344 lines
17 KiB
Python
344 lines
17 KiB
Python
import os
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import sys
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import time
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import numpy as np
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import torch
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from diffusers import FlowMatchEulerDiscreteScheduler
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from omegaconf import OmegaConf
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from PIL import Image
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current_file_path = os.path.abspath(__file__)
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project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path)), os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))]
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for project_root in project_roots:
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sys.path.insert(0, project_root) if project_root not in sys.path else None
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from videox_fun.dist import set_multi_gpus_devices, shard_model
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from videox_fun.models import (AutoencoderKLWan, AutoTokenizer,
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WanT5EncoderModel,
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WanTransformer3DModel_FlexForcing)
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from videox_fun.pipeline import WanFlexForcingPipeline
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from videox_fun.pipeline.pipeline_wan_flex_forcing import \
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PAPER_CHUNK_CONFIGS
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from videox_fun.utils import (register_auto_device_hook,
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safe_enable_group_offload)
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from videox_fun.utils.fm_solvers import FlowDPMSolverMultistepScheduler
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from videox_fun.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
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from videox_fun.utils.fp8_optimization import (convert_model_weight_to_float8,
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convert_weight_dtype_wrapper,
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replace_parameters_by_name)
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from videox_fun.utils.lora_utils import merge_lora, unmerge_lora
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from videox_fun.utils.utils import filter_kwargs, save_videos_grid
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# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
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# model_full_load means that the entire model will be moved to the GPU.
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#
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# model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
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# and the transformer model has been quantized to float8, which can save more GPU memory.
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#
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# model_cpu_offload means that the entire model will be moved to the CPU after use, which can save some GPU memory.
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#
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# model_cpu_offload_and_qfloat8 indicates that the entire model will be moved to the CPU after use,
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# and the transformer model has been quantized to float8, which can save more GPU memory.
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#
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# model_group_offload transfers internal layer groups between CPU/CUDA,
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# balancing memory efficiency and speed between full-module and leaf-level offloading methods.
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#
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# sequential_cpu_offload means that each layer of the model will be moved to the CPU after use,
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# resulting in slower speeds but saving a large amount of GPU memory.
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GPU_memory_mode = "model_full_load"
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# Multi GPUs config
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# Please ensure that the product of ulysses_degree and ring_degree equals the number of GPUs used.
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# For example, if you are using 8 GPUs, you can set ulysses_degree = 2 and ring_degree = 4.
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# If you are using 1 GPU, you can set ulysses_degree = 1 and ring_degree = 1.
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ulysses_degree = 1
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ring_degree = 1
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# Use FSDP to save more GPU memory in multi gpus.
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fsdp_dit = False
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fsdp_text_encoder = True
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# Compile will give a speedup in fixed resolution and need a little GPU memory.
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# The compile_dit is not compatible with the fsdp_dit and sequential_cpu_offload.
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# [NOTE]: flex_attention block masks are rebuilt per partition, so compiling the
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# blocks only pays off when the ladder (`denoise_mode`, `num_inference_steps`)
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# is fixed.
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compile_dit = False
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# Config and model path
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config_path = "config/wan2.1/wan_civitai.yaml"
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# model path
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model_name = "models/Diffusion_Transformer/Wan2.1-T2V-1.3B"
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# Choose the sampler in "Flow", "Flow_Unipc", "Flow_DPM++"
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sampler_name = "Flow"
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# [NOTE]: Noise schedule shift parameter. Affects temporal dynamics.
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# Used when the sampler is in "Flow_Unipc", "Flow_DPM++".
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shift = 5
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# Load pretrained model if need
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# Any Wan2.1 / CausVid / Self-Forcing checkpoint loads as-is: the Flex-Forcing
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# backbone inherits every parameter name and only the new `flex_kproj.*` tensors
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# are reported missing (they are identity-initialised, so step 0 is unchanged).
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transformer_path = "output_dir_wan2.1_flex_forcing_distill/checkpoint-3000/diffusion_pytorch_model.safetensors"
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vae_path = None
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lora_path = None
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# Other params
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# The paper evaluates 5 s clips: 81 pixel frames = 21 latent frames at 832x432.
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sample_size = [432, 832]
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video_length = 81
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fps = 16
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# Flex-Forcing (arXiv 2607.03509) inference config
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# --- Does the frame partition change with the noise level? -----------------
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# "fixed" -> one partition held for every denoising step, i.e. the
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# block-major Self-Forcing schedule (use `num_frame_per_block`).
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# "pyramid" -> Flex-Forcing 3.2: level 0 plans the whole clip in one
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# bidirectional chunk, each further denoising step binary-splits
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# every chunk - coarse (planning) -> fine (refinement), one level
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# per step. The depth follows `num_inference_steps` automatically,
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# so there is no second number to keep in sync. Levels only ever
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# *add* boundaries, so a KV cache written at a coarse level stays
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# valid at a finer one.
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# "full_then_blocks" -> first denoising step runs the whole clip as one
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# bidirectional ("full") chunk, every later step is the block-major
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# Self-Forcing schedule over `num_frame_per_block`. A fixed 2-level
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# ladder - coarser than the binary pyramid, no `min_num_frame_per_
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# block` involvement; needs `num_inference_steps >= 2` for the
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# block-major steps to actually run.
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# An int instead pins a truncated pyramid of exactly that many levels; for 21
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# latent frames (= 81 pixel frames) that ladder is
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# 2 -> [[21], [11, 10]] 3 -> [[21], [11, 10], [6, 5, 5, 5]]
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denoise_mode = "pyramid"
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# How far the splitting above goes: every chunk is binary-split until it is at
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# or below this block size, so it decides how causal the finest level is.
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# 1 -> leaves are single frames, fully causal
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# 3 -> leaves stay 3-frame blocks (classic Self-Forcing granularity); the
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# ladder then converges early and later steps reuse its finest level.
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min_num_frame_per_block = 1
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# --- Causal backbone (inherited from Self-Forcing) -------------------------
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# `num_frame_per_block` only takes effect once the pyramid is off; the rollout
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# derives the block size from the partition itself otherwise. `context_noise`
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# is the noise level the clean context is committed at.
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num_frame_per_block = 3
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independent_first_frame = False
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context_noise = 0.0
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# Local attention window size (-1 for global attention). Must stay -1 whenever
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# the pyramid is used, because a rolling window evicts by `current_start` deltas
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# that a splitting chunk moves backwards. For long videos the paper uses a
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# 21-latent-frame window with a 3-frame sink: local_attn_size = 21, sink_size = 3.
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local_attn_size = -1
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sink_size = 0
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# Use torch.float16 if GPU does not support torch.bfloat16
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# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
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weight_dtype = torch.bfloat16
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prompt = "A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about."
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negative_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"
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guidance_scale = 1.0
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seed = 43
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# The paper's 2-step DMD model denoises at [1000, 500]; 4 steps ([1000, 750,
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# 500, 250]) suit the CCD checkpoint. `denoise_mode = "pyramid"` uses one ladder
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# level per step here, so a deeper pyramid just wants more steps.
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num_inference_steps = 4
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lora_weight = 0.55
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save_path = "samples/wan-videos-flex-forcing-t2v"
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device = set_multi_gpus_devices(ulysses_degree, ring_degree)
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config = OmegaConf.load(config_path)
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# Load transformer with the Flex-Forcing backbone
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transformer_additional_kwargs = OmegaConf.to_container(config['transformer_additional_kwargs'])
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transformer_additional_kwargs['local_attn_size'] = local_attn_size
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transformer_additional_kwargs['sink_size'] = sink_size
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transformer = WanTransformer3DModel_FlexForcing.from_pretrained(
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os.path.join(model_name, config['transformer_additional_kwargs'].get('transformer_subpath', 'transformer')),
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transformer_additional_kwargs=transformer_additional_kwargs,
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low_cpu_mem_usage=True,
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torch_dtype=weight_dtype,
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)
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if transformer_path is not None:
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print(f"From checkpoint: {transformer_path}")
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if transformer_path.endswith("safetensors"):
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from safetensors.torch import load_file, safe_open
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state_dict = load_file(transformer_path)
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else:
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state_dict = torch.load(transformer_path, map_location="cpu")
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state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
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state_dict = state_dict["generator_ema"] if "generator_ema" in state_dict else state_dict
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state_dict = state_dict["generator"] if "generator" in state_dict else state_dict
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if any("._fsdp_wrapped_module." in k for k in state_dict.keys()):
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state_dict = {k.replace("model._fsdp_wrapped_module.", "model.", 1) if k.startswith("model._fsdp_wrapped_module.") else k: v for k, v in state_dict.items()}
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if any(k.startswith("model.") for k in state_dict.keys()):
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state_dict = {k.replace("model.", "", 1) if k.startswith("model.") else k: v for k, v in state_dict.items()}
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m, u = transformer.load_state_dict(state_dict, strict=False)
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# `flex_kproj.*` is expected to be missing when loading a Self-Forcing /
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# CausVid checkpoint that predates Flex-Forcing.
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other_missing = [k for k in m if "flex_kproj" not in k]
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print(f"missing keys: {len(m)} ({len(m) - len(other_missing)} of them flex_kproj), "
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f"unexpected keys: {len(u)}")
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# Get Vae
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vae = AutoencoderKLWan.from_pretrained(
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os.path.join(model_name, config['vae_kwargs'].get('vae_subpath', 'vae')),
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additional_kwargs=OmegaConf.to_container(config['vae_kwargs']),
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).to(weight_dtype)
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if vae_path is not None:
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print(f"From checkpoint: {vae_path}")
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if vae_path.endswith("safetensors"):
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from safetensors.torch import load_file, safe_open
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state_dict = load_file(vae_path)
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else:
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state_dict = torch.load(vae_path, map_location="cpu")
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state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
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m, u = vae.load_state_dict(state_dict, strict=False)
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print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
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# Get Tokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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os.path.join(model_name, config['text_encoder_kwargs'].get('tokenizer_subpath', 'tokenizer')),
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)
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# Get Text encoder
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text_encoder = WanT5EncoderModel.from_pretrained(
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os.path.join(model_name, config['text_encoder_kwargs'].get('text_encoder_subpath', 'text_encoder')),
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additional_kwargs=OmegaConf.to_container(config['text_encoder_kwargs']),
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low_cpu_mem_usage=True,
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torch_dtype=weight_dtype,
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)
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# Get Scheduler
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Chosen_Scheduler = scheduler_dict = {
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"Flow": FlowMatchEulerDiscreteScheduler,
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"Flow_Unipc": FlowUniPCMultistepScheduler,
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"Flow_DPM++": FlowDPMSolverMultistepScheduler,
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}[sampler_name]
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if sampler_name == "Flow_Unipc" or sampler_name == "Flow_DPM++":
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config['scheduler_kwargs']['shift'] = 1
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scheduler = Chosen_Scheduler(
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**filter_kwargs(Chosen_Scheduler, OmegaConf.to_container(config['scheduler_kwargs']))
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)
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# Get Pipeline
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pipeline = WanFlexForcingPipeline(
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transformer=transformer,
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vae=vae,
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tokenizer=tokenizer,
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text_encoder=text_encoder,
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scheduler=scheduler,
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)
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if ulysses_degree > 1 or ring_degree > 1:
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from functools import partial
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transformer.enable_multi_gpus_inference()
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if fsdp_dit:
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shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype)
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pipeline.transformer = shard_fn(pipeline.transformer)
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print("Add FSDP DIT")
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if fsdp_text_encoder:
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shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype)
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pipeline.text_encoder = shard_fn(pipeline.text_encoder)
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print("Add FSDP TEXT ENCODER")
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if compile_dit:
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for i in range(len(pipeline.transformer.blocks)):
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pipeline.transformer.blocks[i] = torch.compile(pipeline.transformer.blocks[i])
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print("Add Compile")
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if GPU_memory_mode == "sequential_cpu_offload":
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replace_parameters_by_name(transformer, ["modulation",], device=device)
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transformer.freqs = transformer.freqs.to(device=device)
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pipeline.enable_sequential_cpu_offload(device=device)
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elif GPU_memory_mode == "model_group_offload":
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register_auto_device_hook(pipeline.transformer)
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safe_enable_group_offload(pipeline, onload_device=device, offload_device="cpu", offload_type="leaf_level", use_stream=True)
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elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
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convert_model_weight_to_float8(transformer, exclude_module_name=["modulation",], device=device)
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convert_weight_dtype_wrapper(transformer, weight_dtype)
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pipeline.enable_model_cpu_offload(device=device)
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elif GPU_memory_mode == "model_cpu_offload":
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pipeline.enable_model_cpu_offload(device=device)
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elif GPU_memory_mode == "model_full_load_and_qfloat8":
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convert_model_weight_to_float8(transformer, exclude_module_name=["modulation",], device=device)
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convert_weight_dtype_wrapper(transformer, weight_dtype)
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pipeline.to(device=device)
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else:
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pipeline.to(device=device)
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print(f"[Flex-Forcing] denoise_mode={denoise_mode}, "
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f"min_num_frame_per_block={min_num_frame_per_block}, "
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f"local_attn_size={local_attn_size}, sink_size={sink_size}")
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print(f"[Flex-Forcing] partitions measured in the paper: "
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f"{[list(c) for c in PAPER_CHUNK_CONFIGS]}")
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generator = torch.Generator(device=device).manual_seed(seed)
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if lora_path is not None:
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pipeline = merge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
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with torch.no_grad():
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video_length = int((video_length - 1) // vae.config.temporal_compression_ratio * vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
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latent_frames = (video_length - 1) // vae.config.temporal_compression_ratio + 1
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torch.cuda.synchronize()
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start_time = time.time()
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sample = pipeline(
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prompt,
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num_frames = video_length,
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negative_prompt = negative_prompt,
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height = sample_size[0],
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width = sample_size[1],
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generator = generator,
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guidance_scale = guidance_scale,
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num_inference_steps = num_inference_steps,
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shift = shift,
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num_frame_per_block = num_frame_per_block,
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independent_first_frame = independent_first_frame,
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context_noise = context_noise,
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denoise_mode = denoise_mode,
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min_num_frame_per_block = min_num_frame_per_block,
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).videos
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torch.cuda.synchronize()
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elapsed = time.time() - start_time
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print(f"[Timing] {video_length} frames ({latent_frames} latent) in {elapsed:.2f}s "
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f"({video_length / elapsed:.2f} frames/s)")
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if getattr(pipeline, "kv_cache_pos", None) is not None:
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kv_tokens = pipeline.kv_cache_pos[0]["k"].shape[1]
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kv_mib = sum(c["k"].numel() + c["v"].numel()
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for c in pipeline.kv_cache_pos + pipeline.kv_cache_neg) \
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* pipeline.kv_cache_pos[0]["k"].element_size() / (1024 ** 2)
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print(f"[KV cache] {kv_tokens} tokens per layer per branch, total {kv_mib:.1f} MiB (pos+neg, all layers)")
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if lora_path is not None:
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pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
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def save_results():
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if not os.path.exists(save_path):
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os.makedirs(save_path, exist_ok=True)
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index = len([path for path in os.listdir(save_path)]) + 1
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prefix = str(index).zfill(8)
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if video_length == 1:
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video_path = os.path.join(save_path, prefix + ".png")
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image = sample[0, :, 0]
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image = image.transpose(0, 1).transpose(1, 2)
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image = (image * 255).numpy().astype(np.uint8)
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image = Image.fromarray(image)
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image.save(video_path)
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else:
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video_path = os.path.join(save_path, prefix + ".mp4")
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save_videos_grid(sample, video_path, fps=fps)
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if ulysses_degree * ring_degree > 1:
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import torch.distributed as dist
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if dist.get_rank() == 0:
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save_results()
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else:
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save_results()
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