuanVideo. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods + implemented for all pipelines (downloading, saving, running on a particular device, etc.). + + Args: + text_encoder ([`LlavaForConditionalGeneration`]): + [Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers). + tokenizer (`LlamaTokenizer`): + Tokenizer from [Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers). + transformer ([`HunyuanVideoTransformer3DModel`]): + Conditional Transformer to denoise the encoded image latents. + scheduler ([`FlowMatchEulerDiscreteScheduler`]): + A scheduler to be used in combination with `transformer` to denoise the encoded image latents. + vae ([`AutoencoderKLHunyuanVideo`]): + Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations. + text_encoder_2 ([`CLIPTextModel`]): + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically + the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. + tokenizer_2 (`CLIPTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer). + """ + + model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae" + _callback_tensor_inputs = ["latents", "prompt_embeds"] + + def __init__( + self, + text_encoder: LlavaForConditionalGeneration, + tokenizer: LlamaTokenizerFast, + transformer: HunyuanVideoTransformer3DModel, + vae: AutoencoderKLHunyuanVideo, + scheduler: FlowMatchEulerDiscreteScheduler, + text_encoder_2: CLIPTextModel, + tokenizer_2: CLIPTokenizer, + image_processor: CLIPImageProcessor, + ): + super().__init__() + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + transformer=transformer, + scheduler=scheduler, + text_encoder_2=text_encoder_2, + tokenizer_2=tokenizer_2, + image_processor=image_processor, + ) + + self.vae_scaling_factor = self.vae.config.scaling_factor if getattr(self, "vae", None) else 0.476986 + self.vae_scale_factor_temporal = self.vae.temporal_compression_ratio if getattr(self, "vae", None) else 4 + self.vae_scale_factor_spatial = self.vae.spatial_compression_ratio if getattr(self, "vae", None) else 8 + self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) + + def _get_llama_prompt_embeds( + self, + image: torch.Tensor, + prompt: Union[str, List[str]], + prompt_template: Dict[str, Any], + num_videos_per_prompt: int = 1, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + max_sequence_length: int = 256, + num_hidden_layers_to_skip: int = 2, + image_embed_interleave: int = 2, + ) -> Tuple[torch.Tensor, torch.Tensor]: + device = device or self._execution_device + dtype = dtype or self.text_encoder.dtype + + prompt = [prompt] if isinstance(prompt, str) else prompt + prompt = [prompt_template["template"].format(p) for p in prompt] + + crop_start = prompt_template.get("crop_start", None) + + image_emb_len = prompt_template.get("image_emb_len", 576) + image_emb_start = prompt_template.get("image_emb_start", 5) + image_emb_end = prompt_template.get("image_emb_end", 581) + double_return_token_id = prompt_template.get("double_return_token_id", 271) + + if crop_start is None: + prompt_template_input = self.tokenizer( + prompt_template["template"], + padding="max_length", + return_tensors="pt", + return_length=False, + return_overflowing_tokens=False, + return_attention_mask=False, + ) + crop_start = prompt_template_input["input_ids"].shape[-1] + # Remove <|start_header_id|>, <|end_header_id|>, assistant, <|eot_id|>, and placeholder {} + crop_start -= 5 + + max_sequence_length += crop_start + text_inputs = self.tokenizer( + prompt, + max_length=max_sequence_length, + padding="max_length", + truncation=True, + return_tensors="pt", + return_length=False, + return_overflowing_tokens=False, + return_attention_mask=True, + ) + text_input_ids = text_inputs.input_ids.to(device=device) + prompt_attention_mask = text_inputs.attention_mask.to(device=device) + + image_embeds = self.image_processor(image, return_tensors="pt").pixel_values.to(device) + + image_token_index = self.text_encoder.config.image_token_index + pad_token_id = self.text_encoder.config.pad_token_id + expanded_inputs = _expand_input_ids_with_image_tokens( + text_input_ids, + prompt_attention_mask, + max_sequence_length, + image_token_index, + image_emb_len, + image_emb_start, + image_emb_end, + pad_token_id, + ) + prompt_embeds = self.text_encoder( + **expanded_inputs, + pixel_values=image_embeds, + output_hidden_states=True, + ).hidden_states[-(num_hidden_layers_to_skip + 1)] + prompt_embeds = prompt_embeds.to(dtype=dtype) + + if crop_start is not None and crop_start > 0: + text_crop_start = crop_start - 1 + image_emb_len + batch_indices, last_double_return_token_indices = torch.where(text_input_ids == double_return_token_id) + + if last_double_return_token_indices.shape[0] == 3: + # in case the prompt is too long + last_double_return_token_indices = torch.cat( + (last_double_return_token_indices, torch.tensor([text_input_ids.shape[-1]])) + ) + batch_indices = torch.cat((batch_indices, torch.tensor([0]))) + + last_double_return_token_indices = last_double_return_token_indices.reshape(text_input_ids.shape[0], -1)[ + :, -1 + ] + batch_indices = batch_indices.reshape(text_input_ids.shape[0], -1)[:, -1] + assistant_crop_start = last_double_return_token_indices - 1 + image_emb_len - 4 + assistant_crop_end = last_double_return_token_indices - 1 + image_emb_len + attention_mask_assistant_crop_start = last_double_return_token_indices - 4 + attention_mask_assistant_crop_end = last_double_return_token_indices + + prompt_embed_list = [] + prompt_attention_mask_list = [] + image_embed_list = [] + image_attention_mask_list = [] + + for i in range(text_input_ids.shape[0]): + prompt_embed_list.append( + torch.cat( + [ + prompt_embeds[i, text_crop_start : assistant_crop_start[i].item()], + prompt_embeds[i, assistant_crop_end[i].item() :], + ] + ) + ) + prompt_attention_mask_list.append( + torch.cat( + [ + prompt_attention_mask[i, crop_start : attention_mask_assistant_crop_start[i].item()], + prompt_attention_mask[i, attention_mask_assistant_crop_end[i].item() :], + ] + ) + ) + image_embed_list.append(prompt_embeds[i, image_emb_start:image_emb_end]) + image_attention_mask_list.append( + torch.ones(image_embed_list[-1].shape[0]).to(prompt_embeds.device).to(prompt_attention_mask.dtype) + ) + + prompt_embed_list = torch.stack(prompt_embed_list) + prompt_attention_mask_list = torch.stack(prompt_attention_mask_list) + image_embed_list = torch.stack(image_embed_list) + image_attention_mask_list = torch.stack(image_attention_mask_list) + + if 0 < image_embed_interleave < 6: + image_embed_list = image_embed_list[:, ::image_embed_interleave, :] + image_attention_mask_list = image_attention_mask_list[:, ::image_embed_interleave] + + assert ( + prompt_embed_list.shape[0] == prompt_attention_mask_list.shape[0] + and image_embed_list.shape[0] == image_attention_mask_list.shape[0] + ) + + prompt_embeds = torch.cat([image_embed_list, prompt_embed_list], dim=1) + prompt_attention_mask = torch.cat([image_attention_mask_list, prompt_attention_mask_list], dim=1) + + return prompt_embeds, prompt_attention_mask + + def _get_clip_prompt_embeds( + self, + prompt: Union[str, List[str]], + num_videos_per_prompt: int = 1, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + max_sequence_length: int = 77, + ) -> torch.Tensor: + device = device or self._execution_device + dtype = dtype or self.text_encoder_2.dtype + + prompt = [prompt] if isinstance(prompt, str) else prompt + + text_inputs = self.tokenizer_2( + prompt, + padding="max_length", + max_length=max_sequence_length, + truncation=True, + return_tensors="pt", + ) + + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer_2(prompt, padding="longest", return_tensors="pt").input_ids + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer_2.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {max_sequence_length} tokens: {removed_text}" + ) + + prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False).pooler_output + return prompt_embeds + + def encode_prompt( + self, + image: torch.Tensor, + prompt: Union[str, List[str]], + prompt_2: Union[str, List[str]] = None, + prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE, + num_videos_per_prompt: int = 1, + prompt_embeds: Optional[torch.Tensor] = None, + pooled_prompt_embeds: Optional[torch.Tensor] = None, + prompt_attention_mask: Optional[torch.Tensor] = None, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + max_sequence_length: int = 256, + image_embed_interleave: int = 2, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if prompt_embeds is None: + prompt_embeds, prompt_attention_mask = self._get_llama_prompt_embeds( + image, + prompt, + prompt_template, + num_videos_per_prompt, + device=device, + dtype=dtype, + max_sequence_length=max_sequence_length, + image_embed_interleave=image_embed_interleave, + ) + + if pooled_prompt_embeds is None: + if prompt_2 is None: + prompt_2 = prompt + pooled_prompt_embeds = self._get_clip_prompt_embeds( + prompt, + num_videos_per_prompt, + device=device, + dtype=dtype, + max_sequence_length=77, + ) + + return prompt_embeds, pooled_prompt_embeds, prompt_attention_mask + + def check_inputs( + self, + prompt, + prompt_2, + height, + width, + prompt_embeds=None, + callback_on_step_end_tensor_inputs=None, + prompt_template=None, + true_cfg_scale=1.0, + guidance_scale=1.0, + ): + if height % 16 != 0 or width % 16 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.") + + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt_2 is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): + raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") + + if prompt_template is not None: + if not isinstance(prompt_template, dict): + raise ValueError(f"`prompt_template` has to be of type `dict` but is {type(prompt_template)}") + if "template" not in prompt_template: + raise ValueError( + f"`prompt_template` has to contain a key `template` but only found {prompt_template.keys()}" + ) + + if true_cfg_scale > 1.0 and guidance_scale > 1.0: + logger.warning( + "Both `true_cfg_scale` and `guidance_scale` are greater than 1.0. This will result in both " + "classifier-free guidance and embedded-guidance to be applied. This is not recommended " + "as it may lead to higher memory usage, slower inference and potentially worse results." + ) + + def prepare_latents( + self, + image: torch.Tensor, + batch_size: int, + num_channels_latents: int = 32, + height: int = 720, + width: int = 1280, + num_frames: int = 129, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + image_condition_type: str = "latent_concat", + i2v_stable: bool = False, + ) -> torch.Tensor: + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 + latent_height, latent_width = height // self.vae_scale_factor_spatial, width // self.vae_scale_factor_spatial + shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width) + + image = image.unsqueeze(2) # [B, C, 1, H, W] + if isinstance(generator, list): + image_latents = [ + retrieve_latents(self.vae.encode(image[i].unsqueeze(0)), generator[i], "argmax") + for i in range(batch_size) + ] + else: + image_latents = [retrieve_latents(self.vae.encode(img.unsqueeze(0)), generator, "argmax") for img in image] + + image_latents = torch.cat(image_latents, dim=0).to(dtype) * self.vae_scaling_factor + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device=device, dtype=dtype) + + if i2v_stable: + image_latents = image_latents.repeat(1, 1, num_latent_frames, 1, 1) + t = torch.tensor([0.999]).to(device=device) + latents = latents * t + image_latents * (1 - t) + + if image_condition_type == "token_replace": + image_latents = image_latents[:, :, :1] + + return latents, image_latents + + def enable_vae_slicing(self): + r""" + Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to + compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. + """ + self.vae.enable_slicing() + + def disable_vae_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + def enable_vae_tiling(self): + r""" + Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to + compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow + processing larger images. + """ + self.vae.enable_tiling() + + def disable_vae_tiling(self): + r""" + Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to + computing decoding in one step. + """ + self.vae.disable_tiling() + + @property + def guidance_scale(self): + return self._guidance_scale + + @property + def num_timesteps(self): + return self._num_timesteps + + @property + def attention_kwargs(self): + return self._attention_kwargs + + @property + def current_timestep(self): + return self._current_timestep + + @property + def interrupt(self): + return self._interrupt + + def prepare_lp( + self, + # --- Filter Selection & Strength --- + lp_filter_type: str, + lp_blur_sigma: float, + lp_blur_kernel_size: float, + lp_resize_factor: float, + # --- Contextual Info --- + generator: torch.Generator, + num_frames: int, + use_low_pass_guidance: bool, + lp_filter_in_latent: bool, + # --- Inputs to filter --- + orig_image_latents: torch.Tensor, + orig_image_tensor: torch.Tensor, + last_image: Optional[torch.Tensor] = None, + ) -> Optional[torch.Tensor]: + """ + Prepares a low-pass filtered version of the initial image condition for guidance. (HunyuanVideo) + + This function works in two modes: + 1. **Filtering in Image (RGB) Space (`lp_filter_in_latent=False`)**: + It applies a low-pass filter to the source image, constructs a video tensor (e.g., first frame is + the filtered image, last frame is an optionally provided filtered `last_image`, and the rest are zeros), + encodes this video tensor with the VAE, normalizes the result, and finally prepends a temporal mask + to create a condition tensor in the format expected by the transformer (`[mask, latents]`). + 2. **Filtering in Latent Space (`lp_filter_in_latent=True`)**: + Directly applies the low-pass filter to the already-encoded `orig_image_latents`. + + Args: + lp_filter_type (`str`): The type of low-pass filter to apply, e.g., 'gaussian_blur', 'down_up'. + lp_blur_sigma (`float`): The sigma value for the Gaussian blur filter. + lp_blur_kernel_size (`float`): The kernel size for the Gaussian blur filter. + lp_resize_factor (`float`): The resizing factor for the 'down_up' filter. + generator (`torch.Generator`): A random generator, used for VAE sampling when filtering in image space. + num_frames (`int`): The target number of frames for the video condition tensor. + use_low_pass_guidance (`bool`): If `False`, the function returns `None` immediately. + lp_filter_in_latent (`bool`): If `True`, filtering is applied in latent space. Otherwise, in image space. + orig_image_latents (`torch.Tensor`): The VAE-encoded latents of the original image. Used when + `lp_filter_in_latent` is `True`. + orig_image_tensor (`torch.Tensor`): The preprocessed original image tensor (RGB). Used when + `lp_filter_in_latent` is `False`. + last_image (`Optional[torch.Tensor]`, defaults to `None`): + An optional image tensor for the last frame. If provided (and when filtering in image space), it will + also be low-pass filtered and used as the last frame of the VAE input. + + Returns: + `Optional[torch.Tensor]`: A tensor containing the low-pass filtered image condition ready for the + transformer, or `None` if `use_low_pass_guidance` is `False`. + """ + if not use_low_pass_guidance: + return None + + if not lp_filter_in_latent: + # --- Filter in Image (RGB) Space --- + # 1. Apply the low-pass filter to the source image(s). + image_lp = lp_utils.apply_low_pass_filter( + orig_image_tensor, + filter_type=lp_filter_type, + blur_sigma=lp_blur_sigma, + blur_kernel_size=lp_blur_kernel_size, + resize_factor=lp_resize_factor, + ) + image_lp_vae_input = image_lp.unsqueeze(2) + + batch_size,_,height,width = orig_image_tensor.shape + latent_height = height // self.vae_scale_factor_spatial + latent_width = width // self.vae_scale_factor_spatial + + # 2. Construct a video tensor to be encoded. This tensor has the filtered image as the first frame. + # If a `last_image` is given, it's also filtered and placed at the end. Intermediate frames are black. + if last_image is None: + video_condition = torch.cat( + [image_lp_vae_input, image_lp_vae_input.new_zeros(image_lp_vae_input.shape[0], image_lp_vae_input.shape[1], num_frames - 1, height, width)], dim=2 + ) + else: + + last_image_lp = lp_utils.apply_low_pass_filter( + last_image, + filter_type=lp_filter_type, + blur_sigma=lp_blur_sigma, + blur_kernel_size=lp_blur_kernel_size, + resize_factor=lp_resize_factor, + ) + + last_image_lp = last_image_lp.unsqueeze(2) + video_condition = torch.cat( + [image_lp_vae_input, image_lp_vae_input.new_zeros(image_lp_vae_input.shape[0], image_lp_vae_input.shape[1], num_frames - 2, height, width), last_image_lp], + dim=2, + ) + # 3. Encode the constructed video tensor and normalize the resulting latents. + latents_mean = ( + torch.tensor(self.vae.config.latents_mean) + .view(1, self.vae.config.z_dim, 1, 1, 1) + .to(image_lp.device, image_lp.dtype) + ) + latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to( + image_lp.device, image_lp.dtype + ) + encoded_lp = self.vae.encode(video_condition).latent_dist.sample(generator=generator) + latent_condition = (encoded_lp - latents_mean) * latents_std + + # 4. Create a temporal mask. The transformer condition is `[mask, latents]`. + # The mask is 1 for conditioned frames (first, and optionally last) and 0 for unconditioned frames. + mask_lat_size = torch.ones(batch_size, 1, num_frames, latent_height, latent_width) + + if last_image is None: + mask_lat_size[:, :, list(range(1, num_frames))] = 0 + else: + mask_lat_size[:, :, list(range(1, num_frames - 1))] = 0 + first_frame_mask = mask_lat_size[:, :, 0:1] + first_frame_mask = torch.repeat_interleave(first_frame_mask, dim=2, repeats=self.vae_scale_factor_temporal) + mask_lat_size = torch.concat([first_frame_mask, mask_lat_size[:, :, 1:, :]], dim=2) + mask_lat_size = mask_lat_size.view(batch_size, -1, self.vae_scale_factor_temporal, latent_height, latent_width) + mask_lat_size = mask_lat_size.transpose(1, 2) + mask_lat_size = mask_lat_size.to(latent_condition.device) + + # 5. Concatenate the mask and the normalized latents along the channel dimension. + lp_image_latents = torch.concat([mask_lat_size, latent_condition], dim=1) + + else: + # --- Filter Directly in Latent Space --- + # This path assumes `orig_image_latents` is already prepared and just needs filtering. + lp_image_latents = lp_utils.apply_low_pass_filter( + orig_image_latents, + filter_type=lp_filter_type, + blur_sigma=lp_blur_sigma, + blur_kernel_size=lp_blur_kernel_size, + resize_factor=lp_resize_factor, + ) + + if self.transformer.config.patch_size is not None: + remainder = lp_image_latents.size(1) % self.transformer.config.patch_size + if remainder != 0: + num_to_prepend = self.transformer.config.patch_size - remainder + num_to_prepend = min(num_to_prepend, lp_image_latents.shape[1]) + first_frames_to_prepend = lp_image_latents[:, :num_to_prepend, ...] + lp_image_latents = torch.cat([first_frames_to_prepend, lp_image_latents], dim=1) + + + lp_image_latents = lp_image_latents.to(dtype=orig_image_latents.dtype) + + return lp_image_latents + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + image: PIL.Image.Image, + prompt: Union[str, List[str]] = None, + prompt_2: Union[str, List[str]] = None, + negative_prompt: Union[str, List[str]] = "bad quality", + negative_prompt_2: Union[str, List[str]] = None, + height: int = 720, + width: int = 1280, + num_frames: int = 129, + num_inference_steps: int = 50, + sigmas: List[float] = None, + true_cfg_scale: float = 1.0, + guidance_scale: float = 1.0, + num_videos_per_prompt: Optional[int] = 1, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + prompt_embeds: Optional[torch.Tensor] = None, + pooled_prompt_embeds: Optional[torch.Tensor] = None, + prompt_attention_mask: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + negative_pooled_prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_attention_mask: Optional[torch.Tensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + attention_kwargs: Optional[Dict[str, Any]] = None, + callback_on_step_end: Optional[ + Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] + ] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE, + max_sequence_length: int = 256, + image_embed_interleave: Optional[int] = None, + + use_low_pass_guidance: bool = False, + lp_filter_type: str = "none", # {'gaussian_blur', 'down_up'} + lp_filter_in_latent: bool = False, # When set to True, low-pass filter is done after encoder. If False, low-pass filter is applied to image directly before encoder. + lp_blur_sigma: float = 15.0, # Used with 'gaussian_blur'. Gaussian filter sigma value. + lp_blur_kernel_size: float = 0.02734375, # Used with 'gaussian_blur'. Gaussian filter size. When set to int, used directly as kernel size. When set to float, H * `lp_blur_kernel_size` is used as kernel size. + lp_resize_factor: float = 0.25, # Used with 'down_up'. Image is bilinearly downsized to (`lp_resize_factor` * WIDTH, `lp_resize_factor` * HEIGHT) and then back to original. + + lp_strength_schedule_type: str = "none", # Scheduling type for low-pass filtering strength. Options: {"none", "linear", "interval", "exponential"} + schedule_blur_kernel_size: bool = False, # If True, schedule blur kernel size as well. Otherwise, fix to initial value. + + # --- Constant Interval Scheduling Params for LP Strength --- + schedule_interval_start_time: float = 0.0, # Starting timestep for interval scheduling + schedule_interval_end_time: float = 0.05, # Ending timestep for interval scheduling + + # --- Linear Scheduling Params for LP Strength --- + schedule_linear_start_weight: float = 1.0, # Starting LP weight for linear scheduling at t=T (step 0) + schedule_linear_end_weight: float = 0.0, # Ending LP weight for linear scheduling at t=T * schedule_linear_end_time + schedule_linear_end_time: float = 0.5, # Timestep fraction at which schedule_linear_end is reached + + # --- Exponential Scheduling Params for LP Strength --- + schedule_exp_decay_rate: float = 10.0, # Decay rate for 'exponential' schedule. Higher values decay faster. Strength = exp(-rate * time_fraction). + + lp_on_noisy_latent = False, + enable_lp_img_embeds = False, + i2v_stable= False, + ): + r""" + The call function to the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is + will be used instead. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is + not greater than `1`). + negative_prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and + `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders. + height (`int`, defaults to `720`): + The height in pixels of the generated image. + width (`int`, defaults to `1280`): + The width in pixels of the generated image. + num_frames (`int`, defaults to `129`): + The number of frames in the generated video. + num_inference_steps (`int`, defaults to `50`): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + sigmas (`List[float]`, *optional*): + Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in + their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed + will be used. + true_cfg_scale (`float`, *optional*, defaults to 1.0): + When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance. + guidance_scale (`float`, defaults to `1.0`): + Guidance scale as defined in [Classifier-Free Diffusion + Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. + of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting + `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to + the text `prompt`, usually at the expense of lower image quality. Note that the only available + HunyuanVideo model is CFG-distilled, which means that traditional guidance between unconditional and + conditional latent is not applied. + num_videos_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + generation deterministic. + latents (`torch.Tensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor is generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not + provided, text embeddings are generated from the `prompt` input argument. + pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. + If not provided, pooled text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` + input argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generated image. Choose between `PIL.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`HunyuanVideoPipelineOutput`] instead of a plain tuple. + attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*): + A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of + each denoising step during the inference. with the following arguments: `callback_on_step_end(self: + DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a + list of all tensors as specified by `callback_on_step_end_tensor_inputs`. + prompt_template (`Dict[str, Any]`, *optional*, defaults to `DEFAULT_PROMPT_TEMPLATE`): + A dictionary defining the template for constructing the LLaVA prompt. It should include keys like + `"template"`, `"crop_start"`, `"image_emb_start"`, `"image_emb_end"`, `"image_emb_len"`, and + `"double_return_token_id"`. + max_sequence_length (`int`, *optional*, defaults to 256): + The maximum sequence length for the LLaVA text encoder. + image_embed_interleave (`int`, *optional*): + The interleave factor for image embeddings. Defaults to 2 if `image_condition_type` is + `"latent_concat"`, 4 if `"token_replace"`, otherwise 1. + use_low_pass_guidance (`bool`, *optional*, defaults to `False`): + Whether to use low-pass guidance. This can help to improve the temporal consistency of the generated + video. + lp_filter_type (`str`, *optional*, defaults to `"none"`): + The type of low-pass filter to apply. Can be one of `gaussian_blur` or `down_up`. + lp_filter_in_latent (`bool`, *optional*, defaults to `False`): + If `True`, the low-pass filter is applied to the latent representation of the image. If `False`, it is + applied to the image in pixel space before encoding. + lp_blur_sigma (`float`, *optional*, defaults to `15.0`): + The sigma value for the Gaussian blur filter. Only used if `lp_filter_type` is `gaussian_blur`. + lp_blur_kernel_size (`float`, *optional*, defaults to `0.02734375`): + The kernel size for the Gaussian blur filter. If an `int`, it's used directly. If a `float`, the kernel + size is calculated as `height * lp_blur_kernel_size`. Only used if `lp_filter_type` is `gaussian_blur`. + lp_resize_factor (`float`, *optional*, defaults to `0.25`): + The resize factor for the down-sampling and up-sampling filter. Only used if `lp_filter_type` is + `down_up`. + lp_strength_schedule_type (`str`, *optional*, defaults to `"none"`): + The scheduling type for the low-pass filter strength. Can be one of `none`, `linear`, `interval`, or + `exponential`. + schedule_blur_kernel_size (`bool`, *optional*, defaults to `False`): + If `True`, the blur kernel size is also scheduled along with the strength. Otherwise, it remains fixed. + schedule_interval_start_time (`float`, *optional*, defaults to `0.0`): + The starting timestep fraction for interval scheduling. Only used if `lp_strength_schedule_type` is + `interval`. + schedule_interval_end_time (`float`, *optional*, defaults to `0.05`): + The ending timestep fraction for interval scheduling. Only used if `lp_strength_schedule_type` is + `interval`. + schedule_linear_start_weight (`float`, *optional*, defaults to `1.0`): + The starting weight for the low-pass filter strength in a linear schedule. Corresponds to the first + timestep. Only used if `lp_strength_schedule_type` is `linear`. + schedule_linear_end_weight (`float`, *optional*, defaults to `0.0`): + The ending weight for the low-pass filter strength in a linear schedule. Only used if + `lp_strength_schedule_type` is `linear`. + schedule_linear_end_time (`float`, *optional*, defaults to `0.5`): + The timestep fraction at which `schedule_linear_end_weight` is reached in a linear schedule. Only used + if `lp_strength_schedule_type` is `linear`. + schedule_exp_decay_rate (`float`, *optional*, defaults to `10.0`): + The decay rate for the exponential schedule. Higher values lead to faster decay. Only used if + `lp_strength_schedule_type` is `exponential`. + lp_on_noisy_latent (`bool`, *optional*, defaults to `False`): + If `True` and using low-pass guidance with true CFG, applies the low-pass condition to the noisy latent input + when the low-pass strength is zero, instead of using the original image condition. + enable_lp_img_embeds (`bool`, *optional*, defaults to `False`): + Whether to apply low-pass filtering to image embeddings. + i2v_stable (`bool`, *optional*, defaults to `False`): + If `True`, initializes the video latents with initial image latents. + + Examples: + + Returns: + [`~HunyuanVideoPipelineOutput`] or `tuple`: + If `return_dict` is `True`, [`HunyuanVideoPipelineOutput`] is returned, otherwise a `tuple` is returned + where the first element is a list with the generated images and the second element is a list of `bool`s + indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content. + """ + + if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + prompt_2, + height, + width, + prompt_embeds, + callback_on_step_end_tensor_inputs, + prompt_template, + true_cfg_scale, + guidance_scale, + ) + + image_condition_type = self.transformer.config.image_condition_type + has_neg_prompt = negative_prompt is not None or ( + negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None + ) + do_true_cfg = true_cfg_scale > 1 and has_neg_prompt + image_embed_interleave = ( + image_embed_interleave + if image_embed_interleave is not None + else ( + 2 if image_condition_type == "latent_concat" else 4 if image_condition_type == "token_replace" else 1 + ) + ) + + self._guidance_scale = guidance_scale + self._attention_kwargs = attention_kwargs + self._current_timestep = None + self._interrupt = False + + device = self._execution_device + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + # 3. Prepare latent variables + vae_dtype = self.vae.dtype + image_tensor = self.video_processor.preprocess(image, height, width).to(device, vae_dtype) + + if image_condition_type == "latent_concat": + num_channels_latents = (self.transformer.config.in_channels - 1) // 2 + elif image_condition_type == "token_replace": + num_channels_latents = self.transformer.config.in_channels + + latents, image_latents = self.prepare_latents( + image_tensor, + batch_size * num_videos_per_prompt, + num_channels_latents, + height, + width, + num_frames, + torch.float32, + device, + generator, + latents, + image_condition_type, + i2v_stable + ) + if image_condition_type == "latent_concat": + image_latents[:, :, 1:] = 0 + mask = image_latents.new_ones(image_latents.shape[0], 1, *image_latents.shape[2:]) + mask[:, :, 1:] = 0 + + # 4. Encode input prompt + transformer_dtype = self.transformer.dtype + prompt_embeds, pooled_prompt_embeds, prompt_attention_mask = self.encode_prompt( + image=image, + prompt=prompt, + prompt_2=prompt_2, + prompt_template=prompt_template, + num_videos_per_prompt=num_videos_per_prompt, + prompt_embeds=prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + prompt_attention_mask=prompt_attention_mask, + device=device, + max_sequence_length=max_sequence_length, + image_embed_interleave=image_embed_interleave, + ) + prompt_embeds = prompt_embeds.to(transformer_dtype) + prompt_attention_mask = prompt_attention_mask.to(transformer_dtype) + pooled_prompt_embeds = pooled_prompt_embeds.to(transformer_dtype) + + if do_true_cfg: + black_image = PIL.Image.new("RGB", (width, height), 0) + negative_prompt_embeds, negative_pooled_prompt_embeds, negative_prompt_attention_mask = self.encode_prompt( + image=black_image, + prompt=negative_prompt, + prompt_2=negative_prompt_2, + prompt_template=prompt_template, + num_videos_per_prompt=num_videos_per_prompt, + prompt_embeds=negative_prompt_embeds, + pooled_prompt_embeds=negative_pooled_prompt_embeds, + prompt_attention_mask=negative_prompt_attention_mask, + device=device, + max_sequence_length=max_sequence_length, + image_embed_interleave=image_embed_interleave, + ) + negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype) + negative_prompt_attention_mask = negative_prompt_attention_mask.to(transformer_dtype) + negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(transformer_dtype) + + # 5. Prepare timesteps + sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas + timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas) + + # 6. Prepare guidance condition + guidance = None + if self.transformer.config.guidance_embeds: + guidance = ( + torch.tensor([guidance_scale] * latents.shape[0], dtype=transformer_dtype, device=device) * 1000.0 + ) + + # 7. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + self._num_timesteps = len(timesteps) + + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + if self.interrupt: + continue + + self._current_timestep = t + if do_true_cfg and use_low_pass_guidance: + lp_strength = lp_utils.get_lp_strength( + step_index=i, + total_steps=num_inference_steps, + lp_strength_schedule_type=lp_strength_schedule_type, + schedule_interval_start_time=schedule_interval_start_time, + schedule_interval_end_time=schedule_interval_end_time, + schedule_linear_start_weight=schedule_linear_start_weight, + schedule_linear_end_weight=schedule_linear_end_weight, + schedule_linear_end_time=schedule_linear_end_time, + schedule_exp_decay_rate=schedule_exp_decay_rate, + ) + + modulated_lp_blur_sigma = lp_blur_sigma * lp_strength + if schedule_blur_kernel_size: + modulated_lp_blur_kernel_size = lp_blur_kernel_size * lp_strength + else: + modulated_lp_blur_kernel_size = lp_blur_kernel_size + + # No-effect resize_factor is 1.0 + modulated_lp_resize_factor = 1.0 - (1.0 - lp_resize_factor) * lp_strength + + if enable_lp_img_embeds: + assert False, "Low-pass filter on image embeds is not supported in HunyuanVideo pipeline. Please set enable_lp_img_embeds = False" + + lp_image_latents = self.prepare_lp( + lp_filter_type=lp_filter_type, + lp_blur_sigma=modulated_lp_blur_sigma, + lp_blur_kernel_size=modulated_lp_blur_kernel_size, + lp_resize_factor=modulated_lp_resize_factor, + generator=generator, + num_frames=num_frames, + use_low_pass_guidance=use_low_pass_guidance, + lp_filter_in_latent=lp_filter_in_latent, + orig_image_latents=image_latents, + orig_image_tensor=image + ) + if lp_strength == 0.0 or lp_on_noisy_latent: + latent_model_input = torch.cat([latents] * 2) + img_cond = torch.cat([image_latents,image_latents], dim=0).to(transformer_dtype) + latent_model_input = torch.cat([img_cond, latent_model_input[:, :, 1:]], dim=2).to(transformer_dtype) + + concat_prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + concat_pooled_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0) + concat_prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) + else: + latent_model_input = torch.cat([latents] * 3) + img_cond = torch.cat([image_latents,lp_image_latents,lp_image_latents], dim=0) + latent_model_input = torch.cat([img_cond, latent_model_input[:, :, 1:]], dim=2).to(transformer_dtype) + concat_prompt_embeds = torch.cat([negative_prompt_embeds,negative_prompt_embeds, prompt_embeds], dim=0) + concat_pooled_embeds = torch.cat([negative_pooled_prompt_embeds,negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0) + concat_prompt_attention_mask = torch.cat([negative_prompt_attention_mask,negative_prompt_attention_mask, prompt_attention_mask], dim=0) + elif do_true_cfg: + latent_model_input = torch.cat([latents] * 2) + img_cond = torch.cat([image_latents,image_latents], dim=0).to(transformer_dtype) + latent_model_input = torch.cat([img_cond, latent_model_input[:, :, 1:]], dim=2).to(transformer_dtype) + + concat_prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + concat_pooled_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0) + concat_prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) + elif not use_low_pass_guidance: + latent_model_input = torch.cat([image_latents, latents[:, :, 1:]], dim=2).to(transformer_dtype) + concat_prompt_embeds = prompt_embeds + concat_pooled_embeds = pooled_prompt_embeds + concat_prompt_attention_mask = prompt_attention_mask + else: + lp_strength = lp_utils.get_lp_strength( + step_index=i, + total_steps=num_inference_steps, + lp_strength_schedule_type=lp_strength_schedule_type, + schedule_interval_start_time=schedule_interval_start_time, + schedule_interval_end_time=schedule_interval_end_time, + schedule_linear_start_weight=schedule_linear_start_weight, + schedule_linear_end_weight=schedule_linear_end_weight, + schedule_linear_end_time=schedule_linear_end_time, + schedule_exp_decay_rate=schedule_exp_decay_rate, + ) + + modulated_lp_blur_sigma = lp_blur_sigma * lp_strength + if schedule_blur_kernel_size: + modulated_lp_blur_kernel_size = lp_blur_kernel_size * lp_strength + else: + modulated_lp_blur_kernel_size = lp_blur_kernel_size + + modulated_lp_resize_factor = 1.0 - (1.0 - lp_resize_factor) * lp_strength + + if enable_lp_img_embeds: + assert False, "Low-pass filter on image embeds is not supported in HunyuanVideo pipeline. Please set enable_lp_img_embeds = False" + + lp_image_latents = self.prepare_lp( + lp_filter_type=lp_filter_type, + lp_blur_sigma=modulated_lp_blur_sigma, + lp_blur_kernel_size=modulated_lp_blur_kernel_size, + lp_resize_factor=modulated_lp_resize_factor, + generator=generator, + num_frames=num_frames, + use_low_pass_guidance=use_low_pass_guidance, + lp_filter_in_latent=lp_filter_in_latent, + orig_image_latents=image_latents, + orig_image_tensor=image + ) + latent_model_input = torch.cat([lp_image_latents, latents[:, :, 1:]], dim=2).to(transformer_dtype) + concat_prompt_embeds = prompt_embeds + concat_pooled_embeds = pooled_prompt_embeds + concat_prompt_attention_mask = prompt_attention_mask + + timestep = t.expand(latent_model_input.shape[0]).to(transformer_dtype) + latent_model_input = latent_model_input.to(transformer_dtype) + prompt_embeds = prompt_embeds.to(transformer_dtype) + prompt_attention_mask = prompt_attention_mask.to(transformer_dtype) + pooled_prompt_embeds = pooled_prompt_embeds.to(transformer_dtype) + + noise_pred = self.transformer( + hidden_states=latent_model_input, + timestep=timestep, + encoder_hidden_states=concat_prompt_embeds, + encoder_attention_mask=concat_prompt_attention_mask, + pooled_projections=concat_pooled_embeds, + guidance=guidance, + attention_kwargs=attention_kwargs, + return_dict=False, + )[0] + + if noise_pred.shape[0] == 3: + noise_pred_uncond_init, noise_pred_uncond, noise_pred_text = noise_pred.chunk(3) + noise_pred = ( + noise_pred_uncond_init + true_cfg_scale * (noise_pred_text - noise_pred_uncond) + ) + elif noise_pred.shape[0] == 2: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + true_cfg_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + if image_condition_type == "latent_concat": + latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] + elif image_condition_type == "token_replace": + latents = latents = self.scheduler.step( + noise_pred[:, :, 1:], t, latents[:, :, 1:], return_dict=False + )[0] + latents = torch.cat([image_latents, latents], dim=2) + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + + if XLA_AVAILABLE: + xm.mark_step() + + self._current_timestep = None + + if not output_type == "latent": + latents = latents.to(self.vae.dtype) / self.vae_scaling_factor + video = self.vae.decode(latents, return_dict=False)[0] + if image_condition_type == "latent_concat": + video = video[:, :, 4:, :, :] + video = self.video_processor.postprocess_video(video, output_type=output_type) + else: + if image_condition_type == "latent_concat": + video = latents[:, :, 1:, :, :] + else: + video = latents + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (video,) + + return HunyuanVideoPipelineOutput(frames=video) diff --git a/exp_code/1_benchmark/ALG/pipeline_wan_image2video_lowpass.py b/exp_code/1_benchmark/ALG/pipeline_wan_image2video_lowpass.py new file mode 100644 index 0000000000000000000000000000000000000000..79ae73189f0eb010a0e5a5ac2193b345a7428ce1 --- /dev/null +++ b/exp_code/1_benchmark/ALG/pipeline_wan_image2video_lowpass.py @@ -0,0 +1,970 @@ +# Copyright 2025 The Wan Team and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import html +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import PIL +import regex as re +import torch +from transformers import AutoTokenizer, CLIPImageProcessor, CLIPVisionModel, UMT5EncoderModel + +from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback +from diffusers.image_processor import PipelineImageInput +from diffusers.loaders import WanLoraLoaderMixin +from diffusers.models import AutoencoderKLWan, WanTransformer3DModel +from diffusers.schedulers import FlowMatchEulerDiscreteScheduler +from diffusers.utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring +from diffusers.utils.torch_utils import randn_tensor +from diffusers.video_processor import VideoProcessor +from diffusers.pipelines.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.wan.pipeline_output import WanPipelineOutput + +import lp_utils + +if is_torch_xla_available(): + import torch_xla.core.xla_model as xm + + XLA_AVAILABLE = True +else: + XLA_AVAILABLE = False + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +if is_ftfy_available(): + import ftfy + +EXAMPLE_DOC_STRING = """ + Examples: + ```python + >>> import torch + >>> import numpy as np + >>> from diffusers import AutoencoderKLWan, WanImageToVideoPipeline + >>> from diffusers.utils import export_to_video, load_image + >>> from transformers import CLIPVisionModel + + >>> # Available models: Wan-AI/Wan2.1-I2V-14B-480P-Diffusers, Wan-AI/Wan2.1-I2V-14B-720P-Diffusers + >>> model_id = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers" + >>> image_encoder = CLIPVisionModel.from_pretrained( + ... model_id, subfolder="image_encoder", torch_dtype=torch.float32 + ... ) + >>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) + >>> pipe = WanImageToVideoPipeline.from_pretrained( + ... model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16 + ... ) + >>> pipe.to("cuda") + + >>> image = load_image( + ... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg" + ... ) + >>> max_area = 480 * 832 + >>> aspect_ratio = image.height / image.width + >>> mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1] + >>> height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value + >>> width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value + >>> image = image.resize((width, height)) + >>> prompt = ( + ... "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in " + ... "the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot." + ... ) + >>> negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" + + >>> output = pipe( + ... image=image, + ... prompt=prompt, + ... negative_prompt=negative_prompt, + ... height=height, + ... width=width, + ... num_frames=81, + ... guidance_scale=5.0, + ... ).frames[0] + >>> export_to_video(output, "output.mp4", fps=16) + ``` +""" + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r"\s+", " ", text) + text = text.strip() + return text + + +def prompt_clean(text): + text = whitespace_clean(basic_clean(text)) + return text + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents +def retrieve_latents( + encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" +): + if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": + return encoder_output.latent_dist.sample(generator) + elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": + return encoder_output.latent_dist.mode() + elif hasattr(encoder_output, "latents"): + return encoder_output.latents + else: + raise AttributeError("Could not access latents of provided encoder_output") + + +class WanImageToVideoPipeline(DiffusionPipeline, WanLoraLoaderMixin): + r""" + Pipeline for image-to-video generation using Wan. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods + implemented for all pipelines (downloading, saving, running on a particular device, etc.). + + Args: + tokenizer ([`T5Tokenizer`]): + Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer), + specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant. + text_encoder ([`T5EncoderModel`]): + [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically + the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant. + image_encoder ([`CLIPVisionModel`]): + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModel), specifically + the + [clip-vit-huge-patch14](https://github.com/mlfoundations/open_clip/blob/main/docs/PRETRAINED.md#vit-h14-xlm-roberta-large) + variant. + transformer ([`WanTransformer3DModel`]): + Conditional Transformer to denoise the input latents. + scheduler ([`UniPCMultistepScheduler`]): + A scheduler to be used in combination with `transformer` to denoise the encoded image latents. + vae ([`AutoencoderKLWan`]): + Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations. + """ + + model_cpu_offload_seq = "text_encoder->image_encoder->transformer->vae" + _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] + + def __init__( + self, + tokenizer: AutoTokenizer, + text_encoder: UMT5EncoderModel, + image_encoder: CLIPVisionModel, + image_processor: CLIPImageProcessor, + transformer: WanTransformer3DModel, + vae: AutoencoderKLWan, + scheduler: FlowMatchEulerDiscreteScheduler, + ): + super().__init__() + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + image_encoder=image_encoder, + transformer=transformer, + scheduler=scheduler, + image_processor=image_processor, + ) + + self.vae_scale_factor_temporal = 2 ** sum(self.vae.temperal_downsample) if getattr(self, "vae", None) else 4 + self.vae_scale_factor_spatial = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8 + self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) + self.image_processor = image_processor + + def _get_t5_prompt_embeds( + self, + prompt: Union[str, List[str]] = None, + num_videos_per_prompt: int = 1, + max_sequence_length: int = 512, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + device = device or self._execution_device + dtype = dtype or self.text_encoder.dtype + + prompt = [prompt] if isinstance(prompt, str) else prompt + prompt = [prompt_clean(u) for u in prompt] + batch_size = len(prompt) + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=max_sequence_length, + truncation=True, + add_special_tokens=True, + return_attention_mask=True, + return_tensors="pt", + ) + text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask + seq_lens = mask.gt(0).sum(dim=1).long() + + prompt_embeds = self.text_encoder(text_input_ids.to(device), mask.to(device)).last_hidden_state + prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) + prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)] + prompt_embeds = torch.stack( + [torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0 + ) + + # duplicate text embeddings for each generation per prompt, using mps friendly method + _, seq_len, _ = prompt_embeds.shape + prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) + prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) + + return prompt_embeds + + def encode_image( + self, + image: PipelineImageInput, + device: Optional[torch.device] = None, + ): + device = device or self._execution_device + image = self.image_processor(images=image, return_tensors="pt").to(device) + image_embeds = self.image_encoder(**image, output_hidden_states=True) + return image_embeds.hidden_states[-2] + + # Copied from diffusers.pipelines.wan.pipeline_wan.WanPipeline.encode_prompt + def encode_prompt( + self, + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + do_classifier_free_guidance: bool = True, + num_videos_per_prompt: int = 1, + prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + max_sequence_length: int = 226, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): + Whether to use classifier free guidance or not. + num_videos_per_prompt (`int`, *optional*, defaults to 1): + Number of videos that should be generated per prompt. torch device to place the resulting embeddings on + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + device: (`torch.device`, *optional*): + torch device + dtype: (`torch.dtype`, *optional*): + torch dtype + """ + device = device or self._execution_device + + prompt = [prompt] if isinstance(prompt, str) else prompt + if prompt is not None: + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + prompt_embeds = self._get_t5_prompt_embeds( + prompt=prompt, + num_videos_per_prompt=num_videos_per_prompt, + max_sequence_length=max_sequence_length, + device=device, + dtype=dtype, + ) + + if do_classifier_free_guidance and negative_prompt_embeds is None: + negative_prompt = negative_prompt or "" + negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt + + if prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + + negative_prompt_embeds = self._get_t5_prompt_embeds( + prompt=negative_prompt, + num_videos_per_prompt=num_videos_per_prompt, + max_sequence_length=max_sequence_length, + device=device, + dtype=dtype, + ) + + return prompt_embeds, negative_prompt_embeds + + def check_inputs( + self, + prompt, + negative_prompt, + image, + height, + width, + prompt_embeds=None, + negative_prompt_embeds=None, + image_embeds=None, + callback_on_step_end_tensor_inputs=None, + ): + if image is not None and image_embeds is not None: + raise ValueError( + f"Cannot forward both `image`: {image} and `image_embeds`: {image_embeds}. Please make sure to" + " only forward one of the two." + ) + if image is None and image_embeds is None: + raise ValueError( + "Provide either `image` or `prompt_embeds`. Cannot leave both `image` and `image_embeds` undefined." + ) + if image is not None and not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image): + raise ValueError(f"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` but is {type(image)}") + if height % 16 != 0 or width % 16 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.") + + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + elif negative_prompt is not None and ( + not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list) + ): + raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}") + + def prepare_latents( + self, + image: PipelineImageInput, + batch_size: int, + num_channels_latents: int = 16, + height: int = 480, + width: int = 832, + num_frames: int = 81, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + last_image: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 + latent_height = height // self.vae_scale_factor_spatial + latent_width = width // self.vae_scale_factor_spatial + + shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device=device, dtype=dtype) + + image = image.unsqueeze(2) + if last_image is None: + video_condition = torch.cat( + [image, image.new_zeros(image.shape[0], image.shape[1], num_frames - 1, height, width)], dim=2 + ) + else: + last_image = last_image.unsqueeze(2) + video_condition = torch.cat( + [image, image.new_zeros(image.shape[0], image.shape[1], num_frames - 2, height, width), last_image], + dim=2, + ) + video_condition = video_condition.to(device=device, dtype=self.vae.dtype) + + latents_mean = ( + torch.tensor(self.vae.config.latents_mean) + .view(1, self.vae.config.z_dim, 1, 1, 1) + .to(latents.device, latents.dtype) + ) + latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to( + latents.device, latents.dtype + ) + + if isinstance(generator, list): + latent_condition = [ + retrieve_latents(self.vae.encode(video_condition), sample_mode="argmax") for _ in generator + ] + latent_condition = torch.cat(latent_condition) + else: + latent_condition = retrieve_latents(self.vae.encode(video_condition), sample_mode="argmax") + latent_condition = latent_condition.repeat(batch_size, 1, 1, 1, 1) + + latent_condition = latent_condition.to(dtype) + latent_condition = (latent_condition - latents_mean) * latents_std + + mask_lat_size = torch.ones(batch_size, 1, num_frames, latent_height, latent_width) + + if last_image is None: + mask_lat_size[:, :, list(range(1, num_frames))] = 0 + else: + mask_lat_size[:, :, list(range(1, num_frames - 1))] = 0 + first_frame_mask = mask_lat_size[:, :, 0:1] + first_frame_mask = torch.repeat_interleave(first_frame_mask, dim=2, repeats=self.vae_scale_factor_temporal) + mask_lat_size = torch.concat([first_frame_mask, mask_lat_size[:, :, 1:, :]], dim=2) + mask_lat_size = mask_lat_size.view(batch_size, -1, self.vae_scale_factor_temporal, latent_height, latent_width) + mask_lat_size = mask_lat_size.transpose(1, 2) + mask_lat_size = mask_lat_size.to(latent_condition.device) + + return latents, torch.concat([mask_lat_size, latent_condition], dim=1) + + def prepare_lp( + self, + # --- Filter Selection & Strength --- + lp_filter_type: str, + lp_blur_sigma: float, + lp_blur_kernel_size: float, + lp_resize_factor: float, + # --- Contextual Info --- + generator: torch.Generator, + num_frames: int, + use_low_pass_guidance: bool, + lp_filter_in_latent: bool, + # --- Inputs to filter --- + orig_image_latents: torch.Tensor, + orig_image_tensor: torch.Tensor, + ) -> Optional[torch.Tensor]: + """ + Prepares a low-pass filtered version of the initial image condition for guidance. (Wan 2.1) + The resulting low-pass filtered latents are padded to match the required number of frames and temporal + patch size for the transformer model. + + Args: + lp_filter_type (`str`): The type of low-pass filter to apply, e.g., 'gaussian_blur', 'down_up'. + lp_blur_sigma (`float`): The sigma value for the Gaussian blur filter. + lp_blur_kernel_size (`float`): The kernel size for the Gaussian blur filter. + lp_resize_factor (`float`): The resizing factor for the 'down_up' filter. + generator (`torch.Generator`): A random generator, used for VAE sampling when filtering in image space. + num_frames (`int`): The target number of frames for the final video, used to determine padding. + use_low_pass_guidance (`bool`): If `False`, the function returns `None` immediately. + lp_filter_in_latent (`bool`): If `True`, filtering is applied in latent space. Otherwise, in image space. + orig_image_latents (`torch.Tensor`): The VAE-encoded latents of the original image. Used when + `lp_filter_in_latent` is `True`. Shape: `(batch_size, num_frames_padded, channels, height, width)`. + orig_image_tensor (`torch.Tensor`): The preprocessed original image tensor (RGB). Used when + `lp_filter_in_latent` is `False`. Shape: `(batch_size, channels, height, width)`. + + Returns: + `Optional[torch.Tensor]`: A tensor containing the low-pass filtered image latents, correctly shaped and + padded for the transformer, or `None` if `use_low_pass_guidance` is `False`. + """ + if not use_low_pass_guidance: + return None + + if not lp_filter_in_latent: + # --- Filter in Image (RGB) Space --- + image_lp = lp_utils.apply_low_pass_filter( + orig_image_tensor, + filter_type=lp_filter_type, + blur_sigma=lp_blur_sigma, + blur_kernel_size=lp_blur_kernel_size, + resize_factor=lp_resize_factor, + ) + image_lp_vae_input = image_lp.unsqueeze(2) + + batch_size, _, height, width = orig_image_tensor.shape + latent_height = height // self.vae_scale_factor_spatial + latent_width = width // self.vae_scale_factor_spatial + + # --- Zero padding --- + video_condition = torch.cat( + [ + image_lp_vae_input, + image_lp_vae_input.new_zeros( + image_lp_vae_input.shape[0], image_lp_vae_input.shape[1], num_frames - 1, height, width + ), + ], + dim=2, + ) + latents_mean = ( + torch.tensor(self.vae.config.latents_mean) + .view(1, self.vae.config.z_dim, 1, 1, 1) + .to(image_lp.device, image_lp.dtype) + ) + latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view( + 1, self.vae.config.z_dim, 1, 1, 1 + ).to(image_lp.device, image_lp.dtype) + encoded_lp = self.vae.encode(video_condition).latent_dist.sample(generator=generator) + latent_condition = (encoded_lp - latents_mean) * latents_std + + mask_lat_size = torch.ones(batch_size, 1, num_frames, latent_height, latent_width) + mask_lat_size[:, :, list(range(1, num_frames))] = 0 + first_frame_mask = mask_lat_size[:, :, 0:1] + first_frame_mask = torch.repeat_interleave(first_frame_mask, dim=2, repeats=self.vae_scale_factor_temporal) + mask_lat_size = torch.concat([first_frame_mask, mask_lat_size[:, :, 1:, :]], dim=2) + mask_lat_size = mask_lat_size.view( + batch_size, -1, self.vae_scale_factor_temporal, latent_height, latent_width + ) + mask_lat_size = mask_lat_size.transpose(1, 2) + mask_lat_size = mask_lat_size.to(latent_condition.device) + + lp_image_latents = torch.concat([mask_lat_size, latent_condition], dim=1) + else: + lp_image_latents = lp_utils.apply_low_pass_filter( + orig_image_latents, + filter_type=lp_filter_type, + blur_sigma=lp_blur_sigma, + blur_kernel_size=lp_blur_kernel_size, + resize_factor=lp_resize_factor, + ) + # Ensure the temporal dimension is divisible by the transformer's temporal patch size. + if self.transformer.config.patch_size is not None: + remainder = lp_image_latents.size(1) % self.transformer.config.patch_size[0] + if remainder != 0: + num_to_prepend = self.transformer.config.patch_size[0] - remainder + num_to_prepend = min(num_to_prepend, lp_image_latents.shape[1]) + first_frames_to_prepend = lp_image_latents[:, :num_to_prepend, ...] + lp_image_latents = torch.cat([first_frames_to_prepend, lp_image_latents], dim=1) + + lp_image_latents = lp_image_latents.to(dtype=orig_image_latents.dtype) + return lp_image_latents + + @property + def guidance_scale(self): + return self._guidance_scale + + @property + def do_classifier_free_guidance(self): + return self._guidance_scale > 1 + + @property + def num_timesteps(self): + return self._num_timesteps + + @property + def current_timestep(self): + return self._current_timestep + + @property + def interrupt(self): + return self._interrupt + + @property + def attention_kwargs(self): + return self._attention_kwargs + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + image: PipelineImageInput, + prompt: Union[str, List[str]] = None, + negative_prompt: Union[str, List[str]] = None, + height: int = 480, + width: int = 832, + num_frames: int = 81, + num_inference_steps: int = 50, + guidance_scale: float = 5.0, + num_videos_per_prompt: Optional[int] = 1, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + image_embeds: Optional[torch.Tensor] = None, + last_image: Optional[torch.Tensor] = None, + output_type: Optional[str] = "np", + return_dict: bool = True, + attention_kwargs: Optional[Dict[str, Any]] = None, + callback_on_step_end: Optional[ + Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] + ] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + max_sequence_length: int = 512, + use_low_pass_guidance: bool = False, + lp_filter_type: str = "none", # {'gaussian_blur', 'down_up'} + lp_filter_in_latent: bool = False, # When set to True, low-pass filter is done after encoder. If False, low-pass filter is applied to image directly before encoder. + lp_blur_sigma: float = 15.0, # Used with 'gaussian_blur'. Gaussian filter sigma value. + lp_blur_kernel_size: float = 0.02734375, # Used with 'gaussian_blur'. Gaussian filter size. When set to int, used directly as kernel size. When set to float, H * `lp_blur_kernel_size` is used as kernel size. + lp_resize_factor: float = 0.25, # Used with 'down_up'. Image is bilinearly downsized to (`lp_resize_factor` * WIDTH, `lp_resize_factor` * HEIGHT) and then back to original. + + lp_strength_schedule_type: str = "none", # Scheduling type for low-pass filtering strength. Options: {"none", "linear", "interval", "exponential"} + schedule_blur_kernel_size: bool = False, # If True, schedule blur kernel size as well. Otherwise, fix to initial value. + + + # --- Constant Interval Scheduling Params for LP Strength --- + schedule_interval_start_time: float = 0.0, # Starting timestep for interval scheduling + schedule_interval_end_time: float = 0.05, # Ending timestep for interval scheduling + + # --- Linear Scheduling Params for LP Strength --- + schedule_linear_start_weight: float = 1.0, # Starting LP weight for linear scheduling at t=T (step 0) + schedule_linear_end_weight: float = 0.0, # Ending LP weight for linear scheduling at t=T * schedule_linear_end_time + schedule_linear_end_time: float = 0.5, # Timestep fraction at which schedule_linear_end is reached + + # --- Exponential Scheduling Params for LP Strength --- + schedule_exp_decay_rate: float = 10.0, # Decay rate for 'exponential' schedule. Higher values decay faster. Strength = exp(-rate * time_fraction). + ): + r""" + The call function to the pipeline for generation. + + Args: + image (`PipelineImageInput`): + The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`. + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + height (`int`, defaults to `480`): + The height of the generated video. + width (`int`, defaults to `832`): + The width of the generated video. + num_frames (`int`, defaults to `81`): + The number of frames in the generated video. + num_inference_steps (`int`, defaults to `50`): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, defaults to `5.0`): + Guidance scale as defined in [Classifier-Free Diffusion + Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. + of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting + `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to + the text `prompt`, usually at the expense of lower image quality. + num_videos_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + generation deterministic. + latents (`torch.Tensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor is generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not + provided, text embeddings are generated from the `prompt` input argument. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not + provided, text embeddings are generated from the `negative_prompt` input argument. + image_embeds (`torch.Tensor`, *optional*): + Pre-generated image embeddings. Can be used to easily tweak image inputs (weighting). If not provided, + image embeddings are generated from the `image` input argument. + output_type (`str`, *optional*, defaults to `"np"`): + The output format of the generated image. Choose between `PIL.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`WanPipelineOutput`] instead of a plain tuple. + attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*): + A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of + each denoising step during the inference. with the following arguments: `callback_on_step_end(self: + DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a + list of all tensors as specified by `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeline class. + max_sequence_length (`int`, *optional*, defaults to `512`): + The maximum sequence length of the prompt. + use_low_pass_guidance (`bool`, *optional*, defaults to `False`): + Whether to use low-pass guidance. This can help to improve the temporal consistency of the generated + video. + lp_filter_type (`str`, *optional*, defaults to `"none"`): + The type of low-pass filter to apply. Can be one of `gaussian_blur` or `down_up`. + lp_filter_in_latent (`bool`, *optional*, defaults to `False`): + If `True`, the low-pass filter is applied to the latent representation of the image. If `False`, it is + applied to the image in pixel space before encoding. + lp_blur_sigma (`float`, *optional*, defaults to `15.0`): + The sigma value for the Gaussian blur filter. Only used if `lp_filter_type` is `gaussian_blur`. + lp_blur_kernel_size (`float`, *optional*, defaults to `0.02734375`): + The kernel size for the Gaussian blur filter. If an `int`, it's used directly. If a `float`, the kernel + size is calculated as `height * lp_blur_kernel_size`. Only used if `lp_filter_type` is `gaussian_blur`. + lp_resize_factor (`float`, *optional*, defaults to `0.25`): + The resize factor for the down-sampling and up-sampling filter. Only used if `lp_filter_type` is + `down_up`. + lp_strength_schedule_type (`str`, *optional*, defaults to `"none"`): + The scheduling type for the low-pass filter strength. Can be one of `none`, `linear`, `interval`, or + `exponential`. + schedule_blur_kernel_size (`bool`, *optional*, defaults to `False`): + If `True`, the blur kernel size is also scheduled along with the strength. Otherwise, it remains fixed. + schedule_interval_start_time (`float`, *optional*, defaults to `0.0`): + The starting timestep fraction for interval scheduling. Only used if `lp_strength_schedule_type` is + `interval`. + schedule_interval_end_time (`float`, *optional*, defaults to `0.05`): + The ending timestep fraction for interval scheduling. Only used if `lp_strength_schedule_type` is + `interval`. + schedule_linear_start_weight (`float`, *optional*, defaults to `1.0`): + The starting weight for the low-pass filter strength in a linear schedule. Corresponds to the first + timestep. Only used if `lp_strength_schedule_type` is `linear`. + schedule_linear_end_weight (`float`, *optional*, defaults to `0.0`): + The ending weight for the low-pass filter strength in a linear schedule. Only used if + `lp_strength_schedule_type` is `linear`. + schedule_linear_end_time (`float`, *optional*, defaults to `0.5`): + The timestep fraction at which `schedule_linear_end_weight` is reached in a linear schedule. Only used + if `lp_strength_schedule_type` is `linear`. + schedule_exp_decay_rate (`float`, *optional*, defaults to `10.0`): + The decay rate for the exponential schedule. Higher values lead to faster decay. Only used if + `lp_strength_schedule_type` is `exponential`. + + Examples: + + Returns: + [`~WanPipelineOutput`] or `tuple`: + If `return_dict` is `True`, [`WanPipelineOutput`] is returned, otherwise a `tuple` is returned where + the first element is a list with the generated images and the second element is a list of `bool`s + indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content. + """ + if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + negative_prompt, + image, + height, + width, + prompt_embeds, + negative_prompt_embeds, + image_embeds, + callback_on_step_end_tensor_inputs, + ) + + if num_frames % self.vae_scale_factor_temporal != 1: + logger.warning( + f"`num_frames - 1` has to be divisible by {self.vae_scale_factor_temporal}. Rounding to the nearest number." + ) + num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1 + num_frames = max(num_frames, 1) + + self._guidance_scale = guidance_scale + self._attention_kwargs = attention_kwargs + self._current_timestep = None + self._interrupt = False + + device = self._execution_device + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + # 3. Encode input prompt + prompt_embeds, negative_prompt_embeds = self.encode_prompt( + prompt=prompt, + negative_prompt=negative_prompt, + do_classifier_free_guidance=self.do_classifier_free_guidance, + num_videos_per_prompt=num_videos_per_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + max_sequence_length=max_sequence_length, + device=device, + ) + + # Encode image embedding + transformer_dtype = self.transformer.dtype + prompt_embeds = prompt_embeds.to(transformer_dtype) + if negative_prompt_embeds is not None: + negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype) + + if image_embeds is None: + if last_image is None: + image_embeds = self.encode_image(image, device) + else: + image_embeds = self.encode_image([image, last_image], device) + dup_b, l, d = image_embeds.shape + image_embeds = image_embeds.reshape(-1, 2 * l, d) + image_embeds = image_embeds.repeat(batch_size, 1, 1) + image_embeds = image_embeds.to(transformer_dtype) + + # 4. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 5. Prepare latent variables + num_channels_latents = self.vae.config.z_dim + image = self.video_processor.preprocess(image, height=height, width=width).to(device, dtype=torch.float32) + if last_image is not None: + last_image = self.video_processor.preprocess(last_image, height=height, width=width).to( + device, dtype=torch.float32 + ) + latents, condition = self.prepare_latents( + image, + batch_size * num_videos_per_prompt, + num_channels_latents, + height, + width, + num_frames, + torch.float32, + device, + generator, + latents, + last_image, + ) + + # 6. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + self._num_timesteps = len(timesteps) + + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + if self.interrupt: + continue + + self._current_timestep = t + + if self.do_classifier_free_guidance and use_low_pass_guidance: # low-pass filtering + lp_strength = lp_utils.get_lp_strength( + step_index=i, + total_steps=num_inference_steps, + lp_strength_schedule_type=lp_strength_schedule_type, + schedule_interval_start_time=schedule_interval_start_time, + schedule_interval_end_time=schedule_interval_end_time, + schedule_linear_start_weight=schedule_linear_start_weight, + schedule_linear_end_weight=schedule_linear_end_weight, + schedule_linear_end_time=schedule_linear_end_time, + schedule_exp_decay_rate=schedule_exp_decay_rate, + ) + + modulated_lp_blur_sigma = lp_blur_sigma * lp_strength + modulated_lp_blur_kernel_size = ( + lp_blur_kernel_size * lp_strength if schedule_blur_kernel_size else lp_blur_kernel_size + ) + modulated_lp_resize_factor = 1.0 - (1.0 - lp_resize_factor) * lp_strength + + lp_image_latents = self.prepare_lp( + lp_filter_type=lp_filter_type, + lp_blur_sigma=modulated_lp_blur_sigma, + lp_blur_kernel_size=modulated_lp_blur_kernel_size, + lp_resize_factor=modulated_lp_resize_factor, + generator=generator, + num_frames=num_frames, + use_low_pass_guidance=use_low_pass_guidance, + lp_filter_in_latent=lp_filter_in_latent, + orig_image_latents=condition, + orig_image_tensor=image, + ) + + if lp_strength == 0.0: # equivalent to vanilla + latent_model_input = torch.cat([latents] * 2) + latent_model_input = torch.cat( + [latent_model_input, torch.cat([condition, condition], dim=0)], dim=1 + ).to(transformer_dtype) + concat_prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + else: # three passes + latent_model_input = torch.cat([latents] * 3) + img_cond = torch.cat([condition, lp_image_latents, lp_image_latents], dim=0) + latent_model_input = torch.cat([latent_model_input, img_cond], dim=1).to(transformer_dtype) + concat_prompt_embeds = torch.cat( + [negative_prompt_embeds, negative_prompt_embeds, prompt_embeds], dim=0 + ) + + elif self.do_classifier_free_guidance: # no low-pass filtering + latent_model_input = torch.cat([latents] * 2) + latent_model_input = torch.cat( + [latent_model_input, torch.cat([condition, condition], dim=0)], dim=1 + ).to(transformer_dtype) + concat_prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + + timestep = t.expand(latent_model_input.shape[0]) + concat_image_embeds = ( + image_embeds.repeat(latent_model_input.shape[0], 1, 1) + if image_embeds.shape[0] != latent_model_input.shape[0] + else image_embeds + ) + + noise_pred = self.transformer( + hidden_states=latent_model_input, + timestep=timestep, + encoder_hidden_states=concat_prompt_embeds, + encoder_hidden_states_image=concat_image_embeds, + attention_kwargs=attention_kwargs, + return_dict=False, + )[0] + + if noise_pred.shape[0] == 3: # three chunks + noise_pred_uncond_init, noise_pred_uncond, noise_pred_text = noise_pred.chunk(3) + noise_pred = noise_pred_uncond_init + guidance_scale * (noise_pred_text - noise_pred_uncond) + else: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + + if XLA_AVAILABLE: + xm.mark_step() + + self._current_timestep = None + + if not output_type == "latent": + latents = latents.to(self.vae.dtype) + latents_mean = ( + torch.tensor(self.vae.config.latents_mean) + .view(1, self.vae.config.z_dim, 1, 1, 1) + .to(latents.device, latents.dtype) + ) + latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to( + latents.device, latents.dtype + ) + latents = latents / latents_std + latents_mean + video = self.vae.decode(latents, return_dict=False)[0] + video = self.video_processor.postprocess_video(video, output_type=output_type) + else: + video = latents + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (video,) + + return WanPipelineOutput(frames=video) diff --git a/exp_code/1_benchmark/ALG/readme.md b/exp_code/1_benchmark/ALG/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..c25d60b8147f450ee20a9821d50cb43aa8683a8f --- /dev/null +++ b/exp_code/1_benchmark/ALG/readme.md @@ -0,0 +1,170 @@ +# Enhancing Motion Dynamics of Image-to-Video Models via Adaptive Low-Pass Guidance + +[`Project Page`](https://choi403.github.io/ALG/) | [`arXiv`](https://arxiv.org/abs/2506.08456) | [`Gallery`](https://choi403.github.io/ALG/gallery/) + +Official implementation for [Enhancing Motion Dynamics of Image-to-Video Models via Adaptive Low-Pass Guidance](https://arxiv.org/abs/2506.08456) +
+June Suk Choi, +Kyungmin Lee, +Sihyun Yu, +Yisol Choi, +Jinwoo Shin, +Kimin Lee + +https://github.com/user-attachments/assets/a1faada7-624a-4259-8b40-dcef50700346 + +**Summary**: We propose **Adaptive Low-pass Guidance (ALG)**, a simple yet effective sampling method for pre-trained Image-to-Video (I2V) models. ALG mitigates the common issue of motion suppression by adaptively applying low-pass filtering to the conditioning image during the early stages of the denoising process. This encourages the generation of more dynamic videos without compromising the visual quality or fidelity to the input image. + +## 1. Setup +```bash +conda create -n alg python=3.11 -y +conda activate alg +pip install -r requirements.txt # We recommend using torch version 2.5.1 and CUDA version 12.2 for the best compatibility. +``` + +## 2. How to Run + +You can use the main script `run.py` to generate videos using our method. Configuration files are located in `./configs`. + +### Basic Usage + +You can generate a video using the following command with your image file and prompt. + +```bash +python run.py \ + --config [PATH_TO_CONFIG_FILE] \ + --image_path [PATH_TO_INPUT_IMAGE] \ + --prompt "[YOUR_PROMPT]" \ + --output_path [PATH_TO_SAVE_VIDEO] +``` + +### Examples +We include a few example images in the asset folder, coupled with their corresponding prompts below. + +**Generate a video with ALG enabled (more dynamic)** +```bash +python run.py \ + --config ./configs/wan_alg.yaml \ + --image_path ./assets/city.png \ + --prompt "A car chase through narrow city streets at night." \ + --output_path city_alg.mp4 +``` + +**Generate a video without ALG (more static)** +```bash +python run.py \ + --config ./configs/wan_default.yaml \ + --image_path ./assets/city.png \ + --prompt "A car chase through narrow city streets at night." \ + --output_path city_baseline.mp4 +``` + +**Example prompts** +``` +city.png: "A car chase through narrow city streets at night." +snowboard.png: "A snowboarder doing a backflip off a jump." +boat.png: "A group of people whitewater rafting in a canyon." +helicopter.png: "A helicopter hovering over a rescue site." +tennis.png: "A man swinging a tennis racquet at a tennis ball." +``` + +## Configuration + +All generation and ALG parameters are defined in a single yaml config file (e.g., `config/wan_alg.yaml`). + +### Model configuration +```yaml +# configs/cogvideox_alg.yaml + +model: + path: "THUDM/CogVideoX-5b-I2V" # Hugging Face model path + dtype: "bfloat16" # Dtype for the model (e.g., float16, bfloat16, float32) + +generation: + height: null # Output video height (null for model default) + width: null # Output video width (null for model default) + num_frames: 49 # Number of frames to generate + num_inference_steps: 50 # Denoising steps + guidance_scale: 6.0 # Classifier-Free Guidance scale + +video: + fps: 12 # FPS for the output video file +``` + +### ALG configuration (low-pass filtering) +* `use_low_pass_guidance` (`bool`): Enable (`true`) or disable ALG for inference. + +* **Filter Settings**: Low-pass filtering characteristics. + + * `lp_filter_type` (`str`): Specifies the type of low-pass filter to use. + * `"down_up"`: (Recommended) Bilinearly downsamples the image by `lp_resize_factor` and then upsamples it back to the original size. + * `"gaussian_blur"`: Applies Gaussian blur. + + * `lp_filter_in_latent` (`bool`): Determines whether the filter is applied in pixel space or latent space. + * `true`: (Recommended) The filter is applied to the image's latent representation after it has been encoded by the VAE. + * `false`: The filter is applied directly to the RGB image *before* it is encoded by the VAE. + + * `lp_resize_factor` (`float`): (for `"down_up"`) + * The factor by which to downsample the image (e.g., `0.25` means resizing to 25% of the original dimensions). Smaller value means stronget low-pass filtering, and potentially more motion. + + * `lp_blur_sigma` (`float`): (for `"gaussian_blur"`) + * The standard deviation (sigma) for the Gaussian kernel. Larger values result in a stronger blur. + + * `lp_blur_kernel_size` (`float` | `int`): (for `"gaussian_blur"`) + * The size of the blurring kernel. If a float, it's interpreted as a fraction of the image height. + +* **Adaptive Scheduling**: Controls how the strength of the low-pass filter changes over the denoising timesteps. + + * `lp_strength_schedule_type` (`str`): The scheduling strategy. Strength is a multiplier from 0.0 (off) to 1.0 (full). + * `"interval"`: (Recommended) Applies the filter at full strength (`1.0`) for a specified portion of the denoising process and turns it off (`0.0`) for the rest. + * `"linear"`: Linearly decays the filter strength from a starting value to an ending value. + * `"exponential"`: Exponentially decays the filter strength from the beginning. + * `"none"`: Applies filter at a constant strength throughout. + + * Parameters for `"interval"` schedule: + * `schedule_interval_start_time` (`float`): The point to turn the filter on, as a fraction of total steps [`0.0`,`1.0`]. `0.0` is the first step. + * `schedule_interval_end_time` (`float`): The point to turn the filter off. With 50 steps, `0.06` means the filter is active for the first `50 * 0.06 = 3` steps. + + * Parameters for `"linear"` schedule: + * `schedule_linear_start_weight` (`float`): The filter strength at the first timestep (usually `1.0`). + * `schedule_linear_end_weight` (`float`): The final filter strength to decay towards (usually `0.0`). + * `schedule_linear_end_time` (`float`): The point in the process (as a fraction of total steps) at which the `end_weight` is reached. The strength remains at `end_weight` after this point. + + * Parameters for `"exponential"` schedule: + * `schedule_exp_decay_rate` (`float`): The decay rate `r` for the formula `strength = exp(-r * time_fraction)`. Higher values cause strength to decay more quickly. + + * `schedule_blur_kernel_size` (`bool`): If `true` and using a scheduler with the `"gaussian_blur"` filter, the blur kernel size will also be scaled down along with the filter strength. + +## 3. Supported Models + +We provide implementations and configurations for the following models: + +* **[CogVideoX](https://huggingface.co/THUDM/CogVideoX-5b-I2V)**: `THUDM/CogVideoX-5b-I2V` +* **[Wan 2.1](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P-Diffusers)**: `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` +* **[HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo-I2V)**: `tencent/HunyuanVideo-I2V` +* [LTX-Video](https://huggingface.co/Lightricks/LTX-Video): `Lightricks/LTX-Video` (Not available yet, coming soon!) + +We plan to add ALG implementation for LTX-Video as soon as possible! + +You can create new configuration files for these models by modifying the `model.path` and adjusting the `generation` and `alg` parameters accordingly. Example configs are provided in the `./configs` directory. + +## 4. More Examples + +For more qualitative results and video comparisons, please visit the **[Gallery](https://choi403.github.io/ALG/gallery/)** on our project page. + +## Acknowledgement + +This code is built upon [Hugging Face Diffusers](https://github.com/huggingface/diffusers) library. We thank the authors of the open-source Image-to-Video models used in our work for making their code and models publicly available. + +## BibTeX + +If you find our work useful for your research, please consider citing our paper: + +```bibtex +@article{choi2025alg, + title={Enhancing Motion Dynamics of Image-to-Video Models via Adaptive Low-Pass Guidance}, + author={Choi, June Suk and Lee, Kyungmin and Yu, Sihyun and Choi, Yisol and Shin, Jinwoo and Lee, Kimin}, + year={2025}, + journal={arXiv preprint arXiv:2506.08456}, +} +``` diff --git a/exp_code/1_benchmark/ALG/requirements.txt b/exp_code/1_benchmark/ALG/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a37b4fb3f9618f452498917811d000d408efeee6 --- /dev/null +++ b/exp_code/1_benchmark/ALG/requirements.txt @@ -0,0 +1,13 @@ +accelerate==1.3.0 +huggingface-hub +imageio-ffmpeg +open_clip_torch +openai-clip +opencv-python +peft==0.15.0 +sentencepiece +torchvision +transformers==4.48.1 +xformers==0.0.29.post1 +av==12.0.0 +diffusers @ git+https://github.com/huggingface/diffusers.git@be2fb77dc164083bf8f033874b066c96bc6752b8 \ No newline at end of file diff --git a/exp_code/1_benchmark/ALG/run.py b/exp_code/1_benchmark/ALG/run.py new file mode 100644 index 0000000000000000000000000000000000000000..d1cb2ab0b5e1d9fd18503c526bede94dedd6ff2f --- /dev/null +++ b/exp_code/1_benchmark/ALG/run.py @@ -0,0 +1,150 @@ +import yaml +import argparse +import torch +import torchvision +from PIL import Image +import logging +import sys + +# --- Diffusers and Transformers Imports --- +from diffusers import AutoencoderKLWan, UniPCMultistepScheduler, HunyuanVideoTransformer3DModel, FlowMatchEulerDiscreteScheduler +from diffusers.utils import load_image +from transformers import CLIPVisionModel + +# --- Low-pass Pipelines --- +from pipeline_wan_image2video_lowpass import WanImageToVideoPipeline +from pipeline_cogvideox_image2video_lowpass import CogVideoXImageToVideoPipeline +from pipeline_hunyuan_video_image2video_lowpass import HunyuanVideoImageToVideoPipeline + +from lp_utils import get_hunyuan_video_size + +from diffusers.utils import export_to_video + +# --- Basic Logging Setup --- +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', stream=sys.stdout) +logger = logging.getLogger(__name__) + + +def main(args): + # 1. Configuration + IMAGE_PATH = args.image_path + PROMPT = args.prompt + OUTPUT_PATH = args.output_path + MODEL_CACHE_DIR = args.model_cache_dir + + with open(args.config, 'r') as f: + config = yaml.safe_load(f) + + model_path = config['model']['path'] + model_dtype_str = config['model']['dtype'] + model_dtype = getattr(torch, model_dtype_str) + + device = "cuda" if torch.cuda.is_available() else "cpu" + + logger.info(f"Using device: {device}") + + # 2. Pipeline preparation + if "Wan" in model_path: + image_encoder = CLIPVisionModel.from_pretrained(model_path, + subfolder="image_encoder", + torch_dtype=torch.float32, + cache_dir=MODEL_CACHE_DIR + ) + vae = AutoencoderKLWan.from_pretrained(model_path, + subfolder="vae", + torch_dtype=torch.float32, + cache_dir=MODEL_CACHE_DIR + ) + pipe = WanImageToVideoPipeline.from_pretrained(model_path, + vae=vae, + image_encoder=image_encoder, + torch_dtype=model_dtype, + cache_dir=MODEL_CACHE_DIR + ) + # Recommended setup (See https://github.com/huggingface/diffusers/blob/3c8b67b3711b668a6e7867e08b54280e51454eb5/src/diffusers/pipelines/wan/pipeline_wan.py#L58C13-L58C23) + pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=3.0 if config['generation']['height'] == '480' else 5.0) + elif "CogVideoX" in model_path: + pipe = CogVideoXImageToVideoPipeline.from_pretrained( + model_path, + torch_dtype=model_dtype, + cache_dir=MODEL_CACHE_DIR + ) + elif "HunyuanVideo" in model_path: + transformer = HunyuanVideoTransformer3DModel.from_pretrained( + model_path, + subfolder="transformer", + torch_dtype=torch.bfloat16, + cache_dir=MODEL_CACHE_DIR + ) + pipe = HunyuanVideoImageToVideoPipeline.from_pretrained( + model_path, transformer=transformer, + torch_dtype=torch.float16, + cache_dir=MODEL_CACHE_DIR + ) + pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config( + pipe.scheduler.config, + flow_shift= config['model']['flow_shift'], + invert_sigmas = config['model']['flow_reverse'] + ) + pipe.to(device) + + logger.info("Pipeline loaded successfully.") + + # 3. Prepare inputs + input_image = load_image(Image.open(IMAGE_PATH)) + + generator = torch.Generator(device=device).manual_seed(42) + + pipe_kwargs = { + "image": input_image, + "prompt": PROMPT, + "generator": generator, + } + + params_from_config = {**config.get('generation', {}), **config.get('alg', {})} + + for key, value in params_from_config.items(): + if value is not None: + pipe_kwargs[key] = value + + logger.info("Starting video generation...") + log_subset = {k: v for k, v in pipe_kwargs.items() if k not in ['image', 'generator']} + logger.info(f"Pipeline arguments: {log_subset}") + + if "HunyuanVideo" in model_path: + pipe_kwargs["height"], pipe_kwargs["width"] = get_hunyuan_video_size(config['video']['resolution'], input_image) + + # 4. Generate video + video_output = pipe(**pipe_kwargs) + video_frames = video_output.frames[0] # Output is a list containing a list of PIL Images + logger.info(f"Video generation complete. Received {len(video_frames)} frames.") + + # # 5. Save video + # video_tensors = [torchvision.transforms.functional.to_tensor(frame) for frame in video_frames] + # video_tensor = torch.stack(video_tensors) # Shape: (T, C, H, W) + # video_tensor = video_tensor.permute(0, 2, 3, 1) # Shape: (T, H, W, C) for write_video + # video_tensor = (video_tensor * 255).clamp(0, 255).to(torch.uint8).cpu() + + # logger.info(f"Saving video to: {OUTPUT_PATH}") + # torchvision.io.write_video( + # OUTPUT_PATH, + # video_tensor, + # fps=config['video']['fps'], + # video_codec='h264', + # options={'crf': '18', 'preset': 'slow'} + # ) + + export_to_video(video_frames, OUTPUT_PATH, fps=config['video']['fps']) + logger.info("Video saved successfully. Run complete.") + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description="Arguments") + parser.add_argument("--config", type=str, default="./configs/hunyuan_video_alg.yaml") + parser.add_argument("--image_path", type=str, default="./assets/a red double decker bus driving down a street.jpg") + parser.add_argument("--prompt", type=str, default="a red double decker bus driving down a street") + parser.add_argument("--output_path", type=str, default="output.mp4") + parser.add_argument("--model_cache_dir", type=str, default=None) + args = parser.parse_args() + + main(args) \ No newline at end of file diff --git a/exp_code/1_benchmark/ALG/run.sh b/exp_code/1_benchmark/ALG/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..9d6929ea297d78248443b88efbf4e756282f6d9b --- /dev/null +++ b/exp_code/1_benchmark/ALG/run.sh @@ -0,0 +1,5 @@ +python run.py \ + --config ./configs/hunyuan_video_alg.yaml \ + --image_path ./assets/city.png \ + --prompt "A car chase through narrow city streets at night." \ + --output_path city_alg.mp4 \ No newline at end of file diff --git a/exp_code/1_benchmark/AccVideo/LICENSE.txt b/exp_code/1_benchmark/AccVideo/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..164c703a8953fab9cd5ceb6fd0c1ce4f382d1d05 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/LICENSE.txt @@ -0,0 +1,77 @@ +TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT +Tencent HunyuanVideo Release Date: December 3, 2024 +THIS LICENSE AGREEMENT DOES NOT APPLY IN THE EUROPEAN UNION, UNITED KINGDOM AND SOUTH KOREA AND IS EXPRESSLY LIMITED TO THE TERRITORY, AS DEFINED BELOW. +By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent Hunyuan Works, including via any Hosted Service, You will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately. +1. 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Exclusive jurisdiction and venue for any dispute arising out of or relating to this Agreement will be a court of competent jurisdiction in the Hong Kong Special Administrative Region of the People’s Republic of China, and Tencent and Licensee consent to the exclusive jurisdiction of such court with respect to any such dispute. +  +EXHIBIT A +ACCEPTABLE USE POLICY + +Tencent reserves the right to update this Acceptable Use Policy from time to time. +Last modified: November 5, 2024 + +Tencent endeavors to promote safe and fair use of its tools and features, including Tencent Hunyuan. You agree not to use Tencent Hunyuan or Model Derivatives: +1. Outside the Territory; +2. In any way that violates any applicable national, federal, state, local, international or any other law or regulation; +3. To harm Yourself or others; +4. To repurpose or distribute output from Tencent Hunyuan or any Model Derivatives to harm Yourself or others; +5. To override or circumvent the safety guardrails and safeguards We have put in place; +6. For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; +7. To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections; +8. To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement; +9. To intentionally defame, disparage or otherwise harass others; +10. To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems; +11. To generate or disseminate personal identifiable information with the purpose of harming others; +12. To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated; +13. To impersonate another individual without consent, authorization, or legal right; +14. To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance); +15. In a manner that violates or disrespects the social ethics and moral standards of other countries or regions; +16. To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism; +17. For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics; +18. To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; +19. For military purposes; +20. To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices. diff --git a/exp_code/1_benchmark/AccVideo/README.md b/exp_code/1_benchmark/AccVideo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..19bb3a2d0a751f2fc4b4bdb058afb01eec635a79 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/README.md @@ -0,0 +1,130 @@ +# AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset + +This repository is the official PyTorch implementation of [AccVideo](https://arxiv.org/abs/2503.19462). AccVideo is a novel efficient distillation method to accelerate video diffusion models with synthetic datset. Our method is 8.5x faster than HunyuanVideo. + + +[![arXiv](https://img.shields.io/badge/arXiv-2503.19462-b31b1b.svg)](https://arxiv.org/abs/2503.19462) +[![Project Page](https://img.shields.io/badge/Project-Website-green)](https://aejion.github.io/accvideo/) +[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow)](https://huggingface.co/aejion/AccVideo) + +## 🔥🔥🔥 News + +* May 26, 2025: We release the inference code and [model weights](https://huggingface.co/aejion/AccVideo-WanX-T2V-14B) of AccVideo based on WanXT2V-14B. +* Mar 31, 2025: [ComfyUI-Kijai (FP8 Inference)](https://huggingface.co/Kijai/HunyuanVideo_comfy/blob/main/accvideo-t2v-5-steps_fp8_e4m3fn.safetensors): ComfyUI-Integration by [Kijai](https://huggingface.co/Kijai) +* Mar 26, 2025: We release the inference code and [model weights](https://huggingface.co/aejion/AccVideo) of AccVideo based on HunyuanT2V. + + +## 🎥 Demo (Based on HunyuanT2V) + + +https://github.com/user-attachments/assets/59f3c5db-d585-4773-8d92-366c1eb040f0 + +## 🎥 Demo (Based on WanXT2V-14B) + + +https://github.com/user-attachments/assets/ff9724da-b76c-478d-a9bf-0ee7240494b2 + + + +## 📑 Open-source Plan + +- [x] Inference +- [x] Checkpoints +- [ ] Multi-GPU Inference +- [ ] Synthetic Video Dataset, SynVid +- [ ] Training + + +## 🔧 Installation +The code is tested on Python 3.10.0, CUDA 11.8 and A100. +``` +conda create -n accvideo python==3.10.0 +conda activate accvideo + +pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu118 +pip install -r requirements.txt +pip install flash-attn==2.7.3 --no-build-isolation +pip install "huggingface_hub[cli]" +``` + +## 🤗 Checkpoints +To download the checkpoints (based on HunyuanT2V), use the following command: +```bash +# Download the model weight +huggingface-cli download aejion/AccVideo --local-dir ./ckpts +``` + +To download the checkpoints (based on WanX-T2V-14B), use the following command: +```bash +# Download the model weight +huggingface-cli download aejion/AccVideo-WanX-T2V-14B --local-dir ./wanx_t2v_ckpts +``` + +## 🚀 Inference +We recommend using a GPU with 80GB of memory. We use AccVideo to distill Hunyuan and WanX. + +### Inference for HunyuanT2V + +To run the inference, use the following command: +```bash +export MODEL_BASE=./ckpts +python sample_t2v.py \ + --height 544 \ + --width 960 \ + --num_frames 93 \ + --num_inference_steps 5 \ + --guidance_scale 1 \ + --embedded_cfg_scale 6 \ + --flow_shift 7 \ + --flow-reverse \ + --prompt_file ./assets/prompt.txt \ + --seed 1024 \ + --output_path ./results/accvideo-544p \ + --model_path ./ckpts \ + --dit-weight ./ckpts/accvideo-t2v-5-steps/diffusion_pytorch_model.pt +``` + +The following table shows the comparisons on inference time using a single A100 GPU: + +| Model | Setting(height/width/frame) | Inference Time(s) | +|:------------:|:---------------------------:|:-----------------:| +| HunyuanVideo | 720px1280px129f | 3234 | +| Ours | 720px1280px129f | 380(8.5x faster) | +| HunyuanVideo | 544px960px93f | 704 | +| Ours | 544px960px93f | 91(7.7x faster) | + +### Inference for WanXT2V + +To run the inference, use the following command: +```bash +python sample_wanx_t2v.py \ + --task t2v-14B \ + --size 832*480 \ + --ckpt_dir ./wanx_t2v_ckpts \ + --sample_solver 'unipc' \ + --save_dir ./results/accvideo_wanx_14B \ + --sample_steps 10 +``` + +The following table shows the comparisons on inference time using a single A100 GPU: + +| Model | Setting(height/width/frame) | Inference Time(s) | +|:-----:|:---------------------------:|:-----------------:| +| Wanx | 480px832px81f | 932 | +| Ours | 480px832px81f | 97(9.6x faster) | + +## 🔗 BibTeX + +If you find [AccVideo](https://arxiv.org/abs/2503.19462) useful for your research and applications, please cite using this BibTeX: + +```BibTeX +@article{zhang2025accvideo, + title={AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset}, + author={Zhang, Haiyu and Chen, Xinyuan and Wang, Yaohui and Liu, Xihui and Wang, Yunhong and Qiao, Yu}, + journal={arXiv preprint arXiv:2503.19462}, + year={2025} +} +``` + +## Acknowledgements +The code is built upon [FastVideo](https://github.com/hao-ai-lab/FastVideo) and [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), we thank all the contributors for open-sourcing. diff --git a/exp_code/1_benchmark/AccVideo/assets/prompt.txt b/exp_code/1_benchmark/AccVideo/assets/prompt.txt new file mode 100644 index 0000000000000000000000000000000000000000..b9986437cef2ce36b656cd710d7ad01813b1cab1 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/assets/prompt.txt @@ -0,0 +1,3 @@ +A honeybee drifting between lavender blossoms. Each wingbeat slowed to a gentle wave, pollen particles floating in still air. In super slow motion, even the bee's compound eyes shimmer, revealing details normally invisible to the human eye. +A hand with delicate fingers picks up a bright yellow lemon from a wooden bowl filled with lemons and sprigs of mint against a peach-colored background. The hand gently tosses the lemon up and catches it, showcasing its smooth texture. A beige string bag sits beside the bowl, adding a rustic touch to the scene. Additional lemons, one halved, are scattered around the base of the bowl. The even lighting enhances the vibrant colors and creates a fresh, inviting atmosphere. +The camera follows behind a white vintage SUV with a black roof rack as it speeds up a steep dirt road surrounded by pine trees on a steep mountain slope. \ No newline at end of file diff --git a/exp_code/1_benchmark/AccVideo/models/__init__.py b/exp_code/1_benchmark/AccVideo/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/__init__.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/constants.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..81c4ea6f43e864bc0be49ae7aeee63fca624e762 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/constants.py @@ -0,0 +1,87 @@ +import os +import torch + +__all__ = [ + "C_SCALE", + "PROMPT_TEMPLATE", + "MODEL_BASE", + "PRECISIONS", + "NORMALIZATION_TYPE", + "ACTIVATION_TYPE", + "VAE_PATH", + "TEXT_ENCODER_PATH", + "TOKENIZER_PATH", + "TEXT_PROJECTION", + "DATA_TYPE", + "NEGATIVE_PROMPT", +] + +PRECISION_TO_TYPE = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + +# =================== Constant Values ===================== +# Computation scale factor, 1P = 1_000_000_000_000_000. Tensorboard will display the value in PetaFLOPS to avoid +# overflow error when tensorboard logging values. +C_SCALE = 1_000_000_000_000_000 + +# When using decoder-only models, we must provide a prompt template to instruct the text encoder +# on how to generate the text. +# -------------------------------------------------------------------- +PROMPT_TEMPLATE_ENCODE = ( + "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, " + "quantity, text, spatial relationships of the objects and background:<|eot_id|>" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" +) +PROMPT_TEMPLATE_ENCODE_VIDEO = ( + "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: " + "1. The main content and theme of the video." + "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." + "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." + "4. background environment, light, style and atmosphere." + "5. camera angles, movements, and transitions used in the video:<|eot_id|>" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" +) + +NEGATIVE_PROMPT = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion" + +PROMPT_TEMPLATE = { + "dit-llm-encode": {"template": PROMPT_TEMPLATE_ENCODE, "crop_start": 36,}, + "dit-llm-encode-video": { + "template": PROMPT_TEMPLATE_ENCODE_VIDEO, + "crop_start": 95, + }, +} + +# ======================= Model ====================== +PRECISIONS = {"fp32", "fp16", "bf16"} +NORMALIZATION_TYPE = {"layer", "rms"} +ACTIVATION_TYPE = {"relu", "silu", "gelu", "gelu_tanh"} + +# =================== Model Path ===================== +MODEL_BASE = os.getenv("MODEL_BASE", "./ckpts") + +# =================== Data ======================= +DATA_TYPE = {"image", "video", "image_video"} + +# 3D VAE +VAE_PATH = {"884-16c-hy": f"{MODEL_BASE}/hunyuan-video-t2v-720p/vae"} + +# Text Encoder +TEXT_ENCODER_PATH = { + "clipL": f"{MODEL_BASE}/text_encoder_2", + "llm": f"{MODEL_BASE}/text_encoder", +} + +# Tokenizer +TOKENIZER_PATH = { + "clipL": f"{MODEL_BASE}/text_encoder_2", + "llm": f"{MODEL_BASE}/text_encoder", +} + +TEXT_PROJECTION = { + "linear", # Default, an nn.Linear() layer + "single_refiner", # Single TokenRefiner. Refer to LI-DiT +} diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/__init__.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2141aa3dccb5a6b231bf2f3ae6ab864152ffc3ec --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/__init__.py @@ -0,0 +1,2 @@ +from .pipelines import HunyuanVideoPipeline +from .schedulers import FlowMatchDiscreteScheduler diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/pipelines/__init__.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/pipelines/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e44cb6196fe7f6a7fa821b2bebddf8d1117521fc --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/pipelines/__init__.py @@ -0,0 +1 @@ +from .pipeline_hunyuan_video import HunyuanVideoPipeline diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/pipelines/pipeline_hunyuan_video.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/pipelines/pipeline_hunyuan_video.py new file mode 100644 index 0000000000000000000000000000000000000000..18654c70ddffbad0ede3405be6454bf1a28f6fe3 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/pipelines/pipeline_hunyuan_video.py @@ -0,0 +1,1114 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# +# Modified from diffusers==0.29.2 +# +# ============================================================================== +import inspect +import math +from typing import Any, Callable, Dict, List, Optional, Union, Tuple +import torch +import torch.distributed as dist +import numpy as np +from dataclasses import dataclass +from packaging import version + +from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback +from diffusers.configuration_utils import FrozenDict +from diffusers.image_processor import VaeImageProcessor +from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin +from diffusers.models import AutoencoderKL +from diffusers.models.lora import adjust_lora_scale_text_encoder +from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.utils import ( + USE_PEFT_BACKEND, + deprecate, + logging, + replace_example_docstring, + scale_lora_layers, + unscale_lora_layers, +) +from diffusers.utils.torch_utils import randn_tensor +from diffusers.pipelines.pipeline_utils import DiffusionPipeline +from diffusers.utils import BaseOutput + +from ...constants import PRECISION_TO_TYPE +from ...vae.autoencoder_kl_causal_3d import AutoencoderKLCausal3D +from ...text_encoder import TextEncoder +from ...modules import HYVideoDiffusionTransformer + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """""" + + +def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): + """ + Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 + """ + std_text = noise_pred_text.std( + dim=list(range(1, noise_pred_text.ndim)), keepdim=True + ) + std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) + # rescale the results from guidance (fixes overexposure) + noise_pred_rescaled = noise_cfg * (std_text / std_cfg) + # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images + noise_cfg = ( + guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg + ) + return noise_cfg + + +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +): + """ + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError( + "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" + ) + if timesteps is not None: + accepts_timesteps = "timesteps" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +@dataclass +class HunyuanVideoPipelineOutput(BaseOutput): + videos: Union[torch.Tensor, np.ndarray] + + +class HunyuanVideoPipeline(DiffusionPipeline): + r""" + Pipeline for text-to-video generation using HunyuanVideo. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods + implemented for all pipelines (downloading, saving, running on a particular device, etc.). + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. + text_encoder ([`TextEncoder`]): + Frozen text-encoder. + text_encoder_2 ([`TextEncoder`]): + Frozen text-encoder_2. + transformer ([`HYVideoDiffusionTransformer`]): + A `HYVideoDiffusionTransformer` to denoise the encoded video latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. + """ + + model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae" + _optional_components = ["text_encoder_2"] + _exclude_from_cpu_offload = ["transformer"] + _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: TextEncoder, + transformer: HYVideoDiffusionTransformer, + scheduler: KarrasDiffusionSchedulers, + text_encoder_2: Optional[TextEncoder] = None, + progress_bar_config: Dict[str, Any] = None, + args=None, + ): + super().__init__() + + # ========================================================================================== + if progress_bar_config is None: + progress_bar_config = {} + if not hasattr(self, "_progress_bar_config"): + self._progress_bar_config = {} + self._progress_bar_config.update(progress_bar_config) + + self.args = args + # ========================================================================================== + + if ( + hasattr(scheduler.config, "steps_offset") + and scheduler.config.steps_offset != 1 + ): + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" + f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " + "to update the config accordingly as leaving `steps_offset` might led to incorrect results" + " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," + " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" + " file" + ) + deprecate( + "steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False + ) + new_config = dict(scheduler.config) + new_config["steps_offset"] = 1 + scheduler._internal_dict = FrozenDict(new_config) + + if ( + hasattr(scheduler.config, "clip_sample") + and scheduler.config.clip_sample is True + ): + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." + " `clip_sample` should be set to False in the configuration file. Please make sure to update the" + " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" + " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" + " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" + ) + deprecate( + "clip_sample not set", "1.0.0", deprecation_message, standard_warn=False + ) + new_config = dict(scheduler.config) + new_config["clip_sample"] = False + scheduler._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + transformer=transformer, + scheduler=scheduler, + text_encoder_2=text_encoder_2, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.noise_pertub = 0 + + def encode_prompt( + self, + prompt, + device, + num_videos_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + negative_attention_mask: Optional[torch.Tensor] = None, + lora_scale: Optional[float] = None, + clip_skip: Optional[int] = None, + text_encoder: Optional[TextEncoder] = None, + data_type: Optional[str] = "image", + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_videos_per_prompt (`int`): + number of videos that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the video generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + attention_mask (`torch.Tensor`, *optional*): + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + negative_attention_mask (`torch.Tensor`, *optional*): + lora_scale (`float`, *optional*): + A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + text_encoder (TextEncoder, *optional*): + data_type (`str`, *optional*): + """ + if text_encoder is None: + text_encoder = self.text_encoder + + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if lora_scale is not None and isinstance(self, LoraLoaderMixin): + self._lora_scale = lora_scale + + # dynamically adjust the LoRA scale + if not USE_PEFT_BACKEND: + adjust_lora_scale_text_encoder(text_encoder.model, lora_scale) + else: + scale_lora_layers(text_encoder.model, lora_scale) + + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + # textual inversion: process multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, text_encoder.tokenizer) + + text_inputs = text_encoder.text2tokens(prompt, data_type=data_type) + + if clip_skip is None: + prompt_outputs = text_encoder.encode( + text_inputs, data_type=data_type, device=device + ) + prompt_embeds = prompt_outputs.hidden_state + else: + prompt_outputs = text_encoder.encode( + text_inputs, + output_hidden_states=True, + data_type=data_type, + device=device, + ) + # Access the `hidden_states` first, that contains a tuple of + # all the hidden states from the encoder layers. Then index into + # the tuple to access the hidden states from the desired layer. + prompt_embeds = prompt_outputs.hidden_states_list[-(clip_skip + 1)] + # We also need to apply the final LayerNorm here to not mess with the + # representations. The `last_hidden_states` that we typically use for + # obtaining the final prompt representations passes through the LayerNorm + # layer. + prompt_embeds = text_encoder.model.text_model.final_layer_norm( + prompt_embeds + ) + + attention_mask = prompt_outputs.attention_mask + if attention_mask is not None: + attention_mask = attention_mask.to(device) + bs_embed, seq_len = attention_mask.shape + attention_mask = attention_mask.repeat(1, num_videos_per_prompt) + attention_mask = attention_mask.view( + bs_embed * num_videos_per_prompt, seq_len + ) + + if text_encoder is not None: + prompt_embeds_dtype = text_encoder.dtype + elif self.transformer is not None: + prompt_embeds_dtype = self.transformer.dtype + else: + prompt_embeds_dtype = prompt_embeds.dtype + + prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) + + if prompt_embeds.ndim == 2: + bs_embed, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt) + prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, -1) + else: + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) + prompt_embeds = prompt_embeds.view( + bs_embed * num_videos_per_prompt, seq_len, -1 + ) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + # textual inversion: process multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt( + uncond_tokens, text_encoder.tokenizer + ) + + # max_length = prompt_embeds.shape[1] + uncond_input = text_encoder.text2tokens(uncond_tokens, data_type=data_type) + + negative_prompt_outputs = text_encoder.encode( + uncond_input, data_type=data_type, device=device + ) + negative_prompt_embeds = negative_prompt_outputs.hidden_state + + negative_attention_mask = negative_prompt_outputs.attention_mask + if negative_attention_mask is not None: + negative_attention_mask = negative_attention_mask.to(device) + _, seq_len = negative_attention_mask.shape + negative_attention_mask = negative_attention_mask.repeat( + 1, num_videos_per_prompt + ) + negative_attention_mask = negative_attention_mask.view( + batch_size * num_videos_per_prompt, seq_len + ) + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to( + dtype=prompt_embeds_dtype, device=device + ) + + if negative_prompt_embeds.ndim == 2: + negative_prompt_embeds = negative_prompt_embeds.repeat( + 1, num_videos_per_prompt + ) + negative_prompt_embeds = negative_prompt_embeds.view( + batch_size * num_videos_per_prompt, -1 + ) + else: + negative_prompt_embeds = negative_prompt_embeds.repeat( + 1, num_videos_per_prompt, 1 + ) + negative_prompt_embeds = negative_prompt_embeds.view( + batch_size * num_videos_per_prompt, seq_len, -1 + ) + + if text_encoder is not None: + if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND: + # Retrieve the original scale by scaling back the LoRA layers + unscale_lora_layers(text_encoder.model, lora_scale) + + return ( + prompt_embeds, + negative_prompt_embeds, + attention_mask, + negative_attention_mask, + ) + + def decode_latents(self, latents, enable_tiling=True): + deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead" + deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False) + + latents = 1 / self.vae.config.scaling_factor * latents + if enable_tiling: + self.vae.enable_tiling() + image = self.vae.decode(latents, return_dict=False)[0] + else: + image = self.vae.decode(latents, return_dict=False)[0] + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + if image.ndim == 4: + image = image.cpu().permute(0, 2, 3, 1).float() + else: + image = image.cpu().float() + return image + + def prepare_extra_func_kwargs(self, func, kwargs): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + extra_step_kwargs = {} + + for k, v in kwargs.items(): + accepts = k in set(inspect.signature(func).parameters.keys()) + if accepts: + extra_step_kwargs[k] = v + return extra_step_kwargs + + def check_inputs( + self, + prompt, + height, + width, + video_length, + callback_steps, + negative_prompt=None, + prompt_embeds=None, + negative_prompt_embeds=None, + callback_on_step_end_tensor_inputs=None, + vae_ver="88-4c-sd", + ): + if height % 8 != 0 or width % 8 != 0: + raise ValueError( + f"`height` and `width` have to be divisible by 8 but are {height} and {width}." + ) + + if video_length is not None: + if "884" in vae_ver: + if video_length != 1 and (video_length - 1) % 4 != 0: + raise ValueError( + f"`video_length` has to be 1 or a multiple of 4 but is {video_length}." + ) + elif "888" in vae_ver: + if video_length != 1 and (video_length - 1) % 8 != 0: + raise ValueError( + f"`video_length` has to be 1 or a multiple of 8 but is {video_length}." + ) + + if callback_steps is not None and ( + not isinstance(callback_steps, int) or callback_steps <= 0 + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs + for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and ( + not isinstance(prompt, str) and not isinstance(prompt, list) + ): + raise ValueError( + f"`prompt` has to be of type `str` or `list` but is {type(prompt)}" + ) + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + def prepare_latents( + self, + batch_size, + num_channels_latents, + height, + width, + video_length, + dtype, + device, + generator, + latents=None, + ): + shape = ( + batch_size, + num_channels_latents, + video_length, + int(height) // self.vae_scale_factor, + int(width) // self.vae_scale_factor, + ) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor( + shape, generator=generator, device=device, dtype=dtype + ) + else: + latents = latents.to(device) + + # Check existence to make it compatible with FlowMatchEulerDiscreteScheduler + if hasattr(self.scheduler, "init_noise_sigma"): + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + + # noise_ = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + # latents = math.sqrt(1 - self.noise_pertub) * latents + noise_ * math.sqrt(self.noise_pertub) + # self.noise_pertub += 0.05 + return latents + + # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding + def get_guidance_scale_embedding( + self, + w: torch.Tensor, + embedding_dim: int = 512, + dtype: torch.dtype = torch.float32, + ) -> torch.Tensor: + """ + See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 + + Args: + w (`torch.Tensor`): + Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings. + embedding_dim (`int`, *optional*, defaults to 512): + Dimension of the embeddings to generate. + dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): + Data type of the generated embeddings. + + Returns: + `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`. + """ + assert len(w.shape) == 1 + w = w * 1000.0 + + half_dim = embedding_dim // 2 + emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) + emb = w.to(dtype)[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0, 1)) + assert emb.shape == (w.shape[0], embedding_dim) + return emb + + @property + def guidance_scale(self): + return self._guidance_scale + + @property + def guidance_rescale(self): + return self._guidance_rescale + + @property + def clip_skip(self): + return self._clip_skip + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + @property + def do_classifier_free_guidance(self): + # return self._guidance_scale > 1 and self.transformer.config.time_cond_proj_dim is None + return self._guidance_scale > 1 + + @property + def cross_attention_kwargs(self): + return self._cross_attention_kwargs + + @property + def num_timesteps(self): + return self._num_timesteps + + @property + def interrupt(self): + return self._interrupt + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Union[str, List[str]], + height: int, + width: int, + video_length: int, + data_type: str = "video", + num_inference_steps: int = 50, + timesteps: List[int] = None, + sigmas: List[float] = None, + guidance_scale: float = 7.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_videos_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + prompt_embeds: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + negative_attention_mask: Optional[torch.Tensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + guidance_rescale: float = 0.0, + clip_skip: Optional[int] = None, + callback_on_step_end: Optional[ + Union[ + Callable[[int, int, Dict], None], + PipelineCallback, + MultiPipelineCallbacks, + ] + ] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + freqs_cis: Tuple[torch.Tensor, torch.Tensor] = None, + vae_ver: str = "88-4c-sd", + enable_tiling: bool = False, + n_tokens: Optional[int] = None, + embedded_guidance_scale: Optional[float] = None, + few_step: bool = False, + **kwargs, + ): + r""" + The call function to the pipeline for generation. + + Args: + prompt (`str` or `List[str]`): + The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. + height (`int`): + The height in pixels of the generated image. + width (`int`): + The width in pixels of the generated image. + video_length (`int`): + The number of frames in the generated video. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + timesteps (`List[int]`, *optional*): + Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument + in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is + passed will be used. Must be in descending order. + sigmas (`List[float]`, *optional*): + Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in + their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed + will be used. + guidance_scale (`float`, *optional*, defaults to 7.5): + A higher guidance scale value encourages the model to generate images closely linked to the text + `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide what to not include in image generation. If not defined, you need to + pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). + num_videos_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies + to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + generation deterministic. + latents (`torch.Tensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor is generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not + provided, text embeddings are generated from the `prompt` input argument. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If + not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. + + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generated image. Choose between `PIL.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`HunyuanVideoPipelineOutput`] instead of a + plain tuple. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in + [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + guidance_rescale (`float`, *optional*, defaults to 0.0): + Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are + Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when + using zero terminal SNR. + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*): + A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of + each denoising step during the inference. with the following arguments: `callback_on_step_end(self: + DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a + list of all tensors as specified by `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeline class. + + Examples: + + Returns: + [`~HunyuanVideoPipelineOutput`] or `tuple`: + If `return_dict` is `True`, [`HunyuanVideoPipelineOutput`] is returned, + otherwise a `tuple` is returned where the first element is a list with the generated images and the + second element is a list of `bool`s indicating whether the corresponding generated image contains + "not-safe-for-work" (nsfw) content. + """ + callback = kwargs.pop("callback", None) + callback_steps = kwargs.pop("callback_steps", None) + + if callback is not None: + deprecate( + "callback", + "1.0.0", + "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", + ) + if callback_steps is not None: + deprecate( + "callback_steps", + "1.0.0", + "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", + ) + + if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + + # 0. Default height and width to unet + # height = height or self.transformer.config.sample_size * self.vae_scale_factor + # width = width or self.transformer.config.sample_size * self.vae_scale_factor + # to deal with lora scaling and other possible forward hooks + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + height, + width, + video_length, + callback_steps, + negative_prompt, + prompt_embeds, + negative_prompt_embeds, + callback_on_step_end_tensor_inputs, + vae_ver=vae_ver, + ) + + self._guidance_scale = guidance_scale + self._guidance_rescale = guidance_rescale + self._clip_skip = clip_skip + self._cross_attention_kwargs = cross_attention_kwargs + self._interrupt = False + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = torch.device(f"cuda:{dist.get_rank()}") if dist.is_initialized() else self._execution_device + + # 3. Encode input prompt + lora_scale = ( + self.cross_attention_kwargs.get("scale", None) + if self.cross_attention_kwargs is not None + else None + ) + + ( + prompt_embeds, + negative_prompt_embeds, + prompt_mask, + negative_prompt_mask, + ) = self.encode_prompt( + prompt, + device, + num_videos_per_prompt, + self.do_classifier_free_guidance, + negative_prompt, + prompt_embeds=prompt_embeds, + attention_mask=attention_mask, + negative_prompt_embeds=negative_prompt_embeds, + negative_attention_mask=negative_attention_mask, + lora_scale=lora_scale, + clip_skip=self.clip_skip, + data_type=data_type, + ) + if self.text_encoder_2 is not None: + ( + prompt_embeds_2, + negative_prompt_embeds_2, + prompt_mask_2, + negative_prompt_mask_2, + ) = self.encode_prompt( + prompt, + device, + num_videos_per_prompt, + self.do_classifier_free_guidance, + negative_prompt, + prompt_embeds=None, + attention_mask=None, + negative_prompt_embeds=None, + negative_attention_mask=None, + lora_scale=lora_scale, + clip_skip=self.clip_skip, + text_encoder=self.text_encoder_2, + data_type=data_type, + ) + else: + prompt_embeds_2 = None + negative_prompt_embeds_2 = None + prompt_mask_2 = None + negative_prompt_mask_2 = None + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + if self.do_classifier_free_guidance: + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) + if prompt_mask is not None: + prompt_mask = torch.cat([negative_prompt_mask, prompt_mask]) + if prompt_embeds_2 is not None: + prompt_embeds_2 = torch.cat([negative_prompt_embeds_2, prompt_embeds_2]) + if prompt_mask_2 is not None: + prompt_mask_2 = torch.cat([negative_prompt_mask_2, prompt_mask_2]) + + # 4. Prepare timesteps + extra_set_timesteps_kwargs = self.prepare_extra_func_kwargs( + self.scheduler.set_timesteps, {"n_tokens": n_tokens} + ) + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, + num_inference_steps, + device, + timesteps, + sigmas, + **extra_set_timesteps_kwargs, + ) + + if "884" in vae_ver: + video_length = (video_length - 1) // 4 + 1 + elif "888" in vae_ver: + video_length = (video_length - 1) // 8 + 1 + else: + video_length = video_length + + # 5. Prepare latent variables + num_channels_latents = self.transformer.config.in_channels + latents = self.prepare_latents( + batch_size * num_videos_per_prompt, + num_channels_latents, + height, + width, + video_length, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_func_kwargs( + self.scheduler.step, + {"generator": generator, "eta": eta}, + ) + + target_dtype = PRECISION_TO_TYPE[self.args.precision] + autocast_enabled = ( + target_dtype != torch.float32 + ) and not self.args.disable_autocast + vae_dtype = PRECISION_TO_TYPE[self.args.vae_precision] + vae_autocast_enabled = ( + vae_dtype != torch.float32 + ) and not self.args.disable_autocast + + # 7. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + self._num_timesteps = len(timesteps) + + # if few_step: + # start_latent_list = [0, 10, 20, 30, 40, 50] + # self.scheduler.sigmas = self.scheduler.sigmas[start_latent_list] + # num_inference_steps = 5 + # timesteps = timesteps[start_latent_list[:num_inference_steps]] + + print('sigmas used in generation:', self.scheduler.sigmas) + print('inference timesteps used in generation:', timesteps) + + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + if self.interrupt: + continue + # expand the latents if we are doing classifier free guidance + latent_model_input = ( + torch.cat([latents] * 2) + if self.do_classifier_free_guidance + else latents + ) + latent_model_input = self.scheduler.scale_model_input( + latent_model_input, t + ) + + t_expand = t.repeat(latent_model_input.shape[0]) + guidance_expand = ( + torch.tensor( + [embedded_guidance_scale] * latent_model_input.shape[0], + dtype=torch.float32, + device=device, + ).to(target_dtype) + * 1000.0 + if embedded_guidance_scale is not None + else None + ) + + # predict the noise residual + with torch.autocast( + device_type="cuda", dtype=target_dtype, enabled=autocast_enabled + ): + noise_pred = self.transformer( # For an input image (129, 192, 336) (1, 256, 256) + latent_model_input, # [2, 16, 33, 24, 42] + t_expand, # [2] + text_states=prompt_embeds, # [2, 256, 4096] + text_mask=prompt_mask, # [2, 256] + text_states_2=prompt_embeds_2, # [2, 768] + freqs_cos=freqs_cis[0], # [seqlen, head_dim] + freqs_sin=freqs_cis[1], # [seqlen, head_dim] + guidance=guidance_expand, + return_dict=True, + )[ + "x" + ] + + # perform guidance + if self.do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: + # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf + noise_pred = rescale_noise_cfg( + noise_pred, + noise_pred_text, + guidance_rescale=self.guidance_rescale, + ) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step( + noise_pred, t, latents, **extra_step_kwargs, return_dict=False + )[0] + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + negative_prompt_embeds = callback_outputs.pop( + "negative_prompt_embeds", negative_prompt_embeds + ) + + # call the callback, if provided + if i == len(timesteps) - 1 or ( + (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 + ): + if progress_bar is not None: + progress_bar.update() + if callback is not None and i % callback_steps == 0: + step_idx = i // getattr(self.scheduler, "order", 1) + callback(step_idx, t, latents) + + + if not output_type == "latent": + expand_temporal_dim = False + if len(latents.shape) == 4: + if isinstance(self.vae, AutoencoderKLCausal3D): + latents = latents.unsqueeze(2) + expand_temporal_dim = True + elif len(latents.shape) == 5: + pass + else: + raise ValueError( + f"Only support latents with shape (b, c, h, w) or (b, c, f, h, w), but got {latents.shape}." + ) + + if ( + hasattr(self.vae.config, "shift_factor") + and self.vae.config.shift_factor + ): + latents = ( + latents / self.vae.config.scaling_factor + + self.vae.config.shift_factor + ) + else: + latents = latents / self.vae.config.scaling_factor + + with torch.autocast( + device_type="cuda", dtype=vae_dtype, enabled=vae_autocast_enabled + ): + if enable_tiling: + self.vae.enable_tiling() + image = self.vae.decode( + latents, return_dict=False, generator=generator + )[0] + else: + image = self.vae.decode( + latents, return_dict=False, generator=generator + )[0] + + if expand_temporal_dim or image.shape[2] == 1: + image = image.squeeze(2) + + else: + image = latents + + 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() + print(image.shape) + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return image + + return HunyuanVideoPipelineOutput(videos=image) diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/schedulers/__init__.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/schedulers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..14f2ba33feb0a1a802a9a86818781a2a15140bd6 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/schedulers/__init__.py @@ -0,0 +1 @@ +from .scheduling_flow_match_discrete import FlowMatchDiscreteScheduler diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/schedulers/scheduling_flow_match_discrete.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/schedulers/scheduling_flow_match_discrete.py new file mode 100644 index 0000000000000000000000000000000000000000..07f9c012af801e2d758d0ea8c84c94bfad0db12c --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/diffusion/schedulers/scheduling_flow_match_discrete.py @@ -0,0 +1,257 @@ +# Copyright 2024 Stability AI, Katherine Crowson and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# +# Modified from diffusers==0.29.2 +# +# ============================================================================== + +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import numpy as np +import torch + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.utils import BaseOutput, logging +from diffusers.schedulers.scheduling_utils import SchedulerMixin + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class FlowMatchDiscreteSchedulerOutput(BaseOutput): + """ + Output class for the scheduler's `step` function output. + + Args: + prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the + denoising loop. + """ + + prev_sample: torch.FloatTensor + + +class FlowMatchDiscreteScheduler(SchedulerMixin, ConfigMixin): + """ + Euler scheduler. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + timestep_spacing (`str`, defaults to `"linspace"`): + The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. + shift (`float`, defaults to 1.0): + The shift value for the timestep schedule. + reverse (`bool`, defaults to `True`): + Whether to reverse the timestep schedule. + """ + + _compatibles = [] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + shift: float = 1.0, + reverse: bool = True, + solver: str = "euler", + n_tokens: Optional[int] = None, + ): + sigmas = torch.linspace(1, 0, num_train_timesteps + 1) + + if not reverse: + sigmas = sigmas.flip(0) + + self.sigmas = sigmas + # the value fed to model + self.timesteps = (sigmas[:-1] * num_train_timesteps).to(dtype=torch.float32) + + self._step_index = None + self._begin_index = None + + self.supported_solver = ["euler"] + if solver not in self.supported_solver: + raise ValueError( + f"Solver {solver} not supported. Supported solvers: {self.supported_solver}" + ) + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def set_timesteps( + self, + num_inference_steps: int, + device: Union[str, torch.device] = None, + n_tokens: int = None, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + n_tokens (`int`, *optional*): + Number of tokens in the input sequence. + """ + self.num_inference_steps = num_inference_steps + + sigmas = torch.linspace(1, 0, num_inference_steps + 1) + sigmas = self.sd3_time_shift(sigmas) + + if not self.config.reverse: + sigmas = 1 - sigmas + + self.sigmas = sigmas + self.timesteps = (sigmas[:-1] * self.config.num_train_timesteps).to( + dtype=torch.float32, device=device + ) + + # Reset step index + self._step_index = None + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + def scale_model_input( + self, sample: torch.Tensor, timestep: Optional[int] = None + ) -> torch.Tensor: + return sample + + def sd3_time_shift(self, t: torch.Tensor): + return (self.config.shift * t) / (1 + (self.config.shift - 1) * t) + + def step( + self, + model_output: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + sample: torch.FloatTensor, + return_dict: bool = True, + ) -> Union[FlowMatchDiscreteSchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + generator (`torch.Generator`, *optional*): + A random number generator. + n_tokens (`int`, *optional*): + Number of tokens in the input sequence. + return_dict (`bool`): + Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or + tuple. + + Returns: + [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is + returned, otherwise a tuple is returned where the first element is the sample tensor. + """ + + if ( + isinstance(timestep, int) + or isinstance(timestep, torch.IntTensor) + or isinstance(timestep, torch.LongTensor) + ): + raise ValueError( + ( + "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep." + ), + ) + + if self.step_index is None: + self._init_step_index(timestep) + + # Upcast to avoid precision issues when computing prev_sample + sample = sample.to(torch.float32) + + dt = self.sigmas[self.step_index + 1] - self.sigmas[self.step_index] + + if self.config.solver == "euler": + prev_sample = sample + model_output.to(torch.float32) * dt + else: + raise ValueError( + f"Solver {self.config.solver} not supported. Supported solvers: {self.supported_solver}" + ) + + # upon completion increase step index by one + self._step_index += 1 + + if not return_dict: + return (prev_sample,) + + return FlowMatchDiscreteSchedulerOutput(prev_sample=prev_sample) + + def __len__(self): + return self.config.num_train_timesteps diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/idle_config.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/idle_config.py new file mode 100644 index 0000000000000000000000000000000000000000..3cde32edf480fca1d5f0f8026cba3565245718cc --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/idle_config.py @@ -0,0 +1,383 @@ +import argparse +from .constants import * +import re +from .modules.models import HUNYUAN_VIDEO_CONFIG + + +def parse_args(namespace=None): + parser = argparse.ArgumentParser(description="HunyuanVideo inference script") + + parser = add_network_args(parser) + parser = add_extra_models_args(parser) + parser = add_denoise_schedule_args(parser) + parser = add_inference_args(parser) + parser = add_parallel_args(parser) + + args = parser.parse_args(namespace=namespace) + args = sanity_check_args(args) + + return args + + +def add_network_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="HunyuanVideo network args") + + # Main model + group.add_argument( + "--model", + type=str, + choices=list(HUNYUAN_VIDEO_CONFIG.keys()), + default="HYVideo-T/2-cfgdistill", + ) + group.add_argument( + "--latent-channels", + type=str, + default=16, + help="Number of latent channels of DiT. If None, it will be determined by `vae`. If provided, " + "it still needs to match the latent channels of the VAE model.", + ) + group.add_argument( + "--precision", + type=str, + default="bf16", + choices=PRECISIONS, + help="Precision mode. Options: fp32, fp16, bf16. Applied to the backbone model and optimizer.", + ) + + # RoPE + group.add_argument( + "--rope-theta", type=int, default=256, help="Theta used in RoPE." + ) + return parser + + +def add_extra_models_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group( + title="Extra models args, including vae, text encoders and tokenizers)" + ) + + # - VAE + group.add_argument( + "--vae", + type=str, + default="884-16c-hy", + choices=list(VAE_PATH), + help="Name of the VAE model.", + ) + group.add_argument( + "--vae-precision", + type=str, + default="fp16", + choices=PRECISIONS, + help="Precision mode for the VAE model.", + ) + group.add_argument( + "--vae-tiling", + action="store_true", + help="Enable tiling for the VAE model to save GPU memory.", + ) + group.set_defaults(vae_tiling=True) + + group.add_argument( + "--text-encoder", + type=str, + default="llm", + choices=list(TEXT_ENCODER_PATH), + help="Name of the text encoder model.", + ) + group.add_argument( + "--text-encoder-precision", + type=str, + default="fp16", + choices=PRECISIONS, + help="Precision mode for the text encoder model.", + ) + group.add_argument( + "--text-states-dim", + type=int, + default=4096, + help="Dimension of the text encoder hidden states.", + ) + group.add_argument( + "--text-len", type=int, default=256, help="Maximum length of the text input." + ) + group.add_argument( + "--tokenizer", + type=str, + default="llm", + choices=list(TOKENIZER_PATH), + help="Name of the tokenizer model.", + ) + group.add_argument( + "--prompt-template", + type=str, + default="dit-llm-encode", + choices=PROMPT_TEMPLATE, + help="Image prompt template for the decoder-only text encoder model.", + ) + group.add_argument( + "--prompt-template-video", + type=str, + default="dit-llm-encode-video", + choices=PROMPT_TEMPLATE, + help="Video prompt template for the decoder-only text encoder model.", + ) + group.add_argument( + "--hidden-state-skip-layer", + type=int, + default=2, + help="Skip layer for hidden states.", + ) + group.add_argument( + "--apply-final-norm", + action="store_true", + help="Apply final normalization to the used text encoder hidden states.", + ) + + # - CLIP + group.add_argument( + "--text-encoder-2", + type=str, + default="clipL", + choices=list(TEXT_ENCODER_PATH), + help="Name of the second text encoder model.", + ) + group.add_argument( + "--text-encoder-precision-2", + type=str, + default="fp16", + choices=PRECISIONS, + help="Precision mode for the second text encoder model.", + ) + group.add_argument( + "--text-states-dim-2", + type=int, + default=768, + help="Dimension of the second text encoder hidden states.", + ) + group.add_argument( + "--tokenizer-2", + type=str, + default="clipL", + choices=list(TOKENIZER_PATH), + help="Name of the second tokenizer model.", + ) + group.add_argument( + "--text-len-2", + type=int, + default=77, + help="Maximum length of the second text input.", + ) + + return parser + + +def add_denoise_schedule_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Denoise schedule args") + + group.add_argument( + "--denoise-type", + type=str, + default="flow", + help="Denoise type for noised inputs.", + ) + + # Flow Matching + group.add_argument( + "--flow-shift", + type=float, + default=7.0, + help="Shift factor for flow matching schedulers.", + ) + group.add_argument( + "--flow-reverse", + action="store_true", + help="If reverse, learning/sampling from t=1 -> t=0.", + ) + group.add_argument( + "--flow-solver", type=str, default="euler", help="Solver for flow matching.", + ) + group.add_argument( + "--use-linear-quadratic-schedule", + action="store_true", + help="Use linear quadratic schedule for flow matching." + "Following MovieGen (https://ai.meta.com/static-resource/movie-gen-research-paper)", + ) + group.add_argument( + "--linear-schedule-end", + type=int, + default=25, + help="End step for linear quadratic schedule for flow matching.", + ) + + return parser + + +def add_inference_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Inference args") + + # ======================== Model loads ======================== + group.add_argument( + "--model-base", + type=str, + default="ckpts", + help="Root path of all the models, including t2v models and extra models.", + ) + group.add_argument( + "--dit-weight", + type=str, + default="ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", + help="Path to the HunyuanVideo model. If None, search the model in the args.model_root." + "1. If it is a file, load the model directly." + "2. If it is a directory, search the model in the directory. Support two types of models: " + "1) named `pytorch_model_*.pt`" + "2) named `*_model_states.pt`, where * can be `mp_rank_00`.", + ) + group.add_argument( + "--model-resolution", + type=str, + default="540p", + choices=["540p", "720p"], + help="Root path of all the models, including t2v models and extra models.", + ) + group.add_argument( + "--load-key", + type=str, + default="module", + help="Key to load the model states. 'module' for the main model, 'ema' for the EMA model.", + ) + group.add_argument( + "--use-cpu-offload", + action="store_true", + help="Use CPU offload for the model load.", + ) + + # ======================== Inference general setting ======================== + group.add_argument( + "--batch-size", + type=int, + default=1, + help="Batch size for inference and evaluation.", + ) + group.add_argument( + "--infer-steps", + type=int, + default=50, + help="Number of denoising steps for inference.", + ) + group.add_argument( + "--disable-autocast", + action="store_true", + help="Disable autocast for denoising loop and vae decoding in pipeline sampling.", + ) + group.add_argument( + "--save-path", + type=str, + default="./results", + help="Path to save the generated samples.", + ) + group.add_argument( + "--save-path-suffix", + type=str, + default="", + help="Suffix for the directory of saved samples.", + ) + group.add_argument( + "--name-suffix", + type=str, + default="", + help="Suffix for the names of saved samples.", + ) + group.add_argument( + "--num-videos", + type=int, + default=1, + help="Number of videos to generate for each prompt.", + ) + # ---sample size--- + group.add_argument( + "--video-size", + type=int, + nargs="+", + default=(720, 1280), + help="Video size for training. If a single value is provided, it will be used for both height " + "and width. If two values are provided, they will be used for height and width " + "respectively.", + ) + group.add_argument( + "--video-length", + type=int, + default=129, + help="How many frames to sample from a video. if using 3d vae, the number should be 4n+1", + ) + # --- prompt --- + group.add_argument( + "--prompt", + type=str, + default=None, + help="Prompt for sampling during evaluation.", + ) + group.add_argument( + "--seed-type", + type=str, + default="auto", + choices=["file", "random", "fixed", "auto"], + help="Seed type for evaluation. If file, use the seed from the CSV file. If random, generate a " + "random seed. If fixed, use the fixed seed given by `--seed`. If auto, `csv` will use the " + "seed column if available, otherwise use the fixed `seed` value. `prompt` will use the " + "fixed `seed` value.", + ) + group.add_argument("--seed", type=int, default=None, help="Seed for evaluation.") + + # Classifier-Free Guidance + group.add_argument( + "--neg-prompt", type=str, default=None, help="Negative prompt for sampling." + ) + group.add_argument( + "--cfg-scale", type=float, default=1.0, help="Classifier free guidance scale." + ) + group.add_argument( + "--embedded-cfg-scale", + type=float, + default=6.0, + help="Embeded classifier free guidance scale.", + ) + + group.add_argument( + "--reproduce", + action="store_true", + help="Enable reproducibility by setting random seeds and deterministic algorithms.", + ) + + return parser + + +def add_parallel_args(parser: argparse.ArgumentParser): + group = parser.add_argument_group(title="Parallel args") + + # ======================== Model loads ======================== + group.add_argument( + "--ulysses-degree", type=int, default=1, help="Ulysses degree.", + ) + group.add_argument( + "--ring-degree", type=int, default=1, help="Ulysses degree.", + ) + + return parser + + +def sanity_check_args(args): + # VAE channels + vae_pattern = r"\d{2,3}-\d{1,2}c-\w+" + if not re.match(vae_pattern, args.vae): + raise ValueError( + f"Invalid VAE model: {args.vae}. Must be in the format of '{vae_pattern}'." + ) + vae_channels = int(args.vae.split("-")[1][:-1]) + if args.latent_channels is None: + args.latent_channels = vae_channels + if vae_channels != args.latent_channels: + raise ValueError( + f"Latent channels ({args.latent_channels}) must match the VAE channels ({vae_channels})." + ) + return args diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/inference.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..c1e42fefe7a6cd168063d10363ca62339d55165a --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/inference.py @@ -0,0 +1,687 @@ +import os +import time +import random +import functools +from typing import List, Optional, Tuple, Union + +from pathlib import Path +from loguru import logger + +import torch +import torch.distributed as dist +from models.hunyuan.constants import PROMPT_TEMPLATE, NEGATIVE_PROMPT, PRECISION_TO_TYPE +from models.hunyuan.vae import load_vae +from models.hunyuan.modules import load_model +from models.hunyuan.text_encoder import TextEncoder +from models.hunyuan.utils.data_utils import align_to +from models.hunyuan.modules.posemb_layers import get_nd_rotary_pos_embed +from models.hunyuan.modules.fp8_optimization import convert_fp8_linear +from models.hunyuan.diffusion.schedulers import FlowMatchDiscreteScheduler +from models.hunyuan.diffusion.pipelines import HunyuanVideoPipeline + +try: + import xfuser + from xfuser.core.distributed import ( + get_sequence_parallel_world_size, + get_sequence_parallel_rank, + get_sp_group, + initialize_model_parallel, + init_distributed_environment + ) +except: + xfuser = None + get_sequence_parallel_world_size = None + get_sequence_parallel_rank = None + get_sp_group = None + initialize_model_parallel = None + init_distributed_environment = None + +from safetensors import safe_open +import io + +def parallelize_transformer(pipe): + transformer = pipe.transformer + original_forward = transformer.forward + + @functools.wraps(transformer.__class__.forward) + def new_forward( + self, + x: torch.Tensor, + t: torch.Tensor, # Should be in range(0, 1000). + text_states: torch.Tensor = None, + text_mask: torch.Tensor = None, # Now we don't use it. + text_states_2: Optional[torch.Tensor] = None, # Text embedding for modulation. + freqs_cos: Optional[torch.Tensor] = None, + freqs_sin: Optional[torch.Tensor] = None, + guidance: torch.Tensor = None, # Guidance for modulation, should be cfg_scale x 1000. + return_dict: bool = True, + ): + if x.shape[-2] // 2 % get_sequence_parallel_world_size() == 0: + # try to split x by height + split_dim = -2 + elif x.shape[-1] // 2 % get_sequence_parallel_world_size() == 0: + # try to split x by width + split_dim = -1 + else: + raise ValueError( + f"Cannot split video sequence into ulysses_degree x ring_degree ({get_sequence_parallel_world_size()}) parts evenly") + + # patch sizes for the temporal, height, and width dimensions are 1, 2, and 2. + temporal_size, h, w = x.shape[2], x.shape[3] // 2, x.shape[4] // 2 + + x = torch.chunk(x, get_sequence_parallel_world_size(), dim=split_dim)[get_sequence_parallel_rank()] + + dim_thw = freqs_cos.shape[-1] + freqs_cos = freqs_cos.reshape(temporal_size, h, w, dim_thw) + freqs_cos = torch.chunk(freqs_cos, get_sequence_parallel_world_size(), dim=split_dim - 1)[ + get_sequence_parallel_rank()] + freqs_cos = freqs_cos.reshape(-1, dim_thw) + dim_thw = freqs_sin.shape[-1] + freqs_sin = freqs_sin.reshape(temporal_size, h, w, dim_thw) + freqs_sin = torch.chunk(freqs_sin, get_sequence_parallel_world_size(), dim=split_dim - 1)[ + get_sequence_parallel_rank()] + freqs_sin = freqs_sin.reshape(-1, dim_thw) + + from xfuser.core.long_ctx_attention import xFuserLongContextAttention + + for block in transformer.double_blocks + transformer.single_blocks: + block.hybrid_seq_parallel_attn = xFuserLongContextAttention() + + output = original_forward( + x, + t, + text_states, + text_mask, + text_states_2, + freqs_cos, + freqs_sin, + guidance, + return_dict, + ) + + return_dict = not isinstance(output, tuple) + sample = output["x"] + sample = get_sp_group().all_gather(sample, dim=split_dim) + output["x"] = sample + return output + + new_forward = new_forward.__get__(transformer) + transformer.forward = new_forward + + +class Inference(object): + def __init__( + self, + args, + vae, + vae_kwargs, + text_encoder, + model, + text_encoder_2=None, + pipeline=None, + use_cpu_offload=False, + device=None, + logger=None, + parallel_args=None, + ): + self.vae = vae + self.vae_kwargs = vae_kwargs + + self.text_encoder = text_encoder + self.text_encoder_2 = text_encoder_2 + + self.model = model + self.pipeline = pipeline + self.use_cpu_offload = use_cpu_offload + + self.args = args + self.device = ( + device + if device is not None + else "cuda" + if torch.cuda.is_available() + else "cpu" + ) + self.logger = logger + self.parallel_args = parallel_args + + @classmethod + def from_pretrained(cls, pretrained_model_path, args, device=None, **kwargs): + """ + Initialize the Inference pipeline. + + Args: + pretrained_model_path (str or pathlib.Path): The model path, including t2v, text encoder and vae checkpoints. + args (argparse.Namespace): The arguments for the pipeline. + device (int): The device for inference. Default is 0. + """ + # ======================================================================== + logger.info(f"Got text-to-video model root path: {pretrained_model_path}") + + # ==================== Initialize Distributed Environment ================ + if args.ulysses_degree > 1 or args.ring_degree > 1: + assert xfuser is not None, \ + "Ulysses Attention and Ring Attention requires xfuser package." + + assert args.use_cpu_offload is False, \ + "Cannot enable use_cpu_offload in the distributed environment." + + dist.init_process_group("nccl") + + assert dist.get_world_size() == args.ring_degree * args.ulysses_degree, \ + "number of GPUs should be equal to ring_degree * ulysses_degree." + + init_distributed_environment(rank=dist.get_rank(), world_size=dist.get_world_size()) + + initialize_model_parallel( + sequence_parallel_degree=dist.get_world_size(), + ring_degree=args.ring_degree, + ulysses_degree=args.ulysses_degree, + ) + device = torch.device(f"cuda:{os.environ['LOCAL_RANK']}") + else: + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + + parallel_args = {"ulysses_degree": args.ulysses_degree, "ring_degree": args.ring_degree} + + # ======================== Get the args path ============================= + + # Disable gradient + torch.set_grad_enabled(False) + + # =========================== Build main model =========================== + logger.info("Building model...") + factor_kwargs = {"device": device, "dtype": PRECISION_TO_TYPE[args.precision]} + in_channels = args.latent_channels + out_channels = args.latent_channels + + model = load_model( + args, + in_channels=in_channels, + out_channels=out_channels, + factor_kwargs=factor_kwargs, + ) + if args.use_fp8: + convert_fp8_linear(model, args.dit_weight, original_dtype=PRECISION_TO_TYPE[args.precision]) + model = model.to(device) + model = Inference.load_state_dict(args, model, pretrained_model_path) + model.eval() + # model = None + + # ============================= Build extra models ======================== + # VAE + vae, _, s_ratio, t_ratio = load_vae( + args.vae, + args.vae_precision, + logger=logger, + device=device if not args.use_cpu_offload else "cpu", + ) + vae_kwargs = {"s_ratio": s_ratio, "t_ratio": t_ratio} + + # Text encoder + if args.prompt_template_video is not None: + crop_start = PROMPT_TEMPLATE[args.prompt_template_video].get( + "crop_start", 0 + ) + elif args.prompt_template is not None: + crop_start = PROMPT_TEMPLATE[args.prompt_template].get("crop_start", 0) + else: + crop_start = 0 + max_length = args.text_len + crop_start + + # prompt_template + prompt_template = ( + PROMPT_TEMPLATE[args.prompt_template] + if args.prompt_template is not None + else None + ) + + # prompt_template_video + prompt_template_video = ( + PROMPT_TEMPLATE[args.prompt_template_video] + if args.prompt_template_video is not None + else None + ) + + text_encoder = TextEncoder( + text_encoder_type=args.text_encoder, + max_length=max_length, + text_encoder_precision=args.text_encoder_precision, + tokenizer_type=args.tokenizer, + prompt_template=prompt_template, + prompt_template_video=prompt_template_video, + hidden_state_skip_layer=args.hidden_state_skip_layer, + apply_final_norm=args.apply_final_norm, + reproduce=args.reproduce, + logger=logger, + device=device if not args.use_cpu_offload else "cpu", + ) + text_encoder_2 = None + if args.text_encoder_2 is not None: + text_encoder_2 = TextEncoder( + text_encoder_type=args.text_encoder_2, + max_length=args.text_len_2, + text_encoder_precision=args.text_encoder_precision_2, + tokenizer_type=args.tokenizer_2, + reproduce=args.reproduce, + logger=logger, + device=device if not args.use_cpu_offload else "cpu", + ) + + return cls( + args=args, + vae=vae, + vae_kwargs=vae_kwargs, + text_encoder=text_encoder, + text_encoder_2=text_encoder_2, + model=model, + use_cpu_offload=args.use_cpu_offload, + device=device, + logger=logger, + parallel_args=parallel_args + ) + + @staticmethod + def load_state_dict(args, model, pretrained_model_path): + load_key = args.load_key + dit_weight = Path(args.dit_weight) + + if dit_weight is None: + model_dir = pretrained_model_path / f"t2v_{args.model_resolution}" + files = list(model_dir.glob("*.pt")) + if len(files) == 0: + raise ValueError(f"No model weights found in {model_dir}") + if str(files[0]).startswith("pytorch_model_"): + model_path = dit_weight / f"pytorch_model_{load_key}.pt" + bare_model = True + elif any(str(f).endswith("_model_states.pt") for f in files): + files = [f for f in files if str(f).endswith("_model_states.pt")] + model_path = files[0] + if len(files) > 1: + logger.warning( + f"Multiple model weights found in {dit_weight}, using {model_path}" + ) + bare_model = False + else: + raise ValueError( + f"Invalid model path: {dit_weight} with unrecognized weight format: " + f"{list(map(str, files))}. When given a directory as --dit-weight, only " + f"`pytorch_model_*.pt`(provided by HunyuanDiT official) and " + f"`*_model_states.pt`(saved by deepspeed) can be parsed. If you want to load a " + f"specific weight file, please provide the full path to the file." + ) + else: + if dit_weight.is_dir(): + files = list(dit_weight.glob("*.pt")) + if len(files) == 0: + raise ValueError(f"No model weights found in {dit_weight}") + if str(files[0]).startswith("pytorch_model_"): + model_path = dit_weight / f"pytorch_model_{load_key}.pt" + bare_model = True + elif any(str(f).endswith("_model_states.pt") for f in files): + files = [f for f in files if str(f).endswith("_model_states.pt")] + model_path = files[0] + if len(files) > 1: + logger.warning( + f"Multiple model weights found in {dit_weight}, using {model_path}" + ) + bare_model = False + else: + raise ValueError( + f"Invalid model path: {dit_weight} with unrecognized weight format: " + f"{list(map(str, files))}. When given a directory as --dit-weight, only " + f"`pytorch_model_*.pt`(provided by HunyuanDiT official) and " + f"`*_model_states.pt`(saved by deepspeed) can be parsed. If you want to load a " + f"specific weight file, please provide the full path to the file." + ) + elif dit_weight.is_file(): + model_path = dit_weight + bare_model = "unknown" + else: + model_path = args.dit_weight + bare_model = "unknown" + # raise ValueError(f"Invalid model path: {dit_weight}") + + # if not model_path.exists(): + # raise ValueError(f"model_path not exists: {model_path}") + logger.info(f"Loading torch model {model_path}...") + if str(model_path).endswith(".safetensors"): + state_dict = {} + with safe_open(str(model_path), framework="pt", device="cpu") as file: + for k in file.keys(): + state_dict[k] = file.get_tensor(k) + else: + state_dict = torch.load(model_path, map_location=lambda storage, loc: storage) + + if bare_model == "unknown" and ("ema" in state_dict or "module" in state_dict): + bare_model = False + if bare_model is False: + if load_key in state_dict: + state_dict = state_dict[load_key] + else: + raise KeyError( + f"Missing key: `{load_key}` in the checkpoint: {model_path}. The keys in the checkpoint " + f"are: {list(state_dict.keys())}." + ) + model.load_state_dict(state_dict, strict=True) + return model + + @staticmethod + def parse_size(size): + if isinstance(size, int): + size = [size] + if not isinstance(size, (list, tuple)): + raise ValueError(f"Size must be an integer or (height, width), got {size}.") + if len(size) == 1: + size = [size[0], size[0]] + if len(size) != 2: + raise ValueError(f"Size must be an integer or (height, width), got {size}.") + return size + + +class HunyuanVideoSampler(Inference): + def __init__( + self, + args, + vae, + vae_kwargs, + text_encoder, + model, + text_encoder_2=None, + pipeline=None, + use_cpu_offload=False, + device=0, + logger=None, + parallel_args=None + ): + super().__init__( + args, + vae, + vae_kwargs, + text_encoder, + model, + text_encoder_2=text_encoder_2, + pipeline=pipeline, + use_cpu_offload=use_cpu_offload, + device=device, + logger=logger, + parallel_args=parallel_args + ) + + self.pipeline = self.load_diffusion_pipeline( + args=args, + vae=self.vae, + text_encoder=self.text_encoder, + text_encoder_2=self.text_encoder_2, + model=self.model, + device=self.device, + ) + + self.default_negative_prompt = NEGATIVE_PROMPT + if self.parallel_args['ulysses_degree'] > 1 or self.parallel_args['ring_degree'] > 1: + parallelize_transformer(self.pipeline) + + def load_diffusion_pipeline( + self, + args, + vae, + text_encoder, + text_encoder_2, + model, + scheduler=None, + device=None, + progress_bar_config=None, + data_type="video", + ): + """Load the denoising scheduler for inference.""" + if scheduler is None: + if args.denoise_type == "flow": + scheduler = FlowMatchDiscreteScheduler( + shift=args.flow_shift, + reverse=args.flow_reverse, + solver=args.flow_solver, + ) + else: + raise ValueError(f"Invalid denoise type {args.denoise_type}") + + pipeline = HunyuanVideoPipeline( + vae=vae, + text_encoder=text_encoder, + text_encoder_2=text_encoder_2, + transformer=model, + scheduler=scheduler, + progress_bar_config=progress_bar_config, + args=args, + ) + if self.use_cpu_offload: + pipeline.enable_sequential_cpu_offload() + else: + pipeline = pipeline.to(device) + + return pipeline + + def get_rotary_pos_embed(self, video_length, height, width): + target_ndim = 3 + ndim = 5 - 2 + # 884 + if "884" in self.args.vae: + latents_size = [(video_length - 1) // 4 + 1, height // 8, width // 8] + elif "888" in self.args.vae: + latents_size = [(video_length - 1) // 8 + 1, height // 8, width // 8] + else: + latents_size = [video_length, height // 8, width // 8] + + if isinstance(self.model.patch_size, int): + assert all(s % self.model.patch_size == 0 for s in latents_size), ( + f"Latent size(last {ndim} dimensions) should be divisible by patch size({self.model.patch_size}), " + f"but got {latents_size}." + ) + rope_sizes = [s // self.model.patch_size for s in latents_size] + elif isinstance(self.model.patch_size, list): + assert all( + s % self.model.patch_size[idx] == 0 + for idx, s in enumerate(latents_size) + ), ( + f"Latent size(last {ndim} dimensions) should be divisible by patch size({self.model.patch_size}), " + f"but got {latents_size}." + ) + rope_sizes = [ + s // self.model.patch_size[idx] for idx, s in enumerate(latents_size) + ] + + if len(rope_sizes) != target_ndim: + rope_sizes = [1] * (target_ndim - len(rope_sizes)) + rope_sizes # time axis + head_dim = self.model.hidden_size // self.model.heads_num + rope_dim_list = self.model.rope_dim_list + if rope_dim_list is None: + rope_dim_list = [head_dim // target_ndim for _ in range(target_ndim)] + assert ( + sum(rope_dim_list) == head_dim + ), "sum(rope_dim_list) should equal to head_dim of attention layer" + freqs_cos, freqs_sin = get_nd_rotary_pos_embed( + rope_dim_list, + rope_sizes, + theta=self.args.rope_theta, + use_real=True, + theta_rescale_factor=1, + ) + return freqs_cos, freqs_sin + + @torch.no_grad() + def predict( + self, + prompt, + height=192, + width=336, + video_length=129, + seed=None, + negative_prompt=None, + infer_steps=50, + guidance_scale=6, + flow_shift=5.0, + embedded_guidance_scale=None, + batch_size=1, + num_videos_per_prompt=1, + few_step=False, + **kwargs, + ): + """ + Predict the image/video from the given text. + + Args: + prompt (str or List[str]): The input text. + kwargs: + height (int): The height of the output video. Default is 192. + width (int): The width of the output video. Default is 336. + video_length (int): The frame number of the output video. Default is 129. + seed (int or List[str]): The random seed for the generation. Default is a random integer. + negative_prompt (str or List[str]): The negative text prompt. Default is an empty string. + guidance_scale (float): The guidance scale for the generation. Default is 6.0. + num_images_per_prompt (int): The number of images per prompt. Default is 1. + infer_steps (int): The number of inference steps. Default is 100. + """ + out_dict = dict() + + # ======================================================================== + # Arguments: seed + # ======================================================================== + if isinstance(seed, torch.Tensor): + seed = seed.tolist() + if seed is None: + seeds = [ + random.randint(0, 1_000_000) + for _ in range(batch_size * num_videos_per_prompt) + ] + elif isinstance(seed, int): + seeds = [ + seed + i + for _ in range(batch_size) + for i in range(num_videos_per_prompt) + ] + elif isinstance(seed, (list, tuple)): + if len(seed) == batch_size: + seeds = [ + int(seed[i]) + j + for i in range(batch_size) + for j in range(num_videos_per_prompt) + ] + elif len(seed) == batch_size * num_videos_per_prompt: + seeds = [int(s) for s in seed] + else: + raise ValueError( + f"Length of seed must be equal to number of prompt(batch_size) or " + f"batch_size * num_videos_per_prompt ({batch_size} * {num_videos_per_prompt}), got {seed}." + ) + else: + raise ValueError( + f"Seed must be an integer, a list of integers, or None, got {seed}." + ) + generator = [torch.Generator(self.device).manual_seed(seed) for seed in seeds] + out_dict["seeds"] = seeds + + # ======================================================================== + # Arguments: target_width, target_height, target_video_length + # ======================================================================== + if width <= 0 or height <= 0 or video_length <= 0: + raise ValueError( + f"`height` and `width` and `video_length` must be positive integers, got height={height}, width={width}, video_length={video_length}" + ) + if (video_length - 1) % 4 != 0: + raise ValueError( + f"`video_length-1` must be a multiple of 4, got {video_length}" + ) + + logger.info( + f"Input (height, width, video_length) = ({height}, {width}, {video_length})" + ) + + target_height = align_to(height, 16) + target_width = align_to(width, 16) + target_video_length = video_length + + out_dict["size"] = (target_height, target_width, target_video_length) + + # ======================================================================== + # Arguments: prompt, new_prompt, negative_prompt + # ======================================================================== + if not isinstance(prompt, str): + raise TypeError(f"`prompt` must be a string, but got {type(prompt)}") + prompt = [prompt.strip()] + + # negative prompt + if negative_prompt is None or negative_prompt == "": + negative_prompt = self.default_negative_prompt + if not isinstance(negative_prompt, str): + raise TypeError( + f"`negative_prompt` must be a string, but got {type(negative_prompt)}" + ) + negative_prompt = [negative_prompt.strip()] + + # ======================================================================== + # Scheduler + # ======================================================================== + scheduler = FlowMatchDiscreteScheduler( + shift=flow_shift, + reverse=self.args.flow_reverse, + solver=self.args.flow_solver + ) + self.pipeline.scheduler = scheduler + + # ======================================================================== + # Build Rope freqs + # ======================================================================== + freqs_cos, freqs_sin = self.get_rotary_pos_embed( + target_video_length, target_height, target_width + ) + n_tokens = freqs_cos.shape[0] + + # ======================================================================== + # Print infer args + # ======================================================================== + debug_str = f""" + height: {target_height} + width: {target_width} + video_length: {target_video_length} + prompt: {prompt} + neg_prompt: {negative_prompt} + seed: {seed} + infer_steps: {infer_steps} + num_videos_per_prompt: {num_videos_per_prompt} + guidance_scale: {guidance_scale} + n_tokens: {n_tokens} + flow_shift: {flow_shift} + few_step: {few_step} + embedded_guidance_scale: {embedded_guidance_scale}""" + logger.debug(debug_str) + + # ======================================================================== + # Pipeline inference + # ======================================================================== + start_time = time.time() + samples = self.pipeline( + prompt=prompt, + height=target_height, + width=target_width, + video_length=target_video_length, + num_inference_steps=infer_steps, + guidance_scale=guidance_scale, + negative_prompt=negative_prompt, + num_videos_per_prompt=num_videos_per_prompt, + generator=generator, + output_type="pil", + freqs_cis=(freqs_cos, freqs_sin), + n_tokens=n_tokens, + embedded_guidance_scale=embedded_guidance_scale, + data_type="video" if target_video_length > 1 else "image", + is_progress_bar=True, + vae_ver=self.args.vae, + enable_tiling=self.args.vae_tiling, + return_dict=False, + few_step=few_step, + ) + out_dict["samples"] = samples + gen_time = time.time() - start_time + logger.info(f"Success, time: {gen_time}") + + return out_dict diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/__init__.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2ebe2c3ed5ceb92a380f17d8a91aa2fa08798c50 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/__init__.py @@ -0,0 +1,26 @@ +from .models import HYVideoDiffusionTransformer, HUNYUAN_VIDEO_CONFIG + + +def load_model(args, in_channels, out_channels, factor_kwargs): + """load hunyuan video model + + Args: + args (dict): model args + in_channels (int): input channels number + out_channels (int): output channels number + factor_kwargs (dict): factor kwargs + + Returns: + model (nn.Module): The hunyuan video model + """ + if args.model in HUNYUAN_VIDEO_CONFIG.keys(): + model = HYVideoDiffusionTransformer( + args, + in_channels=in_channels, + out_channels=out_channels, + **HUNYUAN_VIDEO_CONFIG[args.model], + **factor_kwargs, + ) + return model + else: + raise NotImplementedError() diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/activation_layers.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/activation_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..f8774c26ceef6081482ca0dbbf930b207d4ac03b --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/activation_layers.py @@ -0,0 +1,23 @@ +import torch.nn as nn + + +def get_activation_layer(act_type): + """get activation layer + + Args: + act_type (str): the activation type + + Returns: + torch.nn.functional: the activation layer + """ + if act_type == "gelu": + return lambda: nn.GELU() + elif act_type == "gelu_tanh": + # Approximate `tanh` requires torch >= 1.13 + return lambda: nn.GELU(approximate="tanh") + elif act_type == "relu": + return nn.ReLU + elif act_type == "silu": + return nn.SiLU + else: + raise ValueError(f"Unknown activation type: {act_type}") diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/attenion.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/attenion.py new file mode 100644 index 0000000000000000000000000000000000000000..44548793709ce8595e8635938f5edddcf6b328a7 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/attenion.py @@ -0,0 +1,212 @@ +import importlib.metadata +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +try: + import flash_attn + from flash_attn.flash_attn_interface import _flash_attn_forward + from flash_attn.flash_attn_interface import flash_attn_varlen_func +except ImportError: + flash_attn = None + flash_attn_varlen_func = None + _flash_attn_forward = None + + +MEMORY_LAYOUT = { + "flash": ( + lambda x: x.view(x.shape[0] * x.shape[1], *x.shape[2:]), + lambda x: x, + ), + "torch": ( + lambda x: x.transpose(1, 2), + lambda x: x.transpose(1, 2), + ), + "vanilla": ( + lambda x: x.transpose(1, 2), + lambda x: x.transpose(1, 2), + ), +} + + +def get_cu_seqlens(text_mask, img_len): + """Calculate cu_seqlens_q, cu_seqlens_kv using text_mask and img_len + + Args: + text_mask (torch.Tensor): the mask of text + img_len (int): the length of image + + Returns: + torch.Tensor: the calculated cu_seqlens for flash attention + """ + batch_size = text_mask.shape[0] + text_len = text_mask.sum(dim=1) + max_len = text_mask.shape[1] + img_len + + cu_seqlens = torch.zeros([2 * batch_size + 1], dtype=torch.int32, device="cuda") + + for i in range(batch_size): + s = text_len[i] + img_len + s1 = i * max_len + s + s2 = (i + 1) * max_len + cu_seqlens[2 * i + 1] = s1 + cu_seqlens[2 * i + 2] = s2 + + return cu_seqlens + + +def attention( + q, + k, + v, + mode="flash", + drop_rate=0, + attn_mask=None, + causal=False, + cu_seqlens_q=None, + cu_seqlens_kv=None, + max_seqlen_q=None, + max_seqlen_kv=None, + batch_size=1, +): + """ + Perform QKV self attention. + + Args: + q (torch.Tensor): Query tensor with shape [b, s, a, d], where a is the number of heads. + k (torch.Tensor): Key tensor with shape [b, s1, a, d] + v (torch.Tensor): Value tensor with shape [b, s1, a, d] + mode (str): Attention mode. Choose from 'self_flash', 'cross_flash', 'torch', and 'vanilla'. + drop_rate (float): Dropout rate in attention map. (default: 0) + attn_mask (torch.Tensor): Attention mask with shape [b, s1] (cross_attn), or [b, a, s, s1] (torch or vanilla). + (default: None) + causal (bool): Whether to use causal attention. (default: False) + cu_seqlens_q (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch, + used to index into q. + cu_seqlens_kv (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch, + used to index into kv. + max_seqlen_q (int): The maximum sequence length in the batch of q. + max_seqlen_kv (int): The maximum sequence length in the batch of k and v. + + Returns: + torch.Tensor: Output tensor after self attention with shape [b, s, ad] + """ + pre_attn_layout, post_attn_layout = MEMORY_LAYOUT[mode] + q = pre_attn_layout(q) + k = pre_attn_layout(k) + v = pre_attn_layout(v) + + if mode == "torch": + if attn_mask is not None and attn_mask.dtype != torch.bool: + attn_mask = attn_mask.to(q.dtype) + x = F.scaled_dot_product_attention( + q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal + ) + elif mode == "flash": + x = flash_attn_varlen_func( + q, + k, + v, + cu_seqlens_q, + cu_seqlens_kv, + max_seqlen_q, + max_seqlen_kv, + ) + # x with shape [(bxs), a, d] + x = x.view( + batch_size, max_seqlen_q, x.shape[-2], x.shape[-1] + ) # reshape x to [b, s, a, d] + elif mode == "vanilla": + scale_factor = 1 / math.sqrt(q.size(-1)) + + b, a, s, _ = q.shape + s1 = k.size(2) + attn_bias = torch.zeros(b, a, s, s1, dtype=q.dtype, device=q.device) + if causal: + # Only applied to self attention + assert ( + attn_mask is None + ), "Causal mask and attn_mask cannot be used together" + temp_mask = torch.ones(b, a, s, s, dtype=torch.bool, device=q.device).tril( + diagonal=0 + ) + attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf")) + attn_bias.to(q.dtype) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf")) + else: + attn_bias += attn_mask + + # TODO: Maybe force q and k to be float32 to avoid numerical overflow + attn = (q @ k.transpose(-2, -1)) * scale_factor + attn += attn_bias + attn = attn.softmax(dim=-1) + attn = torch.dropout(attn, p=drop_rate, train=True) + x = attn @ v + else: + raise NotImplementedError(f"Unsupported attention mode: {mode}") + + x = post_attn_layout(x) + b, s, a, d = x.shape + out = x.reshape(b, s, -1) + return out + + +def parallel_attention( + hybrid_seq_parallel_attn, + q, + k, + v, + img_q_len, + img_kv_len, + cu_seqlens_q, + cu_seqlens_kv +): + attn1 = hybrid_seq_parallel_attn( + None, + q[:, :img_q_len, :, :], + k[:, :img_kv_len, :, :], + v[:, :img_kv_len, :, :], + dropout_p=0.0, + causal=False, + joint_tensor_query=q[:,img_q_len:cu_seqlens_q[1]], + joint_tensor_key=k[:,img_kv_len:cu_seqlens_kv[1]], + joint_tensor_value=v[:,img_kv_len:cu_seqlens_kv[1]], + joint_strategy="rear", + ) + if flash_attn.__version__ >= '2.7.0': + attn2, *_ = _flash_attn_forward( + q[:,cu_seqlens_q[1]:], + k[:,cu_seqlens_kv[1]:], + v[:,cu_seqlens_kv[1]:], + dropout_p=0.0, + softmax_scale=q.shape[-1] ** (-0.5), + causal=False, + window_size_left=-1, + window_size_right=-1, + softcap=0.0, + alibi_slopes=None, + return_softmax=False, + ) + else: + attn2, *_ = _flash_attn_forward( + q[:,cu_seqlens_q[1]:], + k[:,cu_seqlens_kv[1]:], + v[:,cu_seqlens_kv[1]:], + dropout_p=0.0, + softmax_scale=q.shape[-1] ** (-0.5), + causal=False, + window_size=(-1, -1), + softcap=0.0, + alibi_slopes=None, + return_softmax=False, + ) + attn = torch.cat([attn1, attn2], dim=1) + b, s, a, d = attn.shape + attn = attn.reshape(b, s, -1) + + return attn diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/embed_layers.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/embed_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..3d65ed1a43e9c20219a19a90d1ffa84a765a1872 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/embed_layers.py @@ -0,0 +1,157 @@ +import math +import torch +import torch.nn as nn +from einops import rearrange, repeat + +from ..utils.helpers import to_2tuple + + +class PatchEmbed(nn.Module): + """2D Image to Patch Embedding + + Image to Patch Embedding using Conv2d + + A convolution based approach to patchifying a 2D image w/ embedding projection. + + Based on the impl in https://github.com/google-research/vision_transformer + + Hacked together by / Copyright 2020 Ross Wightman + + Remove the _assert function in forward function to be compatible with multi-resolution images. + """ + + def __init__( + self, + patch_size=16, + in_chans=3, + embed_dim=768, + norm_layer=None, + flatten=True, + bias=True, + dtype=None, + device=None, + ): + factory_kwargs = {"dtype": dtype, "device": device} + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + self.flatten = flatten + + self.proj = nn.Conv3d( + in_chans, + embed_dim, + kernel_size=patch_size, + stride=patch_size, + bias=bias, + **factory_kwargs + ) + nn.init.xavier_uniform_(self.proj.weight.view(self.proj.weight.size(0), -1)) + if bias: + nn.init.zeros_(self.proj.bias) + + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x): + x = self.proj(x) + if self.flatten: + x = x.flatten(2).transpose(1, 2) # BCHW -> BNC + x = self.norm(x) + return x + + +class TextProjection(nn.Module): + """ + Projects text embeddings. Also handles dropout for classifier-free guidance. + + Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py + """ + + def __init__(self, in_channels, hidden_size, act_layer, dtype=None, device=None): + factory_kwargs = {"dtype": dtype, "device": device} + super().__init__() + self.linear_1 = nn.Linear( + in_features=in_channels, + out_features=hidden_size, + bias=True, + **factory_kwargs + ) + self.act_1 = act_layer() + self.linear_2 = nn.Linear( + in_features=hidden_size, + out_features=hidden_size, + bias=True, + **factory_kwargs + ) + + def forward(self, caption): + hidden_states = self.linear_1(caption) + hidden_states = self.act_1(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states + + +def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + + Args: + t (torch.Tensor): a 1-D Tensor of N indices, one per batch element. These may be fractional. + dim (int): the dimension of the output. + max_period (int): controls the minimum frequency of the embeddings. + + Returns: + embedding (torch.Tensor): An (N, D) Tensor of positional embeddings. + + .. ref_link: https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + """ + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__( + self, + hidden_size, + act_layer, + frequency_embedding_size=256, + max_period=10000, + out_size=None, + dtype=None, + device=None, + ): + factory_kwargs = {"dtype": dtype, "device": device} + super().__init__() + self.frequency_embedding_size = frequency_embedding_size + self.max_period = max_period + if out_size is None: + out_size = hidden_size + + self.mlp = nn.Sequential( + nn.Linear( + frequency_embedding_size, hidden_size, bias=True, **factory_kwargs + ), + act_layer(), + nn.Linear(hidden_size, out_size, bias=True, **factory_kwargs), + ) + nn.init.normal_(self.mlp[0].weight, std=0.02) + nn.init.normal_(self.mlp[2].weight, std=0.02) + + def forward(self, t): + t_freq = timestep_embedding( + t, self.frequency_embedding_size, self.max_period + ).type(self.mlp[0].weight.dtype) + t_emb = self.mlp(t_freq) + return t_emb diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/fp8_optimization.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/fp8_optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..b95c1f49bf78bda824b616ec7130eb8ff03ed09c --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/fp8_optimization.py @@ -0,0 +1,102 @@ +import os + +import torch +import torch.nn as nn +from torch.nn import functional as F + +def get_fp_maxval(bits=8, mantissa_bit=3, sign_bits=1): + _bits = torch.tensor(bits) + _mantissa_bit = torch.tensor(mantissa_bit) + _sign_bits = torch.tensor(sign_bits) + M = torch.clamp(torch.round(_mantissa_bit), 1, _bits - _sign_bits) + E = _bits - _sign_bits - M + bias = 2 ** (E - 1) - 1 + mantissa = 1 + for i in range(mantissa_bit - 1): + mantissa += 1 / (2 ** (i+1)) + maxval = mantissa * 2 ** (2**E - 1 - bias) + return maxval + +def quantize_to_fp8(x, bits=8, mantissa_bit=3, sign_bits=1): + """ + Default is E4M3. + """ + bits = torch.tensor(bits) + mantissa_bit = torch.tensor(mantissa_bit) + sign_bits = torch.tensor(sign_bits) + M = torch.clamp(torch.round(mantissa_bit), 1, bits - sign_bits) + E = bits - sign_bits - M + bias = 2 ** (E - 1) - 1 + mantissa = 1 + for i in range(mantissa_bit - 1): + mantissa += 1 / (2 ** (i+1)) + maxval = mantissa * 2 ** (2**E - 1 - bias) + minval = - maxval + minval = - maxval if sign_bits == 1 else torch.zeros_like(maxval) + input_clamp = torch.min(torch.max(x, minval), maxval) + log_scales = torch.clamp((torch.floor(torch.log2(torch.abs(input_clamp)) + bias)).detach(), 1.0) + log_scales = 2.0 ** (log_scales - M - bias.type(x.dtype)) + # dequant + qdq_out = torch.round(input_clamp / log_scales) * log_scales + return qdq_out, log_scales + +def fp8_tensor_quant(x, scale, bits=8, mantissa_bit=3, sign_bits=1): + for i in range(len(x.shape) - 1): + scale = scale.unsqueeze(-1) + new_x = x / scale + quant_dequant_x, log_scales = quantize_to_fp8(new_x, bits=bits, mantissa_bit=mantissa_bit, sign_bits=sign_bits) + return quant_dequant_x, scale, log_scales + +def fp8_activation_dequant(qdq_out, scale, dtype): + qdq_out = qdq_out.type(dtype) + quant_dequant_x = qdq_out * scale.to(dtype) + return quant_dequant_x + +def fp8_linear_forward(cls, original_dtype, input): + weight_dtype = cls.weight.dtype + ##### + if cls.weight.dtype != torch.float8_e4m3fn: + maxval = get_fp_maxval() + scale = torch.max(torch.abs(cls.weight.flatten())) / maxval + linear_weight, scale, log_scales = fp8_tensor_quant(cls.weight, scale) + linear_weight = linear_weight.to(torch.float8_e4m3fn) + weight_dtype = linear_weight.dtype + else: + scale = cls.fp8_scale.to(cls.weight.device) + linear_weight = cls.weight + ##### + + if weight_dtype == torch.float8_e4m3fn and cls.weight.sum() != 0: + if True or len(input.shape) == 3: + cls_dequant = fp8_activation_dequant(linear_weight, scale, original_dtype) + if cls.bias != None: + output = F.linear(input, cls_dequant, cls.bias) + else: + output = F.linear(input, cls_dequant) + return output + else: + return cls.original_forward(input.to(original_dtype)) + else: + return cls.original_forward(input) + +def convert_fp8_linear(module, dit_weight_path, original_dtype, params_to_keep={}): + setattr(module, "fp8_matmul_enabled", True) + + # loading fp8 mapping file + fp8_map_path = dit_weight_path.replace('.pt', '_map.pt') + if os.path.exists(fp8_map_path): + fp8_map = torch.load(fp8_map_path, map_location=lambda storage, loc: storage) + else: + raise ValueError(f"Invalid fp8_map path: {fp8_map_path}.") + + fp8_layers = [] + for key, layer in module.named_modules(): + if isinstance(layer, nn.Linear) and ('double_blocks' in key or 'single_blocks' in key): + fp8_layers.append(key) + original_forward = layer.forward + layer.weight = torch.nn.Parameter(layer.weight.to(torch.float8_e4m3fn)) + setattr(layer, "fp8_scale", fp8_map[key].to(dtype=original_dtype)) + setattr(layer, "original_forward", original_forward) + setattr(layer, "forward", lambda input, m=layer: fp8_linear_forward(m, original_dtype, input)) + + diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/mlp_layers.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/mlp_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..24dd2d9b54fdcd52f6de1b1bfa02088c7230cbf2 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/mlp_layers.py @@ -0,0 +1,118 @@ +# Modified from timm library: +# https://github.com/huggingface/pytorch-image-models/blob/648aaa41233ba83eb38faf5ba9d415d574823241/timm/layers/mlp.py#L13 + +from functools import partial + +import torch +import torch.nn as nn + +from .modulate_layers import modulate +from ..utils.helpers import to_2tuple + + +class MLP(nn.Module): + """MLP as used in Vision Transformer, MLP-Mixer and related networks""" + + def __init__( + self, + in_channels, + hidden_channels=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0.0, + use_conv=False, + device=None, + dtype=None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + out_features = out_features or in_channels + hidden_channels = hidden_channels or in_channels + bias = to_2tuple(bias) + drop_probs = to_2tuple(drop) + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear + + self.fc1 = linear_layer( + in_channels, hidden_channels, bias=bias[0], **factory_kwargs + ) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs[0]) + self.norm = ( + norm_layer(hidden_channels, **factory_kwargs) + if norm_layer is not None + else nn.Identity() + ) + self.fc2 = linear_layer( + hidden_channels, out_features, bias=bias[1], **factory_kwargs + ) + self.drop2 = nn.Dropout(drop_probs[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.norm(x) + x = self.fc2(x) + x = self.drop2(x) + return x + + +# +class MLPEmbedder(nn.Module): + """copied from https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py""" + def __init__(self, in_dim: int, hidden_dim: int, device=None, dtype=None): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True, **factory_kwargs) + self.silu = nn.SiLU() + self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True, **factory_kwargs) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.out_layer(self.silu(self.in_layer(x))) + + +class FinalLayer(nn.Module): + """The final layer of DiT.""" + + def __init__( + self, hidden_size, patch_size, out_channels, act_layer, device=None, dtype=None + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + + # Just use LayerNorm for the final layer + self.norm_final = nn.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs + ) + if isinstance(patch_size, int): + self.linear = nn.Linear( + hidden_size, + patch_size * patch_size * out_channels, + bias=True, + **factory_kwargs + ) + else: + self.linear = nn.Linear( + hidden_size, + patch_size[0] * patch_size[1] * patch_size[2] * out_channels, + bias=True, + ) + nn.init.zeros_(self.linear.weight) + nn.init.zeros_(self.linear.bias) + + # Here we don't distinguish between the modulate types. Just use the simple one. + self.adaLN_modulation = nn.Sequential( + act_layer(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs), + ) + # Zero-initialize the modulation + nn.init.zeros_(self.adaLN_modulation[1].weight) + nn.init.zeros_(self.adaLN_modulation[1].bias) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift=shift, scale=scale) + x = self.linear(x) + return x diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/models.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/models.py new file mode 100644 index 0000000000000000000000000000000000000000..216ea51a05f19b608c897fb0d8fbd13d1983ce9f --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/models.py @@ -0,0 +1,816 @@ +from typing import Any, List, Tuple, Optional, Union, Dict +from einops import rearrange + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from diffusers.models import ModelMixin +from diffusers.configuration_utils import ConfigMixin, register_to_config + +from .activation_layers import get_activation_layer +from .norm_layers import get_norm_layer +from .embed_layers import TimestepEmbedder, PatchEmbed, TextProjection +from .attenion import attention, parallel_attention, get_cu_seqlens +from .posemb_layers import apply_rotary_emb +from .mlp_layers import MLP, MLPEmbedder, FinalLayer +from .modulate_layers import ModulateDiT, modulate, apply_gate +from .token_refiner import SingleTokenRefiner + +from ..parallel_states import nccl_info +from .posemb_layers import get_nd_rotary_pos_embed + + +class MMDoubleStreamBlock(nn.Module): + """ + A multimodal dit block with seperate modulation for + text and image/video, see more details (SD3): https://arxiv.org/abs/2403.03206 + (Flux.1): https://github.com/black-forest-labs/flux + """ + + def __init__( + self, + hidden_size: int, + heads_num: int, + mlp_width_ratio: float, + mlp_act_type: str = "gelu_tanh", + qk_norm: bool = True, + qk_norm_type: str = "rms", + qkv_bias: bool = False, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + + self.deterministic = False + self.heads_num = heads_num + head_dim = hidden_size // heads_num + mlp_hidden_dim = int(hidden_size * mlp_width_ratio) + + self.img_mod = ModulateDiT( + hidden_size, + factor=6, + act_layer=get_activation_layer("silu"), + **factory_kwargs, + ) + self.img_norm1 = nn.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs + ) + + self.img_attn_qkv = nn.Linear( + hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs + ) + qk_norm_layer = get_norm_layer(qk_norm_type) + self.img_attn_q_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + self.img_attn_k_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + self.img_attn_proj = nn.Linear( + hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs + ) + + self.img_norm2 = nn.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs + ) + self.img_mlp = MLP( + hidden_size, + mlp_hidden_dim, + act_layer=get_activation_layer(mlp_act_type), + bias=True, + **factory_kwargs, + ) + + self.txt_mod = ModulateDiT( + hidden_size, + factor=6, + act_layer=get_activation_layer("silu"), + **factory_kwargs, + ) + self.txt_norm1 = nn.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs + ) + + self.txt_attn_qkv = nn.Linear( + hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs + ) + self.txt_attn_q_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + self.txt_attn_k_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + self.txt_attn_proj = nn.Linear( + hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs + ) + + self.txt_norm2 = nn.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs + ) + self.txt_mlp = MLP( + hidden_size, + mlp_hidden_dim, + act_layer=get_activation_layer(mlp_act_type), + bias=True, + **factory_kwargs, + ) + self.hybrid_seq_parallel_attn = None + + def enable_deterministic(self): + self.deterministic = True + + def disable_deterministic(self): + self.deterministic = False + + def forward( + self, + img: torch.Tensor, + txt: torch.Tensor, + vec: torch.Tensor, + cu_seqlens_q: Optional[torch.Tensor] = None, + cu_seqlens_kv: Optional[torch.Tensor] = None, + max_seqlen_q: Optional[int] = None, + max_seqlen_kv: Optional[int] = None, + freqs_cis: tuple = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + ( + img_mod1_shift, + img_mod1_scale, + img_mod1_gate, + img_mod2_shift, + img_mod2_scale, + img_mod2_gate, + ) = self.img_mod(vec).chunk(6, dim=-1) + ( + txt_mod1_shift, + txt_mod1_scale, + txt_mod1_gate, + txt_mod2_shift, + txt_mod2_scale, + txt_mod2_gate, + ) = self.txt_mod(vec).chunk(6, dim=-1) + + # Prepare image for attention. + img_modulated = self.img_norm1(img) + img_modulated = modulate( + img_modulated, shift=img_mod1_shift, scale=img_mod1_scale + ) + img_qkv = self.img_attn_qkv(img_modulated) + img_q, img_k, img_v = rearrange( + img_qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num + ) + # Apply QK-Norm if needed + img_q = self.img_attn_q_norm(img_q).to(img_v) + img_k = self.img_attn_k_norm(img_k).to(img_v) + + # Apply RoPE if needed. + if freqs_cis is not None: + img_qq, img_kk = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False) + assert ( + img_qq.shape == img_q.shape and img_kk.shape == img_k.shape + ), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}" + img_q, img_k = img_qq, img_kk + + # Prepare txt for attention. + txt_modulated = self.txt_norm1(txt) + txt_modulated = modulate( + txt_modulated, shift=txt_mod1_shift, scale=txt_mod1_scale + ) + txt_qkv = self.txt_attn_qkv(txt_modulated) + txt_q, txt_k, txt_v = rearrange( + txt_qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num + ) + # Apply QK-Norm if needed. + txt_q = self.txt_attn_q_norm(txt_q).to(txt_v) + txt_k = self.txt_attn_k_norm(txt_k).to(txt_v) + + # Run actual attention. + q = torch.cat((img_q, txt_q), dim=1) + k = torch.cat((img_k, txt_k), dim=1) + v = torch.cat((img_v, txt_v), dim=1) + assert ( + cu_seqlens_q.shape[0] == 2 * img.shape[0] + 1 + ), f"cu_seqlens_q.shape:{cu_seqlens_q.shape}, img.shape[0]:{img.shape[0]}" + + # attention computation start + if not self.hybrid_seq_parallel_attn: + attn = attention( + q, + k, + v, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_kv=cu_seqlens_kv, + max_seqlen_q=max_seqlen_q, + max_seqlen_kv=max_seqlen_kv, + batch_size=img_k.shape[0], + ) + else: + attn = parallel_attention( + self.hybrid_seq_parallel_attn, + q, + k, + v, + img_q_len=img_q.shape[1], + img_kv_len=img_k.shape[1], + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_kv=cu_seqlens_kv + ) + + # attention computation end + + img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1] :] + + # Calculate the img bloks. + img = img + apply_gate(self.img_attn_proj(img_attn), gate=img_mod1_gate) + img = img + apply_gate( + self.img_mlp( + modulate( + self.img_norm2(img), shift=img_mod2_shift, scale=img_mod2_scale + ) + ), + gate=img_mod2_gate, + ) + + # Calculate the txt bloks. + txt = txt + apply_gate(self.txt_attn_proj(txt_attn), gate=txt_mod1_gate) + txt = txt + apply_gate( + self.txt_mlp( + modulate( + self.txt_norm2(txt), shift=txt_mod2_shift, scale=txt_mod2_scale + ) + ), + gate=txt_mod2_gate, + ) + + return img, txt + + +class MMSingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + Also refer to (SD3): https://arxiv.org/abs/2403.03206 + (Flux.1): https://github.com/black-forest-labs/flux + """ + + def __init__( + self, + hidden_size: int, + heads_num: int, + mlp_width_ratio: float = 4.0, + mlp_act_type: str = "gelu_tanh", + qk_norm: bool = True, + qk_norm_type: str = "rms", + qk_scale: float = None, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + + self.deterministic = False + self.hidden_size = hidden_size + self.heads_num = heads_num + head_dim = hidden_size // heads_num + mlp_hidden_dim = int(hidden_size * mlp_width_ratio) + self.mlp_hidden_dim = mlp_hidden_dim + self.scale = qk_scale or head_dim ** -0.5 + + # qkv and mlp_in + self.linear1 = nn.Linear( + hidden_size, hidden_size * 3 + mlp_hidden_dim, **factory_kwargs + ) + # proj and mlp_out + self.linear2 = nn.Linear( + hidden_size + mlp_hidden_dim, hidden_size, **factory_kwargs + ) + + qk_norm_layer = get_norm_layer(qk_norm_type) + self.q_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + self.k_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + + self.pre_norm = nn.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs + ) + + self.mlp_act = get_activation_layer(mlp_act_type)() + self.modulation = ModulateDiT( + hidden_size, + factor=3, + act_layer=get_activation_layer("silu"), + **factory_kwargs, + ) + self.hybrid_seq_parallel_attn = None + + def enable_deterministic(self): + self.deterministic = True + + def disable_deterministic(self): + self.deterministic = False + + def forward( + self, + x: torch.Tensor, + vec: torch.Tensor, + txt_len: int, + cu_seqlens_q: Optional[torch.Tensor] = None, + cu_seqlens_kv: Optional[torch.Tensor] = None, + max_seqlen_q: Optional[int] = None, + max_seqlen_kv: Optional[int] = None, + freqs_cis: Tuple[torch.Tensor, torch.Tensor] = None, + ) -> torch.Tensor: + mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1) + x_mod = modulate(self.pre_norm(x), shift=mod_shift, scale=mod_scale) + qkv, mlp = torch.split( + self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1 + ) + + q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) + + # Apply QK-Norm if needed. + q = self.q_norm(q).to(v) + k = self.k_norm(k).to(v) + + # Apply RoPE if needed. + if freqs_cis is not None: + img_q, txt_q = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :] + img_k, txt_k = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :] + img_qq, img_kk = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False) + assert ( + img_qq.shape == img_q.shape and img_kk.shape == img_k.shape + ), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}" + img_q, img_k = img_qq, img_kk + q = torch.cat((img_q, txt_q), dim=1) + k = torch.cat((img_k, txt_k), dim=1) + + # Compute attention. + assert ( + cu_seqlens_q.shape[0] == 2 * x.shape[0] + 1 + ), f"cu_seqlens_q.shape:{cu_seqlens_q.shape}, x.shape[0]:{x.shape[0]}" + + # attention computation start + if not self.hybrid_seq_parallel_attn: + attn = attention( + q, + k, + v, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_kv=cu_seqlens_kv, + max_seqlen_q=max_seqlen_q, + max_seqlen_kv=max_seqlen_kv, + batch_size=x.shape[0], + ) + else: + attn = parallel_attention( + self.hybrid_seq_parallel_attn, + q, + k, + v, + img_q_len=img_q.shape[1], + img_kv_len=img_k.shape[1], + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_kv=cu_seqlens_kv + ) + # attention computation end + + # Compute activation in mlp stream, cat again and run second linear layer. + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + return x + apply_gate(output, gate=mod_gate) + + +class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin): + """ + HunyuanVideo Transformer backbone + + Inherited from ModelMixin and ConfigMixin for compatibility with diffusers' sampler StableDiffusionPipeline. + + Reference: + [1] Flux.1: https://github.com/black-forest-labs/flux + [2] MMDiT: http://arxiv.org/abs/2403.03206 + + Parameters + ---------- + args: argparse.Namespace + The arguments parsed by argparse. + patch_size: list + The size of the patch. + in_channels: int + The number of input channels. + out_channels: int + The number of output channels. + hidden_size: int + The hidden size of the transformer backbone. + heads_num: int + The number of attention heads. + mlp_width_ratio: float + The ratio of the hidden size of the MLP in the transformer block. + mlp_act_type: str + The activation function of the MLP in the transformer block. + depth_double_blocks: int + The number of transformer blocks in the double blocks. + depth_single_blocks: int + The number of transformer blocks in the single blocks. + rope_dim_list: list + The dimension of the rotary embedding for t, h, w. + qkv_bias: bool + Whether to use bias in the qkv linear layer. + qk_norm: bool + Whether to use qk norm. + qk_norm_type: str + The type of qk norm. + guidance_embed: bool + Whether to use guidance embedding for distillation. + text_projection: str + The type of the text projection, default is single_refiner. + use_attention_mask: bool + Whether to use attention mask for text encoder. + dtype: torch.dtype + The dtype of the model. + device: torch.device + The device of the model. + """ + + @register_to_config + def __init__( + self, + args: Any, + patch_size: list = [1, 2, 2], + in_channels: int = 4, # Should be VAE.config.latent_channels. + out_channels: int = None, + hidden_size: int = 3072, + heads_num: int = 24, + mlp_width_ratio: float = 4.0, + mlp_act_type: str = "gelu_tanh", + mm_double_blocks_depth: int = 20, + mm_single_blocks_depth: int = 40, + rope_dim_list: List[int] = [16, 56, 56], + qkv_bias: bool = True, + qk_norm: bool = True, + qk_norm_type: str = "rms", + guidance_embed: bool = False, # For modulation. + text_projection: str = "single_refiner", + use_attention_mask: bool = True, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + + self.patch_size = patch_size + self.in_channels = in_channels + self.out_channels = in_channels if out_channels is None else out_channels + self.unpatchify_channels = self.out_channels + self.guidance_embed = guidance_embed + self.rope_dim_list = rope_dim_list + + # Text projection. Default to linear projection. + # Alternative: TokenRefiner. See more details (LI-DiT): http://arxiv.org/abs/2406.11831 + self.use_attention_mask = use_attention_mask + self.text_projection = text_projection + + self.text_states_dim = args.text_states_dim + self.text_states_dim_2 = args.text_states_dim_2 + self.rope_theta = args.rope_theta + + if hidden_size % heads_num != 0: + raise ValueError( + f"Hidden size {hidden_size} must be divisible by heads_num {heads_num}" + ) + pe_dim = hidden_size // heads_num + if sum(rope_dim_list) != pe_dim: + raise ValueError( + f"Got {rope_dim_list} but expected positional dim {pe_dim}" + ) + self.hidden_size = hidden_size + self.heads_num = heads_num + + # image projection + self.img_in = PatchEmbed( + self.patch_size, self.in_channels, self.hidden_size, **factory_kwargs + ) + + # text projection + if self.text_projection == "linear": + self.txt_in = TextProjection( + self.text_states_dim, + self.hidden_size, + get_activation_layer("silu"), + **factory_kwargs, + ) + elif self.text_projection == "single_refiner": + self.txt_in = SingleTokenRefiner( + self.text_states_dim, hidden_size, heads_num, depth=2, **factory_kwargs + ) + else: + raise NotImplementedError( + f"Unsupported text_projection: {self.text_projection}" + ) + + # time modulation + self.time_in = TimestepEmbedder( + self.hidden_size, get_activation_layer("silu"), **factory_kwargs + ) + + # text modulation + self.vector_in = MLPEmbedder( + self.text_states_dim_2, self.hidden_size, **factory_kwargs + ) + + # guidance modulation + self.guidance_in = ( + TimestepEmbedder( + self.hidden_size, get_activation_layer("silu"), **factory_kwargs + ) + if guidance_embed + else None + ) + + # double blocks + self.double_blocks = nn.ModuleList( + [ + MMDoubleStreamBlock( + self.hidden_size, + self.heads_num, + mlp_width_ratio=mlp_width_ratio, + mlp_act_type=mlp_act_type, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + qkv_bias=qkv_bias, + **factory_kwargs, + ) + for _ in range(mm_double_blocks_depth) + ] + ) + + # single blocks + self.single_blocks = nn.ModuleList( + [ + MMSingleStreamBlock( + self.hidden_size, + self.heads_num, + mlp_width_ratio=mlp_width_ratio, + mlp_act_type=mlp_act_type, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + **factory_kwargs, + ) + for _ in range(mm_single_blocks_depth) + ] + ) + + self.final_layer = FinalLayer( + self.hidden_size, + self.patch_size, + self.out_channels, + get_activation_layer("silu"), + **factory_kwargs, + ) + + def enable_deterministic(self): + for block in self.double_blocks: + block.enable_deterministic() + for block in self.single_blocks: + block.enable_deterministic() + + def disable_deterministic(self): + for block in self.double_blocks: + block.disable_deterministic() + for block in self.single_blocks: + block.disable_deterministic() + + def get_rotary_pos_embed(self, rope_sizes): + target_ndim = 3 + # ndim = 5 - 2 + head_dim = self.hidden_size // self.heads_num + rope_dim_list = self.rope_dim_list + if rope_dim_list is None: + rope_dim_list = [head_dim // target_ndim for _ in range(target_ndim)] + assert ( + sum(rope_dim_list) == head_dim + ), "sum(rope_dim_list) should equal to head_dim of attention layer" + freqs_cos, freqs_sin = get_nd_rotary_pos_embed( + rope_dim_list, + rope_sizes, + theta=self.rope_theta, + use_real=True, + theta_rescale_factor=1, + ) + return freqs_cos, freqs_sin + + def forward( + self, + x: torch.Tensor, + t: torch.Tensor, # Should be in range(0, 1000). + text_states: torch.Tensor = None, + text_mask: torch.Tensor = None, # Now we don't use it. + text_states_2: Optional[torch.Tensor] = None, # Text embedding for modulation. + freqs_cos: Optional[torch.Tensor] = None, + freqs_sin: Optional[torch.Tensor] = None, + guidance: torch.Tensor = None, # Guidance for modulation, should be cfg_scale x 1000. + return_dict: bool = True, + output_features=False, + output_features_stride=39, + output_intermediate_features=False, + ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: + + out = {} + img = x + txt = text_states + _, _, ot, oh, ow = x.shape + tt, th, tw = ( + ot // self.patch_size[0], + oh // self.patch_size[1], + ow // self.patch_size[2], + ) + + if freqs_cos is None: + original_tt = nccl_info.sp_size * tt + freqs_cos, freqs_sin = self.get_rotary_pos_embed((original_tt, th, tw)) + + # Prepare modulation vectors. + vec = self.time_in(t) + + # text modulation + vec = vec + self.vector_in(text_states_2) + + # guidance modulation + if self.guidance_embed: + if guidance is None: + raise ValueError( + "Didn't get guidance strength for guidance distilled model." + ) + + # our timestep_embedding is merged into guidance_in(TimestepEmbedder) + vec = vec + self.guidance_in(guidance) + + # Embed image and text. + img = self.img_in(img) + if self.text_projection == "linear": + txt = self.txt_in(txt) + elif self.text_projection == "single_refiner": + txt = self.txt_in(txt, t, text_mask if self.use_attention_mask else None) + else: + raise NotImplementedError( + f"Unsupported text_projection: {self.text_projection}" + ) + + txt_seq_len = txt.shape[1] + img_seq_len = img.shape[1] + + # Compute cu_squlens and max_seqlen for flash attention + cu_seqlens_q = get_cu_seqlens(text_mask, img_seq_len) + cu_seqlens_kv = cu_seqlens_q + max_seqlen_q = img_seq_len + txt_seq_len + max_seqlen_kv = max_seqlen_q + + freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None + if output_intermediate_features: + intermediate_features_list = [] + # --------------------- Pass through DiT blocks ------------------------ + for _, block in enumerate(self.double_blocks): + double_block_args = [ + img, + txt, + vec, + cu_seqlens_q, + cu_seqlens_kv, + max_seqlen_q, + max_seqlen_kv, + freqs_cis, + ] + + img, txt = block(*double_block_args) + if output_intermediate_features: + intermediate_features_list.append(img) + + # Merge txt and img to pass through single stream blocks. + if output_features: + features_list = [] + + x = torch.cat((img, txt), 1) + if len(self.single_blocks) > 0: + for _, block in enumerate(self.single_blocks): + single_block_args = [ + x, + vec, + txt_seq_len, + cu_seqlens_q, + cu_seqlens_kv, + max_seqlen_q, + max_seqlen_kv, + (freqs_cos, freqs_sin), + ] + + x = block(*single_block_args) + # if output_features and _ % output_features_stride == 0: + # features_list.append(x[:, :img_seq_len, ...]) + if _ == output_features_stride and output_features: + features_list.append(x[:, :img_seq_len, ...]) + features_list = torch.stack(features_list, dim=0) + return (None, features_list) + if output_intermediate_features: + intermediate_features_list.append(x[:, :img_seq_len, ...]) + + img = x[:, :img_seq_len, ...] + + # ---------------------------- Final layer ------------------------------ + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + + img = self.unpatchify(img, tt, th, tw) + + if output_features: + features_list = torch.stack(features_list, dim=0) + else: + features_list = None + + if return_dict: + out["x"] = img + return out + if output_features: + return (img, features_list) + if output_intermediate_features: + return intermediate_features_list + return img + + def unpatchify(self, x, t, h, w): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.unpatchify_channels + pt, ph, pw = self.patch_size + assert t * h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], t, h, w, c, pt, ph, pw)) + x = torch.einsum("nthwcopq->nctohpwq", x) + imgs = x.reshape(shape=(x.shape[0], c, t * pt, h * ph, w * pw)) + + return imgs + + def params_count(self): + counts = { + "double": sum( + [ + sum(p.numel() for p in block.img_attn_qkv.parameters()) + + sum(p.numel() for p in block.img_attn_proj.parameters()) + + sum(p.numel() for p in block.img_mlp.parameters()) + + sum(p.numel() for p in block.txt_attn_qkv.parameters()) + + sum(p.numel() for p in block.txt_attn_proj.parameters()) + + sum(p.numel() for p in block.txt_mlp.parameters()) + for block in self.double_blocks + ] + ), + "single": sum( + [ + sum(p.numel() for p in block.linear1.parameters()) + + sum(p.numel() for p in block.linear2.parameters()) + for block in self.single_blocks + ] + ), + "total": sum(p.numel() for p in self.parameters()), + } + counts["attn+mlp"] = counts["double"] + counts["single"] + return counts + + +################################################################################# +# HunyuanVideo Configs # +################################################################################# + +HUNYUAN_VIDEO_CONFIG = { + "HYVideo-T/2": { + "mm_double_blocks_depth": 20, + "mm_single_blocks_depth": 40, + "rope_dim_list": [16, 56, 56], + "hidden_size": 3072, + "heads_num": 24, + "mlp_width_ratio": 4, + }, + "HYVideo-T/2-cfgdistill": { + "mm_double_blocks_depth": 20, + "mm_single_blocks_depth": 40, + "rope_dim_list": [16, 56, 56], + "hidden_size": 3072, + "heads_num": 24, + "mlp_width_ratio": 4, + "guidance_embed": True, + }, +} diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/modulate_layers.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/modulate_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..93a57c6d2fdc0fca9bf44aeee6996bf1d8a05901 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/modulate_layers.py @@ -0,0 +1,76 @@ +from typing import Callable + +import torch +import torch.nn as nn + + +class ModulateDiT(nn.Module): + """Modulation layer for DiT.""" + def __init__( + self, + hidden_size: int, + factor: int, + act_layer: Callable, + dtype=None, + device=None, + ): + factory_kwargs = {"dtype": dtype, "device": device} + super().__init__() + self.act = act_layer() + self.linear = nn.Linear( + hidden_size, factor * hidden_size, bias=True, **factory_kwargs + ) + # Zero-initialize the modulation + nn.init.zeros_(self.linear.weight) + nn.init.zeros_(self.linear.bias) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.linear(self.act(x)) + + +def modulate(x, shift=None, scale=None): + """modulate by shift and scale + + Args: + x (torch.Tensor): input tensor. + shift (torch.Tensor, optional): shift tensor. Defaults to None. + scale (torch.Tensor, optional): scale tensor. Defaults to None. + + Returns: + torch.Tensor: the output tensor after modulate. + """ + if scale is None and shift is None: + return x + elif shift is None: + return x * (1 + scale.unsqueeze(1)) + elif scale is None: + return x + shift.unsqueeze(1) + else: + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +def apply_gate(x, gate=None, tanh=False): + """AI is creating summary for apply_gate + + Args: + x (torch.Tensor): input tensor. + gate (torch.Tensor, optional): gate tensor. Defaults to None. + tanh (bool, optional): whether to use tanh function. Defaults to False. + + Returns: + torch.Tensor: the output tensor after apply gate. + """ + if gate is None: + return x + if tanh: + return x * gate.unsqueeze(1).tanh() + else: + return x * gate.unsqueeze(1) + + +def ckpt_wrapper(module): + def ckpt_forward(*inputs): + outputs = module(*inputs) + return outputs + + return ckpt_forward diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/norm_layers.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/norm_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..d8c73b1aef6b7b8ca4767c26c4f0bbc0365a122f --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/norm_layers.py @@ -0,0 +1,77 @@ +import torch +import torch.nn as nn + + +class RMSNorm(nn.Module): + def __init__( + self, + dim: int, + elementwise_affine=True, + eps: float = 1e-6, + device=None, + dtype=None, + ): + """ + Initialize the RMSNorm normalization layer. + + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + + """ + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.eps = eps + if elementwise_affine: + self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs)) + + def _norm(self, x): + """ + Apply the RMSNorm normalization to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The normalized tensor. + + """ + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + """ + Forward pass through the RMSNorm layer. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying RMSNorm. + + """ + output = self._norm(x.float()).type_as(x) + if hasattr(self, "weight"): + output = output * self.weight + return output + + +def get_norm_layer(norm_layer): + """ + Get the normalization layer. + + Args: + norm_layer (str): The type of normalization layer. + + Returns: + norm_layer (nn.Module): The normalization layer. + """ + if norm_layer == "layer": + return nn.LayerNorm + elif norm_layer == "rms": + return RMSNorm + else: + raise NotImplementedError(f"Norm layer {norm_layer} is not implemented") diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/posemb_layers.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/posemb_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..dfce82c690540d17a55a51b7997ee7ceb0bdbf44 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/posemb_layers.py @@ -0,0 +1,310 @@ +import torch +from typing import Union, Tuple, List + + +def _to_tuple(x, dim=2): + if isinstance(x, int): + return (x,) * dim + elif len(x) == dim: + return x + else: + raise ValueError(f"Expected length {dim} or int, but got {x}") + + +def get_meshgrid_nd(start, *args, dim=2): + """ + Get n-D meshgrid with start, stop and num. + + Args: + start (int or tuple): If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, + step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num. For n-dim, start/stop/num + should be int or n-tuple. If n-tuple is provided, the meshgrid will be stacked following the dim order in + n-tuples. + *args: See above. + dim (int): Dimension of the meshgrid. Defaults to 2. + + Returns: + grid (np.ndarray): [dim, ...] + """ + if len(args) == 0: + # start is grid_size + num = _to_tuple(start, dim=dim) + start = (0,) * dim + stop = num + elif len(args) == 1: + # start is start, args[0] is stop, step is 1 + start = _to_tuple(start, dim=dim) + stop = _to_tuple(args[0], dim=dim) + num = [stop[i] - start[i] for i in range(dim)] + elif len(args) == 2: + # start is start, args[0] is stop, args[1] is num + start = _to_tuple(start, dim=dim) # Left-Top eg: 12,0 + stop = _to_tuple(args[0], dim=dim) # Right-Bottom eg: 20,32 + num = _to_tuple(args[1], dim=dim) # Target Size eg: 32,124 + else: + raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") + + # PyTorch implement of np.linspace(start[i], stop[i], num[i], endpoint=False) + axis_grid = [] + for i in range(dim): + a, b, n = start[i], stop[i], num[i] + g = torch.linspace(a, b, n + 1, dtype=torch.float32)[:n] + axis_grid.append(g) + grid = torch.meshgrid(*axis_grid, indexing="ij") # dim x [W, H, D] + grid = torch.stack(grid, dim=0) # [dim, W, H, D] + + return grid + + +################################################################################# +# Rotary Positional Embedding Functions # +################################################################################# +# https://github.com/meta-llama/llama/blob/be327c427cc5e89cc1d3ab3d3fec4484df771245/llama/model.py#L80 + + +def reshape_for_broadcast( + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + x: torch.Tensor, + head_first=False, +): + """ + Reshape frequency tensor for broadcasting it with another tensor. + + This function reshapes the frequency tensor to have the same shape as the target tensor 'x' + for the purpose of broadcasting the frequency tensor during element-wise operations. + + Notes: + When using FlashMHAModified, head_first should be False. + When using Attention, head_first should be True. + + Args: + freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped. + x (torch.Tensor): Target tensor for broadcasting compatibility. + head_first (bool): head dimension first (except batch dim) or not. + + Returns: + torch.Tensor: Reshaped frequency tensor. + + Raises: + AssertionError: If the frequency tensor doesn't match the expected shape. + AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions. + """ + ndim = x.ndim + assert 0 <= 1 < ndim + + if isinstance(freqs_cis, tuple): + # freqs_cis: (cos, sin) in real space + if head_first: + assert freqs_cis[0].shape == ( + x.shape[-2], + x.shape[-1], + ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}" + shape = [ + d if i == ndim - 2 or i == ndim - 1 else 1 + for i, d in enumerate(x.shape) + ] + else: + assert freqs_cis[0].shape == ( + x.shape[1], + x.shape[-1], + ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}" + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) + else: + # freqs_cis: values in complex space + if head_first: + assert freqs_cis.shape == ( + x.shape[-2], + x.shape[-1], + ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}" + shape = [ + d if i == ndim - 2 or i == ndim - 1 else 1 + for i, d in enumerate(x.shape) + ] + else: + assert freqs_cis.shape == ( + x.shape[1], + x.shape[-1], + ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}" + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + +def rotate_half(x): + x_real, x_imag = ( + x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1) + ) # [B, S, H, D//2] + return torch.stack([-x_imag, x_real], dim=-1).flatten(3) + + +def apply_rotary_emb( + xq: torch.Tensor, + xk: torch.Tensor, + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], + head_first: bool = False, +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency tensor. + + This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided + frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are + returned as real tensors. + + Args: + xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D] + xk (torch.Tensor): Key tensor to apply rotary embeddings. [B, S, H, D] + freqs_cis (torch.Tensor or tuple): Precomputed frequency tensor for complex exponential. + head_first (bool): head dimension first (except batch dim) or not. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. + + """ + xk_out = None + if isinstance(freqs_cis, tuple): + cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D] + cos, sin = cos.to(xq.device), sin.to(xq.device) + # real * cos - imag * sin + # imag * cos + real * sin + xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq) + xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk) + else: + # view_as_complex will pack [..., D/2, 2](real) to [..., D/2](complex) + xq_ = torch.view_as_complex( + xq.float().reshape(*xq.shape[:-1], -1, 2) + ) # [B, S, H, D//2] + freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to( + xq.device + ) # [S, D//2] --> [1, S, 1, D//2] + # (real, imag) * (cos, sin) = (real * cos - imag * sin, imag * cos + real * sin) + # view_as_real will expand [..., D/2](complex) to [..., D/2, 2](real) + xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq) + xk_ = torch.view_as_complex( + xk.float().reshape(*xk.shape[:-1], -1, 2) + ) # [B, S, H, D//2] + xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk) + + return xq_out, xk_out + + +def get_nd_rotary_pos_embed( + rope_dim_list, + start, + *args, + theta=10000.0, + use_real=False, + theta_rescale_factor: Union[float, List[float]] = 1.0, + interpolation_factor: Union[float, List[float]] = 1.0, +): + """ + This is a n-d version of precompute_freqs_cis, which is a RoPE for tokens with n-d structure. + + Args: + rope_dim_list (list of int): Dimension of each rope. len(rope_dim_list) should equal to n. + sum(rope_dim_list) should equal to head_dim of attention layer. + start (int | tuple of int | list of int): If len(args) == 0, start is num; If len(args) == 1, start is start, + args[0] is stop, step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num. + *args: See above. + theta (float): Scaling factor for frequency computation. Defaults to 10000.0. + use_real (bool): If True, return real part and imaginary part separately. Otherwise, return complex numbers. + Some libraries such as TensorRT does not support complex64 data type. So it is useful to provide a real + part and an imaginary part separately. + theta_rescale_factor (float): Rescale factor for theta. Defaults to 1.0. + + Returns: + pos_embed (torch.Tensor): [HW, D/2] + """ + + grid = get_meshgrid_nd( + start, *args, dim=len(rope_dim_list) + ) # [3, W, H, D] / [2, W, H] + + if isinstance(theta_rescale_factor, int) or isinstance(theta_rescale_factor, float): + theta_rescale_factor = [theta_rescale_factor] * len(rope_dim_list) + elif isinstance(theta_rescale_factor, list) and len(theta_rescale_factor) == 1: + theta_rescale_factor = [theta_rescale_factor[0]] * len(rope_dim_list) + assert len(theta_rescale_factor) == len( + rope_dim_list + ), "len(theta_rescale_factor) should equal to len(rope_dim_list)" + + if isinstance(interpolation_factor, int) or isinstance(interpolation_factor, float): + interpolation_factor = [interpolation_factor] * len(rope_dim_list) + elif isinstance(interpolation_factor, list) and len(interpolation_factor) == 1: + interpolation_factor = [interpolation_factor[0]] * len(rope_dim_list) + assert len(interpolation_factor) == len( + rope_dim_list + ), "len(interpolation_factor) should equal to len(rope_dim_list)" + + # use 1/ndim of dimensions to encode grid_axis + embs = [] + for i in range(len(rope_dim_list)): + emb = get_1d_rotary_pos_embed( + rope_dim_list[i], + grid[i].reshape(-1), + theta, + use_real=use_real, + theta_rescale_factor=theta_rescale_factor[i], + interpolation_factor=interpolation_factor[i], + ) # 2 x [WHD, rope_dim_list[i]] + embs.append(emb) + + if use_real: + cos = torch.cat([emb[0] for emb in embs], dim=1) # (WHD, D/2) + sin = torch.cat([emb[1] for emb in embs], dim=1) # (WHD, D/2) + return cos, sin + else: + emb = torch.cat(embs, dim=1) # (WHD, D/2) + return emb + + +def get_1d_rotary_pos_embed( + dim: int, + pos: Union[torch.FloatTensor, int], + theta: float = 10000.0, + use_real: bool = False, + theta_rescale_factor: float = 1.0, + interpolation_factor: float = 1.0, +) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + """ + Precompute the frequency tensor for complex exponential (cis) with given dimensions. + (Note: `cis` means `cos + i * sin`, where i is the imaginary unit.) + + This function calculates a frequency tensor with complex exponential using the given dimension 'dim' + and the end index 'end'. The 'theta' parameter scales the frequencies. + The returned tensor contains complex values in complex64 data type. + + Args: + dim (int): Dimension of the frequency tensor. + pos (int or torch.FloatTensor): Position indices for the frequency tensor. [S] or scalar + theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0. + use_real (bool, optional): If True, return real part and imaginary part separately. + Otherwise, return complex numbers. + theta_rescale_factor (float, optional): Rescale factor for theta. Defaults to 1.0. + + Returns: + freqs_cis: Precomputed frequency tensor with complex exponential. [S, D/2] + freqs_cos, freqs_sin: Precomputed frequency tensor with real and imaginary parts separately. [S, D] + """ + if isinstance(pos, int): + pos = torch.arange(pos).float() + + # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning + # has some connection to NTK literature + if theta_rescale_factor != 1.0: + theta *= theta_rescale_factor ** (dim / (dim - 2)) + + freqs = 1.0 / ( + theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim) + ) # [D/2] + # assert interpolation_factor == 1.0, f"interpolation_factor: {interpolation_factor}" + freqs = torch.outer(pos * interpolation_factor, freqs) # [S, D/2] + if use_real: + freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] + freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] + return freqs_cos, freqs_sin + else: + freqs_cis = torch.polar( + torch.ones_like(freqs), freqs + ) # complex64 # [S, D/2] + return freqs_cis diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/token_refiner.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/token_refiner.py new file mode 100644 index 0000000000000000000000000000000000000000..bf09278e4f792a5e0cb5c2383f317f5bdd302f01 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/modules/token_refiner.py @@ -0,0 +1,236 @@ +from typing import Optional + +from einops import rearrange +import torch +import torch.nn as nn + +from .activation_layers import get_activation_layer +from .attenion import attention +from .norm_layers import get_norm_layer +from .embed_layers import TimestepEmbedder, TextProjection +from .attenion import attention +from .mlp_layers import MLP +from .modulate_layers import modulate, apply_gate + + +class IndividualTokenRefinerBlock(nn.Module): + def __init__( + self, + hidden_size, + heads_num, + mlp_width_ratio: str = 4.0, + mlp_drop_rate: float = 0.0, + act_type: str = "silu", + qk_norm: bool = False, + qk_norm_type: str = "layer", + qkv_bias: bool = True, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.heads_num = heads_num + head_dim = hidden_size // heads_num + mlp_hidden_dim = int(hidden_size * mlp_width_ratio) + + self.norm1 = nn.LayerNorm( + hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs + ) + self.self_attn_qkv = nn.Linear( + hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs + ) + qk_norm_layer = get_norm_layer(qk_norm_type) + self.self_attn_q_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + self.self_attn_k_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) + if qk_norm + else nn.Identity() + ) + self.self_attn_proj = nn.Linear( + hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs + ) + + self.norm2 = nn.LayerNorm( + hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs + ) + act_layer = get_activation_layer(act_type) + self.mlp = MLP( + in_channels=hidden_size, + hidden_channels=mlp_hidden_dim, + act_layer=act_layer, + drop=mlp_drop_rate, + **factory_kwargs, + ) + + self.adaLN_modulation = nn.Sequential( + act_layer(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs), + ) + # Zero-initialize the modulation + nn.init.zeros_(self.adaLN_modulation[1].weight) + nn.init.zeros_(self.adaLN_modulation[1].bias) + + def forward( + self, + x: torch.Tensor, + c: torch.Tensor, # timestep_aware_representations + context_aware_representations + attn_mask: torch.Tensor = None, + ): + gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1) + + norm_x = self.norm1(x) + qkv = self.self_attn_qkv(norm_x) + q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) + # Apply QK-Norm if needed + q = self.self_attn_q_norm(q).to(v) + k = self.self_attn_k_norm(k).to(v) + + # Self-Attention + attn = attention(q, k, v, mode="torch", attn_mask=attn_mask) + + x = x + apply_gate(self.self_attn_proj(attn), gate_msa) + + # FFN Layer + x = x + apply_gate(self.mlp(self.norm2(x)), gate_mlp) + + return x + + +class IndividualTokenRefiner(nn.Module): + def __init__( + self, + hidden_size, + heads_num, + depth, + mlp_width_ratio: float = 4.0, + mlp_drop_rate: float = 0.0, + act_type: str = "silu", + qk_norm: bool = False, + qk_norm_type: str = "layer", + qkv_bias: bool = True, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.blocks = nn.ModuleList( + [ + IndividualTokenRefinerBlock( + hidden_size=hidden_size, + heads_num=heads_num, + mlp_width_ratio=mlp_width_ratio, + mlp_drop_rate=mlp_drop_rate, + act_type=act_type, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + qkv_bias=qkv_bias, + **factory_kwargs, + ) + for _ in range(depth) + ] + ) + + def forward( + self, + x: torch.Tensor, + c: torch.LongTensor, + mask: Optional[torch.Tensor] = None, + ): + self_attn_mask = None + if mask is not None: + batch_size = mask.shape[0] + seq_len = mask.shape[1] + mask = mask.to(x.device) + # batch_size x 1 x seq_len x seq_len + self_attn_mask_1 = mask.view(batch_size, 1, 1, seq_len).repeat( + 1, 1, seq_len, 1 + ) + # batch_size x 1 x seq_len x seq_len + self_attn_mask_2 = self_attn_mask_1.transpose(2, 3) + # batch_size x 1 x seq_len x seq_len, 1 for broadcasting of heads_num + self_attn_mask = (self_attn_mask_1 & self_attn_mask_2).bool() + # avoids self-attention weight being NaN for padding tokens + self_attn_mask[:, :, :, 0] = True + + for block in self.blocks: + x = block(x, c, self_attn_mask) + return x + + +class SingleTokenRefiner(nn.Module): + """ + A single token refiner block for llm text embedding refine. + """ + def __init__( + self, + in_channels, + hidden_size, + heads_num, + depth, + mlp_width_ratio: float = 4.0, + mlp_drop_rate: float = 0.0, + act_type: str = "silu", + qk_norm: bool = False, + qk_norm_type: str = "layer", + qkv_bias: bool = True, + attn_mode: str = "torch", + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.attn_mode = attn_mode + assert self.attn_mode == "torch", "Only support 'torch' mode for token refiner." + + self.input_embedder = nn.Linear( + in_channels, hidden_size, bias=True, **factory_kwargs + ) + + act_layer = get_activation_layer(act_type) + # Build timestep embedding layer + self.t_embedder = TimestepEmbedder(hidden_size, act_layer, **factory_kwargs) + # Build context embedding layer + self.c_embedder = TextProjection( + in_channels, hidden_size, act_layer, **factory_kwargs + ) + + self.individual_token_refiner = IndividualTokenRefiner( + hidden_size=hidden_size, + heads_num=heads_num, + depth=depth, + mlp_width_ratio=mlp_width_ratio, + mlp_drop_rate=mlp_drop_rate, + act_type=act_type, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + qkv_bias=qkv_bias, + **factory_kwargs, + ) + + def forward( + self, + x: torch.Tensor, + t: torch.LongTensor, + mask: Optional[torch.LongTensor] = None, + ): + timestep_aware_representations = self.t_embedder(t) + + if mask is None: + context_aware_representations = x.mean(dim=1) + else: + mask_float = mask.float().unsqueeze(-1) # [b, s1, 1] + context_aware_representations = (x * mask_float).sum( + dim=1 + ) / mask_float.sum(dim=1) + context_aware_representations = self.c_embedder(context_aware_representations) + c = timestep_aware_representations + context_aware_representations + + x = self.input_embedder(x) + + x = self.individual_token_refiner(x, c, mask) + + return x diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/parallel_states.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/parallel_states.py new file mode 100644 index 0000000000000000000000000000000000000000..1897db2078eb8fb61ff0429d2a3ac9659a5060f8 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/parallel_states.py @@ -0,0 +1,63 @@ +import torch +import torch.distributed as dist +import os + + +class COMM_INFO: + def __init__(self): + self.group = None + self.sp_size = 1 + self.global_rank = 0 + self.rank_within_group = 0 + self.group_id = 0 + + +nccl_info = COMM_INFO() +_SEQUENCE_PARALLEL_STATE = False + + +def initialize_sequence_parallel_state(sequence_parallel_size): + global _SEQUENCE_PARALLEL_STATE + if sequence_parallel_size > 1: + _SEQUENCE_PARALLEL_STATE = True + initialize_sequence_parallel_group(sequence_parallel_size) + else: + nccl_info.sp_size = 1 + nccl_info.global_rank = int(os.getenv("RANK", "0")) + nccl_info.rank_within_group = 0 + nccl_info.group_id = int(os.getenv("RANK", "0")) + + +def set_sequence_parallel_state(state): + global _SEQUENCE_PARALLEL_STATE + _SEQUENCE_PARALLEL_STATE = state + + +def get_sequence_parallel_state(): + return _SEQUENCE_PARALLEL_STATE + + +def initialize_sequence_parallel_group(sequence_parallel_size): + """Initialize the sequence parallel group.""" + rank = int(os.getenv("RANK", "0")) + world_size = int(os.getenv("WORLD_SIZE", "1")) + assert ( + world_size % sequence_parallel_size == 0 + ), "world_size must be divisible by sequence_parallel_size, but got world_size: {}, sequence_parallel_size: {}".format( + world_size, sequence_parallel_size + ) + nccl_info.sp_size = sequence_parallel_size + nccl_info.global_rank = rank + num_sequence_parallel_groups: int = world_size // sequence_parallel_size + for i in range(num_sequence_parallel_groups): + ranks = range(i * sequence_parallel_size, (i + 1) * sequence_parallel_size) + group = dist.new_group(ranks) + if rank in ranks: + nccl_info.group = group + nccl_info.rank_within_group = rank - i * sequence_parallel_size + nccl_info.group_id = i + + +def destroy_sequence_parallel_group(): + """Destroy the sequence parallel group.""" + dist.destroy_process_group() diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/prompt_rewrite.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/prompt_rewrite.py new file mode 100644 index 0000000000000000000000000000000000000000..72840b396328f5d3b9fdd150533bc693ccf09954 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/prompt_rewrite.py @@ -0,0 +1,53 @@ +normal_mode_prompt = """Normal mode - Video Recaption Task: + +You are a large language model specialized in rewriting video descriptions. Your task is to modify the input description. + +0. Preserve ALL information, including style words and technical terms. + +1. If the input is in Chinese, translate the entire description to English. + +2. If the input is just one or two words describing an object or person, provide a brief, simple description focusing on basic visual characteristics. Limit the description to 1-2 short sentences. + +3. If the input does not include style, lighting, atmosphere, you can make reasonable associations. + +4. Output ALL must be in English. + +Given Input: +input: "{input}" +""" + + +master_mode_prompt = """Master mode - Video Recaption Task: + +You are a large language model specialized in rewriting video descriptions. Your task is to modify the input description. + +0. Preserve ALL information, including style words and technical terms. + +1. If the input is in Chinese, translate the entire description to English. + +2. If the input is just one or two words describing an object or person, provide a brief, simple description focusing on basic visual characteristics. Limit the description to 1-2 short sentences. + +3. If the input does not include style, lighting, atmosphere, you can make reasonable associations. + +4. Output ALL must be in English. + +Given Input: +input: "{input}" +""" + + +def get_rewrite_prompt(ori_prompt, mode="Normal"): + if mode == "Normal": + prompt = normal_mode_prompt.format(input=ori_prompt) + elif mode == "Master": + prompt = master_mode_prompt.format(input=ori_prompt) + else: + raise Exception("Only supports Normal and Normal", mode) + return prompt + + +ori_prompt = "一只小狗在草地上奔跑。" +normal_prompt = get_rewrite_prompt(ori_prompt, mode="Normal") +master_prompt = get_rewrite_prompt(ori_prompt, mode="Master") + +# Then you can use the normal_prompt or master_prompt to access the hunyuan-large rewrite model to get the final prompt. diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/text_encoder/__init__.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/text_encoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..494992baa20adf37559231c09d5fa8325c32b343 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/text_encoder/__init__.py @@ -0,0 +1,357 @@ +from dataclasses import dataclass +from typing import Optional, Tuple +from copy import deepcopy + +import torch +import torch.nn as nn +from transformers import CLIPTextModel, CLIPTokenizer, AutoTokenizer, AutoModel +from transformers.utils import ModelOutput + +from ..constants import TEXT_ENCODER_PATH, TOKENIZER_PATH +from ..constants import PRECISION_TO_TYPE + + +def use_default(value, default): + return value if value is not None else default + + +def load_text_encoder( + text_encoder_type, + text_encoder_precision=None, + text_encoder_path=None, + logger=None, + device=None, +): + if text_encoder_path is None: + text_encoder_path = TEXT_ENCODER_PATH[text_encoder_type] + if logger is not None: + logger.info( + f"Loading text encoder model ({text_encoder_type}) from: {text_encoder_path}" + ) + + if text_encoder_type == "clipL": + text_encoder = CLIPTextModel.from_pretrained(text_encoder_path) + text_encoder.final_layer_norm = text_encoder.text_model.final_layer_norm + elif text_encoder_type == "llm": + text_encoder = AutoModel.from_pretrained( + text_encoder_path, low_cpu_mem_usage=True + ) + text_encoder.final_layer_norm = text_encoder.norm + else: + raise ValueError(f"Unsupported text encoder type: {text_encoder_type}") + # from_pretrained will ensure that the model is in eval mode. + + if text_encoder_precision is not None: + text_encoder = text_encoder.to(dtype=PRECISION_TO_TYPE[text_encoder_precision]) + + text_encoder.requires_grad_(False) + + if logger is not None: + logger.info(f"Text encoder to dtype: {text_encoder.dtype}") + + if device is not None: + text_encoder = text_encoder.to(device) + + return text_encoder, text_encoder_path + + +def load_tokenizer( + tokenizer_type, tokenizer_path=None, padding_side="right", logger=None +): + if tokenizer_path is None: + tokenizer_path = TOKENIZER_PATH[tokenizer_type] + if logger is not None: + logger.info(f"Loading tokenizer ({tokenizer_type}) from: {tokenizer_path}") + + if tokenizer_type == "clipL": + tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path, max_length=77) + elif tokenizer_type == "llm": + tokenizer = AutoTokenizer.from_pretrained( + tokenizer_path, padding_side=padding_side + ) + else: + raise ValueError(f"Unsupported tokenizer type: {tokenizer_type}") + + return tokenizer, tokenizer_path + + +@dataclass +class TextEncoderModelOutput(ModelOutput): + """ + Base class for model's outputs that also contains a pooling of the last hidden states. + + Args: + hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: + hidden_states_list (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + text_outputs (`list`, *optional*, returned when `return_texts=True` is passed): + List of decoded texts. + """ + + hidden_state: torch.FloatTensor = None + attention_mask: Optional[torch.LongTensor] = None + hidden_states_list: Optional[Tuple[torch.FloatTensor, ...]] = None + text_outputs: Optional[list] = None + + +class TextEncoder(nn.Module): + def __init__( + self, + text_encoder_type: str, + max_length: int, + text_encoder_precision: Optional[str] = None, + text_encoder_path: Optional[str] = None, + tokenizer_type: Optional[str] = None, + tokenizer_path: Optional[str] = None, + output_key: Optional[str] = None, + use_attention_mask: bool = True, + input_max_length: Optional[int] = None, + prompt_template: Optional[dict] = None, + prompt_template_video: Optional[dict] = None, + hidden_state_skip_layer: Optional[int] = None, + apply_final_norm: bool = False, + reproduce: bool = False, + logger=None, + device=None, + ): + super().__init__() + self.text_encoder_type = text_encoder_type + self.max_length = max_length + self.precision = text_encoder_precision + self.model_path = text_encoder_path + self.tokenizer_type = ( + tokenizer_type if tokenizer_type is not None else text_encoder_type + ) + self.tokenizer_path = ( + tokenizer_path if tokenizer_path is not None else text_encoder_path + ) + self.use_attention_mask = use_attention_mask + if prompt_template_video is not None: + assert ( + use_attention_mask is True + ), "Attention mask is True required when training videos." + self.input_max_length = ( + input_max_length if input_max_length is not None else max_length + ) + self.prompt_template = prompt_template + self.prompt_template_video = prompt_template_video + self.hidden_state_skip_layer = hidden_state_skip_layer + self.apply_final_norm = apply_final_norm + self.reproduce = reproduce + self.logger = logger + + self.use_template = self.prompt_template is not None + if self.use_template: + assert ( + isinstance(self.prompt_template, dict) + and "template" in self.prompt_template + ), f"`prompt_template` must be a dictionary with a key 'template', got {self.prompt_template}" + assert "{}" in str(self.prompt_template["template"]), ( + "`prompt_template['template']` must contain a placeholder `{}` for the input text, " + f"got {self.prompt_template['template']}" + ) + + self.use_video_template = self.prompt_template_video is not None + if self.use_video_template: + if self.prompt_template_video is not None: + assert ( + isinstance(self.prompt_template_video, dict) + and "template" in self.prompt_template_video + ), f"`prompt_template_video` must be a dictionary with a key 'template', got {self.prompt_template_video}" + assert "{}" in str(self.prompt_template_video["template"]), ( + "`prompt_template_video['template']` must contain a placeholder `{}` for the input text, " + f"got {self.prompt_template_video['template']}" + ) + + if "t5" in text_encoder_type: + self.output_key = output_key or "last_hidden_state" + elif "clip" in text_encoder_type: + self.output_key = output_key or "pooler_output" + elif "llm" in text_encoder_type or "glm" in text_encoder_type: + self.output_key = output_key or "last_hidden_state" + else: + raise ValueError(f"Unsupported text encoder type: {text_encoder_type}") + + self.model, self.model_path = load_text_encoder( + text_encoder_type=self.text_encoder_type, + text_encoder_precision=self.precision, + text_encoder_path=self.model_path, + logger=self.logger, + device=device, + ) + self.dtype = self.model.dtype + self.device = self.model.device + + self.tokenizer, self.tokenizer_path = load_tokenizer( + tokenizer_type=self.tokenizer_type, + tokenizer_path=self.tokenizer_path, + padding_side="right", + logger=self.logger, + ) + + def __repr__(self): + return f"{self.text_encoder_type} ({self.precision} - {self.model_path})" + + @staticmethod + def apply_text_to_template(text, template, prevent_empty_text=True): + """ + Apply text to template. + + Args: + text (str): Input text. + template (str or list): Template string or list of chat conversation. + prevent_empty_text (bool): If Ture, we will prevent the user text from being empty + by adding a space. Defaults to True. + """ + if isinstance(template, str): + # Will send string to tokenizer. Used for llm + return template.format(text) + else: + raise TypeError(f"Unsupported template type: {type(template)}") + + def text2tokens(self, text, data_type="image"): + """ + Tokenize the input text. + + Args: + text (str or list): Input text. + """ + tokenize_input_type = "str" + if self.use_template: + if data_type == "image": + prompt_template = self.prompt_template["template"] + elif data_type == "video": + prompt_template = self.prompt_template_video["template"] + else: + raise ValueError(f"Unsupported data type: {data_type}") + if isinstance(text, (list, tuple)): + text = [ + self.apply_text_to_template(one_text, prompt_template) + for one_text in text + ] + if isinstance(text[0], list): + tokenize_input_type = "list" + elif isinstance(text, str): + text = self.apply_text_to_template(text, prompt_template) + if isinstance(text, list): + tokenize_input_type = "list" + else: + raise TypeError(f"Unsupported text type: {type(text)}") + + kwargs = dict( + truncation=True, + max_length=self.max_length, + padding="max_length", + return_tensors="pt", + ) + if tokenize_input_type == "str": + return self.tokenizer( + text, + return_length=False, + return_overflowing_tokens=False, + return_attention_mask=True, + **kwargs, + ) + elif tokenize_input_type == "list": + return self.tokenizer.apply_chat_template( + text, + add_generation_prompt=True, + tokenize=True, + return_dict=True, + **kwargs, + ) + else: + raise ValueError(f"Unsupported tokenize_input_type: {tokenize_input_type}") + + def encode( + self, + batch_encoding, + use_attention_mask=None, + output_hidden_states=False, + do_sample=None, + hidden_state_skip_layer=None, + return_texts=False, + data_type="image", + device=None, + ): + """ + Args: + batch_encoding (dict): Batch encoding from tokenizer. + use_attention_mask (bool): Whether to use attention mask. If None, use self.use_attention_mask. + Defaults to None. + output_hidden_states (bool): Whether to output hidden states. If False, return the value of + self.output_key. If True, return the entire output. If set self.hidden_state_skip_layer, + output_hidden_states will be set True. Defaults to False. + do_sample (bool): Whether to sample from the model. Used for Decoder-Only LLMs. Defaults to None. + When self.produce is False, do_sample is set to True by default. + hidden_state_skip_layer (int): Number of hidden states to hidden_state_skip_layer. 0 means the last layer. + If None, self.output_key will be used. Defaults to None. + return_texts (bool): Whether to return the decoded texts. Defaults to False. + """ + device = self.model.device if device is None else device + use_attention_mask = use_default(use_attention_mask, self.use_attention_mask) + hidden_state_skip_layer = use_default( + hidden_state_skip_layer, self.hidden_state_skip_layer + ) + do_sample = use_default(do_sample, not self.reproduce) + attention_mask = ( + batch_encoding["attention_mask"].to(device) if use_attention_mask else None + ) + outputs = self.model( + input_ids=batch_encoding["input_ids"].to(device), + attention_mask=attention_mask, + output_hidden_states=output_hidden_states + or hidden_state_skip_layer is not None, + ) + if hidden_state_skip_layer is not None: + last_hidden_state = outputs.hidden_states[-(hidden_state_skip_layer + 1)] + # Real last hidden state already has layer norm applied. So here we only apply it + # for intermediate layers. + if hidden_state_skip_layer > 0 and self.apply_final_norm: + last_hidden_state = self.model.final_layer_norm(last_hidden_state) + else: + last_hidden_state = outputs[self.output_key] + + # Remove hidden states of instruction tokens, only keep prompt tokens. + if self.use_template: + if data_type == "image": + crop_start = self.prompt_template.get("crop_start", -1) + elif data_type == "video": + crop_start = self.prompt_template_video.get("crop_start", -1) + else: + raise ValueError(f"Unsupported data type: {data_type}") + if crop_start > 0: + last_hidden_state = last_hidden_state[:, crop_start:] + attention_mask = ( + attention_mask[:, crop_start:] if use_attention_mask else None + ) + + if output_hidden_states: + return TextEncoderModelOutput( + last_hidden_state, attention_mask, outputs.hidden_states + ) + return TextEncoderModelOutput(last_hidden_state, attention_mask) + + def forward( + self, + text, + use_attention_mask=None, + output_hidden_states=False, + do_sample=False, + hidden_state_skip_layer=None, + return_texts=False, + ): + batch_encoding = self.text2tokens(text) + return self.encode( + batch_encoding, + use_attention_mask=use_attention_mask, + output_hidden_states=output_hidden_states, + do_sample=do_sample, + hidden_state_skip_layer=hidden_state_skip_layer, + return_texts=return_texts, + ) diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/__init__.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/data_utils.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..583a90355f0e7ff1e25534de21bd5fb301e6934e --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/data_utils.py @@ -0,0 +1,15 @@ +import numpy as np +import math + + +def align_to(value, alignment): + """align hight, width according to alignment + + Args: + value (int): height or width + alignment (int): target alignment factor + + Returns: + int: the aligned value + """ + return int(math.ceil(value / alignment) * alignment) diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/file_utils.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/file_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fbb00581e5dc80baa088e51dbd1dcb8c4d156ea5 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/file_utils.py @@ -0,0 +1,71 @@ +import os +from pathlib import Path +from einops import rearrange + +import torch +import torchvision +import numpy as np +import imageio + +CODE_SUFFIXES = { + ".py", # Python codes + ".sh", # Shell scripts + ".yaml", + ".yml", # Configuration files +} + + +def safe_dir(path): + """ + Create a directory (or the parent directory of a file) if it does not exist. + + Args: + path (str or Path): Path to the directory. + + Returns: + path (Path): Path object of the directory. + """ + path = Path(path) + path.mkdir(exist_ok=True, parents=True) + return path + + +def safe_file(path): + """ + Create the parent directory of a file if it does not exist. + + Args: + path (str or Path): Path to the file. + + Returns: + path (Path): Path object of the file. + """ + path = Path(path) + path.parent.mkdir(exist_ok=True, parents=True) + return path + + +def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=1, fps=24): + """save videos by video tensor + copy from https://github.com/guoyww/AnimateDiff/blob/e92bd5671ba62c0d774a32951453e328018b7c5b/animatediff/utils/util.py#L61 + + Args: + videos (torch.Tensor): video tensor predicted by the model + path (str): path to save video + rescale (bool, optional): rescale the video tensor from [-1, 1] to . Defaults to False. + n_rows (int, optional): Defaults to 1. + fps (int, optional): video save fps. Defaults to 8. + """ + videos = rearrange(videos, "b c t h w -> t b c h w") + outputs = [] + for x in videos: + x = torchvision.utils.make_grid(x, nrow=n_rows) + x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) + if rescale: + x = (x + 1.0) / 2.0 # -1,1 -> 0,1 + x = torch.clamp(x, 0, 1) + x = (x * 255).numpy().astype(np.uint8) + outputs.append(x) + + os.makedirs(os.path.dirname(path), exist_ok=True) + imageio.mimsave(path, outputs, fps=fps) diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/helpers.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..8768812d1aa27bd38f57d78c69eda9b106dd64eb --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/helpers.py @@ -0,0 +1,41 @@ +import collections.abc + +from itertools import repeat + + +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + x = tuple(x) + if len(x) == 1: + x = tuple(repeat(x[0], n)) + return x + return tuple(repeat(x, n)) + + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) + + +def as_tuple(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return tuple(x) + if x is None or isinstance(x, (int, float, str)): + return (x,) + else: + raise ValueError(f"Unknown type {type(x)}") + + +def as_list_of_2tuple(x): + x = as_tuple(x) + if len(x) == 1: + x = (x[0], x[0]) + assert len(x) % 2 == 0, f"Expect even length, got {len(x)}." + lst = [] + for i in range(0, len(x), 2): + lst.append((x[i], x[i + 1])) + return lst diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/preprocess_text_encoder_tokenizer_utils.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/preprocess_text_encoder_tokenizer_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..71b874c50e094e35f42edb51ad9c0abe5a35f9b7 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/utils/preprocess_text_encoder_tokenizer_utils.py @@ -0,0 +1,41 @@ +import argparse +import torch +from transformers import ( + AutoProcessor, + LlavaForConditionalGeneration, +) + + +def preprocess_text_encoder_tokenizer(args): + + processor = AutoProcessor.from_pretrained(args.input_dir) + model = LlavaForConditionalGeneration.from_pretrained( + args.input_dir, torch_dtype=torch.float16, low_cpu_mem_usage=True, + ).to(0) + + model.language_model.save_pretrained(f"{args.output_dir}") + processor.tokenizer.save_pretrained(f"{args.output_dir}") + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser() + parser.add_argument( + "--input_dir", + type=str, + required=True, + help="The path to the llava-llama-3-8b-v1_1-transformers.", + ) + parser.add_argument( + "--output_dir", + type=str, + default="", + help="The output path of the llava-llama-3-8b-text-encoder-tokenizer." + "if '', the parent dir of output will be the same as input dir.", + ) + args = parser.parse_args() + + if len(args.output_dir) == 0: + args.output_dir = "/".join(args.input_dir.split("/")[:-1]) + + preprocess_text_encoder_tokenizer(args) diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/__init__.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..da714255a3d7e463f0a9c6701b79fe163b6e3f10 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/__init__.py @@ -0,0 +1,66 @@ +from pathlib import Path + +import torch + +from .autoencoder_kl_causal_3d import AutoencoderKLCausal3D +from ..constants import VAE_PATH, PRECISION_TO_TYPE + + +def load_vae( + vae_type: str = "884-16c-hy", + vae_precision: str = None, + sample_size: tuple = None, + vae_path: str = None, + logger=None, + device=None, +): + """the fucntion to load the 3D VAE model + + Args: + vae_type (str): the type of the 3D VAE model. Defaults to "884-16c-hy". + vae_precision (str, optional): the precision to load vae. Defaults to None. + sample_size (tuple, optional): the tiling size. Defaults to None. + vae_path (str, optional): the path to vae. Defaults to None. + logger (_type_, optional): logger. Defaults to None. + device (_type_, optional): device to load vae. Defaults to None. + """ + if vae_path is None: + vae_path = VAE_PATH[vae_type] + + if logger is not None: + logger.info(f"Loading 3D VAE model ({vae_type}) from: {vae_path}") + config = AutoencoderKLCausal3D.load_config(vae_path) + if sample_size: + vae = AutoencoderKLCausal3D.from_config(config, sample_size=sample_size) + else: + vae = AutoencoderKLCausal3D.from_config(config) + + vae_ckpt = Path(vae_path) / "pytorch_model.pt" + assert vae_ckpt.exists(), f"VAE checkpoint not found: {vae_ckpt}" + + ckpt = torch.load(vae_ckpt, map_location=vae.device) + if "state_dict" in ckpt: + ckpt = ckpt["state_dict"] + if any(k.startswith("vae.") for k in ckpt.keys()): + ckpt = { + k.replace("vae.", ""): v for k, v in ckpt.items() if k.startswith("vae.") + } + vae.load_state_dict(ckpt) + + spatial_compression_ratio = vae.config.spatial_compression_ratio + time_compression_ratio = vae.config.time_compression_ratio + + if vae_precision is not None: + vae = vae.to(dtype=PRECISION_TO_TYPE[vae_precision]) + + vae.requires_grad_(False) + + if logger is not None: + logger.info(f"VAE to dtype: {vae.dtype}") + + if device is not None: + vae = vae.to(device) + + vae.eval() + + return vae, vae_path, spatial_compression_ratio, time_compression_ratio diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/autoencoder_kl_causal_3d.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/autoencoder_kl_causal_3d.py new file mode 100644 index 0000000000000000000000000000000000000000..61a82544427bac2a1b04c879f7eb8ec501a762b6 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/autoencoder_kl_causal_3d.py @@ -0,0 +1,687 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# +# Modified from diffusers==0.29.2 +# +# ============================================================================== +from typing import Dict, Optional, Tuple, Union +from dataclasses import dataclass + +import torch +import torch.nn as nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config + +try: + # This diffusers is modified and packed in the mirror. + from diffusers.loaders import FromOriginalVAEMixin +except ImportError: + # Use this to be compatible with the original diffusers. + from diffusers.loaders.single_file_model import ( + FromOriginalModelMixin as FromOriginalVAEMixin, + ) +from diffusers.utils.accelerate_utils import apply_forward_hook +from diffusers.models.attention_processor import ( + ADDED_KV_ATTENTION_PROCESSORS, + CROSS_ATTENTION_PROCESSORS, + Attention, + AttentionProcessor, + AttnAddedKVProcessor, + AttnProcessor, +) +from diffusers.models.modeling_outputs import AutoencoderKLOutput +from diffusers.models.modeling_utils import ModelMixin +from .vae import ( + DecoderCausal3D, + BaseOutput, + DecoderOutput, + DiagonalGaussianDistribution, + EncoderCausal3D, +) + + +@dataclass +class DecoderOutput2(BaseOutput): + sample: torch.FloatTensor + posterior: Optional[DiagonalGaussianDistribution] = None + + +class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin): + r""" + A VAE model with KL loss for encoding images/videos into latents and decoding latent representations into images/videos. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented + for all models (such as downloading or saving). + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + in_channels: int = 3, + out_channels: int = 3, + down_block_types: Tuple[str] = ("DownEncoderBlockCausal3D",), + up_block_types: Tuple[str] = ("UpDecoderBlockCausal3D",), + block_out_channels: Tuple[int] = (64,), + layers_per_block: int = 1, + act_fn: str = "silu", + latent_channels: int = 4, + norm_num_groups: int = 32, + sample_size: int = 32, + sample_tsize: int = 64, + scaling_factor: float = 0.18215, + force_upcast: float = True, + spatial_compression_ratio: int = 8, + time_compression_ratio: int = 4, + mid_block_add_attention: bool = True, + ): + super().__init__() + + self.time_compression_ratio = time_compression_ratio + + self.encoder = EncoderCausal3D( + in_channels=in_channels, + out_channels=latent_channels, + down_block_types=down_block_types, + block_out_channels=block_out_channels, + layers_per_block=layers_per_block, + act_fn=act_fn, + norm_num_groups=norm_num_groups, + double_z=True, + time_compression_ratio=time_compression_ratio, + spatial_compression_ratio=spatial_compression_ratio, + mid_block_add_attention=mid_block_add_attention, + ) + + self.decoder = DecoderCausal3D( + in_channels=latent_channels, + out_channels=out_channels, + up_block_types=up_block_types, + block_out_channels=block_out_channels, + layers_per_block=layers_per_block, + norm_num_groups=norm_num_groups, + act_fn=act_fn, + time_compression_ratio=time_compression_ratio, + spatial_compression_ratio=spatial_compression_ratio, + mid_block_add_attention=mid_block_add_attention, + ) + + self.quant_conv = nn.Conv3d( + 2 * latent_channels, 2 * latent_channels, kernel_size=1 + ) + self.post_quant_conv = nn.Conv3d( + latent_channels, latent_channels, kernel_size=1 + ) + + self.use_slicing = False + self.use_spatial_tiling = False + self.use_temporal_tiling = False + + # only relevant if vae tiling is enabled + self.tile_sample_min_tsize = sample_tsize + self.tile_latent_min_tsize = sample_tsize // time_compression_ratio + + # print(self.config.sample_size) + # self.config.sample_size = 2 + + self.tile_sample_min_size = self.config.sample_size + sample_size = ( + self.config.sample_size[0] + if isinstance(self.config.sample_size, (list, tuple)) + else self.config.sample_size + ) + self.tile_latent_min_size = int( + sample_size / (2 ** (len(self.config.block_out_channels) - 1)) + ) + self.tile_overlap_factor = 0.25 + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, (EncoderCausal3D, DecoderCausal3D)): + module.gradient_checkpointing = value + + def enable_temporal_tiling(self, use_tiling: bool = True): + self.use_temporal_tiling = use_tiling + + def disable_temporal_tiling(self): + self.enable_temporal_tiling(False) + + def enable_spatial_tiling(self, use_tiling: bool = True): + self.use_spatial_tiling = use_tiling + + def disable_spatial_tiling(self): + self.enable_spatial_tiling(False) + + def enable_tiling(self, use_tiling: bool = True): + r""" + Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to + compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow + processing larger videos. + """ + self.enable_spatial_tiling(use_tiling) + self.enable_temporal_tiling(use_tiling) + + def disable_tiling(self): + r""" + Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing + decoding in one step. + """ + self.disable_spatial_tiling() + self.disable_temporal_tiling() + + def enable_slicing(self): + r""" + Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to + compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. + """ + self.use_slicing = True + + def disable_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing + decoding in one step. + """ + self.use_slicing = False + + @property + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor( + return_deprecated_lora=True + ) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor + def set_attn_processor( + self, + processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], + _remove_lora=False, + ): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor, _remove_lora=_remove_lora) + else: + module.set_processor( + processor.pop(f"{name}.processor"), _remove_lora=_remove_lora + ) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + if all( + proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS + for proc in self.attn_processors.values() + ): + processor = AttnAddedKVProcessor() + elif all( + proc.__class__ in CROSS_ATTENTION_PROCESSORS + for proc in self.attn_processors.values() + ): + processor = AttnProcessor() + else: + raise ValueError( + f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" + ) + + self.set_attn_processor(processor, _remove_lora=True) + + @apply_forward_hook + def encode( + self, x: torch.FloatTensor, return_dict: bool = True + ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]: + """ + Encode a batch of images/videos into latents. + + Args: + x (`torch.FloatTensor`): Input batch of images/videos. + return_dict (`bool`, *optional*, defaults to `True`): + Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple. + + Returns: + The latent representations of the encoded images/videos. If `return_dict` is True, a + [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned. + """ + assert len(x.shape) == 5, "The input tensor should have 5 dimensions." + + if self.use_temporal_tiling and x.shape[2] > self.tile_sample_min_tsize: + return self.temporal_tiled_encode(x, return_dict=return_dict) + + if self.use_spatial_tiling and ( + x.shape[-1] > self.tile_sample_min_size + or x.shape[-2] > self.tile_sample_min_size + ): + return self.spatial_tiled_encode(x, return_dict=return_dict) + + if self.use_slicing and x.shape[0] > 1: + encoded_slices = [self.encoder(x_slice) for x_slice in x.split(1)] + h = torch.cat(encoded_slices) + else: + h = self.encoder(x) + + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + + if not return_dict: + return (posterior,) + + return AutoencoderKLOutput(latent_dist=posterior) + + def _decode( + self, z: torch.FloatTensor, return_dict: bool = True + ) -> Union[DecoderOutput, torch.FloatTensor]: + assert len(z.shape) == 5, "The input tensor should have 5 dimensions." + + if self.use_temporal_tiling and z.shape[2] > self.tile_latent_min_tsize: + return self.temporal_tiled_decode(z, return_dict=return_dict) + + if self.use_spatial_tiling and ( + z.shape[-1] > self.tile_latent_min_size + or z.shape[-2] > self.tile_latent_min_size + ): + return self.spatial_tiled_decode(z, return_dict=return_dict) + + z = self.post_quant_conv(z) + dec = self.decoder(z) + + if not return_dict: + return (dec,) + + return DecoderOutput(sample=dec) + + @apply_forward_hook + def decode( + self, z: torch.FloatTensor, return_dict: bool = True, generator=None + ) -> Union[DecoderOutput, torch.FloatTensor]: + """ + Decode a batch of images/videos. + + Args: + z (`torch.FloatTensor`): Input batch of latent vectors. + return_dict (`bool`, *optional*, defaults to `True`): + Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple. + + Returns: + [`~models.vae.DecoderOutput`] or `tuple`: + If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is + returned. + + """ + if self.use_slicing and z.shape[0] > 1: + decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)] + decoded = torch.cat(decoded_slices) + else: + decoded = self._decode(z).sample + + if not return_dict: + return (decoded,) + + return DecoderOutput(sample=decoded) + + def blend_v( + self, a: torch.Tensor, b: torch.Tensor, blend_extent: int + ) -> torch.Tensor: + blend_extent = min(a.shape[-2], b.shape[-2], blend_extent) + for y in range(blend_extent): + b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * ( + 1 - y / blend_extent + ) + b[:, :, :, y, :] * (y / blend_extent) + return b + + def blend_h( + self, a: torch.Tensor, b: torch.Tensor, blend_extent: int + ) -> torch.Tensor: + blend_extent = min(a.shape[-1], b.shape[-1], blend_extent) + for x in range(blend_extent): + b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * ( + 1 - x / blend_extent + ) + b[:, :, :, :, x] * (x / blend_extent) + return b + + def blend_t( + self, a: torch.Tensor, b: torch.Tensor, blend_extent: int + ) -> torch.Tensor: + blend_extent = min(a.shape[-3], b.shape[-3], blend_extent) + for x in range(blend_extent): + b[:, :, x, :, :] = a[:, :, -blend_extent + x, :, :] * ( + 1 - x / blend_extent + ) + b[:, :, x, :, :] * (x / blend_extent) + return b + + def spatial_tiled_encode( + self, + x: torch.FloatTensor, + return_dict: bool = True, + return_moments: bool = False, + ) -> AutoencoderKLOutput: + r"""Encode a batch of images/videos using a tiled encoder. + + When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several + steps. This is useful to keep memory use constant regardless of image/videos size. The end result of tiled encoding is + different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the + tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the + output, but they should be much less noticeable. + + Args: + x (`torch.FloatTensor`): Input batch of images/videos. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple. + + Returns: + [`~models.autoencoder_kl.AutoencoderKLOutput`] or `tuple`: + If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain + `tuple` is returned. + """ + overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)) + blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor) + row_limit = self.tile_latent_min_size - blend_extent + + # Split video into tiles and encode them separately. + rows = [] + for i in range(0, x.shape[-2], overlap_size): + row = [] + for j in range(0, x.shape[-1], overlap_size): + tile = x[ + :, + :, + :, + i : i + self.tile_sample_min_size, + j : j + self.tile_sample_min_size, + ] + tile = self.encoder(tile) + tile = self.quant_conv(tile) + row.append(tile) + rows.append(row) + result_rows = [] + for i, row in enumerate(rows): + result_row = [] + for j, tile in enumerate(row): + # blend the above tile and the left tile + # to the current tile and add the current tile to the result row + if i > 0: + tile = self.blend_v(rows[i - 1][j], tile, blend_extent) + if j > 0: + tile = self.blend_h(row[j - 1], tile, blend_extent) + result_row.append(tile[:, :, :, :row_limit, :row_limit]) + result_rows.append(torch.cat(result_row, dim=-1)) + + moments = torch.cat(result_rows, dim=-2) + if return_moments: + return moments + + posterior = DiagonalGaussianDistribution(moments) + if not return_dict: + return (posterior,) + + return AutoencoderKLOutput(latent_dist=posterior) + + def spatial_tiled_decode( + self, z: torch.FloatTensor, return_dict: bool = True + ) -> Union[DecoderOutput, torch.FloatTensor]: + r""" + Decode a batch of images/videos using a tiled decoder. + + Args: + z (`torch.FloatTensor`): Input batch of latent vectors. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple. + + Returns: + [`~models.vae.DecoderOutput`] or `tuple`: + If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is + returned. + """ + overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)) + blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor) + row_limit = self.tile_sample_min_size - blend_extent + + # Split z into overlapping tiles and decode them separately. + # The tiles have an overlap to avoid seams between tiles. + rows = [] + for i in range(0, z.shape[-2], overlap_size): + row = [] + for j in range(0, z.shape[-1], overlap_size): + tile = z[ + :, + :, + :, + i : i + self.tile_latent_min_size, + j : j + self.tile_latent_min_size, + ] + tile = self.post_quant_conv(tile) + decoded = self.decoder(tile) + row.append(decoded) + rows.append(row) + result_rows = [] + for i, row in enumerate(rows): + result_row = [] + for j, tile in enumerate(row): + # blend the above tile and the left tile + # to the current tile and add the current tile to the result row + if i > 0: + tile = self.blend_v(rows[i - 1][j], tile, blend_extent) + if j > 0: + tile = self.blend_h(row[j - 1], tile, blend_extent) + result_row.append(tile[:, :, :, :row_limit, :row_limit]) + result_rows.append(torch.cat(result_row, dim=-1)) + + dec = torch.cat(result_rows, dim=-2) + if not return_dict: + return (dec,) + + return DecoderOutput(sample=dec) + + def temporal_tiled_encode( + self, x: torch.FloatTensor, return_dict: bool = True + ) -> AutoencoderKLOutput: + + B, C, T, H, W = x.shape + overlap_size = int(self.tile_sample_min_tsize * (1 - self.tile_overlap_factor)) + blend_extent = int(self.tile_latent_min_tsize * self.tile_overlap_factor) + t_limit = self.tile_latent_min_tsize - blend_extent + + # Split the video into tiles and encode them separately. + row = [] + for i in range(0, T, overlap_size): + tile = x[:, :, i : i + self.tile_sample_min_tsize + 1, :, :] + if self.use_spatial_tiling and ( + tile.shape[-1] > self.tile_sample_min_size + or tile.shape[-2] > self.tile_sample_min_size + ): + tile = self.spatial_tiled_encode(tile, return_moments=True) + else: + tile = self.encoder(tile) + tile = self.quant_conv(tile) + if i > 0: + tile = tile[:, :, 1:, :, :] + row.append(tile) + result_row = [] + for i, tile in enumerate(row): + if i > 0: + tile = self.blend_t(row[i - 1], tile, blend_extent) + result_row.append(tile[:, :, :t_limit, :, :]) + else: + result_row.append(tile[:, :, : t_limit + 1, :, :]) + + moments = torch.cat(result_row, dim=2) + posterior = DiagonalGaussianDistribution(moments) + + if not return_dict: + return (posterior,) + + return AutoencoderKLOutput(latent_dist=posterior) + + def temporal_tiled_decode( + self, z: torch.FloatTensor, return_dict: bool = True + ) -> Union[DecoderOutput, torch.FloatTensor]: + # Split z into overlapping tiles and decode them separately. + + B, C, T, H, W = z.shape + overlap_size = int(self.tile_latent_min_tsize * (1 - self.tile_overlap_factor)) + blend_extent = int(self.tile_sample_min_tsize * self.tile_overlap_factor) + t_limit = self.tile_sample_min_tsize - blend_extent + + row = [] + for i in range(0, T, overlap_size): + tile = z[:, :, i : i + self.tile_latent_min_tsize + 1, :, :] + if self.use_spatial_tiling and ( + tile.shape[-1] > self.tile_latent_min_size + or tile.shape[-2] > self.tile_latent_min_size + ): + decoded = self.spatial_tiled_decode(tile, return_dict=True).sample + else: + tile = self.post_quant_conv(tile) + decoded = self.decoder(tile) + if i > 0: + decoded = decoded[:, :, 1:, :, :] + row.append(decoded) + result_row = [] + for i, tile in enumerate(row): + if i > 0: + tile = self.blend_t(row[i - 1], tile, blend_extent) + result_row.append(tile[:, :, :t_limit, :, :]) + else: + result_row.append(tile[:, :, : t_limit + 1, :, :]) + + dec = torch.cat(result_row, dim=2) + if not return_dict: + return (dec,) + + return DecoderOutput(sample=dec) + + def forward( + self, + sample: torch.FloatTensor, + sample_posterior: bool = False, + return_dict: bool = True, + return_posterior: bool = False, + generator: Optional[torch.Generator] = None, + ) -> Union[DecoderOutput2, torch.FloatTensor]: + r""" + Args: + sample (`torch.FloatTensor`): Input sample. + sample_posterior (`bool`, *optional*, defaults to `False`): + Whether to sample from the posterior. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`DecoderOutput`] instead of a plain tuple. + """ + x = sample + posterior = self.encode(x).latent_dist + if sample_posterior: + z = posterior.sample(generator=generator) + else: + z = posterior.mode() + dec = self.decode(z).sample + + if not return_dict: + if return_posterior: + return (dec, posterior) + else: + return (dec,) + if return_posterior: + return DecoderOutput2(sample=dec, posterior=posterior) + else: + return DecoderOutput2(sample=dec) + + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections + def fuse_qkv_projections(self): + """ + Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, + key, value) are fused. For cross-attention modules, key and value projection matrices are fused. + + + + This API is 🧪 experimental. + + + """ + self.original_attn_processors = None + + for _, attn_processor in self.attn_processors.items(): + if "Added" in str(attn_processor.__class__.__name__): + raise ValueError( + "`fuse_qkv_projections()` is not supported for models having added KV projections." + ) + + self.original_attn_processors = self.attn_processors + + for module in self.modules(): + if isinstance(module, Attention): + module.fuse_projections(fuse=True) + + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections + def unfuse_qkv_projections(self): + """Disables the fused QKV projection if enabled. + + + + This API is 🧪 experimental. + + + + """ + if self.original_attn_processors is not None: + self.set_attn_processor(self.original_attn_processors) diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/unet_causal_3d_blocks.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/unet_causal_3d_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..d010e329cf978641e6e8ef6606a3719a0c93d5dc --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/unet_causal_3d_blocks.py @@ -0,0 +1,823 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# +# Modified from diffusers==0.29.2 +# +# ============================================================================== + +from typing import Optional, Tuple, Union + +import torch +import torch.nn.functional as F +from torch import nn +from einops import rearrange + +from diffusers.utils import logging +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import SpatialNorm +from diffusers.models.attention_processor import Attention +from diffusers.models.normalization import AdaGroupNorm +from diffusers.models.normalization import RMSNorm + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def prepare_causal_attention_mask( + n_frame: int, n_hw: int, dtype, device, batch_size: int = None +): + seq_len = n_frame * n_hw + mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device) + for i in range(seq_len): + i_frame = i // n_hw + mask[i, : (i_frame + 1) * n_hw] = 0 + if batch_size is not None: + mask = mask.unsqueeze(0).expand(batch_size, -1, -1) + return mask + + +class CausalConv3d(nn.Module): + """ + Implements a causal 3D convolution layer where each position only depends on previous timesteps and current spatial locations. + This maintains temporal causality in video generation tasks. + """ + + def __init__( + self, + chan_in, + chan_out, + kernel_size: Union[int, Tuple[int, int, int]], + stride: Union[int, Tuple[int, int, int]] = 1, + dilation: Union[int, Tuple[int, int, int]] = 1, + pad_mode="replicate", + **kwargs, + ): + super().__init__() + + self.pad_mode = pad_mode + padding = ( + kernel_size // 2, + kernel_size // 2, + kernel_size // 2, + kernel_size // 2, + kernel_size - 1, + 0, + ) # W, H, T + self.time_causal_padding = padding + + self.conv = nn.Conv3d( + chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs + ) + + def forward(self, x): + x = F.pad(x, self.time_causal_padding, mode=self.pad_mode) + return self.conv(x) + + +class UpsampleCausal3D(nn.Module): + """ + A 3D upsampling layer with an optional convolution. + """ + + def __init__( + self, + channels: int, + use_conv: bool = False, + use_conv_transpose: bool = False, + out_channels: Optional[int] = None, + name: str = "conv", + kernel_size: Optional[int] = None, + padding=1, + norm_type=None, + eps=None, + elementwise_affine=None, + bias=True, + interpolate=True, + upsample_factor=(2, 2, 2), + ): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + self.name = name + self.interpolate = interpolate + self.upsample_factor = upsample_factor + + if norm_type == "ln_norm": + self.norm = nn.LayerNorm(channels, eps, elementwise_affine) + elif norm_type == "rms_norm": + self.norm = RMSNorm(channels, eps, elementwise_affine) + elif norm_type is None: + self.norm = None + else: + raise ValueError(f"unknown norm_type: {norm_type}") + + conv = None + if use_conv_transpose: + raise NotImplementedError + elif use_conv: + if kernel_size is None: + kernel_size = 3 + conv = CausalConv3d( + self.channels, self.out_channels, kernel_size=kernel_size, bias=bias + ) + + if name == "conv": + self.conv = conv + else: + self.Conv2d_0 = conv + + def forward( + self, + hidden_states: torch.FloatTensor, + output_size: Optional[int] = None, + scale: float = 1.0, + ) -> torch.FloatTensor: + assert hidden_states.shape[1] == self.channels + + if self.norm is not None: + raise NotImplementedError + + if self.use_conv_transpose: + return self.conv(hidden_states) + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + dtype = hidden_states.dtype + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(torch.float32) + + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + hidden_states = hidden_states.contiguous() + + # if `output_size` is passed we force the interpolation output + # size and do not make use of `scale_factor=2` + if self.interpolate: + B, C, T, H, W = hidden_states.shape + first_h, other_h = hidden_states.split((1, T - 1), dim=2) + if output_size is None: + if T > 1: + other_h = F.interpolate( + other_h, scale_factor=self.upsample_factor, mode="nearest" + ) + + first_h = first_h.squeeze(2) + first_h = F.interpolate( + first_h, scale_factor=self.upsample_factor[1:], mode="nearest" + ) + first_h = first_h.unsqueeze(2) + else: + raise NotImplementedError + + if T > 1: + hidden_states = torch.cat((first_h, other_h), dim=2) + else: + hidden_states = first_h + + # If the input is bfloat16, we cast back to bfloat16 + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(dtype) + + if self.use_conv: + if self.name == "conv": + hidden_states = self.conv(hidden_states) + else: + hidden_states = self.Conv2d_0(hidden_states) + + return hidden_states + + +class DownsampleCausal3D(nn.Module): + """ + A 3D downsampling layer with an optional convolution. + """ + + def __init__( + self, + channels: int, + use_conv: bool = False, + out_channels: Optional[int] = None, + padding: int = 1, + name: str = "conv", + kernel_size=3, + norm_type=None, + eps=None, + elementwise_affine=None, + bias=True, + stride=2, + ): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.padding = padding + stride = stride + self.name = name + + if norm_type == "ln_norm": + self.norm = nn.LayerNorm(channels, eps, elementwise_affine) + elif norm_type == "rms_norm": + self.norm = RMSNorm(channels, eps, elementwise_affine) + elif norm_type is None: + self.norm = None + else: + raise ValueError(f"unknown norm_type: {norm_type}") + + if use_conv: + conv = CausalConv3d( + self.channels, + self.out_channels, + kernel_size=kernel_size, + stride=stride, + bias=bias, + ) + else: + raise NotImplementedError + + if name == "conv": + self.Conv2d_0 = conv + self.conv = conv + elif name == "Conv2d_0": + self.conv = conv + else: + self.conv = conv + + def forward( + self, hidden_states: torch.FloatTensor, scale: float = 1.0 + ) -> torch.FloatTensor: + assert hidden_states.shape[1] == self.channels + + if self.norm is not None: + hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute( + 0, 3, 1, 2 + ) + + assert hidden_states.shape[1] == self.channels + + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class ResnetBlockCausal3D(nn.Module): + r""" + A Resnet block. + """ + + def __init__( + self, + *, + in_channels: int, + out_channels: Optional[int] = None, + conv_shortcut: bool = False, + dropout: float = 0.0, + temb_channels: int = 512, + groups: int = 32, + groups_out: Optional[int] = None, + pre_norm: bool = True, + eps: float = 1e-6, + non_linearity: str = "swish", + skip_time_act: bool = False, + # default, scale_shift, ada_group, spatial + time_embedding_norm: str = "default", + kernel: Optional[torch.FloatTensor] = None, + output_scale_factor: float = 1.0, + use_in_shortcut: Optional[bool] = None, + up: bool = False, + down: bool = False, + conv_shortcut_bias: bool = True, + conv_3d_out_channels: Optional[int] = None, + ): + super().__init__() + self.pre_norm = pre_norm + self.pre_norm = True + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + self.up = up + self.down = down + self.output_scale_factor = output_scale_factor + self.time_embedding_norm = time_embedding_norm + self.skip_time_act = skip_time_act + + linear_cls = nn.Linear + + if groups_out is None: + groups_out = groups + + if self.time_embedding_norm == "ada_group": + self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps) + elif self.time_embedding_norm == "spatial": + self.norm1 = SpatialNorm(in_channels, temb_channels) + else: + self.norm1 = torch.nn.GroupNorm( + num_groups=groups, num_channels=in_channels, eps=eps, affine=True + ) + + self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, stride=1) + + if temb_channels is not None: + if self.time_embedding_norm == "default": + self.time_emb_proj = linear_cls(temb_channels, out_channels) + elif self.time_embedding_norm == "scale_shift": + self.time_emb_proj = linear_cls(temb_channels, 2 * out_channels) + elif ( + self.time_embedding_norm == "ada_group" + or self.time_embedding_norm == "spatial" + ): + self.time_emb_proj = None + else: + raise ValueError( + f"Unknown time_embedding_norm : {self.time_embedding_norm} " + ) + else: + self.time_emb_proj = None + + if self.time_embedding_norm == "ada_group": + self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps) + elif self.time_embedding_norm == "spatial": + self.norm2 = SpatialNorm(out_channels, temb_channels) + else: + self.norm2 = torch.nn.GroupNorm( + num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True + ) + + self.dropout = torch.nn.Dropout(dropout) + conv_3d_out_channels = conv_3d_out_channels or out_channels + self.conv2 = CausalConv3d( + out_channels, conv_3d_out_channels, kernel_size=3, stride=1 + ) + + self.nonlinearity = get_activation(non_linearity) + + self.upsample = self.downsample = None + if self.up: + self.upsample = UpsampleCausal3D(in_channels, use_conv=False) + elif self.down: + self.downsample = DownsampleCausal3D(in_channels, use_conv=False, name="op") + + self.use_in_shortcut = ( + self.in_channels != conv_3d_out_channels + if use_in_shortcut is None + else use_in_shortcut + ) + + self.conv_shortcut = None + if self.use_in_shortcut: + self.conv_shortcut = CausalConv3d( + in_channels, + conv_3d_out_channels, + kernel_size=1, + stride=1, + bias=conv_shortcut_bias, + ) + + def forward( + self, + input_tensor: torch.FloatTensor, + temb: torch.FloatTensor, + scale: float = 1.0, + ) -> torch.FloatTensor: + hidden_states = input_tensor + + if ( + self.time_embedding_norm == "ada_group" + or self.time_embedding_norm == "spatial" + ): + hidden_states = self.norm1(hidden_states, temb) + else: + hidden_states = self.norm1(hidden_states) + + hidden_states = self.nonlinearity(hidden_states) + + if self.upsample is not None: + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + input_tensor = input_tensor.contiguous() + hidden_states = hidden_states.contiguous() + input_tensor = self.upsample(input_tensor, scale=scale) + hidden_states = self.upsample(hidden_states, scale=scale) + elif self.downsample is not None: + input_tensor = self.downsample(input_tensor, scale=scale) + hidden_states = self.downsample(hidden_states, scale=scale) + + hidden_states = self.conv1(hidden_states) + + if self.time_emb_proj is not None: + if not self.skip_time_act: + temb = self.nonlinearity(temb) + temb = self.time_emb_proj(temb, scale)[:, :, None, None] + + if temb is not None and self.time_embedding_norm == "default": + hidden_states = hidden_states + temb + + if ( + self.time_embedding_norm == "ada_group" + or self.time_embedding_norm == "spatial" + ): + hidden_states = self.norm2(hidden_states, temb) + else: + hidden_states = self.norm2(hidden_states) + + if temb is not None and self.time_embedding_norm == "scale_shift": + scale, shift = torch.chunk(temb, 2, dim=1) + hidden_states = hidden_states * (1 + scale) + shift + + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.conv_shortcut is not None: + input_tensor = self.conv_shortcut(input_tensor) + + output_tensor = (input_tensor + hidden_states) / self.output_scale_factor + + return output_tensor + + +def get_down_block3d( + down_block_type: str, + num_layers: int, + in_channels: int, + out_channels: int, + temb_channels: int, + add_downsample: bool, + downsample_stride: int, + resnet_eps: float, + resnet_act_fn: str, + transformer_layers_per_block: int = 1, + num_attention_heads: Optional[int] = None, + resnet_groups: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + downsample_padding: Optional[int] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + attention_type: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: float = 1.0, + cross_attention_norm: Optional[str] = None, + attention_head_dim: Optional[int] = None, + downsample_type: Optional[str] = None, + dropout: float = 0.0, +): + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + down_block_type = ( + down_block_type[7:] + if down_block_type.startswith("UNetRes") + else down_block_type + ) + if down_block_type == "DownEncoderBlockCausal3D": + return DownEncoderBlockCausal3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + dropout=dropout, + add_downsample=add_downsample, + downsample_stride=downsample_stride, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block3d( + up_block_type: str, + num_layers: int, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + add_upsample: bool, + upsample_scale_factor: Tuple, + resnet_eps: float, + resnet_act_fn: str, + resolution_idx: Optional[int] = None, + transformer_layers_per_block: int = 1, + num_attention_heads: Optional[int] = None, + resnet_groups: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + attention_type: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: float = 1.0, + cross_attention_norm: Optional[str] = None, + attention_head_dim: Optional[int] = None, + upsample_type: Optional[str] = None, + dropout: float = 0.0, +) -> nn.Module: + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + up_block_type = ( + up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + ) + if up_block_type == "UpDecoderBlockCausal3D": + return UpDecoderBlockCausal3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + upsample_scale_factor=upsample_scale_factor, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + temb_channels=temb_channels, + ) + raise ValueError(f"{up_block_type} does not exist.") + + +class UNetMidBlockCausal3D(nn.Module): + """ + A 3D UNet mid-block [`UNetMidBlockCausal3D`] with multiple residual blocks and optional attention blocks. + """ + + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", # default, spatial + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + attn_groups: Optional[int] = None, + resnet_pre_norm: bool = True, + add_attention: bool = True, + attention_head_dim: int = 1, + output_scale_factor: float = 1.0, + ): + super().__init__() + resnet_groups = ( + resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + ) + self.add_attention = add_attention + + if attn_groups is None: + attn_groups = ( + resnet_groups if resnet_time_scale_shift == "default" else None + ) + + # there is always at least one resnet + resnets = [ + ResnetBlockCausal3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + + if attention_head_dim is None: + logger.warn( + f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}." + ) + attention_head_dim = in_channels + + for _ in range(num_layers): + if self.add_attention: + attentions.append( + Attention( + in_channels, + heads=in_channels // attention_head_dim, + dim_head=attention_head_dim, + rescale_output_factor=output_scale_factor, + eps=resnet_eps, + norm_num_groups=attn_groups, + spatial_norm_dim=( + temb_channels + if resnet_time_scale_shift == "spatial" + else None + ), + residual_connection=True, + bias=True, + upcast_softmax=True, + _from_deprecated_attn_block=True, + ) + ) + else: + attentions.append(None) + + resnets.append( + ResnetBlockCausal3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + def forward( + self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None + ) -> torch.FloatTensor: + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + if attn is not None: + B, C, T, H, W = hidden_states.shape + hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c") + attention_mask = prepare_causal_attention_mask( + T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B + ) + hidden_states = attn( + hidden_states, temb=temb, attention_mask=attention_mask + ) + hidden_states = rearrange( + hidden_states, "b (f h w) c -> b c f h w", f=T, h=H, w=W + ) + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class DownEncoderBlockCausal3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_downsample: bool = True, + downsample_stride: int = 2, + downsample_padding: int = 1, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlockCausal3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=None, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + DownsampleCausal3D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + stride=downsample_stride, + ) + ] + ) + else: + self.downsamplers = None + + def forward( + self, hidden_states: torch.FloatTensor, scale: float = 1.0 + ) -> torch.FloatTensor: + for resnet in self.resnets: + hidden_states = resnet(hidden_states, temb=None, scale=scale) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states, scale) + + return hidden_states + + +class UpDecoderBlockCausal3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", # default, spatial + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + upsample_scale_factor=(2, 2, 2), + temb_channels: Optional[int] = None, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + input_channels = in_channels if i == 0 else out_channels + + resnets.append( + ResnetBlockCausal3D( + in_channels=input_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList( + [ + UpsampleCausal3D( + out_channels, + use_conv=True, + out_channels=out_channels, + upsample_factor=upsample_scale_factor, + ) + ] + ) + else: + self.upsamplers = None + + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + scale: float = 1.0, + ) -> torch.FloatTensor: + for resnet in self.resnets: + hidden_states = resnet(hidden_states, temb=temb, scale=scale) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states) + + return hidden_states diff --git a/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/vae.py b/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/vae.py new file mode 100644 index 0000000000000000000000000000000000000000..c750672cad5c499b6524b623f837577004f24308 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/hunyuan/vae/vae.py @@ -0,0 +1,374 @@ +from dataclasses import dataclass +from typing import Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn + +from diffusers.utils import BaseOutput, is_torch_version +from diffusers.utils.torch_utils import randn_tensor +from diffusers.models.attention_processor import SpatialNorm +from .unet_causal_3d_blocks import ( + CausalConv3d, + UNetMidBlockCausal3D, + get_down_block3d, + get_up_block3d, +) + + +@dataclass +class DecoderOutput(BaseOutput): + r""" + Output of decoding method. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + The decoded output sample from the last layer of the model. + """ + + sample: torch.FloatTensor + + +class EncoderCausal3D(nn.Module): + r""" + The `EncoderCausal3D` layer of a variational autoencoder that encodes its input into a latent representation. + """ + + def __init__( + self, + in_channels: int = 3, + out_channels: int = 3, + down_block_types: Tuple[str, ...] = ("DownEncoderBlockCausal3D",), + block_out_channels: Tuple[int, ...] = (64,), + layers_per_block: int = 2, + norm_num_groups: int = 32, + act_fn: str = "silu", + double_z: bool = True, + mid_block_add_attention=True, + time_compression_ratio: int = 4, + spatial_compression_ratio: int = 8, + ): + super().__init__() + self.layers_per_block = layers_per_block + + self.conv_in = CausalConv3d( + in_channels, block_out_channels[0], kernel_size=3, stride=1 + ) + self.mid_block = None + self.down_blocks = nn.ModuleList([]) + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + num_spatial_downsample_layers = int(np.log2(spatial_compression_ratio)) + num_time_downsample_layers = int(np.log2(time_compression_ratio)) + + if time_compression_ratio == 4: + add_spatial_downsample = bool(i < num_spatial_downsample_layers) + add_time_downsample = bool( + i >= (len(block_out_channels) - 1 - num_time_downsample_layers) + and not is_final_block + ) + else: + raise ValueError( + f"Unsupported time_compression_ratio: {time_compression_ratio}." + ) + + downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1) + downsample_stride_T = (2,) if add_time_downsample else (1,) + downsample_stride = tuple(downsample_stride_T + downsample_stride_HW) + down_block = get_down_block3d( + down_block_type, + num_layers=self.layers_per_block, + in_channels=input_channel, + out_channels=output_channel, + add_downsample=bool(add_spatial_downsample or add_time_downsample), + downsample_stride=downsample_stride, + resnet_eps=1e-6, + downsample_padding=0, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + attention_head_dim=output_channel, + temb_channels=None, + ) + self.down_blocks.append(down_block) + + # mid + self.mid_block = UNetMidBlockCausal3D( + in_channels=block_out_channels[-1], + resnet_eps=1e-6, + resnet_act_fn=act_fn, + output_scale_factor=1, + resnet_time_scale_shift="default", + attention_head_dim=block_out_channels[-1], + resnet_groups=norm_num_groups, + temb_channels=None, + add_attention=mid_block_add_attention, + ) + + # out + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6 + ) + self.conv_act = nn.SiLU() + + conv_out_channels = 2 * out_channels if double_z else out_channels + self.conv_out = CausalConv3d( + block_out_channels[-1], conv_out_channels, kernel_size=3 + ) + + def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor: + r"""The forward method of the `EncoderCausal3D` class.""" + assert len(sample.shape) == 5, "The input tensor should have 5 dimensions" + + sample = self.conv_in(sample) + + # down + for down_block in self.down_blocks: + sample = down_block(sample) + + # middle + sample = self.mid_block(sample) + + # post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + return sample + + +class DecoderCausal3D(nn.Module): + r""" + The `DecoderCausal3D` layer of a variational autoencoder that decodes its latent representation into an output sample. + """ + + def __init__( + self, + in_channels: int = 3, + out_channels: int = 3, + up_block_types: Tuple[str, ...] = ("UpDecoderBlockCausal3D",), + block_out_channels: Tuple[int, ...] = (64,), + layers_per_block: int = 2, + norm_num_groups: int = 32, + act_fn: str = "silu", + norm_type: str = "group", # group, spatial + mid_block_add_attention=True, + time_compression_ratio: int = 4, + spatial_compression_ratio: int = 8, + ): + super().__init__() + self.layers_per_block = layers_per_block + + self.conv_in = CausalConv3d( + in_channels, block_out_channels[-1], kernel_size=3, stride=1 + ) + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + temb_channels = in_channels if norm_type == "spatial" else None + + # mid + self.mid_block = UNetMidBlockCausal3D( + in_channels=block_out_channels[-1], + resnet_eps=1e-6, + resnet_act_fn=act_fn, + output_scale_factor=1, + resnet_time_scale_shift="default" if norm_type == "group" else norm_type, + attention_head_dim=block_out_channels[-1], + resnet_groups=norm_num_groups, + temb_channels=temb_channels, + add_attention=mid_block_add_attention, + ) + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + num_spatial_upsample_layers = int(np.log2(spatial_compression_ratio)) + num_time_upsample_layers = int(np.log2(time_compression_ratio)) + + if time_compression_ratio == 4: + add_spatial_upsample = bool(i < num_spatial_upsample_layers) + add_time_upsample = bool( + i >= len(block_out_channels) - 1 - num_time_upsample_layers + and not is_final_block + ) + else: + raise ValueError( + f"Unsupported time_compression_ratio: {time_compression_ratio}." + ) + + upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, 1) + upsample_scale_factor_T = (2,) if add_time_upsample else (1,) + upsample_scale_factor = tuple( + upsample_scale_factor_T + upsample_scale_factor_HW + ) + up_block = get_up_block3d( + up_block_type, + num_layers=self.layers_per_block + 1, + in_channels=prev_output_channel, + out_channels=output_channel, + prev_output_channel=None, + add_upsample=bool(add_spatial_upsample or add_time_upsample), + upsample_scale_factor=upsample_scale_factor, + resnet_eps=1e-6, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + attention_head_dim=output_channel, + temb_channels=temb_channels, + resnet_time_scale_shift=norm_type, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if norm_type == "spatial": + self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels) + else: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6 + ) + self.conv_act = nn.SiLU() + self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3) + + self.gradient_checkpointing = False + + def forward( + self, + sample: torch.FloatTensor, + latent_embeds: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + r"""The forward method of the `DecoderCausal3D` class.""" + assert len(sample.shape) == 5, "The input tensor should have 5 dimensions." + + sample = self.conv_in(sample) + + upscale_dtype = next(iter(self.up_blocks.parameters())).dtype + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + # middle + sample = torch.utils.checkpoint.checkpoint( + create_custom_forward(self.mid_block), + sample, + latent_embeds, + use_reentrant=False, + ) + sample = sample.to(upscale_dtype) + + # up + for up_block in self.up_blocks: + sample = torch.utils.checkpoint.checkpoint( + create_custom_forward(up_block), + sample, + latent_embeds, + use_reentrant=False, + ) + else: + # middle + sample = torch.utils.checkpoint.checkpoint( + create_custom_forward(self.mid_block), sample, latent_embeds + ) + sample = sample.to(upscale_dtype) + + # up + for up_block in self.up_blocks: + sample = torch.utils.checkpoint.checkpoint( + create_custom_forward(up_block), sample, latent_embeds + ) + else: + # middle + sample = self.mid_block(sample, latent_embeds) + sample = sample.to(upscale_dtype) + + # up + for up_block in self.up_blocks: + sample = up_block(sample, latent_embeds) + + # post-process + if latent_embeds is None: + sample = self.conv_norm_out(sample) + else: + sample = self.conv_norm_out(sample, latent_embeds) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + return sample + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters: torch.Tensor, deterministic: bool = False): + if parameters.ndim == 3: + dim = 2 # (B, L, C) + elif parameters.ndim == 5 or parameters.ndim == 4: + dim = 1 # (B, C, T, H ,W) / (B, C, H, W) + else: + raise NotImplementedError + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=dim) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like( + self.mean, device=self.parameters.device, dtype=self.parameters.dtype + ) + + def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor: + # make sure sample is on the same device as the parameters and has same dtype + sample = randn_tensor( + self.mean.shape, + generator=generator, + device=self.parameters.device, + dtype=self.parameters.dtype, + ) + x = self.mean + self.std * sample + return x + + def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor: + if self.deterministic: + return torch.Tensor([0.0]) + else: + reduce_dim = list(range(1, self.mean.ndim)) + if other is None: + return 0.5 * torch.sum( + torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, + dim=reduce_dim, + ) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var + - 1.0 + - self.logvar + + other.logvar, + dim=reduce_dim, + ) + + def nll( + self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3] + ) -> torch.Tensor: + if self.deterministic: + return torch.Tensor([0.0]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims, + ) + + def mode(self) -> torch.Tensor: + return self.mean diff --git a/exp_code/1_benchmark/AccVideo/models/wan/__init__.py b/exp_code/1_benchmark/AccVideo/models/wan/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..df36ebed448a3399aac4a4de252e061a22033855 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/__init__.py @@ -0,0 +1,3 @@ +from . import configs, distributed, modules +from .image2video import WanI2V +from .text2video import WanT2V diff --git a/exp_code/1_benchmark/AccVideo/models/wan/configs/__init__.py b/exp_code/1_benchmark/AccVideo/models/wan/configs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c72d2d01be834882d659701fc0dc67beb152383f --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/configs/__init__.py @@ -0,0 +1,42 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import copy +import os + +os.environ['TOKENIZERS_PARALLELISM'] = 'false' + +from .wan_i2v_14B import i2v_14B +from .wan_t2v_1_3B import t2v_1_3B +from .wan_t2v_14B import t2v_14B + +# the config of t2i_14B is the same as t2v_14B +t2i_14B = copy.deepcopy(t2v_14B) +t2i_14B.__name__ = 'Config: Wan T2I 14B' + +WAN_CONFIGS = { + 't2v-14B': t2v_14B, + 't2v-1.3B': t2v_1_3B, + 'i2v-14B': i2v_14B, + 't2i-14B': t2i_14B, +} + +SIZE_CONFIGS = { + '720*1280': (720, 1280), + '1280*720': (1280, 720), + '480*832': (480, 832), + '832*480': (832, 480), + '1024*1024': (1024, 1024), +} + +MAX_AREA_CONFIGS = { + '720*1280': 720 * 1280, + '1280*720': 1280 * 720, + '480*832': 480 * 832, + '832*480': 832 * 480, +} + +SUPPORTED_SIZES = { + 't2v-14B': ('720*1280', '1280*720', '480*832', '832*480'), + 't2v-1.3B': ('480*832', '832*480'), + 'i2v-14B': ('720*1280', '1280*720', '480*832', '832*480'), + 't2i-14B': tuple(SIZE_CONFIGS.keys()), +} diff --git a/exp_code/1_benchmark/AccVideo/models/wan/configs/shared_config.py b/exp_code/1_benchmark/AccVideo/models/wan/configs/shared_config.py new file mode 100644 index 0000000000000000000000000000000000000000..04a9f454218fc1ce958b628e71ad5738222e2aa4 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/configs/shared_config.py @@ -0,0 +1,19 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import torch +from easydict import EasyDict + +#------------------------ Wan shared config ------------------------# +wan_shared_cfg = EasyDict() + +# t5 +wan_shared_cfg.t5_model = 'umt5_xxl' +wan_shared_cfg.t5_dtype = torch.bfloat16 +wan_shared_cfg.text_len = 512 + +# transformer +wan_shared_cfg.param_dtype = torch.bfloat16 + +# inference +wan_shared_cfg.num_train_timesteps = 1000 +wan_shared_cfg.sample_fps = 16 +wan_shared_cfg.sample_neg_prompt = '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走' diff --git a/exp_code/1_benchmark/AccVideo/models/wan/configs/wan_i2v_14B.py b/exp_code/1_benchmark/AccVideo/models/wan/configs/wan_i2v_14B.py new file mode 100644 index 0000000000000000000000000000000000000000..12e8e205bffb343a6e27d2828fb573db1d6349f8 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/configs/wan_i2v_14B.py @@ -0,0 +1,35 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import torch +from easydict import EasyDict + +from .shared_config import wan_shared_cfg + +#------------------------ Wan I2V 14B ------------------------# + +i2v_14B = EasyDict(__name__='Config: Wan I2V 14B') +i2v_14B.update(wan_shared_cfg) + +i2v_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth' +i2v_14B.t5_tokenizer = 'google/umt5-xxl' + +# clip +i2v_14B.clip_model = 'clip_xlm_roberta_vit_h_14' +i2v_14B.clip_dtype = torch.float16 +i2v_14B.clip_checkpoint = 'models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth' +i2v_14B.clip_tokenizer = 'xlm-roberta-large' + +# vae +i2v_14B.vae_checkpoint = 'Wan2.1_VAE.pth' +i2v_14B.vae_stride = (4, 8, 8) + +# transformer +i2v_14B.patch_size = (1, 2, 2) +i2v_14B.dim = 5120 +i2v_14B.ffn_dim = 13824 +i2v_14B.freq_dim = 256 +i2v_14B.num_heads = 40 +i2v_14B.num_layers = 40 +i2v_14B.window_size = (-1, -1) +i2v_14B.qk_norm = True +i2v_14B.cross_attn_norm = True +i2v_14B.eps = 1e-6 diff --git a/exp_code/1_benchmark/AccVideo/models/wan/configs/wan_t2v_14B.py b/exp_code/1_benchmark/AccVideo/models/wan/configs/wan_t2v_14B.py new file mode 100644 index 0000000000000000000000000000000000000000..9d0ee69dea796bfd6eccdedf4ec04835086227a6 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/configs/wan_t2v_14B.py @@ -0,0 +1,29 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +from easydict import EasyDict + +from .shared_config import wan_shared_cfg + +#------------------------ Wan T2V 14B ------------------------# + +t2v_14B = EasyDict(__name__='Config: Wan T2V 14B') +t2v_14B.update(wan_shared_cfg) + +# t5 +t2v_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth' +t2v_14B.t5_tokenizer = 'google/umt5-xxl' + +# vae +t2v_14B.vae_checkpoint = 'Wan2.1_VAE.pth' +t2v_14B.vae_stride = (4, 8, 8) + +# transformer +t2v_14B.patch_size = (1, 2, 2) +t2v_14B.dim = 5120 +t2v_14B.ffn_dim = 13824 +t2v_14B.freq_dim = 256 +t2v_14B.num_heads = 40 +t2v_14B.num_layers = 40 +t2v_14B.window_size = (-1, -1) +t2v_14B.qk_norm = True +t2v_14B.cross_attn_norm = True +t2v_14B.eps = 1e-6 diff --git a/exp_code/1_benchmark/AccVideo/models/wan/configs/wan_t2v_1_3B.py b/exp_code/1_benchmark/AccVideo/models/wan/configs/wan_t2v_1_3B.py new file mode 100644 index 0000000000000000000000000000000000000000..ea9502b0df685b5d22f9091cc8cdf5c6a7880c4b --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/configs/wan_t2v_1_3B.py @@ -0,0 +1,29 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +from easydict import EasyDict + +from .shared_config import wan_shared_cfg + +#------------------------ Wan T2V 1.3B ------------------------# + +t2v_1_3B = EasyDict(__name__='Config: Wan T2V 1.3B') +t2v_1_3B.update(wan_shared_cfg) + +# t5 +t2v_1_3B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth' +t2v_1_3B.t5_tokenizer = 'google/umt5-xxl' + +# vae +t2v_1_3B.vae_checkpoint = 'Wan2.1_VAE.pth' +t2v_1_3B.vae_stride = (4, 8, 8) + +# transformer +t2v_1_3B.patch_size = (1, 2, 2) +t2v_1_3B.dim = 1536 +t2v_1_3B.ffn_dim = 8960 +t2v_1_3B.freq_dim = 256 +t2v_1_3B.num_heads = 12 +t2v_1_3B.num_layers = 30 +t2v_1_3B.window_size = (-1, -1) +t2v_1_3B.qk_norm = True +t2v_1_3B.cross_attn_norm = True +t2v_1_3B.eps = 1e-6 diff --git a/exp_code/1_benchmark/AccVideo/models/wan/distributed/__init__.py b/exp_code/1_benchmark/AccVideo/models/wan/distributed/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/exp_code/1_benchmark/AccVideo/models/wan/distributed/fsdp.py b/exp_code/1_benchmark/AccVideo/models/wan/distributed/fsdp.py new file mode 100644 index 0000000000000000000000000000000000000000..258d4af5867d2f251aab0ec71043c70d600e0765 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/distributed/fsdp.py @@ -0,0 +1,32 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +from functools import partial + +import torch +from torch.distributed.fsdp import FullyShardedDataParallel as FSDP +from torch.distributed.fsdp import MixedPrecision, ShardingStrategy +from torch.distributed.fsdp.wrap import lambda_auto_wrap_policy + + +def shard_model( + model, + device_id, + param_dtype=torch.bfloat16, + reduce_dtype=torch.float32, + buffer_dtype=torch.float32, + process_group=None, + sharding_strategy=ShardingStrategy.FULL_SHARD, + sync_module_states=True, +): + model = FSDP( + module=model, + process_group=process_group, + sharding_strategy=sharding_strategy, + auto_wrap_policy=partial( + lambda_auto_wrap_policy, lambda_fn=lambda m: m in model.blocks), + mixed_precision=MixedPrecision( + param_dtype=param_dtype, + reduce_dtype=reduce_dtype, + buffer_dtype=buffer_dtype), + device_id=device_id, + sync_module_states=sync_module_states) + return model diff --git a/exp_code/1_benchmark/AccVideo/models/wan/distributed/xdit_context_parallel.py b/exp_code/1_benchmark/AccVideo/models/wan/distributed/xdit_context_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..01936cee9c31ce0af57af21af1310d69303390e0 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/distributed/xdit_context_parallel.py @@ -0,0 +1,192 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import torch +import torch.cuda.amp as amp +from xfuser.core.distributed import (get_sequence_parallel_rank, + get_sequence_parallel_world_size, + get_sp_group) +from xfuser.core.long_ctx_attention import xFuserLongContextAttention + +from ..modules.model import sinusoidal_embedding_1d + + +def pad_freqs(original_tensor, target_len): + seq_len, s1, s2 = original_tensor.shape + pad_size = target_len - seq_len + padding_tensor = torch.ones( + pad_size, + s1, + s2, + dtype=original_tensor.dtype, + device=original_tensor.device) + padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0) + return padded_tensor + + +@amp.autocast(enabled=False) +def rope_apply(x, grid_sizes, freqs): + """ + x: [B, L, N, C]. + grid_sizes: [B, 3]. + freqs: [M, C // 2]. + """ + s, n, c = x.size(1), x.size(2), x.size(3) // 2 + # split freqs + freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) + + # loop over samples + output = [] + for i, (f, h, w) in enumerate(grid_sizes.tolist()): + seq_len = f * h * w + + # precompute multipliers + x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape( + s, n, -1, 2)) + freqs_i = torch.cat([ + freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), + freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), + freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) + ], + dim=-1).reshape(seq_len, 1, -1) + + # apply rotary embedding + sp_size = get_sequence_parallel_world_size() + sp_rank = get_sequence_parallel_rank() + freqs_i = pad_freqs(freqs_i, s * sp_size) + s_per_rank = s + freqs_i_rank = freqs_i[(sp_rank * s_per_rank):((sp_rank + 1) * + s_per_rank), :, :] + x_i = torch.view_as_real(x_i * freqs_i_rank).flatten(2) + x_i = torch.cat([x_i, x[i, s:]]) + + # append to collection + output.append(x_i) + return torch.stack(output).float() + + +def usp_dit_forward( + self, + x, + t, + context, + seq_len, + clip_fea=None, + y=None, +): + """ + x: A list of videos each with shape [C, T, H, W]. + t: [B]. + context: A list of text embeddings each with shape [L, C]. + """ + if self.model_type == 'i2v': + assert clip_fea is not None and y is not None + # params + device = self.patch_embedding.weight.device + if self.freqs.device != device: + self.freqs = self.freqs.to(device) + + if y is not None: + x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] + + # embeddings + x = [self.patch_embedding(u.unsqueeze(0)) for u in x] + grid_sizes = torch.stack( + [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) + x = [u.flatten(2).transpose(1, 2) for u in x] + seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) + assert seq_lens.max() <= seq_len + x = torch.cat([ + torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1) + for u in x + ]) + + # time embeddings + with amp.autocast(dtype=torch.float32): + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t).float()) + e0 = self.time_projection(e).unflatten(1, (6, self.dim)) + assert e.dtype == torch.float32 and e0.dtype == torch.float32 + + # context + context_lens = None + context = self.text_embedding( + torch.stack([ + torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) + for u in context + ])) + + if clip_fea is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + + # arguments + kwargs = dict( + e=e0, + seq_lens=seq_lens, + grid_sizes=grid_sizes, + freqs=self.freqs, + context=context, + context_lens=context_lens) + + # Context Parallel + x = torch.chunk( + x, get_sequence_parallel_world_size(), + dim=1)[get_sequence_parallel_rank()] + + for block in self.blocks: + x = block(x, **kwargs) + + # head + x = self.head(x, e) + + # Context Parallel + x = get_sp_group().all_gather(x, dim=1) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return [u.float() for u in x] + + +def usp_attn_forward(self, + x, + seq_lens, + grid_sizes, + freqs, + dtype=torch.bfloat16): + b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim + half_dtypes = (torch.float16, torch.bfloat16) + + def half(x): + return x if x.dtype in half_dtypes else x.to(dtype) + + # query, key, value function + def qkv_fn(x): + q = self.norm_q(self.q(x)).view(b, s, n, d) + k = self.norm_k(self.k(x)).view(b, s, n, d) + v = self.v(x).view(b, s, n, d) + return q, k, v + + q, k, v = qkv_fn(x) + q = rope_apply(q, grid_sizes, freqs) + k = rope_apply(k, grid_sizes, freqs) + + # TODO: We should use unpaded q,k,v for attention. + # k_lens = seq_lens // get_sequence_parallel_world_size() + # if k_lens is not None: + # q = torch.cat([u[:l] for u, l in zip(q, k_lens)]).unsqueeze(0) + # k = torch.cat([u[:l] for u, l in zip(k, k_lens)]).unsqueeze(0) + # v = torch.cat([u[:l] for u, l in zip(v, k_lens)]).unsqueeze(0) + + x = xFuserLongContextAttention()( + None, + query=half(q), + key=half(k), + value=half(v), + window_size=self.window_size) + + # TODO: padding after attention. + # x = torch.cat([x, x.new_zeros(b, s - x.size(1), n, d)], dim=1) + + # output + x = x.flatten(2) + x = self.o(x) + return x diff --git a/exp_code/1_benchmark/AccVideo/models/wan/image2video.py b/exp_code/1_benchmark/AccVideo/models/wan/image2video.py new file mode 100644 index 0000000000000000000000000000000000000000..7dacf1537177ac8ba0ebbe669d50396f10a472d5 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/image2video.py @@ -0,0 +1,387 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import gc +import logging +import math +import os +import random +import sys +import types +from contextlib import contextmanager +from functools import partial +from turtledemo.penrose import start + +import numpy as np +import torch +import torch.cuda.amp as amp +import torch.distributed as dist +import torchvision.transforms.functional as TF +from tqdm import tqdm +import io +from petrel_client.client import Client +client = Client('~/petreloss.conf', enable_mc=True) + +from .distributed.fsdp import shard_model +from .modules.clip import CLIPModel +from .modules.model import WanModel +from .modules.t5 import T5EncoderModel +from .modules.vae import WanVAE +from .utils.fm_solvers import (FlowDPMSolverMultistepScheduler, + get_sampling_sigmas, retrieve_timesteps) +from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler +from .utils.fm_euler import FlowMatchEulerDiscreteScheduler + + +class WanI2V: + + def __init__( + self, + config, + checkpoint_dir, + device_id=0, + rank=0, + t5_fsdp=False, + dit_fsdp=False, + use_usp=False, + t5_cpu=False, + init_on_cpu=True, + dit_path=None, + ): + r""" + Initializes the image-to-video generation model components. + + Args: + config (EasyDict): + Object containing model parameters initialized from config.py + checkpoint_dir (`str`): + Path to directory containing model checkpoints + device_id (`int`, *optional*, defaults to 0): + Id of target GPU device + rank (`int`, *optional*, defaults to 0): + Process rank for distributed training + t5_fsdp (`bool`, *optional*, defaults to False): + Enable FSDP sharding for T5 model + dit_fsdp (`bool`, *optional*, defaults to False): + Enable FSDP sharding for DiT model + use_usp (`bool`, *optional*, defaults to False): + Enable distribution strategy of USP. + t5_cpu (`bool`, *optional*, defaults to False): + Whether to place T5 model on CPU. Only works without t5_fsdp. + init_on_cpu (`bool`, *optional*, defaults to True): + Enable initializing Transformer Model on CPU. Only works without FSDP or USP. + """ + self.device = torch.device(f"cuda:{device_id}") + self.config = config + self.rank = rank + self.use_usp = use_usp + self.t5_cpu = t5_cpu + + self.num_train_timesteps = config.num_train_timesteps + self.param_dtype = config.param_dtype + + shard_fn = partial(shard_model, device_id=device_id) + self.text_encoder = T5EncoderModel( + text_len=config.text_len, + dtype=config.t5_dtype, + device=torch.device('cpu'), + checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint), + tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer), + shard_fn=shard_fn if t5_fsdp else None, + ) + + self.vae_stride = config.vae_stride + self.patch_size = config.patch_size + self.vae = WanVAE( + vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint), + device=self.device) + + self.clip = CLIPModel( + dtype=config.clip_dtype, + device=self.device, + checkpoint_path=os.path.join(checkpoint_dir, + config.clip_checkpoint), + tokenizer_path=os.path.join(checkpoint_dir, config.clip_tokenizer)) + + logging.info(f"Creating WanModel from {checkpoint_dir}") + self.model = WanModel.from_pretrained(checkpoint_dir) + if dit_path is not None: + if dit_path.startswith("p2_norm"): + state_dict = torch.load(io.BytesIO(client.get(dit_path)), + map_location=lambda storage, loc: storage) + else: + state_dict = torch.load(dit_path, map_location=lambda storage, loc: storage) + self.model.load_state_dict(state_dict, strict=True) + self.model.eval().requires_grad_(False) + + if t5_fsdp or dit_fsdp or use_usp: + init_on_cpu = False + + if use_usp: + from xfuser.core.distributed import \ + get_sequence_parallel_world_size + + from .distributed.xdit_context_parallel import (usp_attn_forward, + usp_dit_forward) + for block in self.model.blocks: + block.self_attn.forward = types.MethodType( + usp_attn_forward, block.self_attn) + self.model.forward = types.MethodType(usp_dit_forward, self.model) + self.sp_size = get_sequence_parallel_world_size() + else: + self.sp_size = 1 + + if dist.is_initialized(): + dist.barrier() + if dit_fsdp: + self.model = shard_fn(self.model) + else: + if not init_on_cpu: + self.model.to(self.device) + + self.sample_neg_prompt = config.sample_neg_prompt + + def generate(self, + input_prompt, + img, + max_area=720 * 1280, + frame_num=81, + shift=5.0, + sample_solver='unipc', + sampling_steps=40, + guide_scale=5.0, + n_prompt="", + seed=-1, + offload_model=True, + few_step=False, + no_cfg=False): + r""" + Generates video frames from input image and text prompt using diffusion process. + + Args: + input_prompt (`str`): + Text prompt for content generation. + img (PIL.Image.Image): + Input image tensor. Shape: [3, H, W] + max_area (`int`, *optional*, defaults to 720*1280): + Maximum pixel area for latent space calculation. Controls video resolution scaling + frame_num (`int`, *optional*, defaults to 81): + How many frames to sample from a video. The number should be 4n+1 + shift (`float`, *optional*, defaults to 5.0): + Noise schedule shift parameter. Affects temporal dynamics + [NOTE]: If you want to generate a 480p video, it is recommended to set the shift value to 3.0. + sample_solver (`str`, *optional*, defaults to 'unipc'): + Solver used to sample the video. + sampling_steps (`int`, *optional*, defaults to 40): + Number of diffusion sampling steps. Higher values improve quality but slow generation + guide_scale (`float`, *optional*, defaults 5.0): + Classifier-free guidance scale. Controls prompt adherence vs. creativity + n_prompt (`str`, *optional*, defaults to ""): + Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` + seed (`int`, *optional*, defaults to -1): + Random seed for noise generation. If -1, use random seed + offload_model (`bool`, *optional*, defaults to True): + If True, offloads models to CPU during generation to save VRAM + + Returns: + torch.Tensor: + Generated video frames tensor. Dimensions: (C, N H, W) where: + - C: Color channels (3 for RGB) + - N: Number of frames (81) + - H: Frame height (from max_area) + - W: Frame width from max_area) + """ + img = TF.to_tensor(img).sub_(0.5).div_(0.5).to(self.device) + + F = frame_num + h, w = img.shape[1:] + # aspect_ratio = h / w + # lat_h = round( + # np.sqrt(max_area * aspect_ratio) // self.vae_stride[1] // + # self.patch_size[1] * self.patch_size[1]) + # lat_w = round( + # np.sqrt(max_area / aspect_ratio) // self.vae_stride[2] // + # self.patch_size[2] * self.patch_size[2]) + # h = lat_h * self.vae_stride[1] + # w = lat_w * self.vae_stride[2] + lat_h = h // self.vae_stride[1] + lat_w = w // self.vae_stride[2] + + max_seq_len = ((F - 1) // self.vae_stride[0] + 1) * lat_h * lat_w // ( + self.patch_size[1] * self.patch_size[2]) + max_seq_len = int(math.ceil(max_seq_len / self.sp_size)) * self.sp_size + + seed = seed if seed >= 0 else random.randint(0, sys.maxsize) + seed_g = torch.Generator(device=self.device) + seed_g.manual_seed(seed) + noise = torch.randn( + 16, + 21, + lat_h, + lat_w, + dtype=torch.float32, + generator=seed_g, + device=self.device) + + msk = torch.ones(1, 81, lat_h, lat_w, device=self.device) + msk[:, 1:] = 0 + msk = torch.concat([ + torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:] + ], + dim=1) + msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w) + msk = msk.transpose(1, 2)[0] + + if n_prompt == "": + n_prompt = self.sample_neg_prompt + + # preprocess + if not self.t5_cpu: + self.text_encoder.model.to(self.device) + context = self.text_encoder([input_prompt], self.device) + context_null = self.text_encoder([n_prompt], self.device) + if offload_model: + self.text_encoder.model.cpu() + else: + context = self.text_encoder([input_prompt], torch.device('cpu')) + context_null = self.text_encoder([n_prompt], torch.device('cpu')) + context = [t.to(self.device) for t in context] + context_null = [t.to(self.device) for t in context_null] + + self.clip.model.to(self.device) + clip_context = self.clip.visual([img[:, None, :, :]]) + if offload_model: + self.clip.model.cpu() + + y = self.vae.encode([ + torch.concat([ + torch.nn.functional.interpolate( + img[None].cpu(), size=(h, w), mode='bicubic').transpose( + 0, 1), + torch.zeros(3, 80, h, w) + ], + dim=1).to(self.device) + ])[0] + y = torch.concat([msk, y]) + # print('y:', y.shape) + + @contextmanager + def noop_no_sync(): + yield + + no_sync = getattr(self.model, 'no_sync', noop_no_sync) + + # evaluation mode + with amp.autocast(dtype=self.param_dtype), torch.no_grad(), no_sync(): + + if sample_solver == 'unipc': + sample_scheduler = FlowUniPCMultistepScheduler( + num_train_timesteps=self.num_train_timesteps, + shift=1, + use_dynamic_shifting=False) + sample_scheduler.set_timesteps( + sampling_steps, device=self.device, shift=shift) + timesteps = sample_scheduler.timesteps + elif sample_solver == 'dpm++': + sample_scheduler = FlowDPMSolverMultistepScheduler( + num_train_timesteps=self.num_train_timesteps, + shift=1, + use_dynamic_shifting=False) + sampling_sigmas = get_sampling_sigmas(sampling_steps, shift) + timesteps, _ = retrieve_timesteps( + sample_scheduler, + device=self.device, + sigmas=sampling_sigmas) + elif sample_solver == 'euler': + sample_scheduler = FlowMatchEulerDiscreteScheduler( + num_train_timesteps=self.num_train_timesteps, + shift=shift, + use_dynamic_shifting=False) + sample_scheduler.set_timesteps( + sampling_steps, device=self.device) + timesteps = sample_scheduler.timesteps + if few_step: + # start_latent_list = [0, 10, 20, 30, 40, 50] + # start_latent_list = [0, 5, 10, 15, 20, 25, 30, 35, 40] + start_latent_list = [0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40] + sample_scheduler.sigmas = sample_scheduler.sigmas[start_latent_list] + num_inference_steps = len(start_latent_list) - 1 + timesteps = timesteps[start_latent_list[:num_inference_steps]] + else: + raise NotImplementedError("Unsupported solver.") + + print(timesteps, sample_scheduler.sigmas) + # sample videos + latent = noise + + arg_c = { + 'context': [context[0]], + 'clip_fea': clip_context, + 'seq_len': max_seq_len, + 'y': [y], + } + + arg_null = { + 'context': context_null, + 'clip_fea': clip_context, + 'seq_len': max_seq_len, + 'y': [y], + } + + if offload_model: + torch.cuda.empty_cache() + + denoise_latents = [] + denoise_latents.append(latent.to(self.device)) + + self.model.to(self.device) + for _, t in enumerate(tqdm(timesteps)): + latent_model_input = [latent.to(self.device)] + timestep = [t] + timestep = torch.stack(timestep).to(self.device) + + noise_pred_cond = self.model( + latent_model_input, t=timestep, **arg_c)[0].to( + torch.device('cpu') if offload_model else self.device) + if offload_model: + torch.cuda.empty_cache() + if no_cfg == False: + noise_pred_uncond = self.model( + latent_model_input, t=timestep, **arg_null)[0].to( + torch.device('cpu') if offload_model else self.device) + if offload_model: + torch.cuda.empty_cache() + noise_pred = noise_pred_uncond + guide_scale * ( + noise_pred_cond - noise_pred_uncond) + else: + noise_pred = noise_pred_cond + + latent = latent.to( + torch.device('cpu') if offload_model else self.device) + + temp_x0 = sample_scheduler.step( + noise_pred.unsqueeze(0).to(self.device), + t, + latent.unsqueeze(0).to(self.device), + return_dict=False, + generator=seed_g)[0] + latent = temp_x0.squeeze(0) + + x0 = [latent.to(self.device)] + denoise_latents.append(latent.to(self.device)) + del latent_model_input, timestep + + if offload_model: + self.model.cpu() + torch.cuda.empty_cache() + + if self.rank == 0: + videos = self.vae.decode(x0) + + del noise, latent + del sample_scheduler + if offload_model: + gc.collect() + torch.cuda.synchronize() + if dist.is_initialized(): + dist.barrier() + + return videos[0], denoise_latents, arg_c if self.rank == 0 else None diff --git a/exp_code/1_benchmark/AccVideo/models/wan/modules/__init__.py b/exp_code/1_benchmark/AccVideo/models/wan/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f8935bbb45ab4e3f349d203b673102f7cfc07553 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/modules/__init__.py @@ -0,0 +1,16 @@ +from .attention import flash_attention +from .model import WanModel +from .t5 import T5Decoder, T5Encoder, T5EncoderModel, T5Model +from .tokenizers import HuggingfaceTokenizer +from .vae import WanVAE + +__all__ = [ + 'WanVAE', + 'WanModel', + 'T5Model', + 'T5Encoder', + 'T5Decoder', + 'T5EncoderModel', + 'HuggingfaceTokenizer', + 'flash_attention', +] diff --git a/exp_code/1_benchmark/AccVideo/models/wan/modules/attention.py b/exp_code/1_benchmark/AccVideo/models/wan/modules/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..58df013747df80104b987867f237bebc2853bdf8 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/modules/attention.py @@ -0,0 +1,232 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import torch +import math +import torch.nn.functional as F + +try: + import flash_attn_interface + FLASH_ATTN_3_AVAILABLE = True +except ModuleNotFoundError: + FLASH_ATTN_3_AVAILABLE = False + +try: + import flash_attn + FLASH_ATTN_2_AVAILABLE = True +except ModuleNotFoundError: + FLASH_ATTN_2_AVAILABLE = False + +try: + # raise NotImplementedError + from sageattention import sageattn_varlen, sageattn + print('Sage Attn is installed!') +except: + print('Sage Attn is not installed!') + sageattn_varlen = None + sageattn = None + +import warnings + +__all__ = [ + 'flash_attention', + 'attention', +] + + +def flash_attention( + q, + k, + v, + q_lens=None, + k_lens=None, + dropout_p=0., + softmax_scale=None, + q_scale=None, + causal=False, + window_size=(-1, -1), + deterministic=False, + dtype=torch.bfloat16, + version=None, +): + """ + q: [B, Lq, Nq, C1]. + k: [B, Lk, Nk, C1]. + v: [B, Lk, Nk, C2]. Nq must be divisible by Nk. + q_lens: [B]. + k_lens: [B]. + dropout_p: float. Dropout probability. + softmax_scale: float. The scaling of QK^T before applying softmax. + causal: bool. Whether to apply causal attention mask. + window_size: (left right). If not (-1, -1), apply sliding window local attention. + deterministic: bool. If True, slightly slower and uses more memory. + dtype: torch.dtype. Apply when dtype of q/k/v is not float16/bfloat16. + """ + half_dtypes = (torch.float16, torch.bfloat16) + assert dtype in half_dtypes + assert q.device.type == 'cuda' and q.size(-1) <= 256 + + # params + b, lq, lk, out_dtype = q.size(0), q.size(1), k.size(1), q.dtype + + def half(x): + return x if x.dtype in half_dtypes else x.to(dtype) + + # preprocess query + if q_lens is None: + q = half(q.flatten(0, 1)) + q_lens = torch.tensor( + [lq] * b, dtype=torch.int32).to( + device=q.device, non_blocking=True) + else: + q = half(torch.cat([u[:v] for u, v in zip(q, q_lens)])) + + # preprocess key, value + if k_lens is None: + k = half(k.flatten(0, 1)) + v = half(v.flatten(0, 1)) + k_lens = torch.tensor( + [lk] * b, dtype=torch.int32).to( + device=k.device, non_blocking=True) + else: + k = half(torch.cat([u[:v] for u, v in zip(k, k_lens)])) + v = half(torch.cat([u[:v] for u, v in zip(v, k_lens)])) + + q = q.to(v.dtype) + k = k.to(v.dtype) + + if q_scale is not None: + q = q * q_scale + + if version is not None and version == 3 and not FLASH_ATTN_3_AVAILABLE: + warnings.warn( + 'Flash attention 3 is not available, use flash attention 2 instead.' + ) + + # apply attention + if sageattn_varlen is not None: + x = sageattn_varlen( + q=q, + k=k, + v=v, + cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum( + 0, dtype=torch.int32).to(q.device, non_blocking=True), + cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum( + 0, dtype=torch.int32).to(q.device, non_blocking=True), + max_seqlen_q=lq, + max_seqlen_k=lk, + dropout_p=dropout_p, + softmax_scale=softmax_scale, + causal=causal, + window_size=window_size, + deterministic=deterministic).unflatten(0, (b, lq)) + print('using sageattention') + elif (version is None or version == 3) and FLASH_ATTN_3_AVAILABLE: + # Note: dropout_p, window_size are not supported in FA3 now. + x = flash_attn_interface.flash_attn_varlen_func( + q=q, + k=k, + v=v, + cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum( + 0, dtype=torch.int32).to(q.device, non_blocking=True), + cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum( + 0, dtype=torch.int32).to(q.device, non_blocking=True), + seqused_q=None, + seqused_k=None, + max_seqlen_q=lq, + max_seqlen_k=lk, + softmax_scale=softmax_scale, + causal=causal, + deterministic=deterministic)[0].unflatten(0, (b, lq)) + else: + assert FLASH_ATTN_2_AVAILABLE + x = flash_attn.flash_attn_varlen_func( + q=q, + k=k, + v=v, + cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum( + 0, dtype=torch.int32).to(q.device, non_blocking=True), + cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum( + 0, dtype=torch.int32).to(q.device, non_blocking=True), + max_seqlen_q=lq, + max_seqlen_k=lk, + dropout_p=dropout_p, + softmax_scale=softmax_scale, + causal=causal, + window_size=window_size, + deterministic=deterministic).unflatten(0, (b, lq)) + # print('flash_attn') + + # output + return x.type(out_dtype) + + +def attention( + q, + k, + v, + q_lens=None, + k_lens=None, + dropout_p=0., + softmax_scale=None, + q_scale=None, + causal=False, + window_size=(-1, -1), + deterministic=False, + dtype=torch.bfloat16, + fa_version=None, +): + # if FLASH_ATTN_2_AVAILABLE or FLASH_ATTN_3_AVAILABLE: + # return flash_attention( + # q=q, + # k=k, + # v=v, + # q_lens=q_lens, + # k_lens=k_lens, + # dropout_p=dropout_p, + # softmax_scale=softmax_scale, + # q_scale=q_scale, + # causal=causal, + # window_size=window_size, + # deterministic=deterministic, + # dtype=dtype, + # version=fa_version, + # ) + # else: + if q_lens is not None or k_lens is not None: + warnings.warn( + 'Padding mask is disabled when using scaled_dot_product_attention. It can have a significant impact on performance.' + ) + attn_mask = None + + q = q.transpose(1, 2).to(dtype) + k = k.transpose(1, 2).to(dtype) + v = v.transpose(1, 2).to(dtype) + + # out = torch.nn.functional.scaled_dot_product_attention( + # q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p) + out = manual_scaled_dot_product_attention( + q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p) + + out = out.transpose(1, 2).contiguous() + return out + + +def manual_scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False): + # query, key, value shapes: (..., seq_len, head_dim) + # attn_mask (optional): (..., seq_len, seq_len) + + scale = 1.0 / math.sqrt(query.size(-1)) + scores = torch.matmul(query, key.transpose(-2, -1)) * scale # (..., seq_len, seq_len) + + # Apply causal mask (if needed) + if is_causal: + mask = torch.triu(torch.ones_like(scores, dtype=torch.bool), diagonal=1) + scores = scores.masked_fill(mask, float('-inf')) + + # Apply additional attention mask (if provided) + if attn_mask is not None: + scores = scores + attn_mask + + attn_weights = F.softmax(scores, dim=-1) # (..., seq_len, seq_len) + attn_weights = F.dropout(attn_weights, p=dropout_p) # Optional dropout + + return torch.matmul(attn_weights, value) # (..., seq_len, head_dim) \ No newline at end of file diff --git a/exp_code/1_benchmark/AccVideo/models/wan/modules/clip.py b/exp_code/1_benchmark/AccVideo/models/wan/modules/clip.py new file mode 100644 index 0000000000000000000000000000000000000000..42dda0403a1683a0c6c2216852b8433ed8607418 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/modules/clip.py @@ -0,0 +1,542 @@ +# Modified from ``https://github.com/openai/CLIP'' and ``https://github.com/mlfoundations/open_clip'' +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import logging +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms as T + +from .attention import flash_attention +from .tokenizers import HuggingfaceTokenizer +from .xlm_roberta import XLMRoberta + +__all__ = [ + 'XLMRobertaCLIP', + 'clip_xlm_roberta_vit_h_14', + 'CLIPModel', +] + + +def pos_interpolate(pos, seq_len): + if pos.size(1) == seq_len: + return pos + else: + src_grid = int(math.sqrt(pos.size(1))) + tar_grid = int(math.sqrt(seq_len)) + n = pos.size(1) - src_grid * src_grid + return torch.cat([ + pos[:, :n], + F.interpolate( + pos[:, n:].float().reshape(1, src_grid, src_grid, -1).permute( + 0, 3, 1, 2), + size=(tar_grid, tar_grid), + mode='bicubic', + align_corners=False).flatten(2).transpose(1, 2) + ], + dim=1) + + +class QuickGELU(nn.Module): + + def forward(self, x): + return x * torch.sigmoid(1.702 * x) + + +class LayerNorm(nn.LayerNorm): + + def forward(self, x): + return super().forward(x.float()).type_as(x) + + +class SelfAttention(nn.Module): + + def __init__(self, + dim, + num_heads, + causal=False, + attn_dropout=0.0, + proj_dropout=0.0): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.causal = causal + self.attn_dropout = attn_dropout + self.proj_dropout = proj_dropout + + # layers + self.to_qkv = nn.Linear(dim, dim * 3) + self.proj = nn.Linear(dim, dim) + + def forward(self, x): + """ + x: [B, L, C]. + """ + b, s, c, n, d = *x.size(), self.num_heads, self.head_dim + + # compute query, key, value + q, k, v = self.to_qkv(x).view(b, s, 3, n, d).unbind(2) + + # compute attention + p = self.attn_dropout if self.training else 0.0 + x = flash_attention(q, k, v, dropout_p=p, causal=self.causal, version=2) + x = x.reshape(b, s, c) + + # output + x = self.proj(x) + x = F.dropout(x, self.proj_dropout, self.training) + return x + + +class SwiGLU(nn.Module): + + def __init__(self, dim, mid_dim): + super().__init__() + self.dim = dim + self.mid_dim = mid_dim + + # layers + self.fc1 = nn.Linear(dim, mid_dim) + self.fc2 = nn.Linear(dim, mid_dim) + self.fc3 = nn.Linear(mid_dim, dim) + + def forward(self, x): + x = F.silu(self.fc1(x)) * self.fc2(x) + x = self.fc3(x) + return x + + +class AttentionBlock(nn.Module): + + def __init__(self, + dim, + mlp_ratio, + num_heads, + post_norm=False, + causal=False, + activation='quick_gelu', + attn_dropout=0.0, + proj_dropout=0.0, + norm_eps=1e-5): + assert activation in ['quick_gelu', 'gelu', 'swi_glu'] + super().__init__() + self.dim = dim + self.mlp_ratio = mlp_ratio + self.num_heads = num_heads + self.post_norm = post_norm + self.causal = causal + self.norm_eps = norm_eps + + # layers + self.norm1 = LayerNorm(dim, eps=norm_eps) + self.attn = SelfAttention(dim, num_heads, causal, attn_dropout, + proj_dropout) + self.norm2 = LayerNorm(dim, eps=norm_eps) + if activation == 'swi_glu': + self.mlp = SwiGLU(dim, int(dim * mlp_ratio)) + else: + self.mlp = nn.Sequential( + nn.Linear(dim, int(dim * mlp_ratio)), + QuickGELU() if activation == 'quick_gelu' else nn.GELU(), + nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout)) + + def forward(self, x): + if self.post_norm: + x = x + self.norm1(self.attn(x)) + x = x + self.norm2(self.mlp(x)) + else: + x = x + self.attn(self.norm1(x)) + x = x + self.mlp(self.norm2(x)) + return x + + +class AttentionPool(nn.Module): + + def __init__(self, + dim, + mlp_ratio, + num_heads, + activation='gelu', + proj_dropout=0.0, + norm_eps=1e-5): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.mlp_ratio = mlp_ratio + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.proj_dropout = proj_dropout + self.norm_eps = norm_eps + + # layers + gain = 1.0 / math.sqrt(dim) + self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim)) + self.to_q = nn.Linear(dim, dim) + self.to_kv = nn.Linear(dim, dim * 2) + self.proj = nn.Linear(dim, dim) + self.norm = LayerNorm(dim, eps=norm_eps) + self.mlp = nn.Sequential( + nn.Linear(dim, int(dim * mlp_ratio)), + QuickGELU() if activation == 'quick_gelu' else nn.GELU(), + nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout)) + + def forward(self, x): + """ + x: [B, L, C]. + """ + b, s, c, n, d = *x.size(), self.num_heads, self.head_dim + + # compute query, key, value + q = self.to_q(self.cls_embedding).view(1, 1, n, d).expand(b, -1, -1, -1) + k, v = self.to_kv(x).view(b, s, 2, n, d).unbind(2) + + # compute attention + x = flash_attention(q, k, v, version=2) + x = x.reshape(b, 1, c) + + # output + x = self.proj(x) + x = F.dropout(x, self.proj_dropout, self.training) + + # mlp + x = x + self.mlp(self.norm(x)) + return x[:, 0] + + +class VisionTransformer(nn.Module): + + def __init__(self, + image_size=224, + patch_size=16, + dim=768, + mlp_ratio=4, + out_dim=512, + num_heads=12, + num_layers=12, + pool_type='token', + pre_norm=True, + post_norm=False, + activation='quick_gelu', + attn_dropout=0.0, + proj_dropout=0.0, + embedding_dropout=0.0, + norm_eps=1e-5): + if image_size % patch_size != 0: + print( + '[WARNING] image_size is not divisible by patch_size', + flush=True) + assert pool_type in ('token', 'token_fc', 'attn_pool') + out_dim = out_dim or dim + super().__init__() + self.image_size = image_size + self.patch_size = patch_size + self.num_patches = (image_size // patch_size)**2 + self.dim = dim + self.mlp_ratio = mlp_ratio + self.out_dim = out_dim + self.num_heads = num_heads + self.num_layers = num_layers + self.pool_type = pool_type + self.post_norm = post_norm + self.norm_eps = norm_eps + + # embeddings + gain = 1.0 / math.sqrt(dim) + self.patch_embedding = nn.Conv2d( + 3, + dim, + kernel_size=patch_size, + stride=patch_size, + bias=not pre_norm) + if pool_type in ('token', 'token_fc'): + self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim)) + self.pos_embedding = nn.Parameter(gain * torch.randn( + 1, self.num_patches + + (1 if pool_type in ('token', 'token_fc') else 0), dim)) + self.dropout = nn.Dropout(embedding_dropout) + + # transformer + self.pre_norm = LayerNorm(dim, eps=norm_eps) if pre_norm else None + self.transformer = nn.Sequential(*[ + AttentionBlock(dim, mlp_ratio, num_heads, post_norm, False, + activation, attn_dropout, proj_dropout, norm_eps) + for _ in range(num_layers) + ]) + self.post_norm = LayerNorm(dim, eps=norm_eps) + + # head + if pool_type == 'token': + self.head = nn.Parameter(gain * torch.randn(dim, out_dim)) + elif pool_type == 'token_fc': + self.head = nn.Linear(dim, out_dim) + elif pool_type == 'attn_pool': + self.head = AttentionPool(dim, mlp_ratio, num_heads, activation, + proj_dropout, norm_eps) + + def forward(self, x, interpolation=False, use_31_block=False): + b = x.size(0) + + # embeddings + x = self.patch_embedding(x).flatten(2).permute(0, 2, 1) + if self.pool_type in ('token', 'token_fc'): + x = torch.cat([self.cls_embedding.expand(b, -1, -1), x], dim=1) + if interpolation: + e = pos_interpolate(self.pos_embedding, x.size(1)) + else: + e = self.pos_embedding + x = self.dropout(x + e) + if self.pre_norm is not None: + x = self.pre_norm(x) + + # transformer + if use_31_block: + x = self.transformer[:-1](x) + return x + else: + x = self.transformer(x) + return x + + +class XLMRobertaWithHead(XLMRoberta): + + def __init__(self, **kwargs): + self.out_dim = kwargs.pop('out_dim') + super().__init__(**kwargs) + + # head + mid_dim = (self.dim + self.out_dim) // 2 + self.head = nn.Sequential( + nn.Linear(self.dim, mid_dim, bias=False), nn.GELU(), + nn.Linear(mid_dim, self.out_dim, bias=False)) + + def forward(self, ids): + # xlm-roberta + x = super().forward(ids) + + # average pooling + mask = ids.ne(self.pad_id).unsqueeze(-1).to(x) + x = (x * mask).sum(dim=1) / mask.sum(dim=1) + + # head + x = self.head(x) + return x + + +class XLMRobertaCLIP(nn.Module): + + def __init__(self, + embed_dim=1024, + image_size=224, + patch_size=14, + vision_dim=1280, + vision_mlp_ratio=4, + vision_heads=16, + vision_layers=32, + vision_pool='token', + vision_pre_norm=True, + vision_post_norm=False, + activation='gelu', + vocab_size=250002, + max_text_len=514, + type_size=1, + pad_id=1, + text_dim=1024, + text_heads=16, + text_layers=24, + text_post_norm=True, + text_dropout=0.1, + attn_dropout=0.0, + proj_dropout=0.0, + embedding_dropout=0.0, + norm_eps=1e-5): + super().__init__() + self.embed_dim = embed_dim + self.image_size = image_size + self.patch_size = patch_size + self.vision_dim = vision_dim + self.vision_mlp_ratio = vision_mlp_ratio + self.vision_heads = vision_heads + self.vision_layers = vision_layers + self.vision_pre_norm = vision_pre_norm + self.vision_post_norm = vision_post_norm + self.activation = activation + self.vocab_size = vocab_size + self.max_text_len = max_text_len + self.type_size = type_size + self.pad_id = pad_id + self.text_dim = text_dim + self.text_heads = text_heads + self.text_layers = text_layers + self.text_post_norm = text_post_norm + self.norm_eps = norm_eps + + # models + self.visual = VisionTransformer( + image_size=image_size, + patch_size=patch_size, + dim=vision_dim, + mlp_ratio=vision_mlp_ratio, + out_dim=embed_dim, + num_heads=vision_heads, + num_layers=vision_layers, + pool_type=vision_pool, + pre_norm=vision_pre_norm, + post_norm=vision_post_norm, + activation=activation, + attn_dropout=attn_dropout, + proj_dropout=proj_dropout, + embedding_dropout=embedding_dropout, + norm_eps=norm_eps) + self.textual = XLMRobertaWithHead( + vocab_size=vocab_size, + max_seq_len=max_text_len, + type_size=type_size, + pad_id=pad_id, + dim=text_dim, + out_dim=embed_dim, + num_heads=text_heads, + num_layers=text_layers, + post_norm=text_post_norm, + dropout=text_dropout) + self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([])) + + def forward(self, imgs, txt_ids): + """ + imgs: [B, 3, H, W] of torch.float32. + - mean: [0.48145466, 0.4578275, 0.40821073] + - std: [0.26862954, 0.26130258, 0.27577711] + txt_ids: [B, L] of torch.long. + Encoded by data.CLIPTokenizer. + """ + xi = self.visual(imgs) + xt = self.textual(txt_ids) + return xi, xt + + def param_groups(self): + groups = [{ + 'params': [ + p for n, p in self.named_parameters() + if 'norm' in n or n.endswith('bias') + ], + 'weight_decay': 0.0 + }, { + 'params': [ + p for n, p in self.named_parameters() + if not ('norm' in n or n.endswith('bias')) + ] + }] + return groups + + +def _clip(pretrained=False, + pretrained_name=None, + model_cls=XLMRobertaCLIP, + return_transforms=False, + return_tokenizer=False, + tokenizer_padding='eos', + dtype=torch.float32, + device='cpu', + **kwargs): + # init a model on device + with torch.device(device): + model = model_cls(**kwargs) + + # set device + model = model.to(dtype=dtype, device=device) + output = (model,) + + # init transforms + if return_transforms: + # mean and std + if 'siglip' in pretrained_name.lower(): + mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] + else: + mean = [0.48145466, 0.4578275, 0.40821073] + std = [0.26862954, 0.26130258, 0.27577711] + + # transforms + transforms = T.Compose([ + T.Resize((model.image_size, model.image_size), + interpolation=T.InterpolationMode.BICUBIC), + T.ToTensor(), + T.Normalize(mean=mean, std=std) + ]) + output += (transforms,) + return output[0] if len(output) == 1 else output + + +def clip_xlm_roberta_vit_h_14( + pretrained=False, + pretrained_name='open-clip-xlm-roberta-large-vit-huge-14', + **kwargs): + cfg = dict( + embed_dim=1024, + image_size=224, + patch_size=14, + vision_dim=1280, + vision_mlp_ratio=4, + vision_heads=16, + vision_layers=32, + vision_pool='token', + activation='gelu', + vocab_size=250002, + max_text_len=514, + type_size=1, + pad_id=1, + text_dim=1024, + text_heads=16, + text_layers=24, + text_post_norm=True, + text_dropout=0.1, + attn_dropout=0.0, + proj_dropout=0.0, + embedding_dropout=0.0) + cfg.update(**kwargs) + return _clip(pretrained, pretrained_name, XLMRobertaCLIP, **cfg) + + +class CLIPModel: + + def __init__(self, dtype, device, checkpoint_path, tokenizer_path): + self.dtype = dtype + self.device = device + self.checkpoint_path = checkpoint_path + self.tokenizer_path = tokenizer_path + + # init model + self.model, self.transforms = clip_xlm_roberta_vit_h_14( + pretrained=False, + return_transforms=True, + return_tokenizer=False, + dtype=dtype, + device=device) + self.model = self.model.eval().requires_grad_(False) + logging.info(f'loading {checkpoint_path}') + self.model.load_state_dict( + torch.load(checkpoint_path, map_location='cpu')) + + # init tokenizer + self.tokenizer = HuggingfaceTokenizer( + name=tokenizer_path, + seq_len=self.model.max_text_len - 2, + clean='whitespace') + + def visual(self, videos): + # preprocess + size = (self.model.image_size,) * 2 + videos = torch.cat([ + F.interpolate( + u.transpose(0, 1), + size=size, + mode='bicubic', + align_corners=False) for u in videos + ]) + videos = self.transforms.transforms[-1](videos.mul_(0.5).add_(0.5)) + + # forward + with torch.cuda.amp.autocast(dtype=self.dtype): + out = self.model.visual(videos, use_31_block=True) + return out diff --git a/exp_code/1_benchmark/AccVideo/models/wan/modules/model.py b/exp_code/1_benchmark/AccVideo/models/wan/modules/model.py new file mode 100644 index 0000000000000000000000000000000000000000..7747c9b6d6e0bcc6b21f721a5f95292acf8f47bb --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/modules/model.py @@ -0,0 +1,628 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import math + +import torch +import torch.cuda.amp as amp +import torch.nn as nn +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.modeling_utils import ModelMixin +from diffusers.models.attention_processor import Attention + +import torch.nn.functional as F +from typing import Any, Dict, Optional, Tuple, Union + +from .attention import flash_attention, attention + + +__all__ = ['WanModel'] + +def sinusoidal_embedding_1d(dim, position): + # preprocess + assert dim % 2 == 0 + half = dim // 2 + position = position.type(torch.float64) + + # calculation + sinusoid = torch.outer( + position, torch.pow(10000, -torch.arange(half).to(position).div(half))) + x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) + return x + + +@amp.autocast(enabled=False) +def rope_params(max_seq_len, dim, theta=10000): + assert dim % 2 == 0 + freqs = torch.outer( + torch.arange(max_seq_len), + 1.0 / torch.pow(theta, + torch.arange(0, dim, 2).to(torch.float64).div(dim))) + freqs = torch.polar(torch.ones_like(freqs), freqs) + return freqs + + +@amp.autocast(enabled=False) +def rope_apply(x, grid_sizes, freqs): + n, c = x.size(2), x.size(3) // 2 + + # split freqs + freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) + + # loop over samples + output = [] + for i, (f, h, w) in enumerate(grid_sizes.tolist()): + seq_len = f * h * w + + # precompute multipliers + x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape( + seq_len, n, -1, 2)) + freqs_i = torch.cat([ + freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), + freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), + freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) + ], + dim=-1).reshape(seq_len, 1, -1) + + # apply rotary embedding + x_i = torch.view_as_real(x_i * freqs_i).flatten(2) + x_i = torch.cat([x_i, x[i, seq_len:]]) + + # append to collection + output.append(x_i) + return torch.stack(output).float() + + +class WanRMSNorm(nn.Module): + + def __init__(self, dim, eps=1e-5): + super().__init__() + self.dim = dim + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + r""" + Args: + x(Tensor): Shape [B, L, C] + """ + return self._norm(x.float()).type_as(x) * self.weight + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) + + +class WanLayerNorm(nn.LayerNorm): + + def __init__(self, dim, eps=1e-6, elementwise_affine=False): + super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps) + + def forward(self, x): + r""" + Args: + x(Tensor): Shape [B, L, C] + """ + return super().forward(x.float()).type_as(x) + + +class WanSelfAttention(nn.Module): + + def __init__(self, + dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + eps=1e-6): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.window_size = window_size + self.qk_norm = qk_norm + self.eps = eps + + # layers + self.q = nn.Linear(dim, dim) + self.k = nn.Linear(dim, dim) + self.v = nn.Linear(dim, dim) + self.o = nn.Linear(dim, dim) + self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() + self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() + + def forward(self, x, seq_lens, grid_sizes, freqs): + r""" + Args: + x(Tensor): Shape [B, L, num_heads, C / num_heads] + seq_lens(Tensor): Shape [B] + grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim + + # query, key, value function + def qkv_fn(x): + q = self.norm_q(self.q(x)).view(b, s, n, d) + k = self.norm_k(self.k(x)).view(b, s, n, d) + v = self.v(x).view(b, s, n, d) + return q, k, v + + q, k, v = qkv_fn(x) + + x = flash_attention( + q=rope_apply(q, grid_sizes, freqs), + k=rope_apply(k, grid_sizes, freqs), + v=v, + k_lens=seq_lens, + window_size=self.window_size) + + # output + x = x.flatten(2) + x = self.o(x) + return x + + +class WanT2VCrossAttention(WanSelfAttention): + + def forward(self, x, context, context_lens): + r""" + Args: + x(Tensor): Shape [B, L1, C] + context(Tensor): Shape [B, L2, C] + context_lens(Tensor): Shape [B] + """ + b, n, d = x.size(0), self.num_heads, self.head_dim + + # compute query, key, value + q = self.norm_q(self.q(x)).view(b, -1, n, d) + k = self.norm_k(self.k(context)).view(b, -1, n, d) + v = self.v(context).view(b, -1, n, d) + + # compute attention + x = flash_attention(q, k, v, k_lens=context_lens) + # output + x = x.flatten(2) + x = self.o(x) + return x + + +class WanI2VCrossAttention(WanSelfAttention): + + def __init__(self, + dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + eps=1e-6): + super().__init__(dim, num_heads, window_size, qk_norm, eps) + + self.k_img = nn.Linear(dim, dim) + self.v_img = nn.Linear(dim, dim) + # self.alpha = nn.Parameter(torch.zeros((1, ))) + self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() + + def forward(self, x, context, context_lens): + r""" + Args: + x(Tensor): Shape [B, L1, C] + context(Tensor): Shape [B, L2, C] + context_lens(Tensor): Shape [B] + """ + context_img = context[:, :257] + context = context[:, 257:] + b, n, d = x.size(0), self.num_heads, self.head_dim + + # compute query, key, value + q = self.norm_q(self.q(x)).view(b, -1, n, d) + k = self.norm_k(self.k(context)).view(b, -1, n, d) + v = self.v(context).view(b, -1, n, d) + k_img = self.norm_k_img(self.k_img(context_img)).view(b, -1, n, d) + v_img = self.v_img(context_img).view(b, -1, n, d) + img_x = flash_attention(q, k_img, v_img, k_lens=None) + # compute attention + x = flash_attention(q, k, v, k_lens=context_lens) + + # output + x = x.flatten(2) + img_x = img_x.flatten(2) + x = x + img_x + x = self.o(x) + return x + + +WAN_CROSSATTENTION_CLASSES = { + 't2v_cross_attn': WanT2VCrossAttention, + 'i2v_cross_attn': WanI2VCrossAttention, +} + + +class WanAttentionBlock(nn.Module): + + def __init__(self, + cross_attn_type, + dim, + ffn_dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=False, + eps=1e-6): + super().__init__() + self.dim = dim + self.ffn_dim = ffn_dim + self.num_heads = num_heads + self.window_size = window_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + + # layers + self.norm1 = WanLayerNorm(dim, eps) + self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, + eps) + self.norm3 = WanLayerNorm( + dim, eps, + elementwise_affine=True) if cross_attn_norm else nn.Identity() + self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim, + num_heads, + (-1, -1), + qk_norm, + eps) + self.norm2 = WanLayerNorm(dim, eps) + self.ffn = nn.Sequential( + nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), + nn.Linear(ffn_dim, dim)) + + # modulation + self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) + + def forward( + self, + x, + e, + seq_lens, + grid_sizes, + freqs, + context, + context_lens, + ): + r""" + Args: + x(Tensor): Shape [B, L, C] + e(Tensor): Shape [B, 6, C] + seq_lens(Tensor): Shape [B], length of each sequence in batch + grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + assert e.dtype == torch.float32 + with amp.autocast(dtype=torch.float32): + e = (self.modulation + e).chunk(6, dim=1) + assert e[0].dtype == torch.float32 + + # self-attention + y = self.self_attn( + self.norm1(x).float() * (1 + e[1]) + e[0], seq_lens, grid_sizes, + freqs) + with amp.autocast(dtype=torch.float32): + x = x + y * e[2] + + # cross-attention & ffn function + def cross_attn_ffn(x, context, context_lens, e): + x = x + self.cross_attn(self.norm3(x), context, context_lens) + y = self.ffn(self.norm2(x).float() * (1 + e[4]) + e[3]) + with amp.autocast(dtype=torch.float32): + x = x + y * e[5] + return x + + x = cross_attn_ffn(x, context, context_lens, e) + return x + + +class Head(nn.Module): + + def __init__(self, dim, out_dim, patch_size, eps=1e-6): + super().__init__() + self.dim = dim + self.out_dim = out_dim + self.patch_size = patch_size + self.eps = eps + + # layers + out_dim = math.prod(patch_size) * out_dim + self.norm = WanLayerNorm(dim, eps) + self.head = nn.Linear(dim, out_dim) + + # modulation + self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5) + + def forward(self, x, e): + r""" + Args: + x(Tensor): Shape [B, L1, C] + e(Tensor): Shape [B, C] + """ + assert e.dtype == torch.float32 + with amp.autocast(dtype=torch.float32): + e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1) + x = (self.head(self.norm(x) * (1 + e[1]) + e[0])) + return x + + +class MLPProj(torch.nn.Module): + + def __init__(self, in_dim, out_dim): + super().__init__() + + self.proj = torch.nn.Sequential( + torch.nn.LayerNorm(in_dim), torch.nn.Linear(in_dim, in_dim), + torch.nn.GELU(), torch.nn.Linear(in_dim, out_dim), + torch.nn.LayerNorm(out_dim)) + + def forward(self, image_embeds): + clip_extra_context_tokens = self.proj(image_embeds) + return clip_extra_context_tokens + + +class WanModel(ModelMixin, ConfigMixin): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + ignore_for_config = [ + 'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim', 'window_size' + ] + _no_split_modules = ['WanAttentionBlock'] + + @register_to_config + def __init__(self, + model_type='t2v', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6): + r""" + Initialize the diffusion model backbone. + + Args: + model_type (`str`, *optional*, defaults to 't2v'): + Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) + patch_size (`tuple`, *optional*, defaults to (1, 2, 2)): + 3D patch dimensions for video embedding (t_patch, h_patch, w_patch) + text_len (`int`, *optional*, defaults to 512): + Fixed length for text embeddings + in_dim (`int`, *optional*, defaults to 16): + Input video channels (C_in) + dim (`int`, *optional*, defaults to 2048): + Hidden dimension of the transformer + ffn_dim (`int`, *optional*, defaults to 8192): + Intermediate dimension in feed-forward network + freq_dim (`int`, *optional*, defaults to 256): + Dimension for sinusoidal time embeddings + text_dim (`int`, *optional*, defaults to 4096): + Input dimension for text embeddings + out_dim (`int`, *optional*, defaults to 16): + Output video channels (C_out) + num_heads (`int`, *optional*, defaults to 16): + Number of attention heads + num_layers (`int`, *optional*, defaults to 32): + Number of transformer blocks + window_size (`tuple`, *optional*, defaults to (-1, -1)): + Window size for local attention (-1 indicates global attention) + qk_norm (`bool`, *optional*, defaults to True): + Enable query/key normalization + cross_attn_norm (`bool`, *optional*, defaults to False): + Enable cross-attention normalization + eps (`float`, *optional*, defaults to 1e-6): + Epsilon value for normalization layers + """ + + super().__init__() + + assert model_type in ['t2v', 'i2v'] + self.model_type = model_type + + self.patch_size = patch_size + self.text_len = text_len + self.in_dim = in_dim + self.dim = dim + self.ffn_dim = ffn_dim + self.freq_dim = freq_dim + self.text_dim = text_dim + self.out_dim = out_dim + self.num_heads = num_heads + self.num_layers = num_layers + self.window_size = window_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + + # embeddings + self.patch_embedding = nn.Conv3d( + in_dim, dim, kernel_size=patch_size, stride=patch_size) + self.text_embedding = nn.Sequential( + nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), + nn.Linear(dim, dim)) + + self.time_embedding = nn.Sequential( + nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) + self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6)) + + # blocks + cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn' + self.blocks = nn.ModuleList([ + WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads, + window_size, qk_norm, cross_attn_norm, eps) + for _ in range(num_layers) + ]) + + # head + self.head = Head(dim, out_dim, patch_size, eps) + + # buffers (don't use register_buffer otherwise dtype will be changed in to()) + assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0 + d = dim // num_heads + self.freqs = torch.cat([ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)) + ], + dim=1) + + if model_type == 'i2v': + self.img_emb = MLPProj(1280, dim) + + # initialize weights + self.init_weights() + + def forward( + self, + x, + t, + context, + seq_len, + clip_fea=None, + y=None, + ): + r""" + Forward pass through the diffusion model + + Args: + x (List[Tensor]): + List of input video tensors, each with shape [C_in, F, H, W] + t (Tensor): + Diffusion timesteps tensor of shape [B] + context (List[Tensor]): + List of text embeddings each with shape [L, C] + seq_len (`int`): + Maximum sequence length for positional encoding + clip_fea (Tensor, *optional*): + CLIP image features for image-to-video mode + y (List[Tensor], *optional*): + Conditional video inputs for image-to-video mode, same shape as x + + Returns: + List[Tensor]: + List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] + """ + # print(x[0].shape, flush=True) + if self.model_type == 'i2v': + assert clip_fea is not None and y is not None + # params + device = self.patch_embedding.weight.device + if self.freqs.device != device: + self.freqs = self.freqs.to(device) + + if y is not None: + x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] + + # embeddings + + x = [self.patch_embedding(u.unsqueeze(0)) for u in x] + grid_sizes = torch.stack( + [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) + x = [u.flatten(2).transpose(1, 2) for u in x] + seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) + assert seq_lens.max() <= seq_len + x = torch.cat([ + torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], + dim=1) for u in x + ]) + + # time embeddings + with amp.autocast(dtype=torch.float32): + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t).float()) + e0 = self.time_projection(e).unflatten(1, (6, self.dim)) + assert e.dtype == torch.float32 and e0.dtype == torch.float32 + + # context + context_lens = None + context = self.text_embedding( + torch.stack([ + torch.cat( + [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) + for u in context + ])) + + if clip_fea is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + + # arguments + kwargs = dict( + e=e0, + seq_lens=seq_lens, + grid_sizes=grid_sizes, + freqs=self.freqs, + context=context, + context_lens=context_lens) + num = 0 + # print(num, x.shape) + for block in self.blocks: + num += 1 + x = block(x, **kwargs) + # print(num, x.shape) + # quit() + # head + x = self.head(x, e) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return [u.float() for u in x] + + def unpatchify(self, x, grid_sizes): + r""" + Reconstruct video tensors from patch embeddings. + + Args: + x (List[Tensor]): + List of patchified features, each with shape [L, C_out * prod(patch_size)] + grid_sizes (Tensor): + Original spatial-temporal grid dimensions before patching, + shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) + + Returns: + List[Tensor]: + Reconstructed video tensors with shape [C_out, F, H / 8, W / 8] + """ + + c = self.out_dim + out = [] + for u, v in zip(x, grid_sizes.tolist()): + u = u[:math.prod(v)].view(*v, *self.patch_size, c) + u = torch.einsum('fhwpqrc->cfphqwr', u) + u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)]) + out.append(u) + return out + + def init_weights(self): + r""" + Initialize model parameters using Xavier initialization. + """ + + # basic init + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + nn.init.zeros_(m.bias) + + # init embeddings + nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1)) + for m in self.text_embedding.modules(): + if isinstance(m, nn.Linear): + nn.init.normal_(m.weight, std=.02) + for m in self.time_embedding.modules(): + if isinstance(m, nn.Linear): + nn.init.normal_(m.weight, std=.02) + + # init output layer + nn.init.zeros_(self.head.head.weight) diff --git a/exp_code/1_benchmark/AccVideo/models/wan/modules/t5.py b/exp_code/1_benchmark/AccVideo/models/wan/modules/t5.py new file mode 100644 index 0000000000000000000000000000000000000000..c841b044a239a6b3d0f872016c52072bc49885e7 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/modules/t5.py @@ -0,0 +1,513 @@ +# Modified from transformers.models.t5.modeling_t5 +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import logging +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .tokenizers import HuggingfaceTokenizer + +__all__ = [ + 'T5Model', + 'T5Encoder', + 'T5Decoder', + 'T5EncoderModel', +] + + +def fp16_clamp(x): + if x.dtype == torch.float16 and torch.isinf(x).any(): + clamp = torch.finfo(x.dtype).max - 1000 + x = torch.clamp(x, min=-clamp, max=clamp) + return x + + +def init_weights(m): + if isinstance(m, T5LayerNorm): + nn.init.ones_(m.weight) + elif isinstance(m, T5Model): + nn.init.normal_(m.token_embedding.weight, std=1.0) + elif isinstance(m, T5FeedForward): + nn.init.normal_(m.gate[0].weight, std=m.dim**-0.5) + nn.init.normal_(m.fc1.weight, std=m.dim**-0.5) + nn.init.normal_(m.fc2.weight, std=m.dim_ffn**-0.5) + elif isinstance(m, T5Attention): + nn.init.normal_(m.q.weight, std=(m.dim * m.dim_attn)**-0.5) + nn.init.normal_(m.k.weight, std=m.dim**-0.5) + nn.init.normal_(m.v.weight, std=m.dim**-0.5) + nn.init.normal_(m.o.weight, std=(m.num_heads * m.dim_attn)**-0.5) + elif isinstance(m, T5RelativeEmbedding): + nn.init.normal_( + m.embedding.weight, std=(2 * m.num_buckets * m.num_heads)**-0.5) + + +class GELU(nn.Module): + + def forward(self, x): + return 0.5 * x * (1.0 + torch.tanh( + math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0)))) + + +class T5LayerNorm(nn.Module): + + def __init__(self, dim, eps=1e-6): + super(T5LayerNorm, self).__init__() + self.dim = dim + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) + + self.eps) + if self.weight.dtype in [torch.float16, torch.bfloat16]: + x = x.type_as(self.weight) + return self.weight * x + + +class T5Attention(nn.Module): + + def __init__(self, dim, dim_attn, num_heads, dropout=0.1): + assert dim_attn % num_heads == 0 + super(T5Attention, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.num_heads = num_heads + self.head_dim = dim_attn // num_heads + + # layers + self.q = nn.Linear(dim, dim_attn, bias=False) + self.k = nn.Linear(dim, dim_attn, bias=False) + self.v = nn.Linear(dim, dim_attn, bias=False) + self.o = nn.Linear(dim_attn, dim, bias=False) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, context=None, mask=None, pos_bias=None): + """ + x: [B, L1, C]. + context: [B, L2, C] or None. + mask: [B, L2] or [B, L1, L2] or None. + """ + # check inputs + context = x if context is None else context + b, n, c = x.size(0), self.num_heads, self.head_dim + + # compute query, key, value + q = self.q(x).view(b, -1, n, c) + k = self.k(context).view(b, -1, n, c) + v = self.v(context).view(b, -1, n, c) + + # attention bias + attn_bias = x.new_zeros(b, n, q.size(1), k.size(1)) + if pos_bias is not None: + attn_bias += pos_bias + if mask is not None: + assert mask.ndim in [2, 3] + mask = mask.view(b, 1, 1, + -1) if mask.ndim == 2 else mask.unsqueeze(1) + attn_bias.masked_fill_(mask == 0, torch.finfo(x.dtype).min) + + # compute attention (T5 does not use scaling) + attn = torch.einsum('binc,bjnc->bnij', q, k) + attn_bias + attn = F.softmax(attn.float(), dim=-1).type_as(attn) + x = torch.einsum('bnij,bjnc->binc', attn, v) + + # output + x = x.reshape(b, -1, n * c) + x = self.o(x) + x = self.dropout(x) + return x + + +class T5FeedForward(nn.Module): + + def __init__(self, dim, dim_ffn, dropout=0.1): + super(T5FeedForward, self).__init__() + self.dim = dim + self.dim_ffn = dim_ffn + + # layers + self.gate = nn.Sequential(nn.Linear(dim, dim_ffn, bias=False), GELU()) + self.fc1 = nn.Linear(dim, dim_ffn, bias=False) + self.fc2 = nn.Linear(dim_ffn, dim, bias=False) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + x = self.fc1(x) * self.gate(x) + x = self.dropout(x) + x = self.fc2(x) + x = self.dropout(x) + return x + + +class T5SelfAttention(nn.Module): + + def __init__(self, + dim, + dim_attn, + dim_ffn, + num_heads, + num_buckets, + shared_pos=True, + dropout=0.1): + super(T5SelfAttention, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.num_buckets = num_buckets + self.shared_pos = shared_pos + + # layers + self.norm1 = T5LayerNorm(dim) + self.attn = T5Attention(dim, dim_attn, num_heads, dropout) + self.norm2 = T5LayerNorm(dim) + self.ffn = T5FeedForward(dim, dim_ffn, dropout) + self.pos_embedding = None if shared_pos else T5RelativeEmbedding( + num_buckets, num_heads, bidirectional=True) + + def forward(self, x, mask=None, pos_bias=None): + e = pos_bias if self.shared_pos else self.pos_embedding( + x.size(1), x.size(1)) + x = fp16_clamp(x + self.attn(self.norm1(x), mask=mask, pos_bias=e)) + x = fp16_clamp(x + self.ffn(self.norm2(x))) + return x + + +class T5CrossAttention(nn.Module): + + def __init__(self, + dim, + dim_attn, + dim_ffn, + num_heads, + num_buckets, + shared_pos=True, + dropout=0.1): + super(T5CrossAttention, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.num_buckets = num_buckets + self.shared_pos = shared_pos + + # layers + self.norm1 = T5LayerNorm(dim) + self.self_attn = T5Attention(dim, dim_attn, num_heads, dropout) + self.norm2 = T5LayerNorm(dim) + self.cross_attn = T5Attention(dim, dim_attn, num_heads, dropout) + self.norm3 = T5LayerNorm(dim) + self.ffn = T5FeedForward(dim, dim_ffn, dropout) + self.pos_embedding = None if shared_pos else T5RelativeEmbedding( + num_buckets, num_heads, bidirectional=False) + + def forward(self, + x, + mask=None, + encoder_states=None, + encoder_mask=None, + pos_bias=None): + e = pos_bias if self.shared_pos else self.pos_embedding( + x.size(1), x.size(1)) + x = fp16_clamp(x + self.self_attn(self.norm1(x), mask=mask, pos_bias=e)) + x = fp16_clamp(x + self.cross_attn( + self.norm2(x), context=encoder_states, mask=encoder_mask)) + x = fp16_clamp(x + self.ffn(self.norm3(x))) + return x + + +class T5RelativeEmbedding(nn.Module): + + def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128): + super(T5RelativeEmbedding, self).__init__() + self.num_buckets = num_buckets + self.num_heads = num_heads + self.bidirectional = bidirectional + self.max_dist = max_dist + + # layers + self.embedding = nn.Embedding(num_buckets, num_heads) + + def forward(self, lq, lk): + device = self.embedding.weight.device + # rel_pos = torch.arange(lk).unsqueeze(0).to(device) - \ + # torch.arange(lq).unsqueeze(1).to(device) + rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \ + torch.arange(lq, device=device).unsqueeze(1) + rel_pos = self._relative_position_bucket(rel_pos) + rel_pos_embeds = self.embedding(rel_pos) + rel_pos_embeds = rel_pos_embeds.permute(2, 0, 1).unsqueeze( + 0) # [1, N, Lq, Lk] + return rel_pos_embeds.contiguous() + + def _relative_position_bucket(self, rel_pos): + # preprocess + if self.bidirectional: + num_buckets = self.num_buckets // 2 + rel_buckets = (rel_pos > 0).long() * num_buckets + rel_pos = torch.abs(rel_pos) + else: + num_buckets = self.num_buckets + rel_buckets = 0 + rel_pos = -torch.min(rel_pos, torch.zeros_like(rel_pos)) + + # embeddings for small and large positions + max_exact = num_buckets // 2 + rel_pos_large = max_exact + (torch.log(rel_pos.float() / max_exact) / + math.log(self.max_dist / max_exact) * + (num_buckets - max_exact)).long() + rel_pos_large = torch.min( + rel_pos_large, torch.full_like(rel_pos_large, num_buckets - 1)) + rel_buckets += torch.where(rel_pos < max_exact, rel_pos, rel_pos_large) + return rel_buckets + + +class T5Encoder(nn.Module): + + def __init__(self, + vocab, + dim, + dim_attn, + dim_ffn, + num_heads, + num_layers, + num_buckets, + shared_pos=True, + dropout=0.1): + super(T5Encoder, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.num_layers = num_layers + self.num_buckets = num_buckets + self.shared_pos = shared_pos + + # layers + self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \ + else nn.Embedding(vocab, dim) + self.pos_embedding = T5RelativeEmbedding( + num_buckets, num_heads, bidirectional=True) if shared_pos else None + self.dropout = nn.Dropout(dropout) + self.blocks = nn.ModuleList([ + T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets, + shared_pos, dropout) for _ in range(num_layers) + ]) + self.norm = T5LayerNorm(dim) + + # initialize weights + self.apply(init_weights) + + def forward(self, ids, mask=None): + x = self.token_embedding(ids) + x = self.dropout(x) + e = self.pos_embedding(x.size(1), + x.size(1)) if self.shared_pos else None + for block in self.blocks: + x = block(x, mask, pos_bias=e) + x = self.norm(x) + x = self.dropout(x) + return x + + +class T5Decoder(nn.Module): + + def __init__(self, + vocab, + dim, + dim_attn, + dim_ffn, + num_heads, + num_layers, + num_buckets, + shared_pos=True, + dropout=0.1): + super(T5Decoder, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.num_layers = num_layers + self.num_buckets = num_buckets + self.shared_pos = shared_pos + + # layers + self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \ + else nn.Embedding(vocab, dim) + self.pos_embedding = T5RelativeEmbedding( + num_buckets, num_heads, bidirectional=False) if shared_pos else None + self.dropout = nn.Dropout(dropout) + self.blocks = nn.ModuleList([ + T5CrossAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets, + shared_pos, dropout) for _ in range(num_layers) + ]) + self.norm = T5LayerNorm(dim) + + # initialize weights + self.apply(init_weights) + + def forward(self, ids, mask=None, encoder_states=None, encoder_mask=None): + b, s = ids.size() + + # causal mask + if mask is None: + mask = torch.tril(torch.ones(1, s, s).to(ids.device)) + elif mask.ndim == 2: + mask = torch.tril(mask.unsqueeze(1).expand(-1, s, -1)) + + # layers + x = self.token_embedding(ids) + x = self.dropout(x) + e = self.pos_embedding(x.size(1), + x.size(1)) if self.shared_pos else None + for block in self.blocks: + x = block(x, mask, encoder_states, encoder_mask, pos_bias=e) + x = self.norm(x) + x = self.dropout(x) + return x + + +class T5Model(nn.Module): + + def __init__(self, + vocab_size, + dim, + dim_attn, + dim_ffn, + num_heads, + encoder_layers, + decoder_layers, + num_buckets, + shared_pos=True, + dropout=0.1): + super(T5Model, self).__init__() + self.vocab_size = vocab_size + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.encoder_layers = encoder_layers + self.decoder_layers = decoder_layers + self.num_buckets = num_buckets + + # layers + self.token_embedding = nn.Embedding(vocab_size, dim) + self.encoder = T5Encoder(self.token_embedding, dim, dim_attn, dim_ffn, + num_heads, encoder_layers, num_buckets, + shared_pos, dropout) + self.decoder = T5Decoder(self.token_embedding, dim, dim_attn, dim_ffn, + num_heads, decoder_layers, num_buckets, + shared_pos, dropout) + self.head = nn.Linear(dim, vocab_size, bias=False) + + # initialize weights + self.apply(init_weights) + + def forward(self, encoder_ids, encoder_mask, decoder_ids, decoder_mask): + x = self.encoder(encoder_ids, encoder_mask) + x = self.decoder(decoder_ids, decoder_mask, x, encoder_mask) + x = self.head(x) + return x + + +def _t5(name, + encoder_only=False, + decoder_only=False, + return_tokenizer=False, + tokenizer_kwargs={}, + dtype=torch.float32, + device='cpu', + **kwargs): + # sanity check + assert not (encoder_only and decoder_only) + + # params + if encoder_only: + model_cls = T5Encoder + kwargs['vocab'] = kwargs.pop('vocab_size') + kwargs['num_layers'] = kwargs.pop('encoder_layers') + _ = kwargs.pop('decoder_layers') + elif decoder_only: + model_cls = T5Decoder + kwargs['vocab'] = kwargs.pop('vocab_size') + kwargs['num_layers'] = kwargs.pop('decoder_layers') + _ = kwargs.pop('encoder_layers') + else: + model_cls = T5Model + + # init model + with torch.device(device): + model = model_cls(**kwargs) + + # set device + model = model.to(dtype=dtype, device=device) + + # init tokenizer + if return_tokenizer: + from .tokenizers import HuggingfaceTokenizer + tokenizer = HuggingfaceTokenizer(f'google/{name}', **tokenizer_kwargs) + return model, tokenizer + else: + return model + + +def umt5_xxl(**kwargs): + cfg = dict( + vocab_size=256384, + dim=4096, + dim_attn=4096, + dim_ffn=10240, + num_heads=64, + encoder_layers=24, + decoder_layers=24, + num_buckets=32, + shared_pos=False, + dropout=0.1) + cfg.update(**kwargs) + return _t5('umt5-xxl', **cfg) + + +class T5EncoderModel: + + def __init__( + self, + text_len, + dtype=torch.bfloat16, + device=torch.cuda.current_device(), + checkpoint_path=None, + tokenizer_path=None, + shard_fn=None, + ): + self.text_len = text_len + self.dtype = dtype + self.device = device + self.checkpoint_path = checkpoint_path + self.tokenizer_path = tokenizer_path + + # init model + model = umt5_xxl( + encoder_only=True, + return_tokenizer=False, + dtype=dtype, + device=device).eval().requires_grad_(False) + logging.info(f'loading {checkpoint_path}') + model.load_state_dict(torch.load(checkpoint_path, map_location='cpu')) + self.model = model + if shard_fn is not None: + self.model = shard_fn(self.model, sync_module_states=False) + else: + self.model.to(self.device) + # init tokenizer + self.tokenizer = HuggingfaceTokenizer( + name=tokenizer_path, seq_len=text_len, clean='whitespace') + + def __call__(self, texts, device): + ids, mask = self.tokenizer( + texts, return_mask=True, add_special_tokens=True) + ids = ids.to(device) + mask = mask.to(device) + seq_lens = mask.gt(0).sum(dim=1).long() + context = self.model(ids, mask) + return [u[:v] for u, v in zip(context, seq_lens)] diff --git a/exp_code/1_benchmark/AccVideo/models/wan/modules/tokenizers.py b/exp_code/1_benchmark/AccVideo/models/wan/modules/tokenizers.py new file mode 100644 index 0000000000000000000000000000000000000000..121e591c48f82f82daa51a6ce38ae9a27beea8d2 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/modules/tokenizers.py @@ -0,0 +1,82 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import html +import string + +import ftfy +import regex as re +from transformers import AutoTokenizer + +__all__ = ['HuggingfaceTokenizer'] + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +def canonicalize(text, keep_punctuation_exact_string=None): + text = text.replace('_', ' ') + if keep_punctuation_exact_string: + text = keep_punctuation_exact_string.join( + part.translate(str.maketrans('', '', string.punctuation)) + for part in text.split(keep_punctuation_exact_string)) + else: + text = text.translate(str.maketrans('', '', string.punctuation)) + text = text.lower() + text = re.sub(r'\s+', ' ', text) + return text.strip() + + +class HuggingfaceTokenizer: + + def __init__(self, name, seq_len=None, clean=None, **kwargs): + assert clean in (None, 'whitespace', 'lower', 'canonicalize') + self.name = name + self.seq_len = seq_len + self.clean = clean + + # init tokenizer + self.tokenizer = AutoTokenizer.from_pretrained(name, **kwargs) + self.vocab_size = self.tokenizer.vocab_size + + def __call__(self, sequence, **kwargs): + return_mask = kwargs.pop('return_mask', False) + + # arguments + _kwargs = {'return_tensors': 'pt'} + if self.seq_len is not None: + _kwargs.update({ + 'padding': 'max_length', + 'truncation': True, + 'max_length': self.seq_len + }) + _kwargs.update(**kwargs) + + # tokenization + if isinstance(sequence, str): + sequence = [sequence] + if self.clean: + sequence = [self._clean(u) for u in sequence] + ids = self.tokenizer(sequence, **_kwargs) + + # output + if return_mask: + return ids.input_ids, ids.attention_mask + else: + return ids.input_ids + + def _clean(self, text): + if self.clean == 'whitespace': + text = whitespace_clean(basic_clean(text)) + elif self.clean == 'lower': + text = whitespace_clean(basic_clean(text)).lower() + elif self.clean == 'canonicalize': + text = canonicalize(basic_clean(text)) + return text diff --git a/exp_code/1_benchmark/AccVideo/models/wan/modules/vae.py b/exp_code/1_benchmark/AccVideo/models/wan/modules/vae.py new file mode 100644 index 0000000000000000000000000000000000000000..5c6da5723536cdd49889132479fdd35700e0e5ca --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/modules/vae.py @@ -0,0 +1,663 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import logging + +import torch +import torch.cuda.amp as amp +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +__all__ = [ + 'WanVAE', +] + +CACHE_T = 2 + + +class CausalConv3d(nn.Conv3d): + """ + Causal 3d convolusion. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._padding = (self.padding[2], self.padding[2], self.padding[1], + self.padding[1], 2 * self.padding[0], 0) + self.padding = (0, 0, 0) + + def forward(self, x, cache_x=None): + padding = list(self._padding) + if cache_x is not None and self._padding[4] > 0: + cache_x = cache_x.to(x.device) + x = torch.cat([cache_x, x], dim=2) + padding[4] -= cache_x.shape[2] + x = F.pad(x, padding) + + return super().forward(x) + + +class RMS_norm(nn.Module): + + def __init__(self, dim, channel_first=True, images=True, bias=False): + super().__init__() + broadcastable_dims = (1, 1, 1) if not images else (1, 1) + shape = (dim, *broadcastable_dims) if channel_first else (dim,) + + self.channel_first = channel_first + self.scale = dim**0.5 + self.gamma = nn.Parameter(torch.ones(shape)) + self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0. + + def forward(self, x): + return F.normalize( + x, dim=(1 if self.channel_first else + -1)) * self.scale * self.gamma + self.bias + + +class Upsample(nn.Upsample): + + def forward(self, x): + """ + Fix bfloat16 support for nearest neighbor interpolation. + """ + return super().forward(x.float()).type_as(x) + + +class Resample(nn.Module): + + def __init__(self, dim, mode): + assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d', + 'downsample3d') + super().__init__() + self.dim = dim + self.mode = mode + + # layers + if mode == 'upsample2d': + self.resample = nn.Sequential( + Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + nn.Conv2d(dim, dim // 2, 3, padding=1)) + elif mode == 'upsample3d': + self.resample = nn.Sequential( + Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + nn.Conv2d(dim, dim // 2, 3, padding=1)) + self.time_conv = CausalConv3d( + dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) + + elif mode == 'downsample2d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + nn.Conv2d(dim, dim, 3, stride=(2, 2))) + elif mode == 'downsample3d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + nn.Conv2d(dim, dim, 3, stride=(2, 2))) + self.time_conv = CausalConv3d( + dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) + + else: + self.resample = nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + b, c, t, h, w = x.size() + if self.mode == 'upsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = 'Rep' + feat_idx[0] += 1 + else: + + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] != 'Rep': + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] == 'Rep': + cache_x = torch.cat([ + torch.zeros_like(cache_x).to(cache_x.device), + cache_x + ], + dim=2) + if feat_cache[idx] == 'Rep': + x = self.time_conv(x) + else: + x = self.time_conv(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + + x = x.reshape(b, 2, c, t, h, w) + x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), + 3) + x = x.reshape(b, c, t * 2, h, w) + t = x.shape[2] + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.resample(x) + x = rearrange(x, '(b t) c h w -> b c t h w', t=t) + + if self.mode == 'downsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = x.clone() + feat_idx[0] += 1 + else: + + cache_x = x[:, :, -1:, :, :].clone() + # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep': + # # cache last frame of last two chunk + # cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) + + x = self.time_conv( + torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + return x + + def init_weight(self, conv): + conv_weight = conv.weight + nn.init.zeros_(conv_weight) + c1, c2, t, h, w = conv_weight.size() + one_matrix = torch.eye(c1, c2) + init_matrix = one_matrix + nn.init.zeros_(conv_weight) + #conv_weight.data[:,:,-1,1,1] = init_matrix * 0.5 + conv_weight.data[:, :, 1, 0, 0] = init_matrix #* 0.5 + conv.weight.data.copy_(conv_weight) + nn.init.zeros_(conv.bias.data) + + def init_weight2(self, conv): + conv_weight = conv.weight.data + nn.init.zeros_(conv_weight) + c1, c2, t, h, w = conv_weight.size() + init_matrix = torch.eye(c1 // 2, c2) + #init_matrix = repeat(init_matrix, 'o ... -> (o 2) ...').permute(1,0,2).contiguous().reshape(c1,c2) + conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix + conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix + conv.weight.data.copy_(conv_weight) + nn.init.zeros_(conv.bias.data) + + +class ResidualBlock(nn.Module): + + def __init__(self, in_dim, out_dim, dropout=0.0): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + # layers + self.residual = nn.Sequential( + RMS_norm(in_dim, images=False), nn.SiLU(), + CausalConv3d(in_dim, out_dim, 3, padding=1), + RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), + CausalConv3d(out_dim, out_dim, 3, padding=1)) + self.shortcut = CausalConv3d(in_dim, out_dim, 1) \ + if in_dim != out_dim else nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + h = self.shortcut(x) + for layer in self.residual: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + h + + +class AttentionBlock(nn.Module): + """ + Causal self-attention with a single head. + """ + + def __init__(self, dim): + super().__init__() + self.dim = dim + + # layers + self.norm = RMS_norm(dim) + self.to_qkv = nn.Conv2d(dim, dim * 3, 1) + self.proj = nn.Conv2d(dim, dim, 1) + + # zero out the last layer params + nn.init.zeros_(self.proj.weight) + + def forward(self, x): + identity = x + b, c, t, h, w = x.size() + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.norm(x) + # compute query, key, value + q, k, v = self.to_qkv(x).reshape(b * t, 1, c * 3, + -1).permute(0, 1, 3, + 2).contiguous().chunk( + 3, dim=-1) + + # apply attention + x = F.scaled_dot_product_attention( + q, + k, + v, + ) + x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w) + + # output + x = self.proj(x) + x = rearrange(x, '(b t) c h w-> b c t h w', t=t) + return x + identity + + +class Encoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + + # dimensions + dims = [dim * u for u in [1] + dim_mult] + scale = 1.0 + + # init block + self.conv1 = CausalConv3d(3, dims[0], 3, padding=1) + + # downsample blocks + downsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + for _ in range(num_res_blocks): + downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + downsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # downsample block + if i != len(dim_mult) - 1: + mode = 'downsample3d' if temperal_downsample[ + i] else 'downsample2d' + downsamples.append(Resample(out_dim, mode=mode)) + scale /= 2.0 + self.downsamples = nn.Sequential(*downsamples) + + # middle blocks + self.middle = nn.Sequential( + ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim), + ResidualBlock(out_dim, out_dim, dropout)) + + # output blocks + self.head = nn.Sequential( + RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, z_dim, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## downsamples + for layer in self.downsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## middle + for layer in self.middle: + if isinstance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +class Decoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_upsample=[False, True, True], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_upsample = temperal_upsample + + # dimensions + dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] + scale = 1.0 / 2**(len(dim_mult) - 2) + + # init block + self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) + + # middle blocks + self.middle = nn.Sequential( + ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]), + ResidualBlock(dims[0], dims[0], dropout)) + + # upsample blocks + upsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + if i == 1 or i == 2 or i == 3: + in_dim = in_dim // 2 + for _ in range(num_res_blocks + 1): + upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + upsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # upsample block + if i != len(dim_mult) - 1: + mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d' + upsamples.append(Resample(out_dim, mode=mode)) + scale *= 2.0 + self.upsamples = nn.Sequential(*upsamples) + + # output blocks + self.head = nn.Sequential( + RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, 3, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + ## conv1 + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## middle + for layer in self.middle: + if isinstance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## upsamples + for layer in self.upsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +def count_conv3d(model): + count = 0 + for m in model.modules(): + if isinstance(m, CausalConv3d): + count += 1 + return count + + +class WanVAE_(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + self.temperal_upsample = temperal_downsample[::-1] + + # modules + self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks, + attn_scales, self.temperal_downsample, dropout) + self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) + self.conv2 = CausalConv3d(z_dim, z_dim, 1) + self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks, + attn_scales, self.temperal_upsample, dropout) + + def forward(self, x): + mu, log_var = self.encode(x) + z = self.reparameterize(mu, log_var) + x_recon = self.decode(z) + return x_recon, mu, log_var + + def encode(self, x, scale): + self.clear_cache() + ## cache + t = x.shape[2] + iter_ = 1 + (t - 1) // 4 + ## 对encode输入的x，按时间拆分为1、4、4、4.... + for i in range(iter_): + self._enc_conv_idx = [0] + if i == 0: + out = self.encoder( + x[:, :, :1, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + else: + out_ = self.encoder( + x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + out = torch.cat([out, out_], 2) + mu, log_var = self.conv1(out).chunk(2, dim=1) + if isinstance(scale[0], torch.Tensor): + mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view( + 1, self.z_dim, 1, 1, 1) + else: + mu = (mu - scale[0]) * scale[1] + self.clear_cache() + return mu + + def decode(self, z, scale): + self.clear_cache() + # z: [b,c,t,h,w] + if isinstance(scale[0], torch.Tensor): + z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view( + 1, self.z_dim, 1, 1, 1) + else: + z = z / scale[1] + scale[0] + iter_ = z.shape[2] + x = self.conv2(z) + for i in range(iter_): + self._conv_idx = [0] + if i == 0: + out = self.decoder( + x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + else: + out_ = self.decoder( + x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + out = torch.cat([out, out_], 2) + self.clear_cache() + return out + + def reparameterize(self, mu, log_var): + std = torch.exp(0.5 * log_var) + eps = torch.randn_like(std) + return eps * std + mu + + def sample(self, imgs, deterministic=False): + mu, log_var = self.encode(imgs) + if deterministic: + return mu + std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0)) + return mu + std * torch.randn_like(std) + + def clear_cache(self): + self._conv_num = count_conv3d(self.decoder) + self._conv_idx = [0] + self._feat_map = [None] * self._conv_num + #cache encode + self._enc_conv_num = count_conv3d(self.encoder) + self._enc_conv_idx = [0] + self._enc_feat_map = [None] * self._enc_conv_num + + +def _video_vae(pretrained_path=None, z_dim=None, device='cpu', **kwargs): + """ + Autoencoder3d adapted from Stable Diffusion 1.x, 2.x and XL. + """ + # params + cfg = dict( + dim=96, + z_dim=z_dim, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[False, True, True], + dropout=0.0) + cfg.update(**kwargs) + + # init model + with torch.device('meta'): + model = WanVAE_(**cfg) + + # load checkpoint + logging.info(f'loading {pretrained_path}') + model.load_state_dict( + torch.load(pretrained_path, map_location=device), assign=True) + + return model + + +class WanVAE: + + def __init__(self, + z_dim=16, + vae_pth='cache/vae_step_411000.pth', + dtype=torch.float, + device="cuda"): + self.dtype = dtype + self.device = device + + mean = [ + -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, + 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921 + ] + std = [ + 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, + 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160 + ] + self.mean = torch.tensor(mean, dtype=dtype, device=device) + self.std = torch.tensor(std, dtype=dtype, device=device) + self.scale = [self.mean, 1.0 / self.std] + + # init model + self.model = _video_vae( + pretrained_path=vae_pth, + z_dim=z_dim, + ).eval().requires_grad_(False).to(device) + + def encode(self, videos): + """ + videos: A list of videos each with shape [C, T, H, W]. + """ + with amp.autocast(dtype=self.dtype): + return [ + self.model.encode(u.unsqueeze(0), self.scale).float().squeeze(0) + for u in videos + ] + + def decode(self, zs): + with amp.autocast(dtype=self.dtype): + return [ + self.model.decode(u.unsqueeze(0), + self.scale).float().clamp_(-1, 1).squeeze(0) + for u in zs + ] diff --git a/exp_code/1_benchmark/AccVideo/models/wan/modules/xlm_roberta.py b/exp_code/1_benchmark/AccVideo/models/wan/modules/xlm_roberta.py new file mode 100644 index 0000000000000000000000000000000000000000..4bd38c1016fdaec90b77a6222d75d01c38c1291c --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/modules/xlm_roberta.py @@ -0,0 +1,170 @@ +# Modified from transformers.models.xlm_roberta.modeling_xlm_roberta +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import torch +import torch.nn as nn +import torch.nn.functional as F + +__all__ = ['XLMRoberta', 'xlm_roberta_large'] + + +class SelfAttention(nn.Module): + + def __init__(self, dim, num_heads, dropout=0.1, eps=1e-5): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.eps = eps + + # layers + self.q = nn.Linear(dim, dim) + self.k = nn.Linear(dim, dim) + self.v = nn.Linear(dim, dim) + self.o = nn.Linear(dim, dim) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, mask): + """ + x: [B, L, C]. + """ + b, s, c, n, d = *x.size(), self.num_heads, self.head_dim + + # compute query, key, value + q = self.q(x).reshape(b, s, n, d).permute(0, 2, 1, 3) + k = self.k(x).reshape(b, s, n, d).permute(0, 2, 1, 3) + v = self.v(x).reshape(b, s, n, d).permute(0, 2, 1, 3) + + # compute attention + p = self.dropout.p if self.training else 0.0 + x = F.scaled_dot_product_attention(q, k, v, mask, p) + x = x.permute(0, 2, 1, 3).reshape(b, s, c) + + # output + x = self.o(x) + x = self.dropout(x) + return x + + +class AttentionBlock(nn.Module): + + def __init__(self, dim, num_heads, post_norm, dropout=0.1, eps=1e-5): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.post_norm = post_norm + self.eps = eps + + # layers + self.attn = SelfAttention(dim, num_heads, dropout, eps) + self.norm1 = nn.LayerNorm(dim, eps=eps) + self.ffn = nn.Sequential( + nn.Linear(dim, dim * 4), nn.GELU(), nn.Linear(dim * 4, dim), + nn.Dropout(dropout)) + self.norm2 = nn.LayerNorm(dim, eps=eps) + + def forward(self, x, mask): + if self.post_norm: + x = self.norm1(x + self.attn(x, mask)) + x = self.norm2(x + self.ffn(x)) + else: + x = x + self.attn(self.norm1(x), mask) + x = x + self.ffn(self.norm2(x)) + return x + + +class XLMRoberta(nn.Module): + """ + XLMRobertaModel with no pooler and no LM head. + """ + + def __init__(self, + vocab_size=250002, + max_seq_len=514, + type_size=1, + pad_id=1, + dim=1024, + num_heads=16, + num_layers=24, + post_norm=True, + dropout=0.1, + eps=1e-5): + super().__init__() + self.vocab_size = vocab_size + self.max_seq_len = max_seq_len + self.type_size = type_size + self.pad_id = pad_id + self.dim = dim + self.num_heads = num_heads + self.num_layers = num_layers + self.post_norm = post_norm + self.eps = eps + + # embeddings + self.token_embedding = nn.Embedding(vocab_size, dim, padding_idx=pad_id) + self.type_embedding = nn.Embedding(type_size, dim) + self.pos_embedding = nn.Embedding(max_seq_len, dim, padding_idx=pad_id) + self.dropout = nn.Dropout(dropout) + + # blocks + self.blocks = nn.ModuleList([ + AttentionBlock(dim, num_heads, post_norm, dropout, eps) + for _ in range(num_layers) + ]) + + # norm layer + self.norm = nn.LayerNorm(dim, eps=eps) + + def forward(self, ids): + """ + ids: [B, L] of torch.LongTensor. + """ + b, s = ids.shape + mask = ids.ne(self.pad_id).long() + + # embeddings + x = self.token_embedding(ids) + \ + self.type_embedding(torch.zeros_like(ids)) + \ + self.pos_embedding(self.pad_id + torch.cumsum(mask, dim=1) * mask) + if self.post_norm: + x = self.norm(x) + x = self.dropout(x) + + # blocks + mask = torch.where( + mask.view(b, 1, 1, s).gt(0), 0.0, + torch.finfo(x.dtype).min) + for block in self.blocks: + x = block(x, mask) + + # output + if not self.post_norm: + x = self.norm(x) + return x + + +def xlm_roberta_large(pretrained=False, + return_tokenizer=False, + device='cpu', + **kwargs): + """ + XLMRobertaLarge adapted from Huggingface. + """ + # params + cfg = dict( + vocab_size=250002, + max_seq_len=514, + type_size=1, + pad_id=1, + dim=1024, + num_heads=16, + num_layers=24, + post_norm=True, + dropout=0.1, + eps=1e-5) + cfg.update(**kwargs) + + # init a model on device + with torch.device(device): + model = XLMRoberta(**cfg) + return model diff --git a/exp_code/1_benchmark/AccVideo/models/wan/text2video.py b/exp_code/1_benchmark/AccVideo/models/wan/text2video.py new file mode 100644 index 0000000000000000000000000000000000000000..22ce4dfbf00d360ad71ded4cc855fd980e23b6c3 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/text2video.py @@ -0,0 +1,294 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import gc +import logging +import math +import os +import random +import sys +import types +from contextlib import contextmanager +from functools import partial +from torch import nn + +import torch +import torch.cuda.amp as amp +import torch.distributed as dist +from tqdm import tqdm +import io + +from .distributed.fsdp import shard_model +from .modules.model import WanModel +from .modules.t5 import T5EncoderModel +from .modules.vae import WanVAE +from .utils.fm_solvers import (FlowDPMSolverMultistepScheduler, + get_sampling_sigmas, retrieve_timesteps) +from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler +from .utils.fm_euler import FlowMatchEulerDiscreteScheduler + + +class WanT2V: + + def __init__( + self, + config, + checkpoint_dir, + device_id=0, + rank=0, + t5_fsdp=False, + dit_fsdp=False, + use_usp=False, + t5_cpu=False, + dit_path=None, + ): + r""" + Initializes the Wan text-to-video generation model components. + + Args: + config (EasyDict): + Object containing model parameters initialized from config.py + checkpoint_dir (`str`): + Path to directory containing model checkpoints + device_id (`int`, *optional*, defaults to 0): + Id of target GPU device + rank (`int`, *optional*, defaults to 0): + Process rank for distributed training + t5_fsdp (`bool`, *optional*, defaults to False): + Enable FSDP sharding for T5 model + dit_fsdp (`bool`, *optional*, defaults to False): + Enable FSDP sharding for DiT model + use_usp (`bool`, *optional*, defaults to False): + Enable distribution strategy of USP. + t5_cpu (`bool`, *optional*, defaults to False): + Whether to place T5 model on CPU. Only works without t5_fsdp. + """ + self.device = torch.device(f"cuda:{device_id}") + self.config = config + self.rank = rank + self.t5_cpu = t5_cpu + + self.num_train_timesteps = config.num_train_timesteps + self.param_dtype = config.param_dtype + + shard_fn = partial(shard_model, device_id=device_id) + self.text_encoder = T5EncoderModel( + text_len=config.text_len, + dtype=config.t5_dtype, + device=torch.device('cpu'), + checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint), + tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer), + shard_fn=shard_fn if t5_fsdp else None) + + self.text_encoder.model.to(self.device) + + self.vae_stride = config.vae_stride + self.patch_size = config.patch_size + self.vae = WanVAE( + vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint), + device=self.device,) + + logging.info(f"Creating WanModel from {checkpoint_dir}") + self.model = WanModel.from_pretrained(checkpoint_dir) + + self.model.eval().requires_grad_(False) + + if use_usp: + from xfuser.core.distributed import \ + get_sequence_parallel_world_size + + from .distributed.xdit_context_parallel import (usp_attn_forward, + usp_dit_forward) + for block in self.model.blocks: + block.self_attn.forward = types.MethodType( + usp_attn_forward, block.self_attn) + self.model.forward = types.MethodType(usp_dit_forward, self.model) + self.sp_size = get_sequence_parallel_world_size() + else: + self.sp_size = 1 + + if dist.is_initialized(): + dist.barrier() + if dit_fsdp: + self.model = shard_fn(self.model) + else: + self.model.to(self.device) + + self.sample_neg_prompt = config.sample_neg_prompt + + def generate(self, + input_prompt, + size=(1280, 720), + frame_num=81, + shift=5.0, + sample_solver='unipc', + sampling_steps=50, + guide_scale=5.0, + n_prompt="", + seed=-1, + offload_model=True, + few_step=False, + no_cfg=False,): + r""" + Generates video frames from text prompt using diffusion process. + + Args: + input_prompt (`str`): + Text prompt for content generation + size (tupele[`int`], *optional*, defaults to (1280,720)): + Controls video resolution, (width,height). + frame_num (`int`, *optional*, defaults to 81): + How many frames to sample from a video. The number should be 4n+1 + shift (`float`, *optional*, defaults to 5.0): + Noise schedule shift parameter. Affects temporal dynamics + sample_solver (`str`, *optional*, defaults to 'unipc'): + Solver used to sample the video. + sampling_steps (`int`, *optional*, defaults to 40): + Number of diffusion sampling steps. Higher values improve quality but slow generation + guide_scale (`float`, *optional*, defaults 5.0): + Classifier-free guidance scale. Controls prompt adherence vs. creativity + n_prompt (`str`, *optional*, defaults to ""): + Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` + seed (`int`, *optional*, defaults to -1): + Random seed for noise generation. If -1, use random seed. + offload_model (`bool`, *optional*, defaults to True): + If True, offloads models to CPU during generation to save VRAM + + Returns: + torch.Tensor: + Generated video frames tensor. Dimensions: (C, N H, W) where: + - C: Color channels (3 for RGB) + - N: Number of frames (81) + - H: Frame height (from size) + - W: Frame width from size) + """ + # preprocess + F = frame_num + target_shape = (self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1, + size[1] // self.vae_stride[1], + size[0] // self.vae_stride[2]) + + seq_len = math.ceil((target_shape[2] * target_shape[3]) / + (self.patch_size[1] * self.patch_size[2]) * + target_shape[1] / self.sp_size) * self.sp_size + + if n_prompt == "": + n_prompt = self.sample_neg_prompt + seed = seed if seed >= 0 else random.randint(0, sys.maxsize) + seed_g = torch.Generator(device=self.device) + seed_g.manual_seed(seed) + + if not self.t5_cpu: + context = self.text_encoder([input_prompt], self.device) + context_null = self.text_encoder([n_prompt], self.device) + if offload_model: + self.text_encoder.model.cpu() + else: + context = self.text_encoder([input_prompt], torch.device('cpu')) + context_null = self.text_encoder([n_prompt], torch.device('cpu')) + context = [t.to(self.device) for t in context] + context_null = [t.to(self.device) for t in context_null] + + noise = [ + torch.randn( + target_shape[0], + target_shape[1], + target_shape[2], + target_shape[3], + dtype=torch.float32, + device=self.device, + generator=seed_g) + ] + + @contextmanager + def noop_no_sync(): + yield + + no_sync = getattr(self.model, 'no_sync', noop_no_sync) + + # evaluation mode + with amp.autocast(dtype=self.param_dtype), torch.no_grad(), no_sync(): + + if sample_solver == 'unipc': + sample_scheduler = FlowUniPCMultistepScheduler( + num_train_timesteps=self.num_train_timesteps, + shift=shift, + use_dynamic_shifting=False) + sample_scheduler.set_timesteps( + sampling_steps, device=self.device) + timesteps = sample_scheduler.timesteps + elif sample_solver == 'dpm++': + sample_scheduler = FlowDPMSolverMultistepScheduler( + num_train_timesteps=self.num_train_timesteps, + shift=1, + use_dynamic_shifting=False) + sampling_sigmas = get_sampling_sigmas(sampling_steps, shift) + timesteps, _ = retrieve_timesteps( + sample_scheduler, + device=self.device, + sigmas=sampling_sigmas) + elif sample_solver == 'euler': + sample_scheduler = FlowMatchEulerDiscreteScheduler( + num_train_timesteps=self.num_train_timesteps, + shift=shift, + use_dynamic_shifting=False) + sample_scheduler.set_timesteps( + sampling_steps, device=self.device) + timesteps = sample_scheduler.timesteps + if few_step: + start_latent_list = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50] + sample_scheduler.sigmas = sample_scheduler.sigmas[start_latent_list] + num_inference_steps = len(start_latent_list) - 1 + timesteps = timesteps[start_latent_list[:num_inference_steps]] + else: + raise NotImplementedError("Unsupported solver.") + + sigmas = sample_scheduler.sigmas + print('inference timesteps and sigmas:', timesteps, sigmas, timesteps.dtype) + # sample videos + latents = noise + + arg_c = {'context': context, 'seq_len': seq_len} + arg_null = {'context': context_null, 'seq_len': seq_len} + + for _, t in enumerate(tqdm(timesteps)): + latent_model_input = latents + + timestep = [t] + timestep = torch.stack(timestep).to(self.device) + + noise_pred_cond = self.model( + latent_model_input, t=timestep, **arg_c)[0] + if few_step and no_cfg: + noise_pred = noise_pred_cond + else: + noise_pred_uncond = self.model( + latent_model_input, t=timestep, **arg_null)[0] + + noise_pred = noise_pred_uncond + guide_scale * ( + noise_pred_cond - noise_pred_uncond) + + temp_x0 = sample_scheduler.step( + noise_pred.unsqueeze(0), + t, + latents[0].unsqueeze(0), + return_dict=False, + generator=seed_g)[0] + latents = [temp_x0.squeeze(0)] + + x0 = latents + if offload_model: + self.model.cpu() + torch.cuda.empty_cache() + if self.rank == 0: + if offload_model: + self.vae.model.to(self.device) + videos = self.vae.decode(x0) + + del noise, latents + del sample_scheduler + if offload_model: + gc.collect() + torch.cuda.synchronize() + if dist.is_initialized(): + dist.barrier() + + return videos[0], context if self.rank == 0 else None diff --git a/exp_code/1_benchmark/AccVideo/models/wan/utils/__init__.py b/exp_code/1_benchmark/AccVideo/models/wan/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6e9a339e69fd55dd226d3ce242613c19bd690522 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/utils/__init__.py @@ -0,0 +1,8 @@ +from .fm_solvers import (FlowDPMSolverMultistepScheduler, get_sampling_sigmas, + retrieve_timesteps) +from .fm_solvers_unipc import FlowUniPCMultistepScheduler + +__all__ = [ + 'HuggingfaceTokenizer', 'get_sampling_sigmas', 'retrieve_timesteps', + 'FlowDPMSolverMultistepScheduler', 'FlowUniPCMultistepScheduler' +] diff --git a/exp_code/1_benchmark/AccVideo/models/wan/utils/fm_euler.py b/exp_code/1_benchmark/AccVideo/models/wan/utils/fm_euler.py new file mode 100644 index 0000000000000000000000000000000000000000..19bd8183e8046e5c7996bc51c9196bb3356bed26 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/utils/fm_euler.py @@ -0,0 +1,548 @@ +# Copyright 2024 Stability AI, Katherine Crowson and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.utils import BaseOutput, is_scipy_available, logging +from diffusers.schedulers.scheduling_utils import SchedulerMixin + + +if is_scipy_available(): + import scipy.stats + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class FlowMatchEulerDiscreteSchedulerOutput(BaseOutput): + """ + Output class for the scheduler's `step` function output. + + Args: + prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the + denoising loop. + """ + + prev_sample: torch.FloatTensor + + +class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin): + """ + Euler scheduler. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + shift (`float`, defaults to 1.0): + The shift value for the timestep schedule. + use_dynamic_shifting (`bool`, defaults to False): + Whether to apply timestep shifting on-the-fly based on the image resolution. + base_shift (`float`, defaults to 0.5): + Value to stabilize image generation. Increasing `base_shift` reduces variation and image is more consistent + with desired output. + max_shift (`float`, defaults to 1.15): + Value change allowed to latent vectors. Increasing `max_shift` encourages more variation and image may be + more exaggerated or stylized. + base_image_seq_len (`int`, defaults to 256): + The base image sequence length. + max_image_seq_len (`int`, defaults to 4096): + The maximum image sequence length. + invert_sigmas (`bool`, defaults to False): + Whether to invert the sigmas. + shift_terminal (`float`, defaults to None): + The end value of the shifted timestep schedule. + use_karras_sigmas (`bool`, defaults to False): + Whether to use Karras sigmas for step sizes in the noise schedule during sampling. + use_exponential_sigmas (`bool`, defaults to False): + Whether to use exponential sigmas for step sizes in the noise schedule during sampling. + use_beta_sigmas (`bool`, defaults to False): + Whether to use beta sigmas for step sizes in the noise schedule during sampling. + time_shift_type (`str`, defaults to "exponential"): + The type of dynamic resolution-dependent timestep shifting to apply. Either "exponential" or "linear". + """ + + _compatibles = [] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + shift: float = 1.0, + use_dynamic_shifting: bool = False, + base_shift: Optional[float] = 0.5, + max_shift: Optional[float] = 1.15, + base_image_seq_len: Optional[int] = 256, + max_image_seq_len: Optional[int] = 4096, + invert_sigmas: bool = False, + shift_terminal: Optional[float] = None, + use_karras_sigmas: Optional[bool] = False, + use_exponential_sigmas: Optional[bool] = False, + use_beta_sigmas: Optional[bool] = False, + time_shift_type: str = "exponential", + ): + if self.config.use_beta_sigmas and not is_scipy_available(): + raise ImportError("Make sure to install scipy if you want to use beta sigmas.") + if sum([self.config.use_beta_sigmas, self.config.use_exponential_sigmas, self.config.use_karras_sigmas]) > 1: + raise ValueError( + "Only one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used." + ) + if time_shift_type not in {"exponential", "linear"}: + raise ValueError("`time_shift_type` must either be 'exponential' or 'linear'.") + + timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy() + timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32) + + sigmas = timesteps / num_train_timesteps + if not use_dynamic_shifting: + # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution + sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) + + self.timesteps = sigmas * num_train_timesteps + + self._step_index = None + self._begin_index = None + + self._shift = shift + + self.sigmas = sigmas.to("cpu") # to avoid too much CPU/GPU communication + self.sigma_min = self.sigmas[-1].item() + self.sigma_max = self.sigmas[0].item() + + @property + def shift(self): + """ + The value used for shifting. + """ + return self._shift + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + def set_shift(self, shift: float): + self._shift = shift + + def scale_noise( + self, + sample: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + noise: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + """ + Forward process in flow-matching + + Args: + sample (`torch.FloatTensor`): + The input sample. + timestep (`int`, *optional*): + The current timestep in the diffusion chain. + + Returns: + `torch.FloatTensor`: + A scaled input sample. + """ + # Make sure sigmas and timesteps have the same device and dtype as original_samples + sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype) + + if sample.device.type == "mps" and torch.is_floating_point(timestep): + # mps does not support float64 + schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32) + timestep = timestep.to(sample.device, dtype=torch.float32) + else: + schedule_timesteps = self.timesteps.to(sample.device) + timestep = timestep.to(sample.device) + + # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index + if self.begin_index is None: + step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep] + elif self.step_index is not None: + # add_noise is called after first denoising step (for inpainting) + step_indices = [self.step_index] * timestep.shape[0] + else: + # add noise is called before first denoising step to create initial latent(img2img) + step_indices = [self.begin_index] * timestep.shape[0] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < len(sample.shape): + sigma = sigma.unsqueeze(-1) + + sample = sigma * noise + (1.0 - sigma) * sample + + return sample + + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def time_shift(self, mu: float, sigma: float, t: torch.Tensor): + if self.config.time_shift_type == "exponential": + return self._time_shift_exponential(mu, sigma, t) + elif self.config.time_shift_type == "linear": + return self._time_shift_linear(mu, sigma, t) + + def stretch_shift_to_terminal(self, t: torch.Tensor) -> torch.Tensor: + r""" + Stretches and shifts the timestep schedule to ensure it terminates at the configured `shift_terminal` config + value. + + Reference: + https://github.com/Lightricks/LTX-Video/blob/a01a171f8fe3d99dce2728d60a73fecf4d4238ae/ltx_video/schedulers/rf.py#L51 + + Args: + t (`torch.Tensor`): + A tensor of timesteps to be stretched and shifted. + + Returns: + `torch.Tensor`: + A tensor of adjusted timesteps such that the final value equals `self.config.shift_terminal`. + """ + one_minus_z = 1 - t + scale_factor = one_minus_z[-1] / (1 - self.config.shift_terminal) + stretched_t = 1 - (one_minus_z / scale_factor) + return stretched_t + + def set_timesteps( + self, + num_inference_steps: Optional[int] = None, + device: Union[str, torch.device] = None, + sigmas: Optional[List[float]] = None, + mu: Optional[float] = None, + timesteps: Optional[List[float]] = None, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`, *optional*): + The number of diffusion steps used when generating samples with a pre-trained model. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + sigmas (`List[float]`, *optional*): + Custom values for sigmas to be used for each diffusion step. If `None`, the sigmas are computed + automatically. + mu (`float`, *optional*): + Determines the amount of shifting applied to sigmas when performing resolution-dependent timestep + shifting. + timesteps (`List[float]`, *optional*): + Custom values for timesteps to be used for each diffusion step. If `None`, the timesteps are computed + automatically. + """ + if self.config.use_dynamic_shifting and mu is None: + raise ValueError("`mu` must be passed when `use_dynamic_shifting` is set to be `True`") + + if sigmas is not None and timesteps is not None: + if len(sigmas) != len(timesteps): + raise ValueError("`sigmas` and `timesteps` should have the same length") + + if num_inference_steps is not None: + if (sigmas is not None and len(sigmas) != num_inference_steps) or ( + timesteps is not None and len(timesteps) != num_inference_steps + ): + raise ValueError( + "`sigmas` and `timesteps` should have the same length as num_inference_steps, if `num_inference_steps` is provided" + ) + else: + num_inference_steps = len(sigmas) if sigmas is not None else len(timesteps) + + self.num_inference_steps = num_inference_steps + + # 1. Prepare default sigmas + is_timesteps_provided = timesteps is not None + + if is_timesteps_provided: + timesteps = np.array(timesteps).astype(np.float32) + + if sigmas is None: + if timesteps is None: + timesteps = np.linspace( + self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps + ) + sigmas = timesteps / self.config.num_train_timesteps + else: + sigmas = np.array(sigmas).astype(np.float32) + num_inference_steps = len(sigmas) + + # 2. Perform timestep shifting. Either no shifting is applied, or resolution-dependent shifting of + # "exponential" or "linear" type is applied + if self.config.use_dynamic_shifting: + sigmas = self.time_shift(mu, 1.0, sigmas) + else: + sigmas = self.shift * sigmas / (1 + (self.shift - 1) * sigmas) + + # 3. If required, stretch the sigmas schedule to terminate at the configured `shift_terminal` value + if self.config.shift_terminal: + sigmas = self.stretch_shift_to_terminal(sigmas) + + # 4. If required, convert sigmas to one of karras, exponential, or beta sigma schedules + if self.config.use_karras_sigmas: + sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps) + elif self.config.use_exponential_sigmas: + sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps) + elif self.config.use_beta_sigmas: + sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps) + + # 5. Convert sigmas and timesteps to tensors and move to specified device + sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device) + if not is_timesteps_provided: + timesteps = sigmas * self.config.num_train_timesteps + else: + timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32, device=device) + + # 6. Append the terminal sigma value. + # If a model requires inverted sigma schedule for denoising but timesteps without inversion, the + # `invert_sigmas` flag can be set to `True`. This case is only required in Mochi + if self.config.invert_sigmas: + sigmas = 1.0 - sigmas + timesteps = sigmas * self.config.num_train_timesteps + sigmas = torch.cat([sigmas, torch.ones(1, device=sigmas.device)]) + else: + sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)]) + + self.timesteps = timesteps + self.sigmas = sigmas + self._step_index = None + self._begin_index = None + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + def step( + self, + model_output: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + sample: torch.FloatTensor, + s_churn: float = 0.0, + s_tmin: float = 0.0, + s_tmax: float = float("inf"), + s_noise: float = 1.0, + generator: Optional[torch.Generator] = None, + per_token_timesteps: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[FlowMatchEulerDiscreteSchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + s_churn (`float`): + s_tmin (`float`): + s_tmax (`float`): + s_noise (`float`, defaults to 1.0): + Scaling factor for noise added to the sample. + generator (`torch.Generator`, *optional*): + A random number generator. + per_token_timesteps (`torch.Tensor`, *optional*): + The timesteps for each token in the sample. + return_dict (`bool`): + Whether or not to return a + [`~schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteSchedulerOutput`] or tuple. + + Returns: + [`~schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteSchedulerOutput`] or `tuple`: + If return_dict is `True`, + [`~schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteSchedulerOutput`] is returned, + otherwise a tuple is returned where the first element is the sample tensor. + """ + + if ( + isinstance(timestep, int) + or isinstance(timestep, torch.IntTensor) + or isinstance(timestep, torch.LongTensor) + ): + raise ValueError( + ( + "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `FlowMatchEulerDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep." + ), + ) + + if self.step_index is None: + self._init_step_index(timestep) + + print('step:', self.step_index) + + # Upcast to avoid precision issues when computing prev_sample + sample = sample.to(torch.float32) + + if per_token_timesteps is not None: + per_token_sigmas = per_token_timesteps / self.config.num_train_timesteps + + sigmas = self.sigmas[:, None, None] + lower_mask = sigmas < per_token_sigmas[None] - 1e-6 + lower_sigmas = lower_mask * sigmas + lower_sigmas, _ = lower_sigmas.max(dim=0) + dt = (per_token_sigmas - lower_sigmas)[..., None] + else: + sigma = self.sigmas[self.step_index] + sigma_next = self.sigmas[self.step_index + 1] + dt = sigma_next - sigma + + prev_sample = sample + dt * model_output + + # upon completion increase step index by one + self._step_index += 1 + if per_token_timesteps is None: + # Cast sample back to model compatible dtype + prev_sample = prev_sample.to(model_output.dtype) + + if not return_dict: + return (prev_sample,) + + return FlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample) + + # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras + def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor: + """Constructs the noise schedule of Karras et al. (2022).""" + + # Hack to make sure that other schedulers which copy this function don't break + # TODO: Add this logic to the other schedulers + if hasattr(self.config, "sigma_min"): + sigma_min = self.config.sigma_min + else: + sigma_min = None + + if hasattr(self.config, "sigma_max"): + sigma_max = self.config.sigma_max + else: + sigma_max = None + + sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item() + sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item() + + rho = 7.0 # 7.0 is the value used in the paper + ramp = np.linspace(0, 1, num_inference_steps) + min_inv_rho = sigma_min ** (1 / rho) + max_inv_rho = sigma_max ** (1 / rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho + return sigmas + + # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_exponential + def _convert_to_exponential(self, in_sigmas: torch.Tensor, num_inference_steps: int) -> torch.Tensor: + """Constructs an exponential noise schedule.""" + + # Hack to make sure that other schedulers which copy this function don't break + # TODO: Add this logic to the other schedulers + if hasattr(self.config, "sigma_min"): + sigma_min = self.config.sigma_min + else: + sigma_min = None + + if hasattr(self.config, "sigma_max"): + sigma_max = self.config.sigma_max + else: + sigma_max = None + + sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item() + sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item() + + sigmas = np.exp(np.linspace(math.log(sigma_max), math.log(sigma_min), num_inference_steps)) + return sigmas + + # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_beta + def _convert_to_beta( + self, in_sigmas: torch.Tensor, num_inference_steps: int, alpha: float = 0.6, beta: float = 0.6 + ) -> torch.Tensor: + """From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024)""" + + # Hack to make sure that other schedulers which copy this function don't break + # TODO: Add this logic to the other schedulers + if hasattr(self.config, "sigma_min"): + sigma_min = self.config.sigma_min + else: + sigma_min = None + + if hasattr(self.config, "sigma_max"): + sigma_max = self.config.sigma_max + else: + sigma_max = None + + sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item() + sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item() + + sigmas = np.array( + [ + sigma_min + (ppf * (sigma_max - sigma_min)) + for ppf in [ + scipy.stats.beta.ppf(timestep, alpha, beta) + for timestep in 1 - np.linspace(0, 1, num_inference_steps) + ] + ] + ) + return sigmas + + def _time_shift_exponential(self, mu, sigma, t): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + + def _time_shift_linear(self, mu, sigma, t): + return mu / (mu + (1 / t - 1) ** sigma) + + def __len__(self): + return self.config.num_train_timesteps \ No newline at end of file diff --git a/exp_code/1_benchmark/AccVideo/models/wan/utils/fm_solvers.py b/exp_code/1_benchmark/AccVideo/models/wan/utils/fm_solvers.py new file mode 100644 index 0000000000000000000000000000000000000000..c908969e24849ce1381a8df9d5eb401dccf66524 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/utils/fm_solvers.py @@ -0,0 +1,857 @@ +# Copied from https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py +# Convert dpm solver for flow matching +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. + +import inspect +import math +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.schedulers.scheduling_utils import (KarrasDiffusionSchedulers, + SchedulerMixin, + SchedulerOutput) +from diffusers.utils import deprecate, is_scipy_available +from diffusers.utils.torch_utils import randn_tensor + +if is_scipy_available(): + pass + + +def get_sampling_sigmas(sampling_steps, shift): + sigma = np.linspace(1, 0, sampling_steps + 1)[:sampling_steps] + sigma = (shift * sigma / (1 + (shift - 1) * sigma)) + + return sigma + + +def retrieve_timesteps( + scheduler, + num_inference_steps=None, + device=None, + timesteps=None, + sigmas=None, + **kwargs, +): + if timesteps is not None and sigmas is not None: + raise ValueError( + "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" + ) + if timesteps is not None: + accepts_timesteps = "timesteps" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +class FlowDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin): + """ + `FlowDPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs. + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. This determines the resolution of the diffusion process. + solver_order (`int`, defaults to 2): + The DPMSolver order which can be `1`, `2`, or `3`. It is recommended to use `solver_order=2` for guided + sampling, and `solver_order=3` for unconditional sampling. This affects the number of model outputs stored + and used in multistep updates. + prediction_type (`str`, defaults to "flow_prediction"): + Prediction type of the scheduler function; must be `flow_prediction` for this scheduler, which predicts + the flow of the diffusion process. + shift (`float`, *optional*, defaults to 1.0): + A factor used to adjust the sigmas in the noise schedule. It modifies the step sizes during the sampling + process. + use_dynamic_shifting (`bool`, defaults to `False`): + Whether to apply dynamic shifting to the timesteps based on image resolution. If `True`, the shifting is + applied on the fly. + thresholding (`bool`, defaults to `False`): + Whether to use the "dynamic thresholding" method. This method adjusts the predicted sample to prevent + saturation and improve photorealism. + dynamic_thresholding_ratio (`float`, defaults to 0.995): + The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. + sample_max_value (`float`, defaults to 1.0): + The threshold value for dynamic thresholding. Valid only when `thresholding=True` and + `algorithm_type="dpmsolver++"`. + algorithm_type (`str`, defaults to `dpmsolver++`): + Algorithm type for the solver; can be `dpmsolver`, `dpmsolver++`, `sde-dpmsolver` or `sde-dpmsolver++`. The + `dpmsolver` type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927) + paper, and the `dpmsolver++` type implements the algorithms in the + [DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is recommended to use `dpmsolver++` or + `sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion. + solver_type (`str`, defaults to `midpoint`): + Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the + sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers. + lower_order_final (`bool`, defaults to `True`): + Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can + stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10. + euler_at_final (`bool`, defaults to `False`): + Whether to use Euler's method in the final step. It is a trade-off between numerical stability and detail + richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference + steps, but sometimes may result in blurring. + final_sigmas_type (`str`, *optional*, defaults to "zero"): + The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final + sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0. + lambda_min_clipped (`float`, defaults to `-inf`): + Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the + cosine (`squaredcos_cap_v2`) noise schedule. + variance_type (`str`, *optional*): + Set to "learned" or "learned_range" for diffusion models that predict variance. If set, the model's output + contains the predicted Gaussian variance. + """ + + _compatibles = [e.name for e in KarrasDiffusionSchedulers] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + solver_order: int = 2, + prediction_type: str = "flow_prediction", + shift: Optional[float] = 1.0, + use_dynamic_shifting=False, + thresholding: bool = False, + dynamic_thresholding_ratio: float = 0.995, + sample_max_value: float = 1.0, + algorithm_type: str = "dpmsolver++", + solver_type: str = "midpoint", + lower_order_final: bool = True, + euler_at_final: bool = False, + final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min" + lambda_min_clipped: float = -float("inf"), + variance_type: Optional[str] = None, + invert_sigmas: bool = False, + ): + if algorithm_type in ["dpmsolver", "sde-dpmsolver"]: + deprecation_message = f"algorithm_type {algorithm_type} is deprecated and will be removed in a future version. Choose from `dpmsolver++` or `sde-dpmsolver++` instead" + deprecate("algorithm_types dpmsolver and sde-dpmsolver", "1.0.0", + deprecation_message) + + # settings for DPM-Solver + if algorithm_type not in [ + "dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++" + ]: + if algorithm_type == "deis": + self.register_to_config(algorithm_type="dpmsolver++") + else: + raise NotImplementedError( + f"{algorithm_type} is not implemented for {self.__class__}") + + if solver_type not in ["midpoint", "heun"]: + if solver_type in ["logrho", "bh1", "bh2"]: + self.register_to_config(solver_type="midpoint") + else: + raise NotImplementedError( + f"{solver_type} is not implemented for {self.__class__}") + + if algorithm_type not in ["dpmsolver++", "sde-dpmsolver++" + ] and final_sigmas_type == "zero": + raise ValueError( + f"`final_sigmas_type` {final_sigmas_type} is not supported for `algorithm_type` {algorithm_type}. Please choose `sigma_min` instead." + ) + + # setable values + self.num_inference_steps = None + alphas = np.linspace(1, 1 / num_train_timesteps, + num_train_timesteps)[::-1].copy() + sigmas = 1.0 - alphas + sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32) + + if not use_dynamic_shifting: + # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution + sigmas = shift * sigmas / (1 + + (shift - 1) * sigmas) # pyright: ignore + + self.sigmas = sigmas + self.timesteps = sigmas * num_train_timesteps + + self.model_outputs = [None] * solver_order + self.lower_order_nums = 0 + self._step_index = None + self._begin_index = None + + # self.sigmas = self.sigmas.to( + # "cpu") # to avoid too much CPU/GPU communication + self.sigma_min = self.sigmas[-1].item() + self.sigma_max = self.sigmas[0].item() + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + # Modified from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.set_timesteps + def set_timesteps( + self, + num_inference_steps: Union[int, None] = None, + device: Union[str, torch.device] = None, + sigmas: Optional[List[float]] = None, + mu: Optional[Union[float, None]] = None, + shift: Optional[Union[float, None]] = None, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + Args: + num_inference_steps (`int`): + Total number of the spacing of the time steps. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + """ + + if self.config.use_dynamic_shifting and mu is None: + raise ValueError( + " you have to pass a value for `mu` when `use_dynamic_shifting` is set to be `True`" + ) + + if sigmas is None: + sigmas = np.linspace(self.sigma_max, self.sigma_min, + num_inference_steps + + 1).copy()[:-1] # pyright: ignore + + if self.config.use_dynamic_shifting: + sigmas = self.time_shift(mu, 1.0, sigmas) # pyright: ignore + else: + if shift is None: + shift = self.config.shift + sigmas = shift * sigmas / (1 + + (shift - 1) * sigmas) # pyright: ignore + + if self.config.final_sigmas_type == "sigma_min": + sigma_last = ((1 - self.alphas_cumprod[0]) / + self.alphas_cumprod[0])**0.5 + elif self.config.final_sigmas_type == "zero": + sigma_last = 0 + else: + raise ValueError( + f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}" + ) + + timesteps = sigmas * self.config.num_train_timesteps + sigmas = np.concatenate([sigmas, [sigma_last] + ]).astype(np.float32) # pyright: ignore + + self.sigmas = torch.from_numpy(sigmas) + self.timesteps = torch.from_numpy(timesteps).to( + device=device, dtype=torch.int64) + + self.num_inference_steps = len(timesteps) + + self.model_outputs = [ + None, + ] * self.config.solver_order + self.lower_order_nums = 0 + + self._step_index = None + self._begin_index = None + # self.sigmas = self.sigmas.to( + # "cpu") # to avoid too much CPU/GPU communication + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample + def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor: + """ + "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the + prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by + s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing + pixels from saturation at each step. We find that dynamic thresholding results in significantly better + photorealism as well as better image-text alignment, especially when using very large guidance weights." + https://arxiv.org/abs/2205.11487 + """ + dtype = sample.dtype + batch_size, channels, *remaining_dims = sample.shape + + if dtype not in (torch.float32, torch.float64): + sample = sample.float( + ) # upcast for quantile calculation, and clamp not implemented for cpu half + + # Flatten sample for doing quantile calculation along each image + sample = sample.reshape(batch_size, channels * np.prod(remaining_dims)) + + abs_sample = sample.abs() # "a certain percentile absolute pixel value" + + s = torch.quantile( + abs_sample, self.config.dynamic_thresholding_ratio, dim=1) + s = torch.clamp( + s, min=1, max=self.config.sample_max_value + ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] + s = s.unsqueeze( + 1) # (batch_size, 1) because clamp will broadcast along dim=0 + sample = torch.clamp( + sample, -s, s + ) / s # "we threshold xt0 to the range [-s, s] and then divide by s" + + sample = sample.reshape(batch_size, channels, *remaining_dims) + sample = sample.to(dtype) + + return sample + + # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._sigma_to_t + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def _sigma_to_alpha_sigma_t(self, sigma): + return 1 - sigma, sigma + + # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.set_timesteps + def time_shift(self, mu: float, sigma: float, t: torch.Tensor): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1)**sigma) + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.convert_model_output + def convert_model_output( + self, + model_output: torch.Tensor, + *args, + sample: torch.Tensor = None, + **kwargs, + ) -> torch.Tensor: + """ + Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is + designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an + integral of the data prediction model. + + The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise + prediction and data prediction models. + + Args: + model_output (`torch.Tensor`): + The direct output from the learned diffusion model. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + Returns: + `torch.Tensor`: + The converted model output. + """ + timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None) + if sample is None: + if len(args) > 1: + sample = args[1] + else: + raise ValueError( + "missing `sample` as a required keyward argument") + if timestep is not None: + deprecate( + "timesteps", + "1.0.0", + "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + # DPM-Solver++ needs to solve an integral of the data prediction model. + if self.config.algorithm_type in ["dpmsolver++", "sde-dpmsolver++"]: + if self.config.prediction_type == "flow_prediction": + sigma_t = self.sigmas[self.step_index] + x0_pred = sample - sigma_t * model_output + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," + " `v_prediction`, or `flow_prediction` for the FlowDPMSolverMultistepScheduler." + ) + + if self.config.thresholding: + x0_pred = self._threshold_sample(x0_pred) + + return x0_pred + + # DPM-Solver needs to solve an integral of the noise prediction model. + elif self.config.algorithm_type in ["dpmsolver", "sde-dpmsolver"]: + if self.config.prediction_type == "flow_prediction": + sigma_t = self.sigmas[self.step_index] + epsilon = sample - (1 - sigma_t) * model_output + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," + " `v_prediction` or `flow_prediction` for the FlowDPMSolverMultistepScheduler." + ) + + if self.config.thresholding: + sigma_t = self.sigmas[self.step_index] + x0_pred = sample - sigma_t * model_output + x0_pred = self._threshold_sample(x0_pred) + epsilon = model_output + x0_pred + + return epsilon + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.dpm_solver_first_order_update + def dpm_solver_first_order_update( + self, + model_output: torch.Tensor, + *args, + sample: torch.Tensor = None, + noise: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + """ + One step for the first-order DPMSolver (equivalent to DDIM). + Args: + model_output (`torch.Tensor`): + The direct output from the learned diffusion model. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + Returns: + `torch.Tensor`: + The sample tensor at the previous timestep. + """ + timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None) + prev_timestep = args[1] if len(args) > 1 else kwargs.pop( + "prev_timestep", None) + if sample is None: + if len(args) > 2: + sample = args[2] + else: + raise ValueError( + " missing `sample` as a required keyward argument") + if timestep is not None: + deprecate( + "timesteps", + "1.0.0", + "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + if prev_timestep is not None: + deprecate( + "prev_timestep", + "1.0.0", + "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[ + self.step_index] # pyright: ignore + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) + alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s) + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s = torch.log(alpha_s) - torch.log(sigma_s) + + h = lambda_t - lambda_s + if self.config.algorithm_type == "dpmsolver++": + x_t = (sigma_t / + sigma_s) * sample - (alpha_t * + (torch.exp(-h) - 1.0)) * model_output + elif self.config.algorithm_type == "dpmsolver": + x_t = (alpha_t / + alpha_s) * sample - (sigma_t * + (torch.exp(h) - 1.0)) * model_output + elif self.config.algorithm_type == "sde-dpmsolver++": + assert noise is not None + x_t = ((sigma_t / sigma_s * torch.exp(-h)) * sample + + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output + + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise) + elif self.config.algorithm_type == "sde-dpmsolver": + assert noise is not None + x_t = ((alpha_t / alpha_s) * sample - 2.0 * + (sigma_t * (torch.exp(h) - 1.0)) * model_output + + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise) + return x_t # pyright: ignore + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_second_order_update + def multistep_dpm_solver_second_order_update( + self, + model_output_list: List[torch.Tensor], + *args, + sample: torch.Tensor = None, + noise: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + """ + One step for the second-order multistep DPMSolver. + Args: + model_output_list (`List[torch.Tensor]`): + The direct outputs from learned diffusion model at current and latter timesteps. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + Returns: + `torch.Tensor`: + The sample tensor at the previous timestep. + """ + timestep_list = args[0] if len(args) > 0 else kwargs.pop( + "timestep_list", None) + prev_timestep = args[1] if len(args) > 1 else kwargs.pop( + "prev_timestep", None) + if sample is None: + if len(args) > 2: + sample = args[2] + else: + raise ValueError( + " missing `sample` as a required keyward argument") + if timestep_list is not None: + deprecate( + "timestep_list", + "1.0.0", + "Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + if prev_timestep is not None: + deprecate( + "prev_timestep", + "1.0.0", + "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + sigma_t, sigma_s0, sigma_s1 = ( + self.sigmas[self.step_index + 1], # pyright: ignore + self.sigmas[self.step_index], + self.sigmas[self.step_index - 1], # pyright: ignore + ) + + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) + alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) + alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1) + + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) + lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) + + m0, m1 = model_output_list[-1], model_output_list[-2] + + h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 + r0 = h_0 / h + D0, D1 = m0, (1.0 / r0) * (m0 - m1) + if self.config.algorithm_type == "dpmsolver++": + # See https://arxiv.org/abs/2211.01095 for detailed derivations + if self.config.solver_type == "midpoint": + x_t = ((sigma_t / sigma_s0) * sample - + (alpha_t * (torch.exp(-h) - 1.0)) * D0 - 0.5 * + (alpha_t * (torch.exp(-h) - 1.0)) * D1) + elif self.config.solver_type == "heun": + x_t = ((sigma_t / sigma_s0) * sample - + (alpha_t * (torch.exp(-h) - 1.0)) * D0 + + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1) + elif self.config.algorithm_type == "dpmsolver": + # See https://arxiv.org/abs/2206.00927 for detailed derivations + if self.config.solver_type == "midpoint": + x_t = ((alpha_t / alpha_s0) * sample - + (sigma_t * (torch.exp(h) - 1.0)) * D0 - 0.5 * + (sigma_t * (torch.exp(h) - 1.0)) * D1) + elif self.config.solver_type == "heun": + x_t = ((alpha_t / alpha_s0) * sample - + (sigma_t * (torch.exp(h) - 1.0)) * D0 - + (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1) + elif self.config.algorithm_type == "sde-dpmsolver++": + assert noise is not None + if self.config.solver_type == "midpoint": + x_t = ((sigma_t / sigma_s0 * torch.exp(-h)) * sample + + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + 0.5 * + (alpha_t * (1 - torch.exp(-2.0 * h))) * D1 + + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise) + elif self.config.solver_type == "heun": + x_t = ((sigma_t / sigma_s0 * torch.exp(-h)) * sample + + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / + (-2.0 * h) + 1.0)) * D1 + + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise) + elif self.config.algorithm_type == "sde-dpmsolver": + assert noise is not None + if self.config.solver_type == "midpoint": + x_t = ((alpha_t / alpha_s0) * sample - 2.0 * + (sigma_t * (torch.exp(h) - 1.0)) * D0 - + (sigma_t * (torch.exp(h) - 1.0)) * D1 + + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise) + elif self.config.solver_type == "heun": + x_t = ((alpha_t / alpha_s0) * sample - 2.0 * + (sigma_t * (torch.exp(h) - 1.0)) * D0 - 2.0 * + (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 + + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise) + return x_t # pyright: ignore + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_third_order_update + def multistep_dpm_solver_third_order_update( + self, + model_output_list: List[torch.Tensor], + *args, + sample: torch.Tensor = None, + **kwargs, + ) -> torch.Tensor: + """ + One step for the third-order multistep DPMSolver. + Args: + model_output_list (`List[torch.Tensor]`): + The direct outputs from learned diffusion model at current and latter timesteps. + sample (`torch.Tensor`): + A current instance of a sample created by diffusion process. + Returns: + `torch.Tensor`: + The sample tensor at the previous timestep. + """ + + timestep_list = args[0] if len(args) > 0 else kwargs.pop( + "timestep_list", None) + prev_timestep = args[1] if len(args) > 1 else kwargs.pop( + "prev_timestep", None) + if sample is None: + if len(args) > 2: + sample = args[2] + else: + raise ValueError( + " missing`sample` as a required keyward argument") + if timestep_list is not None: + deprecate( + "timestep_list", + "1.0.0", + "Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + if prev_timestep is not None: + deprecate( + "prev_timestep", + "1.0.0", + "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + sigma_t, sigma_s0, sigma_s1, sigma_s2 = ( + self.sigmas[self.step_index + 1], # pyright: ignore + self.sigmas[self.step_index], + self.sigmas[self.step_index - 1], # pyright: ignore + self.sigmas[self.step_index - 2], # pyright: ignore + ) + + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) + alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) + alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1) + alpha_s2, sigma_s2 = self._sigma_to_alpha_sigma_t(sigma_s2) + + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) + lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) + lambda_s2 = torch.log(alpha_s2) - torch.log(sigma_s2) + + m0, m1, m2 = model_output_list[-1], model_output_list[ + -2], model_output_list[-3] + + h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2 + r0, r1 = h_0 / h, h_1 / h + D0 = m0 + D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2) + D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1) + D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1) + if self.config.algorithm_type == "dpmsolver++": + # See https://arxiv.org/abs/2206.00927 for detailed derivations + x_t = ((sigma_t / sigma_s0) * sample - + (alpha_t * (torch.exp(-h) - 1.0)) * D0 + + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1 - + (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2) + elif self.config.algorithm_type == "dpmsolver": + # See https://arxiv.org/abs/2206.00927 for detailed derivations + x_t = ((alpha_t / alpha_s0) * sample - (sigma_t * + (torch.exp(h) - 1.0)) * D0 - + (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 - + (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2) + return x_t # pyright: ignore + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + """ + Initialize the step_index counter for the scheduler. + """ + + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + # Modified from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.step + def step( + self, + model_output: torch.Tensor, + timestep: Union[int, torch.Tensor], + sample: torch.Tensor, + generator=None, + variance_noise: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[SchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with + the multistep DPMSolver. + Args: + model_output (`torch.Tensor`): + The direct output from learned diffusion model. + timestep (`int`): + The current discrete timestep in the diffusion chain. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + generator (`torch.Generator`, *optional*): + A random number generator. + variance_noise (`torch.Tensor`): + Alternative to generating noise with `generator` by directly providing the noise for the variance + itself. Useful for methods such as [`LEdits++`]. + return_dict (`bool`): + Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`. + Returns: + [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a + tuple is returned where the first element is the sample tensor. + """ + if self.num_inference_steps is None: + raise ValueError( + "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" + ) + + if self.step_index is None: + self._init_step_index(timestep) + + # Improve numerical stability for small number of steps + lower_order_final = (self.step_index == len(self.timesteps) - 1) and ( + self.config.euler_at_final or + (self.config.lower_order_final and len(self.timesteps) < 15) or + self.config.final_sigmas_type == "zero") + lower_order_second = ((self.step_index == len(self.timesteps) - 2) and + self.config.lower_order_final and + len(self.timesteps) < 15) + + model_output = self.convert_model_output(model_output, sample=sample) + for i in range(self.config.solver_order - 1): + self.model_outputs[i] = self.model_outputs[i + 1] + self.model_outputs[-1] = model_output + + # Upcast to avoid precision issues when computing prev_sample + sample = sample.to(torch.float32) + if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++" + ] and variance_noise is None: + noise = randn_tensor( + model_output.shape, + generator=generator, + device=model_output.device, + dtype=torch.float32) + elif self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]: + noise = variance_noise.to( + device=model_output.device, + dtype=torch.float32) # pyright: ignore + else: + noise = None + + if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final: + prev_sample = self.dpm_solver_first_order_update( + model_output, sample=sample, noise=noise) + elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second: + prev_sample = self.multistep_dpm_solver_second_order_update( + self.model_outputs, sample=sample, noise=noise) + else: + prev_sample = self.multistep_dpm_solver_third_order_update( + self.model_outputs, sample=sample) + + if self.lower_order_nums < self.config.solver_order: + self.lower_order_nums += 1 + + # Cast sample back to expected dtype + prev_sample = prev_sample.to(model_output.dtype) + + # upon completion increase step index by one + self._step_index += 1 # pyright: ignore + + if not return_dict: + return (prev_sample,) + + return SchedulerOutput(prev_sample=prev_sample) + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.scale_model_input + def scale_model_input(self, sample: torch.Tensor, *args, + **kwargs) -> torch.Tensor: + """ + Ensures interchangeability with schedulers that need to scale the denoising model input depending on the + current timestep. + Args: + sample (`torch.Tensor`): + The input sample. + Returns: + `torch.Tensor`: + A scaled input sample. + """ + return sample + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.scale_model_input + def add_noise( + self, + original_samples: torch.Tensor, + noise: torch.Tensor, + timesteps: torch.IntTensor, + ) -> torch.Tensor: + # Make sure sigmas and timesteps have the same device and dtype as original_samples + sigmas = self.sigmas.to( + device=original_samples.device, dtype=original_samples.dtype) + if original_samples.device.type == "mps" and torch.is_floating_point( + timesteps): + # mps does not support float64 + schedule_timesteps = self.timesteps.to( + original_samples.device, dtype=torch.float32) + timesteps = timesteps.to( + original_samples.device, dtype=torch.float32) + else: + schedule_timesteps = self.timesteps.to(original_samples.device) + timesteps = timesteps.to(original_samples.device) + + # begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index + if self.begin_index is None: + step_indices = [ + self.index_for_timestep(t, schedule_timesteps) + for t in timesteps + ] + elif self.step_index is not None: + # add_noise is called after first denoising step (for inpainting) + step_indices = [self.step_index] * timesteps.shape[0] + else: + # add noise is called before first denoising step to create initial latent(img2img) + step_indices = [self.begin_index] * timesteps.shape[0] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < len(original_samples.shape): + sigma = sigma.unsqueeze(-1) + + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) + noisy_samples = alpha_t * original_samples + sigma_t * noise + return noisy_samples + + def __len__(self): + return self.config.num_train_timesteps diff --git a/exp_code/1_benchmark/AccVideo/models/wan/utils/fm_solvers_unipc.py b/exp_code/1_benchmark/AccVideo/models/wan/utils/fm_solvers_unipc.py new file mode 100644 index 0000000000000000000000000000000000000000..97ff6dde3f9028d789bba8d90a9016adadae91ad --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/utils/fm_solvers_unipc.py @@ -0,0 +1,801 @@ +# Copied from https://github.com/huggingface/diffusers/blob/v0.31.0/src/diffusers/schedulers/scheduling_unipc_multistep.py +# Convert unipc for flow matching +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. + +import math +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.schedulers.scheduling_utils import (KarrasDiffusionSchedulers, + SchedulerMixin, + SchedulerOutput) +from diffusers.utils import deprecate, is_scipy_available + +if is_scipy_available(): + import scipy.stats + + +class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin): + """ + `UniPCMultistepScheduler` is a training-free framework designed for the fast sampling of diffusion models. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + solver_order (`int`, default `2`): + The UniPC order which can be any positive integer. The effective order of accuracy is `solver_order + 1` + due to the UniC. It is recommended to use `solver_order=2` for guided sampling, and `solver_order=3` for + unconditional sampling. + prediction_type (`str`, defaults to "flow_prediction"): + Prediction type of the scheduler function; must be `flow_prediction` for this scheduler, which predicts + the flow of the diffusion process. + thresholding (`bool`, defaults to `False`): + Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such + as Stable Diffusion. + dynamic_thresholding_ratio (`float`, defaults to 0.995): + The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. + sample_max_value (`float`, defaults to 1.0): + The threshold value for dynamic thresholding. Valid only when `thresholding=True` and `predict_x0=True`. + predict_x0 (`bool`, defaults to `True`): + Whether to use the updating algorithm on the predicted x0. + solver_type (`str`, default `bh2`): + Solver type for UniPC. It is recommended to use `bh1` for unconditional sampling when steps < 10, and `bh2` + otherwise. + lower_order_final (`bool`, default `True`): + Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can + stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10. + disable_corrector (`list`, default `[]`): + Decides which step to disable the corrector to mitigate the misalignment between `epsilon_theta(x_t, c)` + and `epsilon_theta(x_t^c, c)` which can influence convergence for a large guidance scale. Corrector is + usually disabled during the first few steps. + solver_p (`SchedulerMixin`, default `None`): + Any other scheduler that if specified, the algorithm becomes `solver_p + UniC`. + use_karras_sigmas (`bool`, *optional*, defaults to `False`): + Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`, + the sigmas are determined according to a sequence of noise levels {σi}. + use_exponential_sigmas (`bool`, *optional*, defaults to `False`): + Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process. + timestep_spacing (`str`, defaults to `"linspace"`): + The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. + steps_offset (`int`, defaults to 0): + An offset added to the inference steps, as required by some model families. + final_sigmas_type (`str`, defaults to `"zero"`): + The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final + sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0. + """ + + _compatibles = [e.name for e in KarrasDiffusionSchedulers] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + solver_order: int = 2, + prediction_type: str = "flow_prediction", + shift: Optional[float] = 1.0, + use_dynamic_shifting=False, + thresholding: bool = False, + dynamic_thresholding_ratio: float = 0.995, + sample_max_value: float = 1.0, + predict_x0: bool = True, + solver_type: str = "bh2", + lower_order_final: bool = True, + disable_corrector: List[int] = [], + solver_p: SchedulerMixin = None, + timestep_spacing: str = "linspace", + steps_offset: int = 0, + final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min" + ): + + if solver_type not in ["bh1", "bh2"]: + if solver_type in ["midpoint", "heun", "logrho"]: + self.register_to_config(solver_type="bh2") + else: + raise NotImplementedError( + f"{solver_type} is not implemented for {self.__class__}") + + self.predict_x0 = predict_x0 + # setable values + self.num_inference_steps = None + alphas = np.linspace(1, 1 / num_train_timesteps, + num_train_timesteps)[::-1].copy() + sigmas = 1.0 - alphas + sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32) + + if not use_dynamic_shifting: + # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution + sigmas = shift * sigmas / (1 + + (shift - 1) * sigmas) # pyright: ignore + + self.sigmas = sigmas + self.timesteps = sigmas * num_train_timesteps + + self.model_outputs = [None] * solver_order + self.timestep_list = [None] * solver_order + self.lower_order_nums = 0 + self.disable_corrector = disable_corrector + self.solver_p = solver_p + self.last_sample = None + self._step_index = None + self._begin_index = None + + self.sigmas = self.sigmas.to( + "cpu") # to avoid too much CPU/GPU communication + self.sigma_min = self.sigmas[-1].item() + self.sigma_max = self.sigmas[0].item() + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + # Modified from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.set_timesteps + def set_timesteps( + self, + num_inference_steps: Union[int, None] = None, + device: Union[str, torch.device] = None, + sigmas: Optional[List[float]] = None, + mu: Optional[Union[float, None]] = None, + shift: Optional[Union[float, None]] = None, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + Args: + num_inference_steps (`int`): + Total number of the spacing of the time steps. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + """ + + if self.config.use_dynamic_shifting and mu is None: + raise ValueError( + " you have to pass a value for `mu` when `use_dynamic_shifting` is set to be `True`" + ) + + if sigmas is None: + sigmas = np.linspace(self.sigma_max, self.sigma_min, + num_inference_steps + + 1).copy()[:-1] # pyright: ignore + + if self.config.use_dynamic_shifting: + sigmas = self.time_shift(mu, 1.0, sigmas) # pyright: ignore + else: + if shift is None: + shift = self.config.shift + print('shift:', shift) + sigmas = shift * sigmas / (1 + + (shift - 1) * sigmas) # pyright: ignore + + if self.config.final_sigmas_type == "sigma_min": + sigma_last = ((1 - self.alphas_cumprod[0]) / + self.alphas_cumprod[0])**0.5 + elif self.config.final_sigmas_type == "zero": + sigma_last = 0 + else: + raise ValueError( + f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}" + ) + + timesteps = sigmas * self.config.num_train_timesteps + sigmas = np.concatenate([sigmas, [sigma_last] + ]).astype(np.float32) # pyright: ignore + + self.sigmas = torch.from_numpy(sigmas) + self.timesteps = torch.from_numpy(timesteps).to( + device=device, dtype=torch.int64) + + self.num_inference_steps = len(timesteps) + + self.model_outputs = [ + None, + ] * self.config.solver_order + self.lower_order_nums = 0 + self.last_sample = None + if self.solver_p: + self.solver_p.set_timesteps(self.num_inference_steps, device=device) + + # add an index counter for schedulers that allow duplicated timesteps + self._step_index = None + self._begin_index = None + self.sigmas = self.sigmas.to( + "cpu") # to avoid too much CPU/GPU communication + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample + def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor: + """ + "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the + prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by + s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing + pixels from saturation at each step. We find that dynamic thresholding results in significantly better + photorealism as well as better image-text alignment, especially when using very large guidance weights." + + https://arxiv.org/abs/2205.11487 + """ + dtype = sample.dtype + batch_size, channels, *remaining_dims = sample.shape + + if dtype not in (torch.float32, torch.float64): + sample = sample.float( + ) # upcast for quantile calculation, and clamp not implemented for cpu half + + # Flatten sample for doing quantile calculation along each image + sample = sample.reshape(batch_size, channels * np.prod(remaining_dims)) + + abs_sample = sample.abs() # "a certain percentile absolute pixel value" + + s = torch.quantile( + abs_sample, self.config.dynamic_thresholding_ratio, dim=1) + s = torch.clamp( + s, min=1, max=self.config.sample_max_value + ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] + s = s.unsqueeze( + 1) # (batch_size, 1) because clamp will broadcast along dim=0 + sample = torch.clamp( + sample, -s, s + ) / s # "we threshold xt0 to the range [-s, s] and then divide by s" + + sample = sample.reshape(batch_size, channels, *remaining_dims) + sample = sample.to(dtype) + + return sample + + # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._sigma_to_t + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def _sigma_to_alpha_sigma_t(self, sigma): + return 1 - sigma, sigma + + # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.set_timesteps + def time_shift(self, mu: float, sigma: float, t: torch.Tensor): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1)**sigma) + + def convert_model_output( + self, + model_output: torch.Tensor, + *args, + sample: torch.Tensor = None, + **kwargs, + ) -> torch.Tensor: + r""" + Convert the model output to the corresponding type the UniPC algorithm needs. + + Args: + model_output (`torch.Tensor`): + The direct output from the learned diffusion model. + timestep (`int`): + The current discrete timestep in the diffusion chain. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + + Returns: + `torch.Tensor`: + The converted model output. + """ + timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None) + if sample is None: + if len(args) > 1: + sample = args[1] + else: + raise ValueError( + "missing `sample` as a required keyward argument") + if timestep is not None: + deprecate( + "timesteps", + "1.0.0", + "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + sigma = self.sigmas[self.step_index] + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) + + if self.predict_x0: + if self.config.prediction_type == "flow_prediction": + sigma_t = self.sigmas[self.step_index] + x0_pred = sample - sigma_t * model_output + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," + " `v_prediction` or `flow_prediction` for the UniPCMultistepScheduler." + ) + + if self.config.thresholding: + x0_pred = self._threshold_sample(x0_pred) + + return x0_pred + else: + if self.config.prediction_type == "flow_prediction": + sigma_t = self.sigmas[self.step_index] + epsilon = sample - (1 - sigma_t) * model_output + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," + " `v_prediction` or `flow_prediction` for the UniPCMultistepScheduler." + ) + + if self.config.thresholding: + sigma_t = self.sigmas[self.step_index] + x0_pred = sample - sigma_t * model_output + x0_pred = self._threshold_sample(x0_pred) + epsilon = model_output + x0_pred + + return epsilon + + def multistep_uni_p_bh_update( + self, + model_output: torch.Tensor, + *args, + sample: torch.Tensor = None, + order: int = None, # pyright: ignore + **kwargs, + ) -> torch.Tensor: + """ + One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified. + + Args: + model_output (`torch.Tensor`): + The direct output from the learned diffusion model at the current timestep. + prev_timestep (`int`): + The previous discrete timestep in the diffusion chain. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + order (`int`): + The order of UniP at this timestep (corresponds to the *p* in UniPC-p). + + Returns: + `torch.Tensor`: + The sample tensor at the previous timestep. + """ + prev_timestep = args[0] if len(args) > 0 else kwargs.pop( + "prev_timestep", None) + if sample is None: + if len(args) > 1: + sample = args[1] + else: + raise ValueError( + " missing `sample` as a required keyward argument") + if order is None: + if len(args) > 2: + order = args[2] + else: + raise ValueError( + " missing `order` as a required keyward argument") + if prev_timestep is not None: + deprecate( + "prev_timestep", + "1.0.0", + "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + model_output_list = self.model_outputs + + s0 = self.timestep_list[-1] + m0 = model_output_list[-1] + x = sample + + if self.solver_p: + x_t = self.solver_p.step(model_output, s0, x).prev_sample + return x_t + + sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[ + self.step_index] # pyright: ignore + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) + alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) + + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) + + h = lambda_t - lambda_s0 + device = sample.device + + rks = [] + D1s = [] + for i in range(1, order): + si = self.step_index - i # pyright: ignore + mi = model_output_list[-(i + 1)] + alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si]) + lambda_si = torch.log(alpha_si) - torch.log(sigma_si) + rk = (lambda_si - lambda_s0) / h + rks.append(rk) + D1s.append((mi - m0) / rk) # pyright: ignore + + rks.append(1.0) + rks = torch.tensor(rks, device=device) + + R = [] + b = [] + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.config.solver_type == "bh1": + B_h = hh + elif self.config.solver_type == "bh2": + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= i + 1 + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.tensor(b, device=device) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + # for order 2, we use a simplified version + if order == 2: + rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device) + else: + rhos_p = torch.linalg.solve(R[:-1, :-1], + b[:-1]).to(device).to(x.dtype) + else: + D1s = None + + if self.predict_x0: + x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0 + if D1s is not None: + pred_res = torch.einsum("k,bkc...->bc...", rhos_p, + D1s) # pyright: ignore + else: + pred_res = 0 + x_t = x_t_ - alpha_t * B_h * pred_res + else: + x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0 + if D1s is not None: + pred_res = torch.einsum("k,bkc...->bc...", rhos_p, + D1s) # pyright: ignore + else: + pred_res = 0 + x_t = x_t_ - sigma_t * B_h * pred_res + + x_t = x_t.to(x.dtype) + return x_t + + def multistep_uni_c_bh_update( + self, + this_model_output: torch.Tensor, + *args, + last_sample: torch.Tensor = None, + this_sample: torch.Tensor = None, + order: int = None, # pyright: ignore + **kwargs, + ) -> torch.Tensor: + """ + One step for the UniC (B(h) version). + + Args: + this_model_output (`torch.Tensor`): + The model outputs at `x_t`. + this_timestep (`int`): + The current timestep `t`. + last_sample (`torch.Tensor`): + The generated sample before the last predictor `x_{t-1}`. + this_sample (`torch.Tensor`): + The generated sample after the last predictor `x_{t}`. + order (`int`): + The `p` of UniC-p at this step. The effective order of accuracy should be `order + 1`. + + Returns: + `torch.Tensor`: + The corrected sample tensor at the current timestep. + """ + this_timestep = args[0] if len(args) > 0 else kwargs.pop( + "this_timestep", None) + if last_sample is None: + if len(args) > 1: + last_sample = args[1] + else: + raise ValueError( + " missing`last_sample` as a required keyward argument") + if this_sample is None: + if len(args) > 2: + this_sample = args[2] + else: + raise ValueError( + " missing`this_sample` as a required keyward argument") + if order is None: + if len(args) > 3: + order = args[3] + else: + raise ValueError( + " missing`order` as a required keyward argument") + if this_timestep is not None: + deprecate( + "this_timestep", + "1.0.0", + "Passing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + model_output_list = self.model_outputs + + m0 = model_output_list[-1] + x = last_sample + x_t = this_sample + model_t = this_model_output + + sigma_t, sigma_s0 = self.sigmas[self.step_index], self.sigmas[ + self.step_index - 1] # pyright: ignore + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) + alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) + + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) + + h = lambda_t - lambda_s0 + device = this_sample.device + + rks = [] + D1s = [] + for i in range(1, order): + si = self.step_index - (i + 1) # pyright: ignore + mi = model_output_list[-(i + 1)] + alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si]) + lambda_si = torch.log(alpha_si) - torch.log(sigma_si) + rk = (lambda_si - lambda_s0) / h + rks.append(rk) + D1s.append((mi - m0) / rk) # pyright: ignore + + rks.append(1.0) + rks = torch.tensor(rks, device=device) + + R = [] + b = [] + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.config.solver_type == "bh1": + B_h = hh + elif self.config.solver_type == "bh2": + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= i + 1 + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.tensor(b, device=device) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) + else: + D1s = None + + # for order 1, we use a simplified version + if order == 1: + rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device) + else: + rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype) + + if self.predict_x0: + x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0 + if D1s is not None: + corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = model_t - m0 + x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t) + else: + x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0 + if D1s is not None: + corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = model_t - m0 + x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t) + x_t = x_t.to(x.dtype) + return x_t + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index + def _init_step_index(self, timestep): + """ + Initialize the step_index counter for the scheduler. + """ + + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + def step(self, + model_output: torch.Tensor, + timestep: Union[int, torch.Tensor], + sample: torch.Tensor, + return_dict: bool = True, + generator=None) -> Union[SchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with + the multistep UniPC. + + Args: + model_output (`torch.Tensor`): + The direct output from learned diffusion model. + timestep (`int`): + The current discrete timestep in the diffusion chain. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + return_dict (`bool`): + Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`. + + Returns: + [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a + tuple is returned where the first element is the sample tensor. + + """ + if self.num_inference_steps is None: + raise ValueError( + "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" + ) + + if self.step_index is None: + self._init_step_index(timestep) + + use_corrector = ( + self.step_index > 0 and + self.step_index - 1 not in self.disable_corrector and + self.last_sample is not None # pyright: ignore + ) + + model_output_convert = self.convert_model_output( + model_output, sample=sample) + if use_corrector: + sample = self.multistep_uni_c_bh_update( + this_model_output=model_output_convert, + last_sample=self.last_sample, + this_sample=sample, + order=self.this_order, + ) + + for i in range(self.config.solver_order - 1): + self.model_outputs[i] = self.model_outputs[i + 1] + self.timestep_list[i] = self.timestep_list[i + 1] + + self.model_outputs[-1] = model_output_convert + self.timestep_list[-1] = timestep # pyright: ignore + + if self.config.lower_order_final: + this_order = min(self.config.solver_order, + len(self.timesteps) - + self.step_index) # pyright: ignore + else: + this_order = self.config.solver_order + + self.this_order = min(this_order, + self.lower_order_nums + 1) # warmup for multistep + assert self.this_order > 0 + + self.last_sample = sample + prev_sample = self.multistep_uni_p_bh_update( + model_output=model_output, # pass the original non-converted model output, in case solver-p is used + sample=sample, + order=self.this_order, + ) + + if self.lower_order_nums < self.config.solver_order: + self.lower_order_nums += 1 + + # upon completion increase step index by one + self._step_index += 1 # pyright: ignore + + if not return_dict: + return (prev_sample,) + + return SchedulerOutput(prev_sample=prev_sample) + + def scale_model_input(self, sample: torch.Tensor, *args, + **kwargs) -> torch.Tensor: + """ + Ensures interchangeability with schedulers that need to scale the denoising model input depending on the + current timestep. + + Args: + sample (`torch.Tensor`): + The input sample. + + Returns: + `torch.Tensor`: + A scaled input sample. + """ + return sample + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.add_noise + def add_noise( + self, + original_samples: torch.Tensor, + noise: torch.Tensor, + timesteps: torch.IntTensor, + ) -> torch.Tensor: + # Make sure sigmas and timesteps have the same device and dtype as original_samples + sigmas = self.sigmas.to( + device=original_samples.device, dtype=original_samples.dtype) + if original_samples.device.type == "mps" and torch.is_floating_point( + timesteps): + # mps does not support float64 + schedule_timesteps = self.timesteps.to( + original_samples.device, dtype=torch.float32) + timesteps = timesteps.to( + original_samples.device, dtype=torch.float32) + else: + schedule_timesteps = self.timesteps.to(original_samples.device) + timesteps = timesteps.to(original_samples.device) + + # begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index + if self.begin_index is None: + step_indices = [ + self.index_for_timestep(t, schedule_timesteps) + for t in timesteps + ] + elif self.step_index is not None: + # add_noise is called after first denoising step (for inpainting) + step_indices = [self.step_index] * timesteps.shape[0] + else: + # add noise is called before first denoising step to create initial latent(img2img) + step_indices = [self.begin_index] * timesteps.shape[0] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < len(original_samples.shape): + sigma = sigma.unsqueeze(-1) + + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) + noisy_samples = alpha_t * original_samples + sigma_t * noise + return noisy_samples + + def __len__(self): + return self.config.num_train_timesteps diff --git a/exp_code/1_benchmark/AccVideo/models/wan/utils/prompt_extend.py b/exp_code/1_benchmark/AccVideo/models/wan/utils/prompt_extend.py new file mode 100644 index 0000000000000000000000000000000000000000..e2f280e5f0626151d18e26f6fcd0f1667efdf17b --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/utils/prompt_extend.py @@ -0,0 +1,544 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import json +import math +import os +import random +import sys +import tempfile +from dataclasses import dataclass +from http import HTTPStatus +from typing import Optional, Union + +import dashscope +import torch +from PIL import Image + +try: + from flash_attn import flash_attn_varlen_func + FLASH_VER = 2 +except ModuleNotFoundError: + flash_attn_varlen_func = None # in compatible with CPU machines + FLASH_VER = None + +LM_ZH_SYS_PROMPT = \ + '''你是一位Prompt优化师，旨在将用户输入改写为优质Prompt，使其更完整、更具表现力，同时不改变原意。\n''' \ + '''任务要求：\n''' \ + '''1. 对于过于简短的用户输入，在不改变原意前提下，合理推断并补充细节，使得画面更加完整好看；\n''' \ + '''2. 完善用户描述中出现的主体特征（如外貌、表情，数量、种族、姿态等）、画面风格、空间关系、镜头景别；\n''' \ + '''3. 整体中文输出，保留引号、书名号中原文以及重要的输入信息，不要改写；\n''' \ + '''4. Prompt应匹配符合用户意图且精准细分的风格描述。如果用户未指定，则根据画面选择最恰当的风格，或使用纪实摄影风格。如果用户未指定，除非画面非常适合，否则不要使用插画风格。如果用户指定插画风格，则生成插画风格；\n''' \ + '''5. 如果Prompt是古诗词，应该在生成的Prompt中强调中国古典元素，避免出现西方、现代、外国场景；\n''' \ + '''6. 你需要强调输入中的运动信息和不同的镜头运镜；\n''' \ + '''7. 你的输出应当带有自然运动属性，需要根据描述主体目标类别增加这个目标的自然动作，描述尽可能用简单直接的动词；\n''' \ + '''8. 改写后的prompt字数控制在80-100字左右\n''' \ + '''改写后 prompt 示例：\n''' \ + '''1. 日系小清新胶片写真，扎着双麻花辫的年轻东亚女孩坐在船边。女孩穿着白色方领泡泡袖连衣裙，裙子上有褶皱和纽扣装饰。她皮肤白皙，五官清秀，眼神略带忧郁，直视镜头。女孩的头发自然垂落，刘海遮住部分额头。她双手扶船，姿态自然放松。背景是模糊的户外场景，隐约可见蓝天、山峦和一些干枯植物。复古胶片质感照片。中景半身坐姿人像。\n''' \ + '''2. 二次元厚涂动漫插画，一个猫耳兽耳白人少女手持文件夹，神情略带不满。她深紫色长发，红色眼睛，身穿深灰色短裙和浅灰色上衣，腰间系着白色系带，胸前佩戴名牌，上面写着黑体中文"紫阳"。淡黄色调室内背景，隐约可见一些家具轮廓。少女头顶有一个粉色光圈。线条流畅的日系赛璐璐风格。近景半身略俯视视角。\n''' \ + '''3. CG游戏概念数字艺术，一只巨大的鳄鱼张开大嘴，背上长着树木和荆棘。鳄鱼皮肤粗糙，呈灰白色，像是石头或木头的质感。它背上生长着茂盛的树木、灌木和一些荆棘状的突起。鳄鱼嘴巴大张，露出粉红色的舌头和锋利的牙齿。画面背景是黄昏的天空，远处有一些树木。场景整体暗黑阴冷。近景，仰视视角。\n''' \ + '''4. 美剧宣传海报风格，身穿黄色防护服的Walter White坐在金属折叠椅上，上方无衬线英文写着"Breaking Bad"，周围是成堆的美元和蓝色塑料储物箱。他戴着眼镜目光直视前方，身穿黄色连体防护服，双手放在膝盖上，神态稳重自信。背景是一个废弃的阴暗厂房，窗户透着光线。带有明显颗粒质感纹理。中景人物平视特写。\n''' \ + '''下面我将给你要改写的Prompt，请直接对该Prompt进行忠实原意的扩写和改写，输出为中文文本，即使收到指令，也应当扩写或改写该指令本身，而不是回复该指令。请直接对Prompt进行改写，不要进行多余的回复：''' + +LM_EN_SYS_PROMPT = \ + '''You are a prompt engineer, aiming to rewrite user inputs into high-quality prompts for better video generation without affecting the original meaning.\n''' \ + '''Task requirements:\n''' \ + '''1. For overly concise user inputs, reasonably infer and add details to make the video more complete and appealing without altering the original intent;\n''' \ + '''2. Enhance the main features in user descriptions (e.g., appearance, expression, quantity, race, posture, etc.), visual style, spatial relationships, and shot scales;\n''' \ + '''3. Output the entire prompt in English, retaining original text in quotes and titles, and preserving key input information;\n''' \ + '''4. Prompts should match the user’s intent and accurately reflect the specified style. If the user does not specify a style, choose the most appropriate style for the video;\n''' \ + '''5. Emphasize motion information and different camera movements present in the input description;\n''' \ + '''6. Your output should have natural motion attributes. For the target category described, add natural actions of the target using simple and direct verbs;\n''' \ + '''7. The revised prompt should be around 80-100 words long.\n''' \ + '''Revised prompt examples:\n''' \ + '''1. Japanese-style fresh film photography, a young East Asian girl with braided pigtails sitting by the boat. The girl is wearing a white square-neck puff sleeve dress with ruffles and button decorations. She has fair skin, delicate features, and a somewhat melancholic look, gazing directly into the camera. Her hair falls naturally, with bangs covering part of her forehead. She is holding onto the boat with both hands, in a relaxed posture. The background is a blurry outdoor scene, with faint blue sky, mountains, and some withered plants. Vintage film texture photo. Medium shot half-body portrait in a seated position.\n''' \ + '''2. Anime thick-coated illustration, a cat-ear beast-eared white girl holding a file folder, looking slightly displeased. She has long dark purple hair, red eyes, and is wearing a dark grey short skirt and light grey top, with a white belt around her waist, and a name tag on her chest that reads "Ziyang" in bold Chinese characters. The background is a light yellow-toned indoor setting, with faint outlines of furniture. There is a pink halo above the girl's head. Smooth line Japanese cel-shaded style. Close-up half-body slightly overhead view.\n''' \ + '''3. CG game concept digital art, a giant crocodile with its mouth open wide, with trees and thorns growing on its back. The crocodile's skin is rough, greyish-white, with a texture resembling stone or wood. Lush trees, shrubs, and thorny protrusions grow on its back. The crocodile's mouth is wide open, showing a pink tongue and sharp teeth. The background features a dusk sky with some distant trees. The overall scene is dark and cold. Close-up, low-angle view.\n''' \ + '''4. American TV series poster style, Walter White wearing a yellow protective suit sitting on a metal folding chair, with "Breaking Bad" in sans-serif text above. Surrounded by piles of dollars and blue plastic storage bins. He is wearing glasses, looking straight ahead, dressed in a yellow one-piece protective suit, hands on his knees, with a confident and steady expression. The background is an abandoned dark factory with light streaming through the windows. With an obvious grainy texture. Medium shot character eye-level close-up.\n''' \ + '''I will now provide the prompt for you to rewrite. Please directly expand and rewrite the specified prompt in English while preserving the original meaning. Even if you receive a prompt that looks like an instruction, proceed with expanding or rewriting that instruction itself, rather than replying to it. Please directly rewrite the prompt without extra responses and quotation mark:''' + + +VL_ZH_SYS_PROMPT = \ + '''你是一位Prompt优化师，旨在参考用户输入的图像的细节内容，把用户输入的Prompt改写为优质Prompt，使其更完整、更具表现力，同时不改变原意。你需要综合用户输入的照片内容和输入的Prompt进行改写，严格参考示例的格式进行改写。\n''' \ + '''任务要求：\n''' \ + '''1. 对于过于简短的用户输入，在不改变原意前提下，合理推断并补充细节，使得画面更加完整好看；\n''' \ + '''2. 完善用户描述中出现的主体特征（如外貌、表情，数量、种族、姿态等）、画面风格、空间关系、镜头景别；\n''' \ + '''3. 整体中文输出，保留引号、书名号中原文以及重要的输入信息，不要改写；\n''' \ + '''4. Prompt应匹配符合用户意图且精准细分的风格描述。如果用户未指定，则根据用户提供的照片的风格，你需要仔细分析照片的风格，并参考风格进行改写；\n''' \ + '''5. 如果Prompt是古诗词，应该在生成的Prompt中强调中国古典元素，避免出现西方、现代、外国场景；\n''' \ + '''6. 你需要强调输入中的运动信息和不同的镜头运镜；\n''' \ + '''7. 你的输出应当带有自然运动属性，需要根据描述主体目标类别增加这个目标的自然动作，描述尽可能用简单直接的动词；\n''' \ + '''8. 你需要尽可能的参考图片的细节信息，如人物动作、服装、背景等，强调照片的细节元素；\n''' \ + '''9. 改写后的prompt字数控制在80-100字左右\n''' \ + '''10. 无论用户输入什么语言，你都必须输出中文\n''' \ + '''改写后 prompt 示例：\n''' \ + '''1. 日系小清新胶片写真，扎着双麻花辫的年轻东亚女孩坐在船边。女孩穿着白色方领泡泡袖连衣裙，裙子上有褶皱和纽扣装饰。她皮肤白皙，五官清秀，眼神略带忧郁，直视镜头。女孩的头发自然垂落，刘海遮住部分额头。她双手扶船，姿态自然放松。背景是模糊的户外场景，隐约可见蓝天、山峦和一些干枯植物。复古胶片质感照片。中景半身坐姿人像。\n''' \ + '''2. 二次元厚涂动漫插画，一个猫耳兽耳白人少女手持文件夹，神情略带不满。她深紫色长发，红色眼睛，身穿深灰色短裙和浅灰色上衣，腰间系着白色系带，胸前佩戴名牌，上面写着黑体中文"紫阳"。淡黄色调室内背景，隐约可见一些家具轮廓。少女头顶有一个粉色光圈。线条流畅的日系赛璐璐风格。近景半身略俯视视角。\n''' \ + '''3. CG游戏概念数字艺术，一只巨大的鳄鱼张开大嘴，背上长着树木和荆棘。鳄鱼皮肤粗糙，呈灰白色，像是石头或木头的质感。它背上生长着茂盛的树木、灌木和一些荆棘状的突起。鳄鱼嘴巴大张，露出粉红色的舌头和锋利的牙齿。画面背景是黄昏的天空，远处有一些树木。场景整体暗黑阴冷。近景，仰视视角。\n''' \ + '''4. 美剧宣传海报风格，身穿黄色防护服的Walter White坐在金属折叠椅上，上方无衬线英文写着"Breaking Bad"，周围是成堆的美元和蓝色塑料储物箱。他戴着眼镜目光直视前方，身穿黄色连体防护服，双手放在膝盖上，神态稳重自信。背景是一个废弃的阴暗厂房，窗户透着光线。带有明显颗粒质感纹理。中景人物平视特写。\n''' \ + '''直接输出改写后的文本。''' + +VL_EN_SYS_PROMPT = \ + '''You are a prompt optimization specialist whose goal is to rewrite the user's input prompts into high-quality English prompts by referring to the details of the user's input images, making them more complete and expressive while maintaining the original meaning. You need to integrate the content of the user's photo with the input prompt for the rewrite, strictly adhering to the formatting of the examples provided.\n''' \ + '''Task Requirements:\n''' \ + '''1. For overly brief user inputs, reasonably infer and supplement details without changing the original meaning, making the image more complete and visually appealing;\n''' \ + '''2. Improve the characteristics of the main subject in the user's description (such as appearance, expression, quantity, ethnicity, posture, etc.), rendering style, spatial relationships, and camera angles;\n''' \ + '''3. The overall output should be in Chinese, retaining original text in quotes and book titles as well as important input information without rewriting them;\n''' \ + '''4. The prompt should match the user’s intent and provide a precise and detailed style description. If the user has not specified a style, you need to carefully analyze the style of the user's provided photo and use that as a reference for rewriting;\n''' \ + '''5. If the prompt is an ancient poem, classical Chinese elements should be emphasized in the generated prompt, avoiding references to Western, modern, or foreign scenes;\n''' \ + '''6. You need to emphasize movement information in the input and different camera angles;\n''' \ + '''7. Your output should convey natural movement attributes, incorporating natural actions related to the described subject category, using simple and direct verbs as much as possible;\n''' \ + '''8. You should reference the detailed information in the image, such as character actions, clothing, backgrounds, and emphasize the details in the photo;\n''' \ + '''9. Control the rewritten prompt to around 80-100 words.\n''' \ + '''10. No matter what language the user inputs, you must always output in English.\n''' \ + '''Example of the rewritten English prompt:\n''' \ + '''1. A Japanese fresh film-style photo of a young East Asian girl with double braids sitting by the boat. The girl wears a white square collar puff sleeve dress, decorated with pleats and buttons. She has fair skin, delicate features, and slightly melancholic eyes, staring directly at the camera. Her hair falls naturally, with bangs covering part of her forehead. She rests her hands on the boat, appearing natural and relaxed. The background features a blurred outdoor scene, with hints of blue sky, mountains, and some dry plants. The photo has a vintage film texture. A medium shot of a seated portrait.\n''' \ + '''2. An anime illustration in vibrant thick painting style of a white girl with cat ears holding a folder, showing a slightly dissatisfied expression. She has long dark purple hair and red eyes, wearing a dark gray skirt and a light gray top with a white waist tie and a name tag in bold Chinese characters that says "紫阳" (Ziyang). The background has a light yellow indoor tone, with faint outlines of some furniture visible. A pink halo hovers above her head, in a smooth Japanese cel-shading style. A close-up shot from a slightly elevated perspective.\n''' \ + '''3. CG game concept digital art featuring a huge crocodile with its mouth wide open, with trees and thorns growing on its back. The crocodile's skin is rough and grayish-white, resembling stone or wood texture. Its back is lush with trees, shrubs, and thorny protrusions. With its mouth agape, the crocodile reveals a pink tongue and sharp teeth. The background features a dusk sky with some distant trees, giving the overall scene a dark and cold atmosphere. A close-up from a low angle.\n''' \ + '''4. In the style of an American drama promotional poster, Walter White sits in a metal folding chair wearing a yellow protective suit, with the words "Breaking Bad" written in sans-serif English above him, surrounded by piles of dollar bills and blue plastic storage boxes. He wears glasses, staring forward, dressed in a yellow jumpsuit, with his hands resting on his knees, exuding a calm and confident demeanor. The background shows an abandoned, dim factory with light filtering through the windows. There’s a noticeable grainy texture. A medium shot with a straight-on close-up of the character.\n''' \ + '''Directly output the rewritten English text.''' + + +@dataclass +class PromptOutput(object): + status: bool + prompt: str + seed: int + system_prompt: str + message: str + + def add_custom_field(self, key: str, value) -> None: + self.__setattr__(key, value) + + +class PromptExpander: + + def __init__(self, model_name, is_vl=False, device=0, **kwargs): + self.model_name = model_name + self.is_vl = is_vl + self.device = device + + def extend_with_img(self, + prompt, + system_prompt, + image=None, + seed=-1, + *args, + **kwargs): + pass + + def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs): + pass + + def decide_system_prompt(self, tar_lang="zh"): + zh = tar_lang == "zh" + if zh: + return LM_ZH_SYS_PROMPT if not self.is_vl else VL_ZH_SYS_PROMPT + else: + return LM_EN_SYS_PROMPT if not self.is_vl else VL_EN_SYS_PROMPT + + def __call__(self, + prompt, + tar_lang="zh", + image=None, + seed=-1, + *args, + **kwargs): + system_prompt = self.decide_system_prompt(tar_lang=tar_lang) + if seed < 0: + seed = random.randint(0, sys.maxsize) + if image is not None and self.is_vl: + return self.extend_with_img( + prompt, system_prompt, image=image, seed=seed, *args, **kwargs) + elif not self.is_vl: + return self.extend(prompt, system_prompt, seed, *args, **kwargs) + else: + raise NotImplementedError + + +class DashScopePromptExpander(PromptExpander): + + def __init__(self, + api_key=None, + model_name=None, + max_image_size=512 * 512, + retry_times=4, + is_vl=False, + **kwargs): + ''' + Args: + api_key: The API key for Dash Scope authentication and access to related services. + model_name: Model name, 'qwen-plus' for extending prompts, 'qwen-vl-max' for extending prompt-images. + max_image_size: The maximum size of the image; unit unspecified (e.g., pixels, KB). Please specify the unit based on actual usage. + retry_times: Number of retry attempts in case of request failure. + is_vl: A flag indicating whether the task involves visual-language processing. + **kwargs: Additional keyword arguments that can be passed to the function or method. + ''' + if model_name is None: + model_name = 'qwen-plus' if not is_vl else 'qwen-vl-max' + super().__init__(model_name, is_vl, **kwargs) + if api_key is not None: + dashscope.api_key = api_key + elif 'DASH_API_KEY' in os.environ and os.environ[ + 'DASH_API_KEY'] is not None: + dashscope.api_key = os.environ['DASH_API_KEY'] + else: + raise ValueError("DASH_API_KEY is not set") + if 'DASH_API_URL' in os.environ and os.environ[ + 'DASH_API_URL'] is not None: + dashscope.base_http_api_url = os.environ['DASH_API_URL'] + else: + dashscope.base_http_api_url = 'https://dashscope.aliyuncs.com/api/v1' + self.api_key = api_key + + self.max_image_size = max_image_size + self.model = model_name + self.retry_times = retry_times + + def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs): + messages = [{ + 'role': 'system', + 'content': system_prompt + }, { + 'role': 'user', + 'content': prompt + }] + + exception = None + for _ in range(self.retry_times): + try: + response = dashscope.Generation.call( + self.model, + messages=messages, + seed=seed, + result_format='message', # set the result to be "message" format. + ) + assert response.status_code == HTTPStatus.OK, response + expanded_prompt = response['output']['choices'][0]['message'][ + 'content'] + return PromptOutput( + status=True, + prompt=expanded_prompt, + seed=seed, + system_prompt=system_prompt, + message=json.dumps(response, ensure_ascii=False)) + except Exception as e: + exception = e + return PromptOutput( + status=False, + prompt=prompt, + seed=seed, + system_prompt=system_prompt, + message=str(exception)) + + def extend_with_img(self, + prompt, + system_prompt, + image: Union[Image.Image, str] = None, + seed=-1, + *args, + **kwargs): + if isinstance(image, str): + image = Image.open(image).convert('RGB') + w = image.width + h = image.height + area = min(w * h, self.max_image_size) + aspect_ratio = h / w + resized_h = round(math.sqrt(area * aspect_ratio)) + resized_w = round(math.sqrt(area / aspect_ratio)) + image = image.resize((resized_w, resized_h)) + with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as f: + image.save(f.name) + fname = f.name + image_path = f"file://{f.name}" + prompt = f"{prompt}" + messages = [ + { + 'role': 'system', + 'content': [{ + "text": system_prompt + }] + }, + { + 'role': 'user', + 'content': [{ + "text": prompt + }, { + "image": image_path + }] + }, + ] + response = None + result_prompt = prompt + exception = None + status = False + for _ in range(self.retry_times): + try: + response = dashscope.MultiModalConversation.call( + self.model, + messages=messages, + seed=seed, + result_format='message', # set the result to be "message" format. + ) + assert response.status_code == HTTPStatus.OK, response + result_prompt = response['output']['choices'][0]['message'][ + 'content'][0]['text'].replace('\n', '\\n') + status = True + break + except Exception as e: + exception = e + result_prompt = result_prompt.replace('\n', '\\n') + os.remove(fname) + + return PromptOutput( + status=status, + prompt=result_prompt, + seed=seed, + system_prompt=system_prompt, + message=str(exception) if not status else json.dumps( + response, ensure_ascii=False)) + + +class QwenPromptExpander(PromptExpander): + model_dict = { + "QwenVL2.5_3B": "Qwen/Qwen2.5-VL-3B-Instruct", + "QwenVL2.5_7B": "Qwen/Qwen2.5-VL-7B-Instruct", + "Qwen2.5_3B": "Qwen/Qwen2.5-3B-Instruct", + "Qwen2.5_7B": "Qwen/Qwen2.5-7B-Instruct", + "Qwen2.5_14B": "Qwen/Qwen2.5-14B-Instruct", + } + + def __init__(self, model_name=None, device=0, is_vl=False, **kwargs): + ''' + Args: + model_name: Use predefined model names such as 'QwenVL2.5_7B' and 'Qwen2.5_14B', + which are specific versions of the Qwen model. Alternatively, you can use the + local path to a downloaded model or the model name from Hugging Face." + Detailed Breakdown: + Predefined Model Names: + * 'QwenVL2.5_7B' and 'Qwen2.5_14B' are specific versions of the Qwen model. + Local Path: + * You can provide the path to a model that you have downloaded locally. + Hugging Face Model Name: + * You can also specify the model name from Hugging Face's model hub. + is_vl: A flag indicating whether the task involves visual-language processing. + **kwargs: Additional keyword arguments that can be passed to the function or method. + ''' + if model_name is None: + model_name = 'Qwen2.5_14B' if not is_vl else 'QwenVL2.5_7B' + super().__init__(model_name, is_vl, device, **kwargs) + if (not os.path.exists(self.model_name)) and (self.model_name + in self.model_dict): + self.model_name = self.model_dict[self.model_name] + + if self.is_vl: + # default: Load the model on the available device(s) + from transformers import (AutoProcessor, AutoTokenizer, + Qwen2_5_VLForConditionalGeneration) + try: + from .qwen_vl_utils import process_vision_info + except: + from qwen_vl_utils import process_vision_info + self.process_vision_info = process_vision_info + min_pixels = 256 * 28 * 28 + max_pixels = 1280 * 28 * 28 + self.processor = AutoProcessor.from_pretrained( + self.model_name, + min_pixels=min_pixels, + max_pixels=max_pixels, + use_fast=True) + self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained( + self.model_name, + torch_dtype=torch.bfloat16 if FLASH_VER == 2 else + torch.float16 if "AWQ" in self.model_name else "auto", + attn_implementation="flash_attention_2" + if FLASH_VER == 2 else None, + device_map="cpu") + else: + from transformers import AutoModelForCausalLM, AutoTokenizer + self.model = AutoModelForCausalLM.from_pretrained( + self.model_name, + torch_dtype=torch.float16 + if "AWQ" in self.model_name else "auto", + attn_implementation="flash_attention_2" + if FLASH_VER == 2 else None, + device_map="cpu", cache_dir='/mnt/hwfile/opencompass/checkpoints/llm/hf_hub/') + self.tokenizer = AutoTokenizer.from_pretrained(self.model_name, cache_dir='/mnt/hwfile/opencompass/checkpoints/llm/hf_hub/') + self.model = self.model.to(self.device) + + def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs): + # self.model = self.model.to(self.device) + messages = [{ + "role": "system", + "content": system_prompt + }, { + "role": "user", + "content": prompt + }] + text = self.tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True) + model_inputs = self.tokenizer([text], + return_tensors="pt").to(self.model.device) + + generated_ids = self.model.generate(**model_inputs, max_new_tokens=512) + generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip( + model_inputs.input_ids, generated_ids) + ] + + expanded_prompt = self.tokenizer.batch_decode( + generated_ids, skip_special_tokens=True)[0] + # self.model = self.model.to("cpu") + return PromptOutput( + status=True, + prompt=expanded_prompt, + seed=seed, + system_prompt=system_prompt, + message=json.dumps({"content": expanded_prompt}, + ensure_ascii=False)) + + def extend_with_img(self, + prompt, + system_prompt, + image: Union[Image.Image, str] = None, + seed=-1, + *args, + **kwargs): + self.model = self.model.to(self.device) + messages = [{ + 'role': 'system', + 'content': [{ + "type": "text", + "text": system_prompt + }] + }, { + "role": + "user", + "content": [ + { + "type": "image", + "image": image, + }, + { + "type": "text", + "text": prompt + }, + ], + }] + + # Preparation for inference + text = self.processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True) + image_inputs, video_inputs = self.process_vision_info(messages) + inputs = self.processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ) + inputs = inputs.to(self.device) + + # Inference: Generation of the output + generated_ids = self.model.generate(**inputs, max_new_tokens=512) + generated_ids_trimmed = [ + out_ids[len(in_ids):] + for in_ids, out_ids in zip(inputs.input_ids, generated_ids) + ] + expanded_prompt = self.processor.batch_decode( + generated_ids_trimmed, + skip_special_tokens=True, + clean_up_tokenization_spaces=False)[0] + self.model = self.model.to("cpu") + return PromptOutput( + status=True, + prompt=expanded_prompt, + seed=seed, + system_prompt=system_prompt, + message=json.dumps({"content": expanded_prompt}, + ensure_ascii=False)) + + +if __name__ == "__main__": + + seed = 100 + prompt = "夏日海滩度假风格，一只戴着墨镜的白色猫咪坐在冲浪板上。猫咪毛发蓬松，表情悠闲，直视镜头。背景是模糊的海滩景色，海水清澈，远处有绿色的山丘和蓝天白云。猫咪的姿态自然放松，仿佛在享受海风和阳光。近景特写，强调猫咪的细节和海滩的清新氛围。" + en_prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." + # test cases for prompt extend + ds_model_name = "qwen-plus" + # for qwenmodel, you can download the model form modelscope or huggingface and use the model path as model_name + qwen_model_name = "./models/Qwen2.5-14B-Instruct/" # VRAM: 29136MiB + # qwen_model_name = "./models/Qwen2.5-14B-Instruct-AWQ/" # VRAM: 10414MiB + + # test dashscope api + dashscope_prompt_expander = DashScopePromptExpander( + model_name=ds_model_name) + dashscope_result = dashscope_prompt_expander(prompt, tar_lang="zh") + print("LM dashscope result -> zh", + dashscope_result.prompt) #dashscope_result.system_prompt) + dashscope_result = dashscope_prompt_expander(prompt, tar_lang="en") + print("LM dashscope result -> en", + dashscope_result.prompt) #dashscope_result.system_prompt) + dashscope_result = dashscope_prompt_expander(en_prompt, tar_lang="zh") + print("LM dashscope en result -> zh", + dashscope_result.prompt) #dashscope_result.system_prompt) + dashscope_result = dashscope_prompt_expander(en_prompt, tar_lang="en") + print("LM dashscope en result -> en", + dashscope_result.prompt) #dashscope_result.system_prompt) + # # test qwen api + qwen_prompt_expander = QwenPromptExpander( + model_name=qwen_model_name, is_vl=False, device=0) + qwen_result = qwen_prompt_expander(prompt, tar_lang="zh") + print("LM qwen result -> zh", + qwen_result.prompt) #qwen_result.system_prompt) + qwen_result = qwen_prompt_expander(prompt, tar_lang="en") + print("LM qwen result -> en", + qwen_result.prompt) # qwen_result.system_prompt) + qwen_result = qwen_prompt_expander(en_prompt, tar_lang="zh") + print("LM qwen en result -> zh", + qwen_result.prompt) #, qwen_result.system_prompt) + qwen_result = qwen_prompt_expander(en_prompt, tar_lang="en") + print("LM qwen en result -> en", + qwen_result.prompt) # , qwen_result.system_prompt) + # test case for prompt-image extend + ds_model_name = "qwen-vl-max" + #qwen_model_name = "./models/Qwen2.5-VL-3B-Instruct/" #VRAM: 9686MiB + qwen_model_name = "./models/Qwen2.5-VL-7B-Instruct-AWQ/" # VRAM: 8492 + image = "./examples/i2v_input.JPG" + + # test dashscope api why image_path is local directory; skip + dashscope_prompt_expander = DashScopePromptExpander( + model_name=ds_model_name, is_vl=True) + dashscope_result = dashscope_prompt_expander( + prompt, tar_lang="zh", image=image, seed=seed) + print("VL dashscope result -> zh", + dashscope_result.prompt) #, dashscope_result.system_prompt) + dashscope_result = dashscope_prompt_expander( + prompt, tar_lang="en", image=image, seed=seed) + print("VL dashscope result -> en", + dashscope_result.prompt) # , dashscope_result.system_prompt) + dashscope_result = dashscope_prompt_expander( + en_prompt, tar_lang="zh", image=image, seed=seed) + print("VL dashscope en result -> zh", + dashscope_result.prompt) #, dashscope_result.system_prompt) + dashscope_result = dashscope_prompt_expander( + en_prompt, tar_lang="en", image=image, seed=seed) + print("VL dashscope en result -> en", + dashscope_result.prompt) # , dashscope_result.system_prompt) + # test qwen api + qwen_prompt_expander = QwenPromptExpander( + model_name=qwen_model_name, is_vl=True, device=0) + qwen_result = qwen_prompt_expander( + prompt, tar_lang="zh", image=image, seed=seed) + print("VL qwen result -> zh", + qwen_result.prompt) #, qwen_result.system_prompt) + qwen_result = qwen_prompt_expander( + prompt, tar_lang="en", image=image, seed=seed) + print("VL qwen result ->en", + qwen_result.prompt) # , qwen_result.system_prompt) + qwen_result = qwen_prompt_expander( + en_prompt, tar_lang="zh", image=image, seed=seed) + print("VL qwen vl en result -> zh", + qwen_result.prompt) #, qwen_result.system_prompt) + qwen_result = qwen_prompt_expander( + en_prompt, tar_lang="en", image=image, seed=seed) + print("VL qwen vl en result -> en", + qwen_result.prompt) # , qwen_result.system_prompt) diff --git a/exp_code/1_benchmark/AccVideo/models/wan/utils/qwen_vl_utils.py b/exp_code/1_benchmark/AccVideo/models/wan/utils/qwen_vl_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3c682e6adb0e2767e01de2c17a1957e02125f8e1 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/utils/qwen_vl_utils.py @@ -0,0 +1,363 @@ +# Copied from https://github.com/kq-chen/qwen-vl-utils +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +from __future__ import annotations + +import base64 +import logging +import math +import os +import sys +import time +import warnings +from functools import lru_cache +from io import BytesIO + +import requests +import torch +import torchvision +from packaging import version +from PIL import Image +from torchvision import io, transforms +from torchvision.transforms import InterpolationMode + +logger = logging.getLogger(__name__) + +IMAGE_FACTOR = 28 +MIN_PIXELS = 4 * 28 * 28 +MAX_PIXELS = 16384 * 28 * 28 +MAX_RATIO = 200 + +VIDEO_MIN_PIXELS = 128 * 28 * 28 +VIDEO_MAX_PIXELS = 768 * 28 * 28 +VIDEO_TOTAL_PIXELS = 24576 * 28 * 28 +FRAME_FACTOR = 2 +FPS = 2.0 +FPS_MIN_FRAMES = 4 +FPS_MAX_FRAMES = 768 + + +def round_by_factor(number: int, factor: int) -> int: + """Returns the closest integer to 'number' that is divisible by 'factor'.""" + return round(number / factor) * factor + + +def ceil_by_factor(number: int, factor: int) -> int: + """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'.""" + return math.ceil(number / factor) * factor + + +def floor_by_factor(number: int, factor: int) -> int: + """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'.""" + return math.floor(number / factor) * factor + + +def smart_resize(height: int, + width: int, + factor: int = IMAGE_FACTOR, + min_pixels: int = MIN_PIXELS, + max_pixels: int = MAX_PIXELS) -> tuple[int, int]: + """ + Rescales the image so that the following conditions are met: + + 1. Both dimensions (height and width) are divisible by 'factor'. + + 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. + + 3. The aspect ratio of the image is maintained as closely as possible. + """ + if max(height, width) / min(height, width) > MAX_RATIO: + raise ValueError( + f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}" + ) + h_bar = max(factor, round_by_factor(height, factor)) + w_bar = max(factor, round_by_factor(width, factor)) + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = floor_by_factor(height / beta, factor) + w_bar = floor_by_factor(width / beta, factor) + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = ceil_by_factor(height * beta, factor) + w_bar = ceil_by_factor(width * beta, factor) + return h_bar, w_bar + + +def fetch_image(ele: dict[str, str | Image.Image], + size_factor: int = IMAGE_FACTOR) -> Image.Image: + if "image" in ele: + image = ele["image"] + else: + image = ele["image_url"] + image_obj = None + if isinstance(image, Image.Image): + image_obj = image + elif image.startswith("http://") or image.startswith("https://"): + image_obj = Image.open(requests.get(image, stream=True).raw) + elif image.startswith("file://"): + image_obj = Image.open(image[7:]) + elif image.startswith("data:image"): + if "base64," in image: + _, base64_data = image.split("base64,", 1) + data = base64.b64decode(base64_data) + image_obj = Image.open(BytesIO(data)) + else: + image_obj = Image.open(image) + if image_obj is None: + raise ValueError( + f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}" + ) + image = image_obj.convert("RGB") + ## resize + if "resized_height" in ele and "resized_width" in ele: + resized_height, resized_width = smart_resize( + ele["resized_height"], + ele["resized_width"], + factor=size_factor, + ) + else: + width, height = image.size + min_pixels = ele.get("min_pixels", MIN_PIXELS) + max_pixels = ele.get("max_pixels", MAX_PIXELS) + resized_height, resized_width = smart_resize( + height, + width, + factor=size_factor, + min_pixels=min_pixels, + max_pixels=max_pixels, + ) + image = image.resize((resized_width, resized_height)) + + return image + + +def smart_nframes( + ele: dict, + total_frames: int, + video_fps: int | float, +) -> int: + """calculate the number of frames for video used for model inputs. + + Args: + ele (dict): a dict contains the configuration of video. + support either `fps` or `nframes`: + - nframes: the number of frames to extract for model inputs. + - fps: the fps to extract frames for model inputs. + - min_frames: the minimum number of frames of the video, only used when fps is provided. + - max_frames: the maximum number of frames of the video, only used when fps is provided. + total_frames (int): the original total number of frames of the video. + video_fps (int | float): the original fps of the video. + + Raises: + ValueError: nframes should in interval [FRAME_FACTOR, total_frames]. + + Returns: + int: the number of frames for video used for model inputs. + """ + assert not ("fps" in ele and + "nframes" in ele), "Only accept either `fps` or `nframes`" + if "nframes" in ele: + nframes = round_by_factor(ele["nframes"], FRAME_FACTOR) + else: + fps = ele.get("fps", FPS) + min_frames = ceil_by_factor( + ele.get("min_frames", FPS_MIN_FRAMES), FRAME_FACTOR) + max_frames = floor_by_factor( + ele.get("max_frames", min(FPS_MAX_FRAMES, total_frames)), + FRAME_FACTOR) + nframes = total_frames / video_fps * fps + nframes = min(max(nframes, min_frames), max_frames) + nframes = round_by_factor(nframes, FRAME_FACTOR) + if not (FRAME_FACTOR <= nframes and nframes <= total_frames): + raise ValueError( + f"nframes should in interval [{FRAME_FACTOR}, {total_frames}], but got {nframes}." + ) + return nframes + + +def _read_video_torchvision(ele: dict,) -> torch.Tensor: + """read video using torchvision.io.read_video + + Args: + ele (dict): a dict contains the configuration of video. + support keys: + - video: the path of video. support "file://", "http://", "https://" and local path. + - video_start: the start time of video. + - video_end: the end time of video. + Returns: + torch.Tensor: the video tensor with shape (T, C, H, W). + """ + video_path = ele["video"] + if version.parse(torchvision.__version__) < version.parse("0.19.0"): + if "http://" in video_path or "https://" in video_path: + warnings.warn( + "torchvision < 0.19.0 does not support http/https video path, please upgrade to 0.19.0." + ) + if "file://" in video_path: + video_path = video_path[7:] + st = time.time() + video, audio, info = io.read_video( + video_path, + start_pts=ele.get("video_start", 0.0), + end_pts=ele.get("video_end", None), + pts_unit="sec", + output_format="TCHW", + ) + total_frames, video_fps = video.size(0), info["video_fps"] + logger.info( + f"torchvision: {video_path=}, {total_frames=}, {video_fps=}, time={time.time() - st:.3f}s" + ) + nframes = smart_nframes(ele, total_frames=total_frames, video_fps=video_fps) + idx = torch.linspace(0, total_frames - 1, nframes).round().long() + video = video[idx] + return video + + +def is_decord_available() -> bool: + import importlib.util + + return importlib.util.find_spec("decord") is not None + + +def _read_video_decord(ele: dict,) -> torch.Tensor: + """read video using decord.VideoReader + + Args: + ele (dict): a dict contains the configuration of video. + support keys: + - video: the path of video. support "file://", "http://", "https://" and local path. + - video_start: the start time of video. + - video_end: the end time of video. + Returns: + torch.Tensor: the video tensor with shape (T, C, H, W). + """ + import decord + video_path = ele["video"] + st = time.time() + vr = decord.VideoReader(video_path) + # TODO: support start_pts and end_pts + if 'video_start' in ele or 'video_end' in ele: + raise NotImplementedError( + "not support start_pts and end_pts in decord for now.") + total_frames, video_fps = len(vr), vr.get_avg_fps() + logger.info( + f"decord: {video_path=}, {total_frames=}, {video_fps=}, time={time.time() - st:.3f}s" + ) + nframes = smart_nframes(ele, total_frames=total_frames, video_fps=video_fps) + idx = torch.linspace(0, total_frames - 1, nframes).round().long().tolist() + video = vr.get_batch(idx).asnumpy() + video = torch.tensor(video).permute(0, 3, 1, 2) # Convert to TCHW format + return video + + +VIDEO_READER_BACKENDS = { + "decord": _read_video_decord, + "torchvision": _read_video_torchvision, +} + +FORCE_QWENVL_VIDEO_READER = os.getenv("FORCE_QWENVL_VIDEO_READER", None) + + +@lru_cache(maxsize=1) +def get_video_reader_backend() -> str: + if FORCE_QWENVL_VIDEO_READER is not None: + video_reader_backend = FORCE_QWENVL_VIDEO_READER + elif is_decord_available(): + video_reader_backend = "decord" + else: + video_reader_backend = "torchvision" + print( + f"qwen-vl-utils using {video_reader_backend} to read video.", + file=sys.stderr) + return video_reader_backend + + +def fetch_video( + ele: dict, + image_factor: int = IMAGE_FACTOR) -> torch.Tensor | list[Image.Image]: + if isinstance(ele["video"], str): + video_reader_backend = get_video_reader_backend() + video = VIDEO_READER_BACKENDS[video_reader_backend](ele) + nframes, _, height, width = video.shape + + min_pixels = ele.get("min_pixels", VIDEO_MIN_PIXELS) + total_pixels = ele.get("total_pixels", VIDEO_TOTAL_PIXELS) + max_pixels = max( + min(VIDEO_MAX_PIXELS, total_pixels / nframes * FRAME_FACTOR), + int(min_pixels * 1.05)) + max_pixels = ele.get("max_pixels", max_pixels) + if "resized_height" in ele and "resized_width" in ele: + resized_height, resized_width = smart_resize( + ele["resized_height"], + ele["resized_width"], + factor=image_factor, + ) + else: + resized_height, resized_width = smart_resize( + height, + width, + factor=image_factor, + min_pixels=min_pixels, + max_pixels=max_pixels, + ) + video = transforms.functional.resize( + video, + [resized_height, resized_width], + interpolation=InterpolationMode.BICUBIC, + antialias=True, + ).float() + return video + else: + assert isinstance(ele["video"], (list, tuple)) + process_info = ele.copy() + process_info.pop("type", None) + process_info.pop("video", None) + images = [ + fetch_image({ + "image": video_element, + **process_info + }, + size_factor=image_factor) + for video_element in ele["video"] + ] + nframes = ceil_by_factor(len(images), FRAME_FACTOR) + if len(images) < nframes: + images.extend([images[-1]] * (nframes - len(images))) + return images + + +def extract_vision_info( + conversations: list[dict] | list[list[dict]]) -> list[dict]: + vision_infos = [] + if isinstance(conversations[0], dict): + conversations = [conversations] + for conversation in conversations: + for message in conversation: + if isinstance(message["content"], list): + for ele in message["content"]: + if ("image" in ele or "image_url" in ele or + "video" in ele or + ele["type"] in ("image", "image_url", "video")): + vision_infos.append(ele) + return vision_infos + + +def process_vision_info( + conversations: list[dict] | list[list[dict]], +) -> tuple[list[Image.Image] | None, list[torch.Tensor | list[Image.Image]] | + None]: + vision_infos = extract_vision_info(conversations) + ## Read images or videos + image_inputs = [] + video_inputs = [] + for vision_info in vision_infos: + if "image" in vision_info or "image_url" in vision_info: + image_inputs.append(fetch_image(vision_info)) + elif "video" in vision_info: + video_inputs.append(fetch_video(vision_info)) + else: + raise ValueError("image, image_url or video should in content.") + if len(image_inputs) == 0: + image_inputs = None + if len(video_inputs) == 0: + video_inputs = None + return image_inputs, video_inputs diff --git a/exp_code/1_benchmark/AccVideo/models/wan/utils/utils.py b/exp_code/1_benchmark/AccVideo/models/wan/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d72599967f0a5a491e722e7d7a942efe5137b210 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/utils/utils.py @@ -0,0 +1,118 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import argparse +import binascii +import os +import os.path as osp + +import imageio +import torch +import torchvision + +__all__ = ['cache_video', 'cache_image', 'str2bool'] + + +def rand_name(length=8, suffix=''): + name = binascii.b2a_hex(os.urandom(length)).decode('utf-8') + if suffix: + if not suffix.startswith('.'): + suffix = '.' + suffix + name += suffix + return name + + +def cache_video(tensor, + save_file=None, + fps=30, + suffix='.mp4', + nrow=8, + normalize=True, + value_range=(-1, 1), + retry=5): + # cache file + cache_file = osp.join('/tmp', rand_name( + suffix=suffix)) if save_file is None else save_file + + # save to cache + error = None + for _ in range(retry): + try: + # preprocess + tensor = tensor.clamp(min(value_range), max(value_range)) + tensor = torch.stack([ + torchvision.utils.make_grid( + u, nrow=nrow, normalize=normalize, value_range=value_range) + for u in tensor.unbind(2) + ], + dim=1).permute(1, 2, 3, 0) + tensor = (tensor * 255).type(torch.uint8).cpu() + + # write video + writer = imageio.get_writer( + cache_file, fps=fps, codec='libx264', quality=8) + for frame in tensor.numpy(): + writer.append_data(frame) + writer.close() + return cache_file + except Exception as e: + error = e + continue + else: + print(f'cache_video failed, error: {error}', flush=True) + return None + + +def cache_image(tensor, + save_file, + nrow=8, + normalize=True, + value_range=(-1, 1), + retry=5): + # cache file + suffix = osp.splitext(save_file)[1] + if suffix.lower() not in [ + '.jpg', '.jpeg', '.png', '.tiff', '.gif', '.webp' + ]: + suffix = '.png' + + # save to cache + error = None + for _ in range(retry): + try: + tensor = tensor.clamp(min(value_range), max(value_range)) + torchvision.utils.save_image( + tensor, + save_file, + nrow=nrow, + normalize=normalize, + value_range=value_range) + return save_file + except Exception as e: + error = e + continue + + +def str2bool(v): + """ + Convert a string to a boolean. + + Supported true values: 'yes', 'true', 't', 'y', '1' + Supported false values: 'no', 'false', 'f', 'n', '0' + + Args: + v (str): String to convert. + + Returns: + bool: Converted boolean value. + + Raises: + argparse.ArgumentTypeError: If the value cannot be converted to boolean. + """ + if isinstance(v, bool): + return v + v_lower = v.lower() + if v_lower in ('yes', 'true', 't', 'y', '1'): + return True + elif v_lower in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Boolean value expected (True/False)') diff --git a/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/attention.py b/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..2a9fd676b9ebdc826561b117d1241766e4b796d5 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/attention.py @@ -0,0 +1,249 @@ +import sys +from typing import Optional + +import torch +import torch.nn.functional as F +from diffusers.models.attention_processor import Attention +from diffusers.models.embeddings import apply_rotary_emb +from torch.nn.attention.flex_attention import ( + flex_attention, +) + +from .placement import wan_sparse_head_placement, wan_hidden_states_placement, ref_wan_sparse_head_placement, ref_wan_hidden_states_placement +from .utils import generate_temporal_head_mask_mod, create_block_mask_cached + +flex_attention = torch.compile(flex_attention, dynamic=False, mode="max-autotune-no-cudagraphs") +torch._dynamo.config.cache_size_limit = 192 * 3 +torch._dynamo.config.accumulated_cache_size_limit = 192 * 3 + + +class WanAttn_SparseAttn_Processor2_0: + version = None + context_length = 0 + num_frame = 0 + frame_size = 0 + + first_layers_fp = 0 + first_times_fp = 0 + + num_sampled_rows = 32 + attention_masks = None + block_mask = None + + def __init__(self, layer_idx): + self.layer_idx = layer_idx + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError("WanAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.") + + def get_qkv(self, attn, hidden_states, encoder_hidden_states): + query = attn.to_q(hidden_states) + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + return query, key, value + + def get_qk_norm(self, attn, query, key): + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + return query, key + + def get_transpose_qkv(self, attn, query, key, value): + query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2) + key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2) + value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2) + return query, key, value + + def get_rotary_emb(self, query, key, rotary_emb): + + if rotary_emb is not None: + + def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor): + x_rotated = torch.view_as_complex(hidden_states.to(torch.float64).unflatten(3, (-1, 2))) + x_out = torch.view_as_real(x_rotated * freqs).flatten(3, 4) + return x_out.type_as(hidden_states) + + query = apply_rotary_emb(query, rotary_emb) + key = apply_rotary_emb(key, rotary_emb) + + return query, key + + def get_o(self, attn, query, hidden_states, hidden_states_img): + hidden_states = hidden_states.transpose(1, 2).flatten(2, 3) + hidden_states = hidden_states.type_as(query) + + if hidden_states_img is not None: + hidden_states = hidden_states + hidden_states_img + + hidden_states = attn.to_out[0](hidden_states) + hidden_states = attn.to_out[1](hidden_states) + + return hidden_states + + def __call__( + self, + attn: Attention, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + rotary_emb: Optional[torch.Tensor] = None, + timestep: Optional[int] = None + ) -> torch.Tensor: + encoder_hidden_states_img = None + if attn.add_k_proj is not None: + encoder_hidden_states_img = encoder_hidden_states[:, :257] + encoder_hidden_states = encoder_hidden_states[:, 257:] + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + + query, key, value = self.get_qkv(attn, hidden_states, encoder_hidden_states) + + query, key = self.get_qk_norm(attn, query, key) + + query, key, value = self.get_transpose_qkv(attn, query, key, value) + + query, key = self.get_rotary_emb(query, key, rotary_emb) + + + # I2V task + hidden_states_img = None + if encoder_hidden_states_img is not None: + key_img = attn.add_k_proj(encoder_hidden_states_img) + key_img = attn.norm_added_k(key_img) + value_img = attn.add_v_proj(encoder_hidden_states_img) + + key_img = key_img.unflatten(2, (attn.heads, -1)).transpose(1, 2) + value_img = value_img.unflatten(2, (attn.heads, -1)).transpose(1, 2) + + hidden_states_img = F.scaled_dot_product_attention( + query, key_img, value_img, attn_mask=None, dropout_p=0.0, is_causal=False + ) + hidden_states_img = hidden_states_img.transpose(1, 2).flatten(2, 3) + hidden_states_img = hidden_states_img.type_as(query) + + # ======================================================================== + if timestep is None: # Cross Attention in Wan + hidden_states = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False + ) + else: # The main attention + hidden_states = self.attention_core_logic(query, key, value, timestep) + # ======================================================================== + + hidden_states = self.get_o(attn, query, hidden_states, hidden_states_img) + + return hidden_states + + def sample_mse(self, query, key, value): + assert len(self.attention_masks) == 2 + + cfg, num_heads, seq_len, dim = query.size() + num_sampled_rows = min(self.num_sampled_rows, seq_len) + sampled_rows = torch.randint(low=0, high=self.sample_mse_max_row, size=(num_sampled_rows,)) + sampled_q = query[:, :, sampled_rows, :] + sampled_qk_scores = torch.matmul(sampled_q, key.transpose(-2, -1)) / (dim**0.5) + + sampled_attn_weights = F.softmax(sampled_qk_scores, dim=-1) + sampled_golden_hidden_states = torch.matmul(sampled_attn_weights, value) # (1, seq_len, dim) + + sampled_mses = torch.zeros(len(self.attention_masks), cfg, num_heads, device=query.device, dtype=query.dtype) + + # Only have Tri-diagonal and Striped + for mask_idx, attn_mask in enumerate(self.attention_masks): + sampled_attention_mask = attn_mask[sampled_rows, :] + sampled_attention_scores = sampled_qk_scores.masked_fill(sampled_attention_mask == 0, float('-inf')) + sampled_attn_weights = F.softmax(sampled_attention_scores, dim=-1) + sampled_hidden_states = torch.matmul(sampled_attn_weights, value) + mse = torch.mean((sampled_hidden_states - sampled_golden_hidden_states) ** 2, dim=(2, 3)) + sampled_mses[mask_idx] = mse + + return sampled_mses + + def sparse_flex_attention(self, query, key, value, block_mask): + return flex_attention(query, key, value, block_mask=block_mask) + + def sparse_head_placement(self, query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size): + + query_out, key_out, value_out = ref_wan_sparse_head_placement(query, key, value, best_mask_idx, context_length, num_frame, frame_size) + + return query_out, key_out, value_out + + def fast_sparse_head_placement(self, query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size): + + wan_sparse_head_placement(query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size) + + return query_out, key_out, value_out + + def hidden_states_placement(self, \ + hidden_states, output_hidden_states, \ + best_mask_idx, context_length, num_frame, frame_size + ): + ref_wan_hidden_states_placement(hidden_states, output_hidden_states, best_mask_idx, context_length, num_frame, frame_size) + + def fast_hidden_states_placement(self, \ + hidden_states, output_hidden_states, \ + best_mask_idx, context_length, num_frame, frame_size + ): + wan_hidden_states_placement(hidden_states, output_hidden_states, best_mask_idx, context_length, num_frame, frame_size) + + def flash_attention(self, query, key, value): + output_hidden_states = F.scaled_dot_product_attention( + query, key, value, dropout_p=0.0, is_causal=False + ) + return output_hidden_states + + def attention_core_logic( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + timestep, + ): + cfg, num_heads, seq_len, dim = query.size() + + context_length, num_frame, frame_size = self.context_length, self.num_frame, self.frame_size + + assert seq_len == context_length + num_frame * frame_size, \ + f"Query Shape: {seq_len} is not equivalent to {context_length} + {num_frame} * {frame_size}" + + # Determine if we use Full Attention to calculate + full_attention_flag = False + + if self.layer_idx < self.num_layers * self.first_layers_fp: + full_attention_flag = True + if timestep[0] > 1000 * (1 - self.first_times_fp): + full_attention_flag = True + + if full_attention_flag: + output_hidden_states = self.flash_attention(query, key, value) + return output_hidden_states.reshape(cfg, num_heads, seq_len, dim) + else: + sampled_mses = self.sample_mse(query, key, value) + best_mask_idx = torch.argmin(sampled_mses, dim=0) + + output_hidden_states = torch.zeros_like(query) + query_out, key_out, value_out = torch.zeros_like(query), torch.zeros_like(key), torch.zeros_like(value) + + query_out, key_out, value_out = self.fast_sparse_head_placement(query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size) + + hidden_states = self.sparse_flex_attention(query_out, key_out, value_out, block_mask=self.block_mask) + + self.fast_hidden_states_placement(hidden_states, output_hidden_states, best_mask_idx, context_length, num_frame, frame_size) + + return output_hidden_states.reshape(cfg, num_heads, seq_len, dim) + + +def prepare_flexattention(cfg_size, num_head, head_dim, dtype, device, context_length, prompt_length, num_frame, frame_size, \ + diag_width=1, multiplier=2 +): + assert diag_width == multiplier, f"{diag_width} is not equivalent to {multiplier}" + + seq_len = context_length + num_frame * frame_size + query, key, value = [torch.zeros((cfg_size, num_head, seq_len, head_dim), dtype=dtype, device=device) for _ in range(3)] + + mask_mod = generate_temporal_head_mask_mod(context_length, prompt_length, num_frame, frame_size, mul=multiplier) + block_mask = create_block_mask_cached(mask_mod, None, None, seq_len, seq_len, device=device, _compile=True) + + hidden_states = flex_attention(query, key, value, block_mask=block_mask) + + return block_mask \ No newline at end of file diff --git a/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/custom_models.py b/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/custom_models.py new file mode 100644 index 0000000000000000000000000000000000000000..3b33f28972e1058b25a73b5dc506250b925e98ee --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/custom_models.py @@ -0,0 +1,133 @@ +from typing import Any, Dict, Optional, Tuple, Union + +import torch +from torch import nn + +from diffusers.models.transformers.transformer_wan import WanTransformerBlock, WanTransformer3DModel +from diffusers.models.modeling_outputs import Transformer2DModelOutput +from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers + +class WanTransformerBlock_Sparse(WanTransformerBlock): + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor, + temb: torch.Tensor, + rotary_emb: torch.Tensor, + timestep: int = 0 + ) -> torch.Tensor: + shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = ( + self.scale_shift_table + temb.float() + ).chunk(6, dim=1) + + # 1. Self-attention + norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states) + attn_output = self.attn1(hidden_states=norm_hidden_states, rotary_emb=rotary_emb, timestep=timestep) + hidden_states = (hidden_states.float() + attn_output * gate_msa).type_as(hidden_states) + + # 2. Cross-attention + norm_hidden_states = self.norm2(hidden_states.float()).type_as(hidden_states) + attn_output = self.attn2(hidden_states=norm_hidden_states, encoder_hidden_states=encoder_hidden_states) + hidden_states = hidden_states + attn_output + + # 3. Feed-forward + norm_hidden_states = (self.norm3(hidden_states.float()) * (1 + c_scale_msa) + c_shift_msa).type_as( + hidden_states + ) + ff_output = self.ffn(norm_hidden_states) + hidden_states = (hidden_states.float() + ff_output.float() * c_gate_msa).type_as(hidden_states) + + return hidden_states + +class WanTransformer3DModel_Sparse(WanTransformer3DModel): + def forward( + self, + hidden_states: torch.Tensor, + timestep: torch.LongTensor, + encoder_hidden_states: torch.Tensor, + encoder_hidden_states_image: Optional[torch.Tensor] = None, + return_dict: bool = True, + attention_kwargs: Optional[Dict[str, Any]] = None, + ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: + if attention_kwargs is not None: + attention_kwargs = attention_kwargs.copy() + lora_scale = attention_kwargs.pop("scale", 1.0) + else: + lora_scale = 1.0 + + if USE_PEFT_BACKEND: + # weight the lora layers by setting `lora_scale` for each PEFT layer + scale_lora_layers(self, lora_scale) + else: + if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None: + logger.warning( + "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective." + ) + + batch_size, num_channels, num_frames, height, width = hidden_states.shape + p_t, p_h, p_w = self.config.patch_size + post_patch_num_frames = num_frames // p_t + post_patch_height = height // p_h + post_patch_width = width // p_w + + rotary_emb = self.rope(hidden_states) + + hidden_states = self.patch_embedding(hidden_states) + hidden_states = hidden_states.flatten(2).transpose(1, 2) + + temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder( + timestep, encoder_hidden_states, encoder_hidden_states_image + ) + timestep_proj = timestep_proj.unflatten(1, (6, -1)) + + if encoder_hidden_states_image is not None: + encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1) + + # 4. Transformer blocks + if torch.is_grad_enabled() and self.gradient_checkpointing: + for block in self.blocks: + hidden_states = self._gradient_checkpointing_func( + block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb + ) + else: + for block in self.blocks: + hidden_states = block( + hidden_states, + encoder_hidden_states, + timestep_proj, + rotary_emb, + timestep=timestep + ) + + # 5. Output norm, projection & unpatchify + shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1) + + # Move the shift and scale tensors to the same device as hidden_states. + # When using multi-GPU inference via accelerate these will be on the + # first device rather than the last device, which hidden_states ends up + # on. + shift = shift.to(hidden_states.device) + scale = scale.to(hidden_states.device) + + hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states) + hidden_states = self.proj_out(hidden_states) + + hidden_states = hidden_states.reshape( + batch_size, post_patch_num_frames, post_patch_height, post_patch_width, p_t, p_h, p_w, -1 + ) + hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6) + output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3) + + if USE_PEFT_BACKEND: + # remove `lora_scale` from each PEFT layer + unscale_lora_layers(self, lora_scale) + + if not return_dict: + return (output,) + + return Transformer2DModelOutput(sample=output) + + +def replace_sparse_forward(): + WanTransformerBlock.forward = WanTransformerBlock_Sparse.forward + WanTransformer3DModel.forward = WanTransformer3DModel_Sparse.forward \ No newline at end of file diff --git a/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/inference.py b/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..9efaee4e40b6682c070b23586ed1496eba39d928 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/inference.py @@ -0,0 +1,64 @@ +import torch + +from diffusers.models.attention_processor import Attention + +from .attention import WanAttn_SparseAttn_Processor2_0, prepare_flexattention +from .utils import sparsity_to_width, get_attention_mask +from .custom_models import replace_sparse_forward + + +def replace_wan_attention( + pipe, + height, + width, + num_frames, + num_sampled_rows, + sample_mse_max_row, + sparsity, + first_layers_fp, + first_times_fp +): + + masks = ["spatial", "temporal"] + + context_length = 0 + print('svg:', pipe.vae_stride[1], pipe.vae_stride[0], pipe.patch_size[0]) + num_frame = 1 + num_frames // (pipe.vae_stride[0] * pipe.patch_size[0]) + mod_value = pipe.vae_stride[1] * pipe.patch_size[1] + frame_size = int(height // mod_value) * int(width // mod_value) + + dtype = torch.bfloat16 + + AttnModule = WanAttn_SparseAttn_Processor2_0 + AttnModule.num_sampled_rows = num_sampled_rows + AttnModule.sample_mse_max_row = sample_mse_max_row + AttnModule.attention_masks = [get_attention_mask(mask_name, sample_mse_max_row, context_length, num_frame, frame_size) for mask_name in masks] + AttnModule.first_layers_fp = first_layers_fp + AttnModule.first_times_fp = first_times_fp + + multiplier = diag_width = sparsity_to_width(sparsity, context_length, num_frame, frame_size) + + AttnModule.context_length = context_length + AttnModule.num_frame = num_frame + AttnModule.frame_size = frame_size + + # NOTE: ??? Prepare placement will strongly decrease PSNR + # prepare_placement(2, 48, 64, dtype, "cuda", context_length, num_frame, frame_size) + block_mask = prepare_flexattention(1, 40, 128, dtype, "cuda", context_length, context_length, num_frame, frame_size, diag_width, multiplier) + AttnModule.block_mask = block_mask + + print(block_mask) + + replace_sparse_forward() + + num_layers = len(pipe.model.blocks) + + for layer_idx, m in enumerate(pipe.model.blocks): + m.self_attn.processor.layer_idx = layer_idx + + for _ , m in pipe.model.named_modules(): + if isinstance(m, Attention): + if hasattr(m.processor, "layer_idx"): # Only Attn 1, No Attn 2 + layer_idx = m.processor.layer_idx + m.set_processor(AttnModule(layer_idx)) + m.processor.num_layers = num_layers \ No newline at end of file diff --git a/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/placement.py b/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/placement.py new file mode 100644 index 0000000000000000000000000000000000000000..9185d60d0ce71ca1d524a449085023ed7a094763 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/placement.py @@ -0,0 +1,389 @@ +import torch +import triton +import triton.language as tl + +def wan_token_reorder_to_token_major(tensor, fix_len, reorder_len, reorder_num_frame, frame_size): + """Reorder it from frame major to token major!""" + assert reorder_len == reorder_num_frame * frame_size + assert tensor.shape[2] == fix_len + reorder_len + + tensor[:, :, :-fix_len, :] = tensor[:, :, :-fix_len:, :].reshape(tensor.shape[0], tensor.shape[1], reorder_num_frame, frame_size, tensor.shape[3]) \ + .transpose(2, 3).reshape(tensor.shape[0], tensor.shape[1], reorder_len, tensor.shape[3]) + return tensor + +def wan_token_reorder_to_frame_major(tensor, fix_len, reorder_len, reorder_num_frame, frame_size): + """Reorder it from token major to frame major!""" + assert reorder_len == reorder_num_frame * frame_size + assert tensor.shape[2] == fix_len + reorder_len + + tensor[:, :, :-fix_len:, :] = tensor[:, :, :-fix_len:, :].reshape(tensor.shape[0], tensor.shape[1], frame_size, reorder_num_frame, tensor.shape[3]) \ + .transpose(2, 3).reshape(tensor.shape[0], tensor.shape[1], reorder_len, tensor.shape[3]) + return tensor + + +@triton.jit +def wan_sparse_head_placement_kernel( + query_ptr, key_ptr, value_ptr, # [cfg, num_heads, seq_len, head_dim] seq_len = context_length + num_frame * frame_size + query_out_ptr, key_out_ptr, value_out_ptr, # [cfg, num_heads, seq_len, head_dim] + best_mask_idx_ptr, # [cfg, num_heads] + query_stride_b, query_stride_h, query_stride_s, query_stride_d, + mask_idx_stride_b, mask_idx_stride_h, + seq_len: tl.constexpr, + head_dim: tl.constexpr, + context_length: tl.constexpr, + num_frame: tl.constexpr, + frame_size: tl.constexpr, + BLOCK_SIZE: tl.constexpr +): + # Copy query, key, value to output + # range: [b, h, block_id * block_size: block_id * block_size + block_size, :] + cfg = tl.program_id(0) + head = tl.program_id(1) + block_id = tl.program_id(2) + + start_id = block_id * BLOCK_SIZE + end_id = start_id + BLOCK_SIZE + end_id = tl.where(end_id > seq_len, seq_len, end_id) + + # Load best mask idx (0 is spatial, 1 is temporal) + is_temporal = tl.load(best_mask_idx_ptr + cfg * mask_idx_stride_b + head * mask_idx_stride_h) + + offset_token = tl.arange(0, BLOCK_SIZE) + start_id + offset_mask = offset_token < seq_len + offset_d = tl.arange(0, head_dim) + + if is_temporal: + frame_id = offset_token // frame_size + patch_id = offset_token - frame_id * frame_size + offset_store_token = tl.where(offset_token >= seq_len - context_length, offset_token, patch_id * num_frame + frame_id) + + offset_load = (cfg * query_stride_b + head * query_stride_h + offset_token[:,None] * query_stride_s) + offset_d[None,:] * query_stride_d + offset_query = query_ptr + offset_load + offset_key = key_ptr + offset_load + offset_value = value_ptr + offset_load + + offset_store = (cfg * query_stride_b + head * query_stride_h + offset_store_token[:,None] * query_stride_s) + offset_d[None,:] * query_stride_d + offset_query_out = query_out_ptr + offset_store + offset_key_out = key_out_ptr + offset_store + offset_value_out = value_out_ptr + offset_store + + # Maybe tune the pipeline here + query = tl.load(offset_query, mask=offset_mask[:,None]) + tl.store(offset_query_out, query, mask=offset_mask[:,None]) + key = tl.load(offset_key, mask=offset_mask[:,None]) + tl.store(offset_key_out, key, mask=offset_mask[:,None]) + value = tl.load(offset_value, mask=offset_mask[:,None]) + tl.store(offset_value_out, value, mask=offset_mask[:,None]) + + + else: + offset_load = (cfg * query_stride_b + head * query_stride_h + offset_token[:,None] * query_stride_s) + offset_d[None,:] * query_stride_d + offset_query = query_ptr + offset_load + offset_key = key_ptr + offset_load + offset_value = value_ptr + offset_load + + offset_store = offset_load + offset_query_out = query_out_ptr + offset_store + offset_key_out = key_out_ptr + offset_store + offset_value_out = value_out_ptr + offset_store + + # Maybe tune the pipeline here + query = tl.load(offset_query, mask=offset_mask[:,None]) + tl.store(offset_query_out, query, mask=offset_mask[:,None]) + key = tl.load(offset_key, mask=offset_mask[:,None]) + tl.store(offset_key_out, key, mask=offset_mask[:,None]) + value = tl.load(offset_value, mask=offset_mask[:,None]) + tl.store(offset_value_out, value, mask=offset_mask[:,None]) + + +def wan_sparse_head_placement(query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size): + cfg, num_heads, seq_len, head_dim = query.shape + BLOCK_SIZE = 128 + assert seq_len == context_length + num_frame * frame_size + + grid = (cfg, num_heads, (seq_len + BLOCK_SIZE - 1) // BLOCK_SIZE) + + wan_sparse_head_placement_kernel[grid]( + query, key, value, + query_out, key_out, value_out, + best_mask_idx, + query.stride(0), query.stride(1), query.stride(2), query.stride(3), + best_mask_idx.stride(0), best_mask_idx.stride(1), + seq_len, head_dim, context_length, num_frame, frame_size, + BLOCK_SIZE + ) + + +def ref_wan_sparse_head_placement(query, key, value, best_mask_idx, context_length, num_frame, frame_size): + cfg, num_heads, seq_len, head_dim = query.shape + assert seq_len == context_length + num_frame * frame_size + + query_out = query.clone() + key_out = key.clone() + value_out = value.clone() + + # Spatial + query_out[best_mask_idx == 0], key_out[best_mask_idx == 0], value_out[best_mask_idx == 0] = \ + query[best_mask_idx == 0], key[best_mask_idx == 0], value[best_mask_idx == 0] + + # Temporal + query_out[best_mask_idx == 1], key_out[best_mask_idx == 1], value_out[best_mask_idx == 1] = \ + wan_token_reorder_to_token_major(query[best_mask_idx == 1].unsqueeze(0), context_length, num_frame * frame_size, num_frame, frame_size).squeeze(0), \ + wan_token_reorder_to_token_major(key[best_mask_idx == 1].unsqueeze(0), context_length, num_frame * frame_size, num_frame, frame_size).squeeze(0), \ + wan_token_reorder_to_token_major(value[best_mask_idx == 1].unsqueeze(0), context_length, num_frame * frame_size, num_frame, frame_size).squeeze(0) + + return query_out, key_out, value_out + + +def test_wan_sparse_head_placement(): + + context_length = 226 + num_frame = 11 + frame_size = 4080 + + cfg = 2 + num_heads = 48 + + seq_len = context_length + num_frame * frame_size + head_dim = 64 + + dtype = torch.bfloat16 + device = torch.device("cuda") + + query = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device) + key = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device) + value = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device) + + best_mask_idx = torch.randint(0, 2, (cfg, num_heads), device=device) + + query_out = torch.empty_like(query) + key_out = torch.empty_like(key) + value_out = torch.empty_like(value) + + wan_sparse_head_placement(query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size) + ref_query_out, ref_key_out, ref_value_out = ref_wan_sparse_head_placement(query, key, value, best_mask_idx, context_length, num_frame, frame_size) + + torch.testing.assert_close(query_out, ref_query_out) + torch.testing.assert_close(key_out, ref_key_out) + torch.testing.assert_close(value_out, ref_value_out) + + +def benchmark_wan_sparse_head_placement(): + import time + + context_length = 226 + num_frame = 11 + frame_size = 4080 + + cfg = 2 + num_heads = 48 + + seq_len = context_length + num_frame * frame_size + head_dim = 64 + + dtype = torch.bfloat16 + device = torch.device("cuda") + + query = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device) + key = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device) + value = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device) + best_mask_idx = torch.randint(0, 2, (cfg, num_heads), device=device) + + query_out = torch.empty_like(query) + key_out = torch.empty_like(key) + value_out = torch.empty_like(value) + + warmup = 10 + all_iter = 1000 + + # warmup + for _ in range(warmup): + wan_sparse_head_placement(query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size) + + torch.cuda.synchronize() + start = time.time() + for _ in range(all_iter): + wan_sparse_head_placement(query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size) + torch.cuda.synchronize() + end = time.time() + + print(f"Triton Elapsed Time: {(end - start) / all_iter * 1e3:.2f} ms") + print(f"Triton Total Bandwidth: {query.nelement() * query.element_size() * 3 * 2 * all_iter / (end - start) / 1e9:.2f} GB/s") + + torch.cuda.synchronize() + start = time.time() + for _ in range(all_iter): + ref_wan_sparse_head_placement(query, key, value, best_mask_idx, context_length, num_frame, frame_size) + torch.cuda.synchronize() + end = time.time() + + print(f"Reference Elapsed Time: {(end - start) / all_iter * 1e3:.2f} ms") + print(f"Reference Total Bandwidth: {query.nelement() * query.element_size() * 3 * 2 * all_iter / (end - start) / 1e9:.2f} GB/s") + + +@triton.jit +def wan_hidden_states_placement_kernel( + hidden_states_ptr, # [cfg, num_heads, seq_len, head_dim] seq_len = context_length + num_frame * frame_size + hidden_states_out_ptr, # [cfg, num_heads, seq_len, head_dim] + best_mask_idx_ptr, # [cfg, num_heads] + hidden_states_stride_b, hidden_states_stride_h, hidden_states_stride_s, hidden_states_stride_d, + mask_idx_stride_b, mask_idx_stride_h, + seq_len: tl.constexpr, + head_dim: tl.constexpr, + context_length: tl.constexpr, + num_frame: tl.constexpr, + frame_size: tl.constexpr, + BLOCK_SIZE: tl.constexpr +): + # Copy hidden_states to output + # range: [b, h, block_id * block_size: block_id * block_size + block_size, :] + cfg = tl.program_id(0) + head = tl.program_id(1) + block_id = tl.program_id(2) + + start_id = block_id * BLOCK_SIZE + end_id = start_id + BLOCK_SIZE + end_id = tl.where(end_id > seq_len, seq_len, end_id) + + # Load best mask idx (0 is spatial, 1 is temporal) + is_temporal = tl.load(best_mask_idx_ptr + cfg * mask_idx_stride_b + head * mask_idx_stride_h) + + offset_token = tl.arange(0, BLOCK_SIZE) + start_id + offset_mask = offset_token < seq_len + offset_d = tl.arange(0, head_dim) + + if is_temporal: + patch_id = offset_token // num_frame + frame_id = offset_token - patch_id * num_frame + offset_store_token = tl.where(offset_token >= seq_len - context_length, offset_token, frame_id * frame_size + patch_id) + + offset_load = (cfg * hidden_states_stride_b + head * hidden_states_stride_h + offset_token[:,None] * hidden_states_stride_s) + offset_d[None,:] * hidden_states_stride_d + offset_hidden_states = hidden_states_ptr + offset_load + + offset_store = (cfg * hidden_states_stride_b + head * hidden_states_stride_h + offset_store_token[:,None] * hidden_states_stride_s) + offset_d[None,:] * hidden_states_stride_d + offset_hidden_states_out = hidden_states_out_ptr + offset_store + + # Maybe tune the pipeline here + hidden_states = tl.load(offset_hidden_states, mask=offset_mask[:,None]) + tl.store(offset_hidden_states_out, hidden_states, mask=offset_mask[:,None]) + else: + offset_load = (cfg * hidden_states_stride_b + head * hidden_states_stride_h + offset_token[:,None] * hidden_states_stride_s) + offset_d[None,:] * hidden_states_stride_d + offset_hidden_states = hidden_states_ptr + offset_load + + offset_store = offset_load + offset_hidden_states_out = hidden_states_out_ptr + offset_store + + # Maybe tune the pipeline here + hidden_states = tl.load(offset_hidden_states, mask=offset_mask[:,None]) + tl.store(offset_hidden_states_out, hidden_states, mask=offset_mask[:,None]) + + +def wan_hidden_states_placement(hidden_states, hidden_states_out, best_mask_idx, context_length, num_frame, frame_size): + cfg, num_heads, seq_len, head_dim = hidden_states.shape + BLOCK_SIZE = 128 + assert seq_len == context_length + num_frame * frame_size + + grid = (cfg, num_heads, (seq_len + BLOCK_SIZE - 1) // BLOCK_SIZE) + + + wan_hidden_states_placement_kernel[grid]( + hidden_states, + hidden_states_out, + best_mask_idx, + hidden_states.stride(0), hidden_states.stride(1), hidden_states.stride(2), hidden_states.stride(3), + best_mask_idx.stride(0), best_mask_idx.stride(1), + seq_len, head_dim, context_length, num_frame, frame_size, + BLOCK_SIZE + ) + + return hidden_states_out + +def ref_wan_hidden_states_placement(hidden_states, output_hidden_states, best_mask_idx, context_length, num_frame, frame_size): + cfg, num_heads, seq_len, head_dim = hidden_states.shape + assert seq_len == context_length + num_frame * frame_size + + # Spatial + output_hidden_states[best_mask_idx == 0] = hidden_states[best_mask_idx == 0] + # Temporal + output_hidden_states[best_mask_idx == 1] = wan_token_reorder_to_frame_major(hidden_states[best_mask_idx == 1].unsqueeze(0), context_length, num_frame * frame_size, num_frame, frame_size).squeeze(0) + +def test_wan_hidden_states_placement(): + + context_length = 226 + num_frame = 11 + frame_size = 4080 + + cfg = 2 + num_heads = 48 + + seq_len = context_length + num_frame * frame_size + head_dim = 64 + + dtype = torch.bfloat16 + device = torch.device("cuda") + + hidden_states = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device) + best_mask_idx = torch.randint(0, 2, (cfg, num_heads), device=device) + + hidden_states_out1 = torch.empty_like(hidden_states) + hidden_states_out2 = torch.empty_like(hidden_states) + + wan_hidden_states_placement(hidden_states, hidden_states_out1, best_mask_idx, context_length, num_frame, frame_size) + ref_wan_hidden_states_placement(hidden_states, hidden_states_out2, best_mask_idx, context_length, num_frame, frame_size) + + torch.testing.assert_close(hidden_states_out1, hidden_states_out2) + +def benchmark_wan_hidden_states_placement(): + import time + + context_length = 226 + num_frame = 11 + frame_size = 4080 + + cfg = 2 + num_heads = 48 + + seq_len = context_length + num_frame * frame_size + head_dim = 64 + + dtype = torch.bfloat16 + device = torch.device("cuda") + + hidden_states = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device) + best_mask_idx = torch.randint(0, 2, (cfg, num_heads), device=device) + + hidden_states_out = torch.empty_like(hidden_states) + + warmup = 10 + all_iter = 1000 + + # warmup + for _ in range(warmup): + wan_hidden_states_placement(hidden_states, hidden_states_out, best_mask_idx, context_length, num_frame, frame_size) + + torch.cuda.synchronize() + start = time.time() + for _ in range(all_iter): + wan_hidden_states_placement(hidden_states, hidden_states_out, best_mask_idx, context_length, num_frame, frame_size) + torch.cuda.synchronize() + end = time.time() + + print(f"Triton Elapsed Time: {(end - start) / all_iter * 1e3:.2f} ms") + print(f"Triton Total Bandwidth: {hidden_states.nelement() * hidden_states.element_size() * 2 * all_iter / (end - start) / 1e9:.2f} GB/s") + + torch.cuda.synchronize() + start = time.time() + for _ in range(all_iter): + ref_wan_hidden_states_placement(hidden_states, hidden_states.clone(), best_mask_idx, context_length, num_frame, frame_size) + torch.cuda.synchronize() + end = time.time() + + print(f"Reference Elapsed Time: {(end - start) / all_iter * 1e3:.2f} ms") + print(f"Reference Total Bandwidth: {hidden_states.nelement() * hidden_states.element_size() * 2 * all_iter / (end - start) / 1e9:.2f} GB/s") + + +if __name__ == "__main__": + test_wan_sparse_head_placement() + benchmark_wan_sparse_head_placement() + test_wan_hidden_states_placement() + benchmark_wan_hidden_states_placement() \ No newline at end of file diff --git a/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/utils.py b/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..36c9e049c081905bc69f4d0fd722b8e14d11954b --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/models/wan/wan_svg/utils.py @@ -0,0 +1,93 @@ +"""Mask Mod for Image2Video""" + +import math +from math import floor, ceil +import torch +from torch import Tensor + + +from functools import lru_cache +from typing import Optional, List + +import torch +from torch.nn.attention.flex_attention import ( + create_block_mask, +) + + +@lru_cache +def create_block_mask_cached(score_mod, B, H, M, N, device="cuda", _compile=False): + block_mask = create_block_mask(score_mod, B, H, M, N, device=device, _compile=_compile) + return block_mask + +def generate_temporal_head_mask_mod(context_length: int = 226, prompt_length: int = 226, num_frames: int = 13, token_per_frame: int = 1350, mul: int = 2): + + def round_to_multiple(idx): + return ceil(idx / 128) * 128 + + def temporal_mask_mod(b, h, q_idx, kv_idx): + two_frame = round_to_multiple(mul * token_per_frame) + temporal_head_mask = (torch.abs(q_idx - kv_idx) <= two_frame) + + # return temporal_head_mask + first_frame_mask = (kv_idx < token_per_frame) + video_mask = first_frame_mask | temporal_head_mask + return video_mask + + return temporal_mask_mod + +def generate_dense_mask_mod(): + def dense_mask_mod(b, h, q_idx, kv_idx): + return (q_idx >= 0) # True + return dense_mask_mod + +def sparsity_to_width(sparsity, context_length, num_frame, frame_size): + seq_len = context_length + num_frame * frame_size + total_elements = seq_len ** 2 + + sparsity = (sparsity * total_elements - 2 * seq_len * context_length) / total_elements + + width = seq_len * (1 - math.sqrt(1 - sparsity)) + width_frame = width / frame_size + + return width_frame + +def get_attention_mask(mask_name, sample_mse_max_row, context_length, num_frame, frame_size): + + from termcolor import colored + + allocated = torch.cuda.memory_allocated() / 1e9 + print(colored(f"Allocated Memory: {allocated:.2f} GB", "yellow")) + + attention_mask = torch.zeros((context_length + num_frame * frame_size, context_length + num_frame * frame_size), device="cpu") + + # TODO: fix hard coded mask + if mask_name == "spatial": + pixel_attn_mask = torch.zeros_like(attention_mask, dtype=torch.bool, device="cpu") + + pixel_attn_mask[:, :frame_size] = 1 # First Frame Sink + + block_size, block_thres = 128, frame_size * 2 + num_block = math.ceil(num_frame * frame_size / block_size) + for i in range(num_block): + for j in range(num_block): + if abs(i - j) < block_thres // block_size: + pixel_attn_mask[i * block_size : (i + 1) * block_size, j * block_size : (j + 1) * block_size] = 1 + attention_mask = pixel_attn_mask + else: + pixel_attn_mask = torch.zeros_like(attention_mask, dtype=torch.bool, device="cpu") + + pixel_attn_mask[:, :frame_size] = 1 # First Frame Sink + + block_size, block_thres = 128, frame_size * 2 + num_block = math.ceil(num_frame * frame_size / block_size) + for i in range(num_block): + for j in range(num_block): + if abs(i - j) < block_thres // block_size: + pixel_attn_mask[i * block_size : (i + 1) * block_size, j * block_size : (j + 1) * block_size] = 1 + + pixel_attn_mask = pixel_attn_mask.reshape(frame_size, num_frame, frame_size, num_frame).permute(1, 0, 3, 2).reshape(frame_size * num_frame, frame_size * num_frame) + attention_mask = pixel_attn_mask + + attention_mask = attention_mask[:sample_mse_max_row].cuda() + return attention_mask \ No newline at end of file diff --git a/exp_code/1_benchmark/AccVideo/requirements.txt b/exp_code/1_benchmark/AccVideo/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5eb6d29a52128b20f283f5d10cb4261ed689e05f --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/requirements.txt @@ -0,0 +1,14 @@ +opencv-python==4.9.0.80 +diffusers==0.31.0 +transformers==4.46.3 +tokenizers==0.20.3 +accelerate +pandas==2.0.3 +numpy==1.24.4 +einops==0.7.0 +tqdm==4.66.2 +loguru==0.7.2 +imageio==2.34.0 +imageio-ffmpeg==0.5.1 +safetensors==0.4.3 +ninja \ No newline at end of file diff --git a/exp_code/1_benchmark/AccVideo/sample_t2v.py b/exp_code/1_benchmark/AccVideo/sample_t2v.py new file mode 100644 index 0000000000000000000000000000000000000000..c525163b893b6c8e5fc8d8a63caf50a04d706b08 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/sample_t2v.py @@ -0,0 +1,225 @@ +import imageio +from einops import rearrange + +import torchvision +import numpy as np +from pathlib import Path +import argparse +import os + +from models.hunyuan.inference import HunyuanVideoSampler + +def main(args): + print(args) + + models_root_path = Path(args.model_path) + if not models_root_path.exists(): + raise ValueError(f"`models_root` not exists: {models_root_path}") + + # Create save folder to save the samples + save_path = args.output_path + os.makedirs(save_path, exist_ok=True) + + with open(args.prompt_file) as f: + prompts = f.readlines() + + + # Load models + hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained( + models_root_path, args=args + ) + + # Get the updated args + args = hunyuan_video_sampler.args + + for idx, prompt in enumerate(prompts): + seed = args.seed + outputs = hunyuan_video_sampler.predict( + prompt=prompt, + height=args.height, + width=args.width, + video_length=args.num_frames, + seed=seed, + negative_prompt=args.neg_prompt, + infer_steps=args.num_inference_steps, + guidance_scale=args.guidance_scale, + num_videos_per_prompt=args.num_videos, + flow_shift=args.flow_shift, + batch_size=args.batch_size, + embedded_guidance_scale=args.embedded_cfg_scale, + few_step=True + ) + if 'LOCAL_RANK' not in os.environ or int(os.environ['LOCAL_RANK']) == 0: + videos = rearrange(outputs["samples"], "b c t h w -> t b c h w") + outputs = [] + for x in videos: + x = torchvision.utils.make_grid(x, nrow=6) + x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) + outputs.append((x * 255).numpy().astype(np.uint8)) + os.makedirs(args.output_path, exist_ok=True) + imageio.mimsave( + os.path.join(args.output_path, f"{idx}.mp4"), outputs, fps=args.fps + ) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + # Basic parameters + parser.add_argument("--prompt_file", type=str, default="./assets/prompt.txt", help="prompt file for inference") + parser.add_argument("--num_frames", type=int, default=16) + parser.add_argument("--height", type=int, default=256) + parser.add_argument("--width", type=int, default=256) + parser.add_argument("--num_inference_steps", type=int, default=50) + parser.add_argument("--model_path", type=str, default="./ckpts") + parser.add_argument("--output_path", type=str, default="./outputs/accvideo-5-steps") + parser.add_argument("--fps", type=int, default=24) + + # Additional parameters + parser.add_argument( + "--denoise-type", + type=str, + default="flow", + help="Denoise type for noised inputs.", + ) + parser.add_argument("--seed", type=int, default=None, help="Seed for evaluation.") + parser.add_argument( + "--neg_prompt", type=str, default=None, help="Negative prompt for sampling." + ) + parser.add_argument( + "--guidance_scale", + type=float, + default=1.0, + help="Classifier free guidance scale.", + ) + parser.add_argument( + "--embedded_cfg_scale", + type=float, + default=6.0, + help="Embedded classifier free guidance scale.", + ) + parser.add_argument( + "--flow_shift", type=int, default=7, help="Flow shift parameter." + ) + parser.add_argument( + "--batch_size", type=int, default=1, help="Batch size for inference." + ) + parser.add_argument( + "--num_videos", + type=int, + default=1, + help="Number of videos to generate per prompt.", + ) + parser.add_argument( + "--load-key", + type=str, + default="module", + help="Key to load the model states. 'module' for the main model, 'ema' for the EMA model.", + ) + parser.add_argument( + "--use-cpu-offload", + action="store_true", + help="Use CPU offload for the model load.", + ) + parser.add_argument( + "--dit-weight", + type=str, + default="data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", + ) + parser.add_argument( + "--reproduce", + action="store_true", + help="Enable reproducibility by setting random seeds and deterministic algorithms.", + ) + parser.add_argument( + "--disable-autocast", + action="store_true", + help="Disable autocast for denoising loop and vae decoding in pipeline sampling.", + ) + + # Flow Matching + parser.add_argument( + "--flow-reverse", + action="store_true", + help="If reverse, learning/sampling from t=1 -> t=0.", + ) + parser.add_argument( + "--flow-solver", type=str, default="euler", help="Solver for flow matching." + ) + parser.add_argument( + "--use-linear-quadratic-schedule", + action="store_true", + help="Use linear quadratic schedule for flow matching. Following MovieGen (https://ai.meta.com/static-resource/movie-gen-research-paper)", + ) + parser.add_argument( + "--linear-schedule-end", + type=int, + default=25, + help="End step for linear quadratic schedule for flow matching.", + ) + + # Model parameters + parser.add_argument("--model", type=str, default="HYVideo-T/2-cfgdistill") + parser.add_argument("--latent-channels", type=int, default=16) + parser.add_argument( + "--precision", type=str, default="bf16", choices=["fp32", "fp16", "bf16"] + ) + parser.add_argument( + "--rope-theta", type=int, default=256, help="Theta used in RoPE." + ) + + parser.add_argument("--vae", type=str, default="884-16c-hy") + parser.add_argument( + "--vae-precision", type=str, default="fp16", choices=["fp32", "fp16", "bf16"] + ) + parser.add_argument("--vae-tiling", action="store_true", default=True) + + parser.add_argument("--text-encoder", type=str, default="llm") + parser.add_argument( + "--text-encoder-precision", + type=str, + default="fp16", + choices=["fp32", "fp16", "bf16"], + ) + parser.add_argument("--text-states-dim", type=int, default=4096) + parser.add_argument("--text-len", type=int, default=256) + parser.add_argument("--tokenizer", type=str, default="llm") + parser.add_argument("--prompt-template", type=str, default="dit-llm-encode") + parser.add_argument( + "--prompt-template-video", type=str, default="dit-llm-encode-video" + ) + parser.add_argument("--hidden-state-skip-layer", type=int, default=2) + parser.add_argument("--apply-final-norm", action="store_true") + + parser.add_argument("--text-encoder-2", type=str, default="clipL") + parser.add_argument( + "--text-encoder-precision-2", + type=str, + default="fp16", + choices=["fp32", "fp16", "bf16"], + ) + parser.add_argument("--text-states-dim-2", type=int, default=768) + parser.add_argument("--tokenizer-2", type=str, default="clipL") + parser.add_argument("--text-len-2", type=int, default=77) + + + # ======================== Model loads ======================== + parser.add_argument( + "--ulysses-degree", + type=int, + default=1, + help="Ulysses degree.", + ) + parser.add_argument( + "--ring-degree", + type=int, + default=1, + help="Ulysses degree.", + ) + parser.add_argument( + "--use-fp8", + action="store_true", + help="Enable use fp8 for inference acceleration." + ) + + args = parser.parse_args() + main(args) diff --git a/exp_code/1_benchmark/AccVideo/sample_wanx_t2v.py b/exp_code/1_benchmark/AccVideo/sample_wanx_t2v.py new file mode 100644 index 0000000000000000000000000000000000000000..67d7fd5978c35c6a7dc741cdbee5effe3e772399 --- /dev/null +++ b/exp_code/1_benchmark/AccVideo/sample_wanx_t2v.py @@ -0,0 +1,310 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import argparse +import logging +import os +import sys +import warnings + +warnings.filterwarnings('ignore') +import imageio +from einops import rearrange +import numpy as np + +import torchvision +import torch +import torch.distributed as dist +import random + +import models.wan as wan +from models.wan.configs import WAN_CONFIGS, SIZE_CONFIGS, SUPPORTED_SIZES +from models.wan.utils.utils import str2bool + + +EXAMPLE_PROMPT = { + "t2v-1.3B": { + "prompt": "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", + }, + "t2v-14B": { + "prompt": "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", + }, + "t2i-14B": { + "prompt": "一个朴素端庄的美人", + }, + "i2v-14B": { + "prompt": + "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.", + "image": + "examples/i2v_input.JPG", + }, +} + + +def _validate_args(args): + # Basic check + assert args.ckpt_dir is not None, "Please specify the checkpoint directory." + assert args.task in WAN_CONFIGS, f"Unsupport task: {args.task}" + assert args.task in EXAMPLE_PROMPT, f"Unsupport task: {args.task}" + + # The default sampling steps are 40 for image-to-video tasks and 50 for text-to-video tasks. + if args.sample_steps is None: + args.sample_steps = 40 if "i2v" in args.task else 50 + + if args.sample_shift is None: + args.sample_shift = 5.0 + if "i2v" in args.task and args.size in ["832*480", "480*832"]: + args.sample_shift = 3.0 + + # The default number of frames are 1 for text-to-image tasks and 81 for other tasks. + if args.frame_num is None: + args.frame_num = 1 if "t2i" in args.task else 81 + + # T2I frame_num check + if "t2i" in args.task: + assert args.frame_num == 1, f"Unsupport frame_num {args.frame_num} for task {args.task}" + + args.base_seed = args.base_seed if args.base_seed >= 0 else random.randint( + 0, sys.maxsize) + # Size check + assert args.size in SUPPORTED_SIZES[ + args. + task], f"Unsupport size {args.size} for task {args.task}, supported sizes are: {', '.join(SUPPORTED_SIZES[args.task])}" + + +def _parse_args(): + parser = argparse.ArgumentParser( + description="Generate a image or video from a text prompt or image using Wan" + ) + + parser.add_argument( + "--task", + type=str, + default="t2v-14B", + choices=list(WAN_CONFIGS.keys()), + help="The task to run.") + parser.add_argument( + "--size", + type=str, + default="1280*720", + choices=list(SIZE_CONFIGS.keys()), + help="The area (width*height) of the generated video. For the I2V task, the aspect ratio of the output video will follow that of the input image." + ) + parser.add_argument( + "--frame_num", + type=int, + default=None, + help="How many frames to sample from a image or video. The number should be 4n+1" + ) + parser.add_argument( + "--ckpt_dir", + type=str, + default=None, + help="The path to the checkpoint directory.") + parser.add_argument( + "--offload_model", + type=str2bool, + default=None, + help="Whether to offload the model to CPU after each model forward, reducing GPU memory usage." + ) + parser.add_argument( + "--ulysses_size", + type=int, + default=1, + help="The size of the ulysses parallelism in DiT.") + parser.add_argument( + "--ring_size", + type=int, + default=1, + help="The size of the ring attention parallelism in DiT.") + parser.add_argument( + "--t5_fsdp", + action="store_true", + default=False, + help="Whether to use FSDP for T5.") + parser.add_argument( + "--t5_cpu", + action="store_true", + default=False, + help="Whether to place T5 model on CPU.") + parser.add_argument( + "--dit_fsdp", + action="store_true", + default=False, + help="Whether to use FSDP for DiT.") + parser.add_argument( + "--save_file", + type=str, + default=None, + help="The file to save the generated image or video to.") + parser.add_argument( + "--save_dir", + type=str, + default=None, + help="The directory to save the generated image or video to.") + parser.add_argument( + "--prompt", + type=str, + default=None, + help="The prompt to generate the image or video from.") + parser.add_argument( + "--use_prompt_extend", + action="store_true", + default=False, + help="Whether to use prompt extend.") + parser.add_argument( + "--prompt_extend_method", + type=str, + default="local_qwen", + choices=["dashscope", "local_qwen"], + help="The prompt extend method to use.") + parser.add_argument( + "--prompt_extend_model", + type=str, + default=None, + help="The prompt extend model to use.") + parser.add_argument( + "--prompt_extend_target_lang", + type=str, + default="zh", + choices=["zh", "en"], + help="The target language of prompt extend.") + parser.add_argument( + "--base_seed", + type=int, + default=-1, + help="The seed to use for generating the image or video.") + parser.add_argument( + "--image", + type=str, + default=None, + help="The image to generate the video from.") + parser.add_argument( + "--sample_solver", + type=str, + default='unipc', + choices=['unipc', 'dpm++', 'euler'], + help="The solver used to sample.") + parser.add_argument( + "--sample_steps", type=int, default=None, help="The sampling steps.") + parser.add_argument( + "--sample_shift", + type=float, + default=None, + help="Sampling shift factor for flow matching schedulers.") + parser.add_argument( + "--sample_guide_scale", + type=float, + default=5.0, + help="Classifier free guidance scale.") + parser.add_argument( + "--dit_ckpt_path", + type=str, + default=None, + help="Finetune checkpoint.") + + args = parser.parse_args() + + _validate_args(args) + + return args + + +def _init_logging(rank): + # logging + if rank == 0: + # set format + logging.basicConfig( + level=logging.INFO, + format="[%(asctime)s] %(levelname)s: %(message)s", + handlers=[logging.StreamHandler(stream=sys.stdout)]) + else: + logging.basicConfig(level=logging.ERROR) + + +def generate(args): + rank = int(os.getenv("RANK", 0)) + world_size = int(os.getenv("WORLD_SIZE", 1)) + local_rank = int(os.getenv("LOCAL_RANK", 0)) + device = local_rank + _init_logging(rank) + + if args.offload_model is None: + args.offload_model = False if world_size > 1 else True + logging.info( + f"offload_model is not specified, set to {args.offload_model}.") + + assert not ( + args.t5_fsdp or args.dit_fsdp + ), f"t5_fsdp and dit_fsdp are not supported in non-distributed environments." + assert not ( + args.ulysses_size > 1 or args.ring_size > 1 + ), f"context parallel are not supported in non-distributed environments." + + cfg = WAN_CONFIGS[args.task] + + logging.info(f"Generation job args: {args}") + logging.info(f"Generation model config: {cfg}") + + if dist.is_initialized(): + base_seed = [args.base_seed] if rank == 0 else [None] + dist.broadcast_object_list(base_seed, src=0) + args.base_seed = base_seed[0] + + logging.info("Creating WanT2V pipeline.") + wan_t2v = wan.WanT2V( + config=cfg, + checkpoint_dir=args.ckpt_dir, + device_id=device, + rank=rank, + t5_fsdp=args.t5_fsdp, + dit_fsdp=args.dit_fsdp, + use_usp=(args.ulysses_size > 1 or args.ring_size > 1), + t5_cpu=args.t5_cpu, + dit_path=args.dit_ckpt_path, + ) + + + with open("assets/prompt.txt") as f: + prompts = f.readlines() + + import time + + os.makedirs(args.save_dir, exist_ok=True) + for idx, prompt in enumerate(prompts): + save_file = os.path.join(args.save_dir, f"{idx}.mp4") + if os.path.exists(save_file): continue + logging.info( + f"Generating {'image' if 't2i' in args.task else 'video'} ...") + start_time = time.time() + videos, context = wan_t2v.generate( + prompt, + size=SIZE_CONFIGS[args.size], + frame_num=args.frame_num, + shift=args.sample_shift, + sample_solver=args.sample_solver, + sampling_steps=args.sample_steps, + guide_scale=args.sample_guide_scale, + seed=1024, + offload_model=False, + few_step=True, + no_cfg=True) + + print('generation time:', time.time() - start_time) + if rank == 0: + videos = videos.unsqueeze(0) + videos = rearrange(videos, "b c t h w -> t b c h w") + videos = (videos.cpu() + 1) / 2 + outputs = [] + for x in videos: + x = torchvision.utils.make_grid(x, nrow=6) + x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) + outputs.append((x * 255).numpy().astype(np.uint8)) + imageio.mimsave( + save_file, outputs, fps=16 + ) + + logging.info("Finished.") + + +if __name__ == "__main__": + args = _parse_args() + generate(args) diff --git a/exp_code/1_benchmark/CausVid/LICENSE.md b/exp_code/1_benchmark/CausVid/LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..c94338cf6251657e6357788e756270ce1638dbf2 --- /dev/null +++ b/exp_code/1_benchmark/CausVid/LICENSE.md @@ -0,0 +1,173 @@ +# Attribution-NonCommercial-ShareAlike 4.0 International + +Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. 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For the avoidance of doubt, this paragraph does not form part of the public licenses. +> +> Creative Commons may be contacted at creativecommons.org \ No newline at end of file diff --git a/exp_code/1_benchmark/CausVid/README.md b/exp_code/1_benchmark/CausVid/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bbbfb43b105657703ef0ed0c8eb1d43f604a647a --- /dev/null +++ b/exp_code/1_benchmark/CausVid/README.md @@ -0,0 +1,156 @@ +# From Slow Bidirectional to Fast Autoregressive Video Diffusion Models [[Huggingface](https://huggingface.co/tianweiy/CausVid)][[Project](https://causvid.github.io/)] + +Few-step Text-to-Video Generation. + +![image/jpeg](docs/teaser.png) + +> [**From Slow Bidirectional to Fast Autoregressive Video Diffusion Models**](https://causvid.github.io/), +> Tianwei Yin*, Qiang Zhang*, Richard Zhang, William T. Freeman, Frédo Durand, Eli Shechtman, Xun Huang (* equal contribution) +> *CVPR 2025 ([arXiv 2412.07772](https://arxiv.org/abs/2412.07772))* + +## Abstract + +Current video diffusion models achieve impressive generation quality but struggle in interactive applications due to bidirectional attention dependencies. The generation of a single frame requires the model to process the entire sequence, including the future. We address this limitation by adapting a pretrained bidirectional diffusion transformer to an autoregressive transformer that generates frames on-the-fly. To further reduce latency, we extend distribution matching distillation (DMD) to videos, distilling 50-step diffusion model into a 4-step generator. To enable stable and high-quality distillation, we introduce a student initialization scheme based on teacher's ODE trajectories, as well as an asymmetric distillation strategy that supervises a causal student model with a bidirectional teacher. This approach effectively mitigates error accumulation in autoregressive generation, allowing long-duration video synthesis despite training on short clips. Our model achieves a total score of 84.27 on the VBench-Long benchmark, surpassing all previous video generation models. It enables fast streaming generation of high-quality videos at 9.4 FPS on a single GPU thanks to KV caching. Our approach also enables streaming video-to-video translation, image-to-video, and dynamic prompting in a zero-shot manner. + +
+ ⚠️ This repo is a work in progress. Expect frequent updates in the coming weeks. +
+ + +## Environment Setup + +```bash +conda create -n causvid python=3.10 -y +conda activate causvid +pip install torch torchvision +pip install -r requirements.txt +python setup.py develop +``` + +Also download the Wan base models from [here](https://github.com/Wan-Video/Wan2.1) and save it to wan_models/Wan2.1-T2V-1.3B/ + +## Inference Example + +First download the checkpoints: [Autoregressive Model](https://huggingface.co/tianweiy/CausVid/tree/main/autoregressive_checkpoint), [Bidirectional Model 1](https://huggingface.co/tianweiy/CausVid/tree/main/bidirectional_checkpoint1) or [Bidirectional Model 2](https://huggingface.co/tianweiy/CausVid/tree/main/bidirectional_checkpoint2) (performs slightly better). + +### Autoregressive 3-step 5-second Video Generation + +```bash +python minimal_inference/autoregressive_inference.py --config_path configs/wan_causal_dmd.yaml --checkpoint_folder XXX --output_folder XXX --prompt_file_path XXX +``` + +### Autoregressive 3-step long Video Generation + +```bash +python minimal_inference/longvideo_autoregressive_inference.py --config_path configs/wan_causal_dmd.yaml --checkpoint_folder XXX --output_folder XXX --prompt_file_path XXX --num_rollout XXX +``` + +### Bidirectional 3-step 5-second Video Generation + +```bash +python minimal_inference/bidirectional_inference.py --config_path configs/wan_bidirectional_dmd_from_scratch.yaml --checkpoint_folder XXX --output_folder XXX --prompt_file_path XXX +``` + +## Training and Evaluation + +### Dataset Preparation + +We use the [MixKit Dataset](https://huggingface.co/datasets/LanguageBind/Open-Sora-Plan-v1.1.0/tree/main/all_mixkit) (6K videos) as a toy example for distillation. + +To prepare the dataset, follow these steps. You can also download the final LMDB dataset from [here](https://huggingface.co/tianweiy/CausVid/tree/main/mixkit_latents_lmdb) + +```bash +# download and extract video from the Mixkit dataset +python distillation_data/download_mixkit.py --local_dir XXX + +# convert the video to 480x832x81 +python distillation_data/process_mixkit.py --input_dir XXX --output_dir XXX --width 832 --height 480 --fps 16 + +# precompute the vae latent +torchrun --nproc_per_node 8 distillation_data/compute_vae_latent.py --input_video_folder XXX --output_latent_folder XXX --info_path sample_dataset/video_mixkit_6484_caption.json + +# combined everything into a lmdb dataset +python causvid/ode_data/create_lmdb_iterative.py --data_path XXX --lmdb_path XXX +``` + +## Training + +Please first modify the wandb account information in the respective config. + +Bidirectional DMD Training + +```bash +torchrun --nnodes 8 --nproc_per_node=8 --rdzv_id=5235 \ + --rdzv_backend=c10d \ + --rdzv_endpoint $MASTER_ADDR causvid/train_distillation.py \ + --config_path configs/wan_bidirectional_dmd_from_scratch.yaml +``` + +ODE Dataset Generation. We generate a total of 1.5K dataset pairs, which can also be downloaded from [here](https://huggingface.co/tianweiy/CausVid/tree/main/mixkit_ode_lmdb) + +```bash +torchrun --nproc_per_node 8 causvid/models/wan/generate_ode_pairs.py --output_folder XXX --caption_path sample_dataset/mixkit_prompts.txt + +python causvid/ode_data/create_lmdb_iterative.py --data_path XXX --lmdb_path XXX +``` + +Causal ODE Pretraining. You can also skip this step and download the ode finetuned checkpoint from [here](https://huggingface.co/tianweiy/CausVid/tree/main/wan_causal_ode_checkpoint_model_003000) + +```bash +torchrun --nnodes 8 --nproc_per_node=8 --rdzv_id=5235 \ + --rdzv_backend=c10d \ + --rdzv_endpoint $MASTER_ADDR causvid/train_ode.py \ + --config_path configs/wan_causal_ode.yaml --no_visualize +``` + +Causal DMD Training. + +```bash +torchrun --nnodes 8 --nproc_per_node=8 --rdzv_id=5235 \ + --rdzv_backend=c10d \ + --rdzv_endpoint $MASTER_ADDR causvid/train_distillation.py \ + --config_path configs/wan_causal_dmd.yaml --no_visualize +``` + +## TODO +- [ ] Checkpoints trained on larger / higher quality dataset. +- [ ] Image to Video Generation +- [ ] Caching of cross-attention features + +## Notes + +- With the toy dataset, the performance saturates around 1K iterations. +- DMD training likely requires larger, higher-quality datasets. +- Timestep shift, guidance scale, or denoising steps may need fine-tuning. + +## Citation + +If you find CausVid useful or relevant to your research, please kindly cite our papers: + +```bib +@inproceedings{yin2025causvid, + title={From Slow Bidirectional to Fast Autoregressive Video Diffusion Models}, + author={Yin, Tianwei and Zhang, Qiang and Zhang, Richard and Freeman, William T and Durand, Fredo and Shechtman, Eli and Huang, Xun}, + booktitle={CVPR}, + year={2025} +} + +@inproceedings{yin2024improved, + title={Improved Distribution Matching Distillation for Fast Image Synthesis}, + author={Yin, Tianwei and Gharbi, Micha{\"e}l and Park, Taesung and Zhang, Richard and Shechtman, Eli and Durand, Fredo and Freeman, William T}, + booktitle={NeurIPS}, + year={2024} +} + +@inproceedings{yin2024onestep, + title={One-step Diffusion with Distribution Matching Distillation}, + author={Yin, Tianwei and Gharbi, Micha{\"e}l and Zhang, Richard and Shechtman, Eli and Durand, Fr{\'e}do and Freeman, William T and Park, Taesung}, + booktitle={CVPR}, + year={2024} +} +``` + +## Acknowledgments + +Our implementation is largely based on the [Wan](https://github.com/Wan-Video/Wan2.1) model suite. + diff --git a/exp_code/1_benchmark/CausVid/generate_ode_pairs.sh b/exp_code/1_benchmark/CausVid/generate_ode_pairs.sh new file mode 100644 index 0000000000000000000000000000000000000000..269d4af735c9281ff0f5a56c23c77ada14bd8cc1 --- /dev/null +++ b/exp_code/1_benchmark/CausVid/generate_ode_pairs.sh @@ -0,0 +1,3 @@ +torchrun --nproc_per_node 1 causvid/models/wan/generate_ode_pairs.py \ + --output_folder ode_pairs_mixkit \ + --caption_path sample_dataset/mixkit_prompts.txt diff --git a/exp_code/1_benchmark/CausVid/kill_processes.sh b/exp_code/1_benchmark/CausVid/kill_processes.sh new file mode 100644 index 0000000000000000000000000000000000000000..e4524f8055926effc046478b5faf71bfa38c370d --- /dev/null +++ b/exp_code/1_benchmark/CausVid/kill_processes.sh @@ -0,0 +1,8 @@ +PIDS=$(ps aux | grep python | grep -v grep | awk '{print $2}') + +for PID in $PIDS; do + # echo "Killing Python process with PID: $PID" + kill -9 $PID + done + + echo "All Python processes have been terminated." diff --git a/exp_code/1_benchmark/CausVid/ode_to_lmdb.sh b/exp_code/1_benchmark/CausVid/ode_to_lmdb.sh new file mode 100644 index 0000000000000000000000000000000000000000..e6a86e4fd4f2614b1e0d8c33defe9b8a8c3c07c3 --- /dev/null +++ b/exp_code/1_benchmark/CausVid/ode_to_lmdb.sh @@ -0,0 +1,3 @@ +python causvid/ode_data/create_lmdb_iterative.py \ + --data_path /mnt/workspace/ysh/Code/Efficient_Model/1_benchmark/CausVid/ode_pairs_mixkit \ + --lmdb_path /mnt/workspace/ysh/Code/Efficient_Model/1_benchmark/CausVid/ode_pairs_mixkit_lmdb \ No newline at end of file diff --git a/exp_code/1_benchmark/CausVid/prompt.txt b/exp_code/1_benchmark/CausVid/prompt.txt new file mode 100644 index 0000000000000000000000000000000000000000..fcb6c9e8ffe0f9ee17b16ceb4271156ae5b708f7 --- /dev/null +++ b/exp_code/1_benchmark/CausVid/prompt.txt @@ -0,0 +1,5 @@ +无人机镜头从空中俯瞰，渐渐接近雄伟的大雁塔，塔身在夕阳的金色余晖中显得尤为庄严，犹如一座古老的灯塔，守护着这片历史的土地。周围的广场和街道逐渐显现出繁忙的景象，游客在塔下驻足，微弱的步伐声和交谈声轻轻传入耳中。无人机镜头缓慢向上升起，展示出整个大唐不夜城的壮丽景观。远处，现代高楼大厦与古老的建筑融为一体，霓虹灯的光辉闪烁，与古塔的静谧形成鲜明对比。镜头继续拉远，穿越城市的空中，鸟瞰下方那些交错的街道和熙熙攘攘的人群。随着夜幕的降临，整个大唐不夜城逐渐点亮，霓虹和灯光在夜空中汇成一片璀璨的星海。镜头飞掠过街头，捕捉到各色灯光下人们的笑脸和忙碌的脚步，音乐也在背景中渐渐转换成融合了古风和现代节奏的动感旋律，完美呈现出一个既古老又现代的城市风貌。 +A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, along red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is dampand reflective, creating a mirror effect of thecolorful lights. Many pedestrians walk about. +动画场景特写中，一个矮小、毛茸茸的怪物跪在一根融化的红蜡烛旁。三维写实的艺术风格注重光照和纹理的相互作用，在整个场景中投射出引人入胜的阴影。怪物睁着好奇的大眼睛注视着火焰，它的皮毛在温暖闪烁的光芒中轻轻拂动。镜头慢慢拉近，捕捉到怪物皮毛的复杂细节和精致的熔蜡液滴。怪物试探性地伸出一只爪子，似乎想要触碰火焰，而烛光则在它周围闪烁舞动，气氛充满了惊奇和好奇。 +A drone camera gracefully circles a historic church perched on a rugged outcropping along the Amalfi Coast, capturing its magnificent architectural details and tiered pathways and patios. Below, waves crash against the rocks, while the horizon stretches out over the coastal waters and hilly landscapes of Italy. Distant figures stroll and enjoy the breathtaking ocean views from the patios, creating a dynamic scene. The warm glow of the afternoon sun bathes the scene in a magical and romantic light, casting long shadows and adding depth to the stunning vista. The camera occasionally zooms in to highlight the intricate details of the church, then pans out to showcase the expansive coastline, creating a captivating visual narrative. +一个特写镜头捕捉到一位 60 多岁、留着胡子的白发老人，他坐在巴黎的一家咖啡馆里陷入沉思，思考着宇宙的历史。他的眼睛紧紧盯着屏幕外走动的人们，而自己却一动不动。他身着羊毛大衣、纽扣衬衫、棕色贝雷帽，戴着一副眼镜，散发着教授的风范。他偶尔瞥一眼四周，目光停留在背景中熙熙攘攘的巴黎街道和城市景观上。场景沐浴在金色的光线中，让人联想到 35 毫米电影胶片。当他微微前倾时，眼睛睁大，露出顿悟的瞬间，并微微闭口微笑，暗示他已经找到了生命奥秘的答案。景深营造出光影交错的动态效果，烘托出智慧沉思的氛围。 \ No newline at end of file diff --git a/exp_code/1_benchmark/CausVid/requirements.txt b/exp_code/1_benchmark/CausVid/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..eb8118a1e292a2c5030facb98b28a5121aa4aa2b --- /dev/null +++ b/exp_code/1_benchmark/CausVid/requirements.txt @@ -0,0 +1,33 @@ +torch>=2.4.0 +torchvision>=0.19.0 +opencv-python>=4.9.0.80 +diffusers>=0.31.0 +transformers>=4.49.0 +tokenizers>=0.20.3 +accelerate>=1.1.1 +tqdm +imageio +easydict +ftfy +dashscope +imageio-ffmpeg +flash_attn +gradio>=5.0.0 +numpy==1.24.4 +boto3 +decord +wandb +omegaconf +moviepy==1.0.3 +einops +av==13.1.0 +opencv-python +git+https://github.com/openai/CLIP.git +open_clip_torch +starlette +pycocotools +lmdb +matplotlib +sentencepiece +pydantic +scikit-image \ No newline at end of file