1109 lines
47 KiB
Python
1109 lines
47 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from Helios (https://github.com/BestWishYsh/Helios)
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from __future__ import annotations
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import json
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import logging
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import math
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import os
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from collections.abc import Iterable
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from typing import TYPE_CHECKING, Any
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import numpy as np
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import torch
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import torch.nn.functional as F
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from diffusers import AutoencoderKLWan
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from diffusers.utils.torch_utils import randn_tensor
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from torch import nn
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from transformers import AutoConfig, AutoTokenizer, UMT5EncoderModel
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from vllm.model_executor.models.utils import AutoWeightsLoader
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from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
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from vllm_omni.diffusion.distributed.cfg_parallel import CFGParallelMixin
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from vllm_omni.diffusion.distributed.utils import get_local_device
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from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
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from vllm_omni.diffusion.models.helios.helios_transformer import HeliosTransformer3DModel
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from vllm_omni.diffusion.models.helios.scheduling_helios import HeliosScheduler
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from vllm_omni.diffusion.models.progress_bar import ProgressBarMixin
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from vllm_omni.diffusion.profiler.diffusion_pipeline_profiler import DiffusionPipelineProfilerMixin
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from vllm_omni.diffusion.request import OmniDiffusionRequest
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from vllm_omni.platforms import current_omni_platform
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if TYPE_CHECKING:
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig,
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)
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logger = logging.getLogger(__name__)
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def calculate_shift(
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image_seq_len,
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base_seq_len: int = 256,
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max_seq_len: int = 4096,
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base_shift: float = 0.5,
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max_shift: float = 1.15,
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):
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m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
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b = base_shift - m * base_seq_len
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mu = image_seq_len * m + b
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return mu
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def optimized_scale(positive_flat, negative_flat):
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positive_flat = positive_flat.float()
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negative_flat = negative_flat.float()
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dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
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squared_norm = torch.sum(negative_flat**2, dim=1, keepdim=True) + 1e-8
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st_star = dot_product / squared_norm
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return st_star
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def load_json_config(model_path: str, subfolder: str, filename: str, local_files_only: bool = True) -> dict:
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"""Load a JSON config file from a local path or HuggingFace Hub repo."""
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if local_files_only:
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config_path = os.path.join(model_path, subfolder, filename)
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if os.path.exists(config_path):
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with open(config_path) as f:
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return json.load(f)
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else:
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try:
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from huggingface_hub import hf_hub_download
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config_path = hf_hub_download(
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repo_id=model_path,
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filename=f"{subfolder}/{filename}",
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)
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with open(config_path) as f:
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return json.load(f)
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except Exception:
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pass
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return {}
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def load_transformer_config(model_path: str, subfolder: str = "transformer", local_files_only: bool = True) -> dict:
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return load_json_config(model_path, subfolder, "config.json", local_files_only)
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def create_transformer_from_config(
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config: dict, quant_config: QuantizationConfig | None = None
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) -> HeliosTransformer3DModel:
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kwargs = {}
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key_map = [
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"patch_size",
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"num_attention_heads",
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"attention_head_dim",
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"in_channels",
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"out_channels",
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"text_dim",
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"freq_dim",
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"ffn_dim",
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"num_layers",
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"cross_attn_norm",
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"qk_norm",
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"eps",
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"added_kv_proj_dim",
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"rope_dim",
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"rope_theta",
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"guidance_cross_attn",
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"zero_history_timestep",
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"has_multi_term_memory_patch",
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"is_amplify_history",
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"history_scale_mode",
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]
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for key in key_map:
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if key in config:
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val = config[key]
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if key in ("patch_size", "rope_dim") and isinstance(val, list):
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val = tuple(val)
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kwargs[key] = val
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return HeliosTransformer3DModel(quant_config=quant_config, **kwargs)
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def get_helios_post_process_func(
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od_config: OmniDiffusionConfig,
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):
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from diffusers.video_processor import VideoProcessor
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video_processor = VideoProcessor(vae_scale_factor=8)
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def post_process_func(
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video: torch.Tensor,
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output_type: str = "np",
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):
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if output_type == "latent":
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return video
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return video_processor.postprocess_video(video, output_type=output_type)
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return post_process_func
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def get_helios_pre_process_func(
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od_config: OmniDiffusionConfig,
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):
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def pre_process_func(request: OmniDiffusionRequest) -> OmniDiffusionRequest:
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return request
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return pre_process_func
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class HeliosPipeline(nn.Module, CFGParallelMixin, ProgressBarMixin, DiffusionPipelineProfilerMixin):
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"""Helios text-to-video / image-to-video / video-to-video pipeline for vllm-omni.
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Supports T2V, I2V (with image input), and V2V (with video input).
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Implements chunked video generation with multi-term memory history context.
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"""
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def __init__(
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self,
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*,
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od_config: OmniDiffusionConfig,
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prefix: str = "",
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):
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super().__init__()
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self.od_config = od_config
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self.device = get_local_device()
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dtype = getattr(od_config, "dtype", torch.bfloat16)
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model = od_config.model
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local_files_only = os.path.exists(model)
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self.weights_sources = [
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DiffusersPipelineLoader.ComponentSource(
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model_or_path=od_config.model,
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subfolder="transformer",
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revision=None,
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prefix="transformer.",
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fall_back_to_pt=True,
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)
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]
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self.tokenizer = AutoTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
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# Helios checkpoints store embed_tokens under ``shared.weight`` only,
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# but the published config sets ``tie_word_embeddings=False``. When
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# transformers sees ``tie=False`` it creates a separate
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# ``encoder.embed_tokens.weight`` that is never loaded from the
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# checkpoint, leaving it as all-zeros. This silently destroys prompt
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# encoding and produces grey/meaningless video output. Force tying so
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# that ``embed_tokens`` shares ``shared.weight`` as intended.
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text_enc_cfg = AutoConfig.from_pretrained(model, subfolder="text_encoder", local_files_only=local_files_only)
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text_enc_cfg.tie_word_embeddings = True
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self.text_encoder = UMT5EncoderModel.from_pretrained(
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model, subfolder="text_encoder", config=text_enc_cfg, torch_dtype=dtype, local_files_only=local_files_only
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).to(self.device)
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self.vae = AutoencoderKLWan.from_pretrained(
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model, subfolder="vae", torch_dtype=torch.float32, local_files_only=local_files_only
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).to(self.device)
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transformer_config = load_transformer_config(model, "transformer", local_files_only)
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self.transformer = create_transformer_from_config(
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transformer_config, quant_config=od_config.quantization_config
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)
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# Read scheduler config to determine scheduler type
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sched_cfg = load_json_config(model, "scheduler", "scheduler_config.json", local_files_only)
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scheduler_kwargs = {}
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passthrough_keys = [
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"num_train_timesteps",
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"shift",
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"stages",
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"stage_range",
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"gamma",
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"thresholding",
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"prediction_type",
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"solver_order",
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"predict_x0",
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"solver_type",
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"lower_order_final",
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"disable_corrector",
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"use_flow_sigmas",
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"scheduler_type",
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"use_dynamic_shifting",
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"time_shift_type",
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]
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for key in passthrough_keys:
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if key in sched_cfg:
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scheduler_kwargs[key] = sched_cfg[key]
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self.scheduler = HeliosScheduler(**scheduler_kwargs)
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self.is_distilled = scheduler_kwargs.get("scheduler_type") == "dmd"
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self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal if getattr(self, "vae", None) else 4
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self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 8
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self._guidance_scale = None
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self._num_timesteps = None
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self._current_timestep = None
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self.setup_diffusion_pipeline_profiler(
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enable_diffusion_pipeline_profiler=self.od_config.enable_diffusion_pipeline_profiler
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)
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@property
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def guidance_scale(self):
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return self._guidance_scale
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@property
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def do_classifier_free_guidance(self):
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return self._guidance_scale is not None and self._guidance_scale > 1.0
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@property
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def num_timesteps(self):
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return self._num_timesteps
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@property
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def current_timestep(self):
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return self._current_timestep
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def forward(
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self,
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req: OmniDiffusionRequest,
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prompt: str | None = None,
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negative_prompt: str | None = None,
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height: int = 384,
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width: int = 640,
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num_inference_steps: int = 50,
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guidance_scale: float = 5.0,
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frame_num: int = 132,
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output_type: str | None = "np",
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generator: torch.Generator | list[torch.Generator] | None = None,
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prompt_embeds: torch.Tensor | None = None,
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negative_prompt_embeds: torch.Tensor | None = None,
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attention_kwargs: dict | None = None,
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# Helios-specific
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history_sizes: list | None = None,
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num_latent_frames_per_chunk: int = 9,
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keep_first_frame: bool = True,
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# I2V
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image: torch.Tensor | None = None,
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add_noise_to_image_latents: bool = True,
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image_noise_sigma_min: float = 0.111,
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image_noise_sigma_max: float = 0.135,
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# V2V
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video: torch.Tensor | None = None,
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add_noise_to_video_latents: bool = True,
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video_noise_sigma_min: float = 0.111,
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video_noise_sigma_max: float = 0.135,
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# Stage 2 (pyramid multi-stage denoising)
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is_enable_stage2: bool = False,
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pyramid_num_stages: int = 3,
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pyramid_num_inference_steps_list: list | None = None,
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is_skip_first_chunk: bool = False,
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# DMD
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is_amplify_first_chunk: bool = False,
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# CFG Zero Star
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use_cfg_zero_star: bool = False,
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use_zero_init: bool = True,
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zero_steps: int = 1,
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**kwargs,
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) -> DiffusionOutput:
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if pyramid_num_inference_steps_list is None:
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pyramid_num_inference_steps_list = [10, 10, 10]
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if history_sizes is None:
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history_sizes = [16, 2, 1]
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# Read Helios-specific params from extra_args
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extra = getattr(req.sampling_params, "extra_args", {}) or {}
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is_enable_stage2 = extra.get("is_enable_stage2", is_enable_stage2)
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pyramid_num_stages = extra.get("pyramid_num_stages", pyramid_num_stages)
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pyramid_num_inference_steps_list = extra.get(
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"pyramid_num_inference_steps_list", pyramid_num_inference_steps_list
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)
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is_amplify_first_chunk = extra.get("is_amplify_first_chunk", is_amplify_first_chunk)
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use_cfg_zero_star = extra.get("use_cfg_zero_star", use_cfg_zero_star)
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use_zero_init = extra.get("use_zero_init", use_zero_init)
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zero_steps = extra.get("zero_steps", zero_steps)
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is_skip_first_chunk = extra.get("is_skip_first_chunk", is_skip_first_chunk)
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image = extra.get("image", image)
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video = extra.get("video", video)
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add_noise_to_image_latents = extra.get("add_noise_to_image_latents", add_noise_to_image_latents)
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image_noise_sigma_min = extra.get("image_noise_sigma_min", image_noise_sigma_min)
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image_noise_sigma_max = extra.get("image_noise_sigma_max", image_noise_sigma_max)
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add_noise_to_video_latents = extra.get("add_noise_to_video_latents", add_noise_to_video_latents)
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video_noise_sigma_min = extra.get("video_noise_sigma_min", video_noise_sigma_min)
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video_noise_sigma_max = extra.get("video_noise_sigma_max", video_noise_sigma_max)
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if image is not None and video is not None:
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raise ValueError("image and video cannot be provided simultaneously")
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if len(req.prompts) > 1:
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raise ValueError("This model only supports a single prompt, not a batched request.")
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if len(req.prompts) == 1:
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prompt = req.prompts[0] if isinstance(req.prompts[0], str) else req.prompts[0].get("prompt")
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negative_prompt = None if isinstance(req.prompts[0], str) else req.prompts[0].get("negative_prompt")
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if prompt is None and prompt_embeds is None:
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raise ValueError("Prompt or prompt_embeds is required for Helios generation.")
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height = req.sampling_params.height or height
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width = req.sampling_params.width or width
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num_frames = req.sampling_params.num_frames if req.sampling_params.num_frames else frame_num
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num_steps = req.sampling_params.num_inference_steps or num_inference_steps
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if req.sampling_params.guidance_scale_provided:
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guidance_scale = req.sampling_params.guidance_scale
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self._guidance_scale = guidance_scale
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height = (height // 16) * 16
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width = (width // 16) * 16
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num_frames = max(num_frames, 1)
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device = self.device
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dtype = self.transformer.dtype
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if generator is None:
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generator = req.sampling_params.generator
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if generator is None and req.sampling_params.seed is not None:
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generator = torch.Generator(device=device).manual_seed(req.sampling_params.seed)
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# Encode prompts
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if prompt_embeds is None:
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prompt_embeds, negative_prompt_embeds = self.encode_prompt(
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prompt=prompt,
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negative_prompt=negative_prompt,
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do_classifier_free_guidance=self.do_classifier_free_guidance,
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num_videos_per_prompt=req.sampling_params.num_outputs_per_prompt or 1,
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max_sequence_length=req.sampling_params.max_sequence_length or 226,
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device=device,
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dtype=dtype,
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)
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else:
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prompt_embeds = prompt_embeds.to(device=device, dtype=dtype)
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if negative_prompt_embeds is not None:
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negative_prompt_embeds = negative_prompt_embeds.to(device=device, dtype=dtype)
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batch_size = prompt_embeds.shape[0]
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history_sizes = sorted(history_sizes, reverse=True)
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latents_mean = (
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torch.tensor(self.vae.config.latents_mean)
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.view(1, self.vae.config.z_dim, 1, 1, 1)
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.to(self.vae.device, self.vae.dtype)
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)
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latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
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self.vae.device, self.vae.dtype
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)
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# Prepare I2V image latents
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fake_image_latents = None
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image_latents = None
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if image is not None:
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image_latents, fake_image_latents = self.prepare_image_latents(
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image,
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latents_mean=latents_mean,
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latents_std=latents_std,
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num_latent_frames_per_chunk=num_latent_frames_per_chunk,
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dtype=torch.float32,
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device=device,
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generator=generator,
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)
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if image_latents is not None and add_noise_to_image_latents:
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image_noise_sigma = (
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torch.rand(1, device=device, generator=generator) * (image_noise_sigma_max - image_noise_sigma_min)
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+ image_noise_sigma_min
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)
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image_latents = (
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image_noise_sigma * randn_tensor(image_latents.shape, generator=generator, device=device)
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+ (1 - image_noise_sigma) * image_latents
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)
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fake_image_noise_sigma = (
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torch.rand(1, device=device, generator=generator) * (video_noise_sigma_max - video_noise_sigma_min)
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+ video_noise_sigma_min
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)
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fake_image_latents = (
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fake_image_noise_sigma * randn_tensor(fake_image_latents.shape, generator=generator, device=device)
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+ (1 - fake_image_noise_sigma) * fake_image_latents
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)
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# Prepare V2V video latents
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video_latents = None
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if video is not None:
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image_latents, video_latents = self.prepare_video_latents(
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video,
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latents_mean=latents_mean,
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latents_std=latents_std,
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num_latent_frames_per_chunk=num_latent_frames_per_chunk,
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dtype=torch.float32,
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device=device,
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generator=generator,
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)
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if video_latents is not None and add_noise_to_video_latents:
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image_noise_sigma = (
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torch.rand(1, device=device, generator=generator) * (image_noise_sigma_max - image_noise_sigma_min)
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+ image_noise_sigma_min
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)
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image_latents = (
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image_noise_sigma * randn_tensor(image_latents.shape, generator=generator, device=device)
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+ (1 - image_noise_sigma) * image_latents
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)
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noisy_latents_chunks = []
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num_latent_chunks = video_latents.shape[2] // num_latent_frames_per_chunk
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for i in range(num_latent_chunks):
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chunk_start = i * num_latent_frames_per_chunk
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chunk_end = chunk_start + num_latent_frames_per_chunk
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latent_chunk = video_latents[:, :, chunk_start:chunk_end, :, :]
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chunk_frames = latent_chunk.shape[2]
|
|
frame_sigmas = (
|
|
torch.rand(chunk_frames, device=device, generator=generator)
|
|
* (video_noise_sigma_max - video_noise_sigma_min)
|
|
+ video_noise_sigma_min
|
|
)
|
|
frame_sigmas = frame_sigmas.view(1, 1, chunk_frames, 1, 1)
|
|
noisy_chunk = (
|
|
frame_sigmas * randn_tensor(latent_chunk.shape, generator=generator, device=device)
|
|
+ (1 - frame_sigmas) * latent_chunk
|
|
)
|
|
noisy_latents_chunks.append(noisy_chunk)
|
|
video_latents = torch.cat(noisy_latents_chunks, dim=2)
|
|
|
|
# Prepare latent variables
|
|
num_channels_latents = self.transformer.config.in_channels
|
|
window_num_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1
|
|
num_latent_chunk = max(1, (num_frames + window_num_frames - 1) // window_num_frames)
|
|
num_history_latent_frames = sum(history_sizes)
|
|
history_video = None
|
|
total_generated_latent_frames = 0
|
|
|
|
if not keep_first_frame:
|
|
history_sizes[-1] = history_sizes[-1] + 1
|
|
|
|
history_latents = torch.zeros(
|
|
batch_size,
|
|
num_channels_latents,
|
|
num_history_latent_frames,
|
|
height // self.vae_scale_factor_spatial,
|
|
width // self.vae_scale_factor_spatial,
|
|
device=device,
|
|
dtype=torch.float32,
|
|
)
|
|
|
|
if fake_image_latents is not None:
|
|
history_latents = torch.cat([history_latents[:, :, :-1, :, :], fake_image_latents], dim=2)
|
|
total_generated_latent_frames += 1
|
|
|
|
if video_latents is not None:
|
|
history_frames = history_latents.shape[2]
|
|
video_frames = video_latents.shape[2]
|
|
if video_frames < history_frames:
|
|
keep_frames = history_frames - video_frames
|
|
history_latents = torch.cat([history_latents[:, :, :keep_frames, :, :], video_latents], dim=2)
|
|
else:
|
|
history_latents = video_latents
|
|
total_generated_latent_frames += video_latents.shape[2]
|
|
|
|
# Prepare frame indices
|
|
if keep_first_frame:
|
|
indices = torch.arange(0, sum([1, *history_sizes, num_latent_frames_per_chunk]))
|
|
(
|
|
indices_prefix,
|
|
indices_latents_history_long,
|
|
indices_latents_history_mid,
|
|
indices_latents_history_1x,
|
|
indices_hidden_states,
|
|
) = indices.split([1, *history_sizes, num_latent_frames_per_chunk], dim=0)
|
|
indices_latents_history_short = torch.cat([indices_prefix, indices_latents_history_1x], dim=0)
|
|
else:
|
|
indices = torch.arange(0, sum([*history_sizes, num_latent_frames_per_chunk]))
|
|
(
|
|
indices_latents_history_long,
|
|
indices_latents_history_mid,
|
|
indices_latents_history_short,
|
|
indices_hidden_states,
|
|
) = indices.split([*history_sizes, num_latent_frames_per_chunk], dim=0)
|
|
|
|
indices_hidden_states = indices_hidden_states.unsqueeze(0)
|
|
indices_latents_history_short = indices_latents_history_short.unsqueeze(0)
|
|
indices_latents_history_mid = indices_latents_history_mid.unsqueeze(0)
|
|
indices_latents_history_long = indices_latents_history_long.unsqueeze(0)
|
|
|
|
if attention_kwargs is None:
|
|
attention_kwargs = {}
|
|
|
|
vae_dtype = self.vae.dtype
|
|
|
|
# Chunked denoising loop
|
|
for k in range(num_latent_chunk):
|
|
is_first_chunk = k == 0
|
|
is_second_chunk = k == 1
|
|
|
|
if keep_first_frame:
|
|
latents_history_long, latents_history_mid, latents_history_1x = history_latents[
|
|
:, :, -num_history_latent_frames:
|
|
].split(history_sizes, dim=2)
|
|
if image_latents is None and is_first_chunk:
|
|
latents_prefix = torch.zeros(
|
|
(
|
|
batch_size,
|
|
num_channels_latents,
|
|
1,
|
|
latents_history_1x.shape[-2],
|
|
latents_history_1x.shape[-1],
|
|
),
|
|
device=latents_history_1x.device,
|
|
dtype=latents_history_1x.dtype,
|
|
)
|
|
else:
|
|
latents_prefix = image_latents
|
|
latents_history_short = torch.cat([latents_prefix, latents_history_1x], dim=2)
|
|
else:
|
|
latents_history_long, latents_history_mid, latents_history_short = history_latents[
|
|
:, :, -num_history_latent_frames:
|
|
].split(history_sizes, dim=2)
|
|
|
|
# Prepare noise for this chunk
|
|
latents = self.prepare_latents(
|
|
batch_size,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
window_num_frames,
|
|
dtype=torch.float32,
|
|
device=device,
|
|
generator=generator,
|
|
latents=None,
|
|
)
|
|
|
|
if not is_enable_stage2:
|
|
# Stage 1 only: single-stage denoising
|
|
patch_size = self.transformer.config.patch_size
|
|
image_seq_len = (latents.shape[-1] * latents.shape[-2] * latents.shape[-3]) // (
|
|
patch_size[0] * patch_size[1] * patch_size[2]
|
|
)
|
|
sigmas = np.linspace(0.999, 0.0, num_steps + 1)[:-1]
|
|
mu = calculate_shift(image_seq_len)
|
|
self.scheduler.set_timesteps(num_steps, device=device, sigmas=sigmas, mu=mu)
|
|
timesteps = self.scheduler.timesteps
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
latents = self._stage1_sample(
|
|
latents=latents,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
timesteps=timesteps,
|
|
guidance_scale=guidance_scale,
|
|
indices_hidden_states=indices_hidden_states,
|
|
indices_latents_history_short=indices_latents_history_short,
|
|
indices_latents_history_mid=indices_latents_history_mid,
|
|
indices_latents_history_long=indices_latents_history_long,
|
|
latents_history_short=latents_history_short,
|
|
latents_history_mid=latents_history_mid,
|
|
latents_history_long=latents_history_long,
|
|
attention_kwargs=attention_kwargs,
|
|
transformer_dtype=dtype,
|
|
use_cfg_zero_star=use_cfg_zero_star,
|
|
use_zero_init=use_zero_init,
|
|
zero_steps=zero_steps,
|
|
)
|
|
else:
|
|
# Stage 2: pyramid multi-stage denoising
|
|
latents = self._stage2_sample(
|
|
latents=latents,
|
|
pyramid_num_stages=pyramid_num_stages,
|
|
pyramid_num_inference_steps_list=pyramid_num_inference_steps_list,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
guidance_scale=guidance_scale,
|
|
indices_hidden_states=indices_hidden_states,
|
|
indices_latents_history_short=indices_latents_history_short,
|
|
indices_latents_history_mid=indices_latents_history_mid,
|
|
indices_latents_history_long=indices_latents_history_long,
|
|
latents_history_short=latents_history_short,
|
|
latents_history_mid=latents_history_mid,
|
|
latents_history_long=latents_history_long,
|
|
attention_kwargs=attention_kwargs,
|
|
transformer_dtype=dtype,
|
|
is_amplify_first_chunk=is_amplify_first_chunk and is_first_chunk,
|
|
use_cfg_zero_star=use_cfg_zero_star,
|
|
use_zero_init=use_zero_init,
|
|
zero_steps=zero_steps,
|
|
device=device,
|
|
generator=generator,
|
|
)
|
|
|
|
if keep_first_frame and (
|
|
(is_first_chunk and image_latents is None) or (is_skip_first_chunk and is_second_chunk)
|
|
):
|
|
image_latents = latents[:, :, 0:1, :, :]
|
|
|
|
total_generated_latent_frames += latents.shape[2]
|
|
history_latents = torch.cat([history_latents, latents], dim=2)
|
|
real_history_latents = history_latents[:, :, -total_generated_latent_frames:]
|
|
index_slice = (
|
|
slice(None),
|
|
slice(None),
|
|
slice(-num_latent_frames_per_chunk, None),
|
|
)
|
|
|
|
current_latents = real_history_latents[index_slice].to(vae_dtype) / latents_std + latents_mean
|
|
current_video = self.vae.decode(current_latents, return_dict=False)[0]
|
|
|
|
if history_video is None:
|
|
history_video = current_video
|
|
else:
|
|
history_video = torch.cat([history_video, current_video], dim=2)
|
|
|
|
if current_omni_platform.is_available():
|
|
current_omni_platform.empty_cache()
|
|
self._current_timestep = None
|
|
|
|
if output_type == "latent":
|
|
output = real_history_latents
|
|
else:
|
|
output = history_video
|
|
|
|
return DiffusionOutput(
|
|
output=output, stage_durations=self.stage_durations if hasattr(self, "stage_durations") else None
|
|
)
|
|
|
|
def _stage1_sample(
|
|
self,
|
|
latents: torch.Tensor,
|
|
prompt_embeds: torch.Tensor,
|
|
negative_prompt_embeds: torch.Tensor | None,
|
|
timesteps: torch.Tensor,
|
|
guidance_scale: float,
|
|
indices_hidden_states: torch.Tensor,
|
|
indices_latents_history_short: torch.Tensor,
|
|
indices_latents_history_mid: torch.Tensor,
|
|
indices_latents_history_long: torch.Tensor,
|
|
latents_history_short: torch.Tensor,
|
|
latents_history_mid: torch.Tensor,
|
|
latents_history_long: torch.Tensor,
|
|
attention_kwargs: dict,
|
|
transformer_dtype: torch.dtype,
|
|
use_cfg_zero_star: bool = False,
|
|
use_zero_init: bool = True,
|
|
zero_steps: int = 1,
|
|
) -> torch.Tensor:
|
|
"""Single-stage denoising loop for one chunk."""
|
|
batch_size = latents.shape[0]
|
|
do_true_cfg = guidance_scale > 1.0 and negative_prompt_embeds is not None
|
|
|
|
with self.progress_bar(total=len(timesteps)) as pbar:
|
|
for i, t in enumerate(timesteps):
|
|
self._current_timestep = t
|
|
timestep = t.expand(batch_size)
|
|
|
|
transformer_kwargs = {
|
|
"hidden_states": latents.to(transformer_dtype),
|
|
"timestep": timestep,
|
|
"indices_hidden_states": indices_hidden_states,
|
|
"indices_latents_history_short": indices_latents_history_short,
|
|
"indices_latents_history_mid": indices_latents_history_mid,
|
|
"indices_latents_history_long": indices_latents_history_long,
|
|
"latents_history_short": latents_history_short.to(transformer_dtype),
|
|
"latents_history_mid": latents_history_mid.to(transformer_dtype),
|
|
"latents_history_long": latents_history_long.to(transformer_dtype),
|
|
"attention_kwargs": attention_kwargs,
|
|
"return_dict": False,
|
|
}
|
|
|
|
if use_cfg_zero_star and do_true_cfg:
|
|
noise_pred = self.transformer(
|
|
encoder_hidden_states=prompt_embeds,
|
|
**transformer_kwargs,
|
|
)[0]
|
|
|
|
noise_uncond = self.transformer(
|
|
encoder_hidden_states=negative_prompt_embeds,
|
|
**transformer_kwargs,
|
|
)[0]
|
|
|
|
positive_flat = noise_pred.view(batch_size, -1)
|
|
negative_flat = noise_uncond.view(batch_size, -1)
|
|
alpha_cfg = optimized_scale(positive_flat, negative_flat)
|
|
alpha_cfg = alpha_cfg.view(batch_size, *([1] * (len(noise_pred.shape) - 1)))
|
|
alpha_cfg = alpha_cfg.to(noise_pred.dtype)
|
|
|
|
if (i <= zero_steps) and use_zero_init:
|
|
noise_pred = noise_pred * 0.0
|
|
else:
|
|
noise_pred = noise_uncond * alpha_cfg + guidance_scale * (noise_pred - noise_uncond * alpha_cfg)
|
|
else:
|
|
positive_kwargs = {
|
|
"encoder_hidden_states": prompt_embeds,
|
|
**transformer_kwargs,
|
|
}
|
|
if do_true_cfg:
|
|
negative_kwargs = {
|
|
"encoder_hidden_states": negative_prompt_embeds,
|
|
**transformer_kwargs,
|
|
}
|
|
else:
|
|
negative_kwargs = None
|
|
|
|
noise_pred = self.predict_noise_maybe_with_cfg(
|
|
do_true_cfg=do_true_cfg,
|
|
true_cfg_scale=guidance_scale,
|
|
positive_kwargs=positive_kwargs,
|
|
negative_kwargs=negative_kwargs,
|
|
cfg_normalize=False,
|
|
)
|
|
|
|
latents = self.scheduler_step_maybe_with_cfg(noise_pred, t, latents, do_true_cfg)
|
|
|
|
pbar.update()
|
|
|
|
return latents
|
|
|
|
def _stage2_sample(
|
|
self,
|
|
latents: torch.Tensor,
|
|
pyramid_num_stages: int,
|
|
pyramid_num_inference_steps_list: list[int],
|
|
prompt_embeds: torch.Tensor,
|
|
negative_prompt_embeds: torch.Tensor | None,
|
|
guidance_scale: float,
|
|
indices_hidden_states: torch.Tensor,
|
|
indices_latents_history_short: torch.Tensor,
|
|
indices_latents_history_mid: torch.Tensor,
|
|
indices_latents_history_long: torch.Tensor,
|
|
latents_history_short: torch.Tensor,
|
|
latents_history_mid: torch.Tensor,
|
|
latents_history_long: torch.Tensor,
|
|
attention_kwargs: dict,
|
|
transformer_dtype: torch.dtype,
|
|
is_amplify_first_chunk: bool = False,
|
|
use_cfg_zero_star: bool = False,
|
|
use_zero_init: bool = True,
|
|
zero_steps: int = 1,
|
|
device: torch.device | None = None,
|
|
generator: torch.Generator | list[torch.Generator] | None = None,
|
|
) -> torch.Tensor:
|
|
"""Pyramid multi-stage denoising for one chunk."""
|
|
batch_size, num_channel, num_frames_lat, height, width = latents.shape
|
|
|
|
# Downsample latents to the smallest pyramid level
|
|
latents_flat = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames_lat, num_channel, height, width)
|
|
for _ in range(pyramid_num_stages - 1):
|
|
height //= 2
|
|
width //= 2
|
|
latents_flat = F.interpolate(latents_flat, size=(height, width), mode="bilinear") * 2
|
|
latents = latents_flat.reshape(batch_size, num_frames_lat, num_channel, height, width).permute(0, 2, 1, 3, 4)
|
|
|
|
start_point_list = None
|
|
if self.is_distilled:
|
|
start_point_list = [latents]
|
|
|
|
do_true_cfg = guidance_scale > 1.0 and negative_prompt_embeds is not None
|
|
|
|
for i_s in range(pyramid_num_stages):
|
|
patch_size = self.transformer.config.patch_size
|
|
image_seq_len = (latents.shape[-1] * latents.shape[-2] * latents.shape[-3]) // (
|
|
patch_size[0] * patch_size[1] * patch_size[2]
|
|
)
|
|
mu = calculate_shift(image_seq_len)
|
|
self.scheduler.set_timesteps(
|
|
pyramid_num_inference_steps_list[i_s],
|
|
stage_index=i_s,
|
|
device=device,
|
|
mu=mu,
|
|
is_amplify_first_chunk=is_amplify_first_chunk,
|
|
)
|
|
timesteps = self.scheduler.timesteps
|
|
|
|
if i_s > 0:
|
|
# Upsample latents to next pyramid level
|
|
height *= 2
|
|
width *= 2
|
|
num_frames_cur = latents.shape[2]
|
|
latents_flat = latents.permute(0, 2, 1, 3, 4).reshape(
|
|
batch_size * num_frames_cur, num_channel, height // 2, width // 2
|
|
)
|
|
latents_flat = F.interpolate(latents_flat, size=(height, width), mode="nearest")
|
|
latents = latents_flat.reshape(batch_size, num_frames_cur, num_channel, height, width).permute(
|
|
0, 2, 1, 3, 4
|
|
)
|
|
|
|
# Add block noise to fix artifacts between stages
|
|
ori_sigma = 1 - self.scheduler.ori_start_sigmas[i_s]
|
|
gamma = self.scheduler.config.gamma
|
|
alpha = 1 / (math.sqrt(1 + (1 / gamma)) * (1 - ori_sigma) + ori_sigma)
|
|
beta = alpha * (1 - ori_sigma) / math.sqrt(gamma)
|
|
|
|
noise = self.sample_block_noise(
|
|
batch_size, num_channel, latents.shape[2], height, width, patch_size, generator=generator
|
|
)
|
|
noise = noise.to(device=device, dtype=transformer_dtype)
|
|
latents = alpha * latents + beta * noise
|
|
|
|
if self.is_distilled and start_point_list is not None:
|
|
start_point_list.append(latents)
|
|
|
|
with self.progress_bar(total=len(timesteps)) as pbar:
|
|
for idx, t in enumerate(timesteps):
|
|
self._current_timestep = t
|
|
timestep = t.expand(latents.shape[0]).to(torch.int64)
|
|
|
|
transformer_kwargs = {
|
|
"hidden_states": latents.to(transformer_dtype),
|
|
"timestep": timestep,
|
|
"indices_hidden_states": indices_hidden_states,
|
|
"indices_latents_history_short": indices_latents_history_short,
|
|
"indices_latents_history_mid": indices_latents_history_mid,
|
|
"indices_latents_history_long": indices_latents_history_long,
|
|
"latents_history_short": latents_history_short.to(transformer_dtype),
|
|
"latents_history_mid": latents_history_mid.to(transformer_dtype),
|
|
"latents_history_long": latents_history_long.to(transformer_dtype),
|
|
"attention_kwargs": attention_kwargs,
|
|
"return_dict": False,
|
|
}
|
|
|
|
noise_pred = self.transformer(
|
|
encoder_hidden_states=prompt_embeds,
|
|
**transformer_kwargs,
|
|
)[0]
|
|
|
|
if do_true_cfg:
|
|
noise_uncond = self.transformer(
|
|
encoder_hidden_states=negative_prompt_embeds,
|
|
**transformer_kwargs,
|
|
)[0]
|
|
|
|
if use_cfg_zero_star:
|
|
positive_flat = noise_pred.view(batch_size, -1)
|
|
negative_flat = noise_uncond.view(batch_size, -1)
|
|
alpha_cfg = optimized_scale(positive_flat, negative_flat)
|
|
alpha_cfg = alpha_cfg.view(batch_size, *([1] * (len(noise_pred.shape) - 1)))
|
|
alpha_cfg = alpha_cfg.to(noise_pred.dtype)
|
|
|
|
if (i_s == 0 and idx <= zero_steps) and use_zero_init:
|
|
noise_pred = noise_pred * 0.0
|
|
else:
|
|
noise_pred = noise_uncond * alpha_cfg + guidance_scale * (
|
|
noise_pred - noise_uncond * alpha_cfg
|
|
)
|
|
else:
|
|
noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
|
|
|
|
latents = self.scheduler.step(
|
|
noise_pred,
|
|
t,
|
|
latents,
|
|
return_dict=False,
|
|
cur_sampling_step=idx,
|
|
dmd_noisy_tensor=start_point_list[i_s] if start_point_list is not None else None,
|
|
dmd_sigmas=self.scheduler.sigmas,
|
|
dmd_timesteps=self.scheduler.timesteps,
|
|
all_timesteps=timesteps,
|
|
)[0]
|
|
|
|
pbar.update()
|
|
|
|
return latents
|
|
|
|
def sample_block_noise(self, batch_size, channel, num_frames, height, width, patch_size=(1, 2, 2), generator=None):
|
|
gamma = self.scheduler.config.gamma
|
|
_, ph, pw = patch_size
|
|
block_size = ph * pw
|
|
|
|
device = generator.device if generator is not None else self.device
|
|
|
|
cov = torch.eye(block_size) * (1 + gamma) - torch.ones(block_size, block_size) * gamma
|
|
cov += torch.eye(block_size) * 1e-8
|
|
cov = cov.float() # Upcast to fp32 for numerical stability — cholesky is unreliable in fp16/bf16.
|
|
|
|
L = torch.linalg.cholesky(cov).to(device)
|
|
block_number = batch_size * channel * num_frames * (height // ph) * (width // pw)
|
|
z = torch.randn(block_number, block_size, generator=generator, device=device)
|
|
noise = z @ L.T
|
|
|
|
noise = noise.view(batch_size, channel, num_frames, height // ph, width // pw, ph, pw)
|
|
noise = noise.permute(0, 1, 2, 3, 5, 4, 6).reshape(batch_size, channel, num_frames, height, width)
|
|
|
|
return noise
|
|
|
|
def predict_noise(self, **kwargs: Any) -> torch.Tensor:
|
|
return self.transformer(**kwargs)[0]
|
|
|
|
def encode_prompt(
|
|
self,
|
|
prompt: str | list[str],
|
|
negative_prompt: str | list[str] | None = None,
|
|
do_classifier_free_guidance: bool = True,
|
|
num_videos_per_prompt: int = 1,
|
|
max_sequence_length: int = 226,
|
|
device: torch.device | None = None,
|
|
dtype: torch.dtype | None = None,
|
|
):
|
|
device = device or self.device
|
|
dtype = dtype or self.text_encoder.dtype
|
|
|
|
prompt = [prompt] if isinstance(prompt, str) else prompt
|
|
prompt_clean = [self._prompt_clean(p) for p in prompt]
|
|
batch_size = len(prompt_clean)
|
|
|
|
text_inputs = self.tokenizer(
|
|
prompt_clean,
|
|
padding="max_length",
|
|
max_length=max_sequence_length,
|
|
truncation=True,
|
|
add_special_tokens=True,
|
|
return_attention_mask=True,
|
|
return_tensors="pt",
|
|
)
|
|
ids, mask = text_inputs.input_ids, text_inputs.attention_mask
|
|
seq_lens = mask.gt(0).sum(dim=1).long()
|
|
|
|
prompt_embeds = self.text_encoder(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
|
|
)
|
|
|
|
_, 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)
|
|
|
|
negative_prompt_embeds = None
|
|
if do_classifier_free_guidance:
|
|
negative_prompt = negative_prompt or ""
|
|
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
|
|
neg_text_inputs = self.tokenizer(
|
|
[self._prompt_clean(p) for p in negative_prompt],
|
|
padding="max_length",
|
|
max_length=max_sequence_length,
|
|
truncation=True,
|
|
add_special_tokens=True,
|
|
return_attention_mask=True,
|
|
return_tensors="pt",
|
|
)
|
|
ids_neg, mask_neg = neg_text_inputs.input_ids, neg_text_inputs.attention_mask
|
|
seq_lens_neg = mask_neg.gt(0).sum(dim=1).long()
|
|
negative_prompt_embeds = self.text_encoder(ids_neg.to(device), mask_neg.to(device)).last_hidden_state
|
|
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
|
|
negative_prompt_embeds = [u[:v] for u, v in zip(negative_prompt_embeds, seq_lens_neg)]
|
|
negative_prompt_embeds = torch.stack(
|
|
[
|
|
torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))])
|
|
for u in negative_prompt_embeds
|
|
],
|
|
dim=0,
|
|
)
|
|
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)
|
|
|
|
return prompt_embeds, negative_prompt_embeds
|
|
|
|
@staticmethod
|
|
def _prompt_clean(text: str) -> str:
|
|
return " ".join(text.strip().split())
|
|
|
|
def prepare_latents(
|
|
self,
|
|
batch_size: int,
|
|
num_channels_latents: int,
|
|
height: int,
|
|
width: int,
|
|
num_frames: int,
|
|
dtype: torch.dtype | None,
|
|
device: torch.device | None,
|
|
generator: torch.Generator | list[torch.Generator] | None,
|
|
latents: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
if latents is not None:
|
|
return latents.to(device=device, dtype=dtype)
|
|
|
|
num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
|
|
shape = (
|
|
batch_size,
|
|
num_channels_latents,
|
|
num_latent_frames,
|
|
int(height) // self.vae_scale_factor_spatial,
|
|
int(width) // self.vae_scale_factor_spatial,
|
|
)
|
|
if isinstance(generator, list) and len(generator) != batch_size:
|
|
raise ValueError(f"Generator list length {len(generator)} does not match batch size {batch_size}.")
|
|
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
return latents
|
|
|
|
def prepare_image_latents(
|
|
self,
|
|
image: torch.Tensor,
|
|
latents_mean: torch.Tensor,
|
|
latents_std: torch.Tensor,
|
|
num_latent_frames_per_chunk: int,
|
|
dtype: torch.dtype | None = None,
|
|
device: torch.device | None = None,
|
|
generator: torch.Generator | list[torch.Generator] | None = None,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""Encode a single image into VAE latent space for I2V generation.
|
|
|
|
Returns (image_latents, fake_image_latents) where fake_image_latents
|
|
is the last-frame latent of a repeated-frame video, used to seed the
|
|
history buffer for the first denoising chunk.
|
|
"""
|
|
device = device or self.device
|
|
image = image.unsqueeze(2).to(device=device, dtype=self.vae.dtype)
|
|
latents = self.vae.encode(image).latent_dist.sample(generator=generator)
|
|
latents = (latents - latents_mean) * latents_std
|
|
|
|
min_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1
|
|
fake_video = image.repeat(1, 1, min_frames, 1, 1)
|
|
fake_latents_full = self.vae.encode(fake_video).latent_dist.sample(generator=generator)
|
|
fake_latents_full = (fake_latents_full - latents_mean) * latents_std
|
|
fake_latents = fake_latents_full[:, :, -1:, :, :]
|
|
|
|
return latents.to(device=device, dtype=dtype), fake_latents.to(device=device, dtype=dtype)
|
|
|
|
def prepare_video_latents(
|
|
self,
|
|
video: torch.Tensor,
|
|
latents_mean: torch.Tensor,
|
|
latents_std: torch.Tensor,
|
|
num_latent_frames_per_chunk: int,
|
|
dtype: torch.dtype | None = None,
|
|
device: torch.device | None = None,
|
|
generator: torch.Generator | list[torch.Generator] | None = None,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""Encode a video into VAE latent space for V2V generation.
|
|
|
|
Returns (first_frame_latent, video_latents) where first_frame_latent
|
|
is used as the image prefix, and video_latents fills the history buffer.
|
|
"""
|
|
device = device or self.device
|
|
video = video.to(device=device, dtype=self.vae.dtype)
|
|
|
|
num_frames = video.shape[2]
|
|
min_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1
|
|
num_chunks = num_frames // min_frames
|
|
if num_chunks == 0:
|
|
raise ValueError(
|
|
f"Video must have at least {min_frames} frames (got {num_frames}). "
|
|
f"Required: (num_latent_frames_per_chunk - 1) * {self.vae_scale_factor_temporal} + 1"
|
|
)
|
|
total_valid_frames = num_chunks * min_frames
|
|
start_frame = num_frames - total_valid_frames
|
|
|
|
first_frame = video[:, :, 0:1, :, :]
|
|
first_frame_latent = self.vae.encode(first_frame).latent_dist.sample(generator=generator)
|
|
first_frame_latent = (first_frame_latent - latents_mean) * latents_std
|
|
|
|
latents_chunks = []
|
|
for i in range(num_chunks):
|
|
chunk_start = start_frame + i * min_frames
|
|
chunk_end = chunk_start + min_frames
|
|
video_chunk = video[:, :, chunk_start:chunk_end, :, :]
|
|
chunk_latents = self.vae.encode(video_chunk).latent_dist.sample(generator=generator)
|
|
chunk_latents = (chunk_latents - latents_mean) * latents_std
|
|
latents_chunks.append(chunk_latents)
|
|
video_latents = torch.cat(latents_chunks, dim=2)
|
|
|
|
return first_frame_latent.to(device=device, dtype=dtype), video_latents.to(device=device, dtype=dtype)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|