328 lines
15 KiB
Python
328 lines
15 KiB
Python
import os
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import sys
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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_SelfForcing)
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from videox_fun.pipeline import WanSelfForcingPipeline
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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, get_image_to_video_latent,
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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_cpu_offload"
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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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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 = 5.0
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# `stochastic_sampling`: False: ar
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# True : ccd and dmd
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stochastic_sampling = True
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# Causal-Forcing checkpoint to overlay on top of the Wan2.1 base model.
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transformer_path = "output_dir_wan2.1_causal_forcing_dmd/checkpoint-2000/diffusion_pytorch_model.safetensors"
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use_ema = False
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vae_path = None
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lora_path = None
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# Other params
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sample_size = [480, 832]
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video_length = 81
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fps = 16
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# Causal-Forcing causal inference config
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# `num_frame_per_block`: 3 = chunk-wise:
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# 1 = frame-wise:
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num_frame_per_block = 3
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# Local attention window size (-1 for global attention)
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local_attn_size = -1
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# Others
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independent_first_frame = False
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context_noise = 0.0
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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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# -------- Stage selector (uncomment ONE block) --------
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# All few-step distilled stages (2, 3) bake CFG into the student weights, so
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# inference MUST use guidance_scale=1.0 — CF's CausalInferencePipeline does
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# zero CFG (grep "unconditional/cfg/guidance" in pipeline/causal_inference.py
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# returns 0). Using gs>1 stacks CFG on top of a CFG-baked model and produces
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# over-saturated, AR-unstable outputs.
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#
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# Stage 1 — AR diffusion (`ar_diffusion.pt`): 50-step UniPC + CFG.
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# guidance_scale = 3.0
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# num_inference_steps = 50
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#
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# Stage 2 — CCD (`causal_cd.pt`): 4-step consistency-distilled.
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# guidance_scale = 1.0
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# num_inference_steps = 4
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#
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# Stage 3 — DMD (`causal_forcing.pt`): 4-step distribution-matching distilled.
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# guidance_scale = 1.0
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# num_inference_steps = 4
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guidance_scale = 1.0
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num_inference_steps = 4
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seed = 43
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lora_weight = 0.55
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save_path = "samples/wan-videos-causal-forcing"
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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 causal inference support if enabled
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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 = WanTransformer3DModel_SelfForcing.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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def _resolve_transformer_path(raw_path: str, prefer_ema: bool) -> str:
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"""Resolve a checkpoint path to the actual weights file to load.
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File paths are returned unchanged so external `.pt` ckpts (CF official) keep
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working. For trainer-output dirs, prefer EMA when asked and available,
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otherwise fall back to live `transformer/` weights.
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"""
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if os.path.isfile(raw_path):
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return raw_path
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if os.path.isdir(raw_path):
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candidates = []
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if prefer_ema:
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candidates.append(os.path.join(raw_path, "ema_transformer", "diffusion_pytorch_model.safetensors"))
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candidates.append(os.path.join(raw_path, "transformer", "diffusion_pytorch_model.safetensors"))
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candidates.append(os.path.join(raw_path, "diffusion_pytorch_model.safetensors"))
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for c in candidates:
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if os.path.isfile(c):
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return c
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raise FileNotFoundError(
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f"transformer_path={raw_path!r} is neither a file nor a checkpoint dir "
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f"with a known safetensors layout (transformer/ or ema_transformer/)."
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)
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_raw_transformer_path = transformer_path
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transformer_path = _resolve_transformer_path(transformer_path, prefer_ema=use_ema)
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if transformer_path != _raw_transformer_path:
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print(f"use_ema={use_ema}: resolved {_raw_transformer_path} -> {transformer_path}")
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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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# Causal-Forcing's FSDP-saved ckpts (causal_cd.pt / causal_forcing.pt) keep the
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# `model._fsdp_wrapped_module.` prefix; strip it before the generic `model.` strip
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# so both kinds of ckpt land at bare parameter names.
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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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print(f"missing keys: {len(m)}, 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 = WanSelfForcingPipeline(
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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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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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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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stochastic_sampling = stochastic_sampling,
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).videos
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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() |