686 lines
27 KiB
Python
686 lines
27 KiB
Python
"""Wan2.2-Lightning image-to-video wrapper via LightX2V.
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This wrapper targets LightX2V's actual Python entry points (verified against
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the upstream ``lightx2v.infer.main`` in ModelTC/LightX2V@main):
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from lightx2v.utils.set_config import set_config
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from lightx2v.utils.input_info import init_empty_input_info, update_input_info_from_dict
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from lightx2v.infer import init_runner
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args = argparse.Namespace(model_cls=..., task="i2v", model_path=..., config_json=..., ...)
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config = set_config(args)
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input_info = init_empty_input_info(args.task, args.support_tasks)
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runner = init_runner(config) # loads all weights — done ONCE
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update_input_info_from_dict(input_info, {"seed": ..., "prompt": ..., "image_path": ..., "save_result_path": ...})
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runner.run_pipeline(input_info) # per-turn; MP4 written to save_result_path
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# LoRA hot-swap:
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runner.switch_lora(lora_path, strength) # swap in
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runner.switch_lora("", 0.0) # remove
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Model weights are loaded once at construction and held resident across turns
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so reflective mode doesn't re-pay the load cost each reply.
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Two HuggingFace repos are consumed on first run (cached under HF_HOME):
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- Wan-AI/Wan2.2-I2V-A14B — T5 encoder, VAE, tokenizer/config only.
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The bf16 DIT shards under high_noise_model/
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and low_noise_model/ are SKIPPED via
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ignore_patterns — we replace them with
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quantised checkpoints from dit_repo.
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- dit_repo (configurable) — quantised DIT checkpoints. Supported
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formats:
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* fp8 safetensors (lightx2v/Wan2.2-Distill-Models)
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* GGUF (QuantStack/Wan2.2-I2V-A14B-GGUF)
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"""
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from __future__ import annotations
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import argparse
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import json
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import logging
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import os
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import random
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import tempfile
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from typing import TYPE_CHECKING
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import numpy as np
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if TYPE_CHECKING:
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from server.video import LoRASpec
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log = logging.getLogger(__name__)
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# --- fp8 distill filenames --------------------------------------------------
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FP8_HIGH_NOISE_FILE = "wan2.2_i2v_A14b_high_noise_scaled_fp8_e4m3_lightx2v_4step.safetensors"
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FP8_LOW_NOISE_FILE = "wan2.2_i2v_A14b_low_noise_scaled_fp8_e4m3_lightx2v_4step.safetensors"
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# --- GGUF filenames (QuantStack layout: HighNoise/<name>.gguf) ---------------
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GGUF_HIGH_NOISE_TEMPLATE = "HighNoise/Wan2.2-I2V-A14B-HighNoise-{quant}.gguf"
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GGUF_LOW_NOISE_TEMPLATE = "LowNoise/Wan2.2-I2V-A14B-LowNoise-{quant}.gguf"
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# --- fp8 T5 encoder (lightx2v/Encoders repo) --------------------------------
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T5_FP8_REPO = "lightx2v/Encoders"
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T5_FP8_FILE = "models_t5_umt5-xxl-enc-fp8.safetensors"
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# The Wan-AI base repo ships bf16 DIT weight shards (~28 GB) alongside the
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# T5/VAE/tokenizer support files (~12 GB). We only need the latter — the
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# quantised files from dit_repo replace the DIT weights entirely. We must
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# keep the config.json / index.json metadata under high_noise_model/ and
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# low_noise_model/ (LightX2V's set_config reads architecture params like
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# ``dim`` from them) and the tokenizer files under google/.
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BASE_REPO_IGNORE_PATTERNS = [
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"high_noise_model/*.safetensors",
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"low_noise_model/*.safetensors",
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"assets/*",
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"examples/*",
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"nohup.out",
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"*.md",
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]
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def _patch_fp8_scaled_mm_for_blackwell() -> None:
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"""Replace sgl_kernel.fp8_scaled_mm with torch._scaled_mm on Blackwell.
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sgl_kernel's CUTLASS-based fp8 GEMM doesn't ship SM120 kernels yet.
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PyTorch 2.8+'s native ``_scaled_mm`` works on all architectures
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including Blackwell. This patch is idempotent.
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"""
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try:
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import sgl_kernel # type: ignore[import-not-found]
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except ImportError:
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return # no sgl_kernel → fp8 T5 not in use
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if getattr(sgl_kernel, "_fp8_patched_for_blackwell", False):
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return
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import torch
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if not torch.cuda.is_available():
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return
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cap = torch.cuda.get_device_capability()
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if cap[0] < 12:
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return # only patch on Blackwell+
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_orig = sgl_kernel.fp8_scaled_mm
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def _torch_fp8_scaled_mm(
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a: torch.Tensor,
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b: torch.Tensor,
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a_scale: torch.Tensor,
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b_scale: torch.Tensor,
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out_dtype: torch.dtype,
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bias: torch.Tensor | None = None,
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) -> torch.Tensor:
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# torch._scaled_mm expects (M,K) @ (N,K).t() with:
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# scale_a: scalar or (M,1)
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# scale_b: scalar or (1,N)
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# sgl_kernel provides scale_b as (N,1) — transpose it.
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if b_scale.dim() == 2 and b_scale.shape[1] == 1:
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b_scale = b_scale.t()
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# _scaled_mm requires B to be column-major (stride(0)==1).
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bt = b.t().contiguous().t()
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out = torch._scaled_mm(
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a, bt,
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scale_a=a_scale, scale_b=b_scale,
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out_dtype=out_dtype, bias=bias,
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)
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return out
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sgl_kernel.fp8_scaled_mm = _torch_fp8_scaled_mm
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sgl_kernel._fp8_patched_for_blackwell = True
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log.info("Patched sgl_kernel.fp8_scaled_mm → torch._scaled_mm for Blackwell (SM%d%d).", *cap)
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class Wan22Pipeline:
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"""Wrapper around LightX2V's Wan2.2 MoE distill runner.
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Supports two DIT quantisation formats:
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* **fp8** — ``dit_quant_scheme="fp8-sgl"`` (default, from
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``lightx2v/Wan2.2-Distill-Models``)
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* **GGUF** — ``dit_quant_scheme="gguf-Q4_K_M"`` (or any quant level,
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from ``QuantStack/Wan2.2-I2V-A14B-GGUF``)
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Constructor downloads (if needed) both HF repos, writes a runtime JSON
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config with absolute ckpt paths, then drives ``lightx2v.infer.init_runner``.
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``generate_i2v`` runs one inference turn against the already-loaded runner.
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"""
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def __init__(
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self,
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base_repo: str,
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dit_repo: str,
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config_json: str,
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model_cls: str = "wan2.2_moe_distill",
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resolution: int = 480,
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fps: int = 16,
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dit_quant_scheme: str = "fp8-sgl",
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t5_quantized: bool = False,
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):
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self.base_repo = base_repo
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self.dit_repo = dit_repo
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self.config_json_template = config_json
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self.model_cls = model_cls
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self.resolution = resolution
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self.fps = fps
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self.dit_quant_scheme = dit_quant_scheme
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self.t5_quantized = t5_quantized
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self._applied_loras: list[LoRASpec] = []
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self._is_gguf = dit_quant_scheme.startswith("gguf-")
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# 1. Resolve / download base repo (T5/VAE/config) and DIT ckpts.
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self._model_root = self._ensure_base_repo(base_repo)
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self._dit_high, self._dit_low = self._ensure_dit_checkpoints(
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dit_repo, dit_quant_scheme,
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)
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self._t5_fp8_ckpt = (
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self._ensure_t5_fp8() if t5_quantized else None
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)
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# 2. Materialize a runtime JSON config with absolute ckpt paths.
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self._runtime_json_path = self._build_runtime_config()
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# 3. Build the argparse-like namespace LightX2V.set_config() expects.
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args = self._build_args(
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model_cls=model_cls,
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model_path=self._model_root,
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config_json=self._runtime_json_path,
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)
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# 4. Import LightX2V (scoped here so ``import server.video_models.wan22``
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# never pulls in lightx2v — tests can import this module on CPU).
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from lightx2v.utils.set_config import set_config # type: ignore[import-not-found]
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from lightx2v.utils.input_info import init_empty_input_info # type: ignore[import-not-found]
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from lightx2v.infer import init_runner # type: ignore[import-not-found]
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_patch_fp8_scaled_mm_for_blackwell()
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# 5. Load all models under default DTYPE=BF16 so T5 (which is
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# hardcoded to bf16 weights) initialises its offload buffers
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# correctly. We flip to FP16 *after* init_runner completes.
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log.info("LightX2V set_config (model_cls=%s, model_path=%s)",
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model_cls, self._model_root)
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self._config = set_config(args)
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self._input_info_template = init_empty_input_info(
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args.task, args.support_tasks
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)
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log.info("LightX2V init_runner — loading weights (this takes a while)...")
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self._runner = init_runner(self._config)
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log.info("LightX2V runner loaded; weights resident.")
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# 6. GGUF: switch global DTYPE to FP16 for inference. GGUF DIT
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# dequantises to fp16, and many intermediate tensors inside the
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# DIT forward pass are allocated via GET_DTYPE(). The T5 encoder
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# is wrapped to temporarily restore BF16 during its forward.
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if self._is_gguf:
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os.environ["DTYPE"] = "FP16"
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from lightx2v.utils.envs import GET_DTYPE # type: ignore[import-not-found]
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GET_DTYPE.cache_clear()
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log.info("Set DTYPE=FP16 for GGUF (GET_DTYPE()=%s)", GET_DTYPE())
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self._patch_t5_dtype_for_gguf()
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self._patch_vae_dtype_for_gguf()
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self._patch_dit_fp32_weights_for_gguf()
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# --- GGUF dtype compatibility patch ----------------------------------------
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def _patch_t5_dtype_for_gguf(self) -> None:
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"""Wrap the T5 encoder so it temporarily restores DTYPE=BF16.
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The T5 encoder is hardcoded to bfloat16 weights (wan_runner.py). When
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the global DTYPE is FP16 (required for GGUF DIT), the T5's CPU-offload
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path breaks because intermediate tensor dtypes no longer match the bf16
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weights. We wrap ``run_text_encoder`` to temporarily flip GET_DTYPE()
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back to bf16, then restore fp16 before the DIT runs.
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"""
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import os
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import types
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from lightx2v.utils.envs import GET_DTYPE, GET_SENSITIVE_DTYPE # type: ignore[import-not-found]
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runner = self._runner
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orig_run_text_encoder = runner.run_text_encoder.__func__
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def bf16_text_encoder(self_runner, *args, **kwargs):
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import torch
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# Flip DTYPE to BF16 so the T5 encoder works with its bf16 weights.
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os.environ["DTYPE"] = "BF16"
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GET_DTYPE.cache_clear()
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GET_SENSITIVE_DTYPE.cache_clear()
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try:
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result = orig_run_text_encoder(self_runner, *args, **kwargs)
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finally:
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# Restore FP16 for the DIT / rest of the pipeline.
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os.environ["DTYPE"] = "FP16"
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GET_DTYPE.cache_clear()
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GET_SENSITIVE_DTYPE.cache_clear()
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# Cast bf16 T5 outputs to fp16 so they match the GGUF DIT dtype.
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def _to_fp16(x):
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if isinstance(x, torch.Tensor) and x.dtype == torch.bfloat16:
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return x.to(torch.float16)
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if isinstance(x, list):
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return [_to_fp16(v) for v in x]
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if isinstance(x, tuple):
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return tuple(_to_fp16(v) for v in x)
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if isinstance(x, dict):
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return {k: _to_fp16(v) for k, v in x.items()}
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return x
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return _to_fp16(result)
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runner.run_text_encoder = types.MethodType(bf16_text_encoder, runner)
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log.info("Patched T5 encoder to use BF16 under GGUF FP16 pipeline.")
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def _patch_vae_dtype_for_gguf(self) -> None:
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"""Cast VAE encoder/decoder weights to fp16 to match GGUF DIT dtype.
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The VAE weights load as bf16 (the default). Under GGUF the DIT runs in
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fp16 and the runner casts VAE inputs via ``.to(GET_DTYPE())`` — which
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under DTYPE=FP16 collides with bf16 VAE weights in Conv3d. Since the
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VAE is a plain float model (not quantized), simply converting its
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weights to fp16 avoids both input-vs-weight mismatches and the need
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for any runtime dtype juggling.
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"""
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import torch
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runner = self._runner
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for name in ("vae_encoder", "vae_decoder"):
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mod = getattr(runner, name, None)
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if mod is None:
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continue
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inner = getattr(mod, "model", mod)
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if hasattr(inner, "to"):
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inner.to(dtype=torch.float16)
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# The outer WanVAE wrapper also holds mean/inv_std/scale tensors
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# used by encode/decode (z = z/inv_std + mean). Cast them too, or
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# the first op upcasts fp16 latents back to fp32/bf16.
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for attr in ("mean", "inv_std"):
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t = getattr(mod, attr, None)
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if isinstance(t, torch.Tensor):
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setattr(mod, attr, t.to(torch.float16))
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scale = getattr(mod, "scale", None)
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if isinstance(scale, list):
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mod.scale = [
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t.to(torch.float16) if isinstance(t, torch.Tensor) else t
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for t in scale
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]
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log.info("Cast VAE encoder/decoder weights + scale to fp16 for GGUF FP16 pipeline.")
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def _patch_dit_fp32_weights_for_gguf(self) -> None:
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"""Cast leftover fp32 DIT pre/post weights to fp16.
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GGUF Q4_K_M dequantises the transformer blocks to fp16, but a handful
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of non-quantised weights (notably ``patch_embedding.pin_weight``) end
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up loaded as fp32. That breaks the first conv in the DIT forward pass
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(fp16 input vs fp32 weight). Cast any such tensors to fp16 so the DIT
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runs uniformly in fp16.
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"""
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import torch
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runner = self._runner
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models = getattr(runner.model, "model", None)
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if models is None:
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return
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if not isinstance(models, (list, tuple)):
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models = [models]
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n_cast = 0
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for m in models:
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for weights_attr in ("pre_weight", "post_weight"):
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w = getattr(m, weights_attr, None)
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if w is None:
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continue
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for sub_name in dir(w):
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if sub_name.startswith("_"):
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continue
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try:
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sub = getattr(w, sub_name)
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except Exception:
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continue
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if sub is None:
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continue
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for t_name in ("weight", "bias", "pin_weight", "pin_bias"):
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t = getattr(sub, t_name, None)
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if isinstance(t, torch.Tensor) and t.dtype == torch.float32:
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casted = t.to(torch.float16)
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# Preserve pinned-memory status on pin_* tensors so
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# move_attr_to_cuda's non-blocking H2D copy is safe.
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if t_name.startswith("pin_") and t.is_pinned() and not casted.is_pinned():
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try:
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casted = casted.pin_memory()
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except RuntimeError:
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pass
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setattr(sub, t_name, casted)
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n_cast += 1
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log.info("Cast %d fp32 DIT weight tensors to fp16 for GGUF pipeline.", n_cast)
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# --- Weight provisioning -------------------------------------------------
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@staticmethod
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def _ensure_base_repo(base_repo: str) -> str:
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"""Return a local directory containing the Wan2.2 base support files.
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If ``base_repo`` is already a local directory, use it as-is. Otherwise
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snapshot_download the HF repo into HF_HOME, skipping the bf16 DIT
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shards (they're replaced by the quantised files).
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"""
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if os.path.isdir(base_repo):
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return base_repo
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from huggingface_hub import snapshot_download
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log.info("Downloading Wan2.2 base support files from %s "
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"(skipping bf16 DIT shards)...", base_repo)
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return snapshot_download(
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repo_id=base_repo,
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ignore_patterns=BASE_REPO_IGNORE_PATTERNS,
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)
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@staticmethod
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def _ensure_dit_checkpoints(
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dit_repo: str,
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dit_quant_scheme: str,
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) -> tuple[str, str]:
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"""Return (high_noise_path, low_noise_path) for the DIT pair.
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Supports both fp8 safetensors and GGUF formats.
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"""
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if not dit_repo:
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raise ValueError("dit_repo must be a HF repo id or local directory.")
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is_gguf = dit_quant_scheme.startswith("gguf-")
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if is_gguf:
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# Extract quant level, e.g. "gguf-Q4_K_M" → "Q4_K_M"
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quant = dit_quant_scheme.replace("gguf-", "")
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high_file = GGUF_HIGH_NOISE_TEMPLATE.format(quant=quant)
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low_file = GGUF_LOW_NOISE_TEMPLATE.format(quant=quant)
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else:
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high_file = FP8_HIGH_NOISE_FILE
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low_file = FP8_LOW_NOISE_FILE
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# Local directory?
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if os.path.isdir(dit_repo):
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high = os.path.join(dit_repo, high_file)
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low = os.path.join(dit_repo, low_file)
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if not (os.path.isfile(high) and os.path.isfile(low)):
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raise FileNotFoundError(
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f"DIT checkpoints not found in {dit_repo}: expected "
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f"{high_file} and {low_file}"
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)
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return high, low
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# HuggingFace download.
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from huggingface_hub import hf_hub_download
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log.info("Downloading %s DIT checkpoints from %s ...",
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dit_quant_scheme, dit_repo)
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high = hf_hub_download(repo_id=dit_repo, filename=high_file)
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low = hf_hub_download(repo_id=dit_repo, filename=low_file)
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return high, low
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@staticmethod
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def _ensure_t5_fp8() -> str:
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"""Download the fp8 T5 encoder from lightx2v/Encoders (if not cached).
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Returns the local path to the safetensors file (~6 GB).
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"""
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from huggingface_hub import hf_hub_download
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log.info("Downloading fp8 T5 encoder from %s ...", T5_FP8_REPO)
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return hf_hub_download(repo_id=T5_FP8_REPO, filename=T5_FP8_FILE)
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def _build_runtime_config(self) -> str:
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"""Load the template JSON, inject absolute ckpt paths, persist to temp."""
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with open(self.config_json_template, "r", encoding="utf-8") as f:
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cfg = json.load(f)
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# Drop editorial comments before passing to LightX2V.
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cfg.pop("_comment", None)
|
|
cfg["high_noise_quantized_ckpt"] = self._dit_high
|
|
cfg["low_noise_quantized_ckpt"] = self._dit_low
|
|
cfg.setdefault("fps", self.fps)
|
|
|
|
# T5 fp8 quantization.
|
|
if self._t5_fp8_ckpt:
|
|
cfg["t5_quantized"] = True
|
|
cfg["t5_quant_scheme"] = "fp8-sgl"
|
|
cfg["t5_quantized_ckpt"] = self._t5_fp8_ckpt
|
|
|
|
tmp = tempfile.NamedTemporaryFile(
|
|
prefix="wan22_dit_", suffix=".json",
|
|
mode="w", delete=False, encoding="utf-8",
|
|
)
|
|
json.dump(cfg, tmp, indent=2)
|
|
tmp.close()
|
|
log.info("Runtime LightX2V config: %s", tmp.name)
|
|
return tmp.name
|
|
|
|
@staticmethod
|
|
def _build_args(
|
|
*, model_cls: str, model_path: str, config_json: str
|
|
) -> argparse.Namespace:
|
|
"""Mirror every field from ``lightx2v.infer.main``'s argparse so
|
|
``set_config`` finds the attributes it expects. We only customize the
|
|
model/task/path fields; everything else stays at the CLI defaults.
|
|
"""
|
|
return argparse.Namespace(
|
|
seed=42,
|
|
model_cls=model_cls,
|
|
task="i2v",
|
|
support_tasks=[],
|
|
model_path=model_path,
|
|
sf_model_path=None,
|
|
config_json=config_json,
|
|
use_prompt_enhancer=False,
|
|
prompt="",
|
|
negative_prompt="",
|
|
image_path="",
|
|
last_frame_path="",
|
|
audio_path="",
|
|
image_strength="1.0",
|
|
image_frame_idx="",
|
|
src_ref_images=None,
|
|
src_video=None,
|
|
src_mask=None,
|
|
src_pose_path=None,
|
|
src_face_path=None,
|
|
src_bg_path=None,
|
|
src_mask_path=None,
|
|
pose=None,
|
|
action_path=None,
|
|
action_ckpt=None,
|
|
save_result_path=None,
|
|
return_result_tensor=False,
|
|
target_shape=[],
|
|
target_video_length=81,
|
|
aspect_ratio="",
|
|
video_path=None,
|
|
sr_ratio=2.0,
|
|
)
|
|
|
|
# --- LoRA --------------------------------------------------------------
|
|
|
|
def load_loras(self, specs: list["LoRASpec"]) -> None:
|
|
"""Apply LoRAs to the Wan2.2 MoE distill pipeline.
|
|
|
|
Each spec's ``target`` must be ``"high_noise"`` or ``"low_noise"``
|
|
to route the LoRA to the correct expert.
|
|
|
|
With ``lazy_load`` the DIT models are ``None`` at this point, so
|
|
runtime ``switch_lora`` is impossible. Instead we inject
|
|
``lora_configs`` + ``lora_dynamic_apply`` into the runner config so
|
|
the LoRAs are applied when the models materialise on first inference.
|
|
|
|
Without ``lazy_load`` (models already resident) we call
|
|
``switch_lora`` with explicit high/low keyword args.
|
|
"""
|
|
if not specs:
|
|
return
|
|
|
|
# Resolve every path up-front (may trigger HF download).
|
|
resolved: list[tuple["LoRASpec", str]] = []
|
|
for spec in specs:
|
|
local_path = self._resolve_lora_path(spec.path)
|
|
log.info(" LoRA %s → strength=%.2f target=%s (%s)",
|
|
spec.name or spec.path, spec.weight, spec.target,
|
|
local_path)
|
|
resolved.append((spec, local_path))
|
|
|
|
lazy = self._config.get("lazy_load", False)
|
|
if lazy:
|
|
# Build the lora_configs list that LightX2V's lazy-load path
|
|
# reads inside MultiDistillModelStruct.infer().
|
|
lora_cfgs = []
|
|
for spec, local_path in resolved:
|
|
# LightX2V expects name "high_noise_model" / "low_noise_model"
|
|
cfg_name = {
|
|
"high_noise": "high_noise_model",
|
|
"low_noise": "low_noise_model",
|
|
}.get(spec.target)
|
|
if cfg_name is None:
|
|
raise ValueError(
|
|
f"LoRA target must be 'high_noise' or 'low_noise', "
|
|
f"got {spec.target!r}")
|
|
lora_cfgs.append({
|
|
"name": cfg_name,
|
|
"path": local_path,
|
|
"strength": spec.weight,
|
|
})
|
|
self._runner.set_config({
|
|
"lora_configs": lora_cfgs,
|
|
"lora_dynamic_apply": True,
|
|
})
|
|
else:
|
|
# Models are loaded — use runtime hot-swap.
|
|
high_path = high_strength = None
|
|
low_path = low_strength = None
|
|
for spec, local_path in resolved:
|
|
if spec.target == "high_noise":
|
|
high_path, high_strength = local_path, spec.weight
|
|
elif spec.target == "low_noise":
|
|
low_path, low_strength = local_path, spec.weight
|
|
else:
|
|
raise ValueError(
|
|
f"LoRA target must be 'high_noise' or 'low_noise', "
|
|
f"got {spec.target!r}")
|
|
|
|
kwargs: dict = {}
|
|
if high_path is not None:
|
|
kwargs["high_lora_path"] = high_path
|
|
kwargs["high_lora_strength"] = high_strength
|
|
if low_path is not None:
|
|
kwargs["low_lora_path"] = low_path
|
|
kwargs["low_lora_strength"] = low_strength
|
|
ok = self._runner.switch_lora(**kwargs)
|
|
if not ok:
|
|
raise RuntimeError(
|
|
"runner.switch_lora returned False. Check that your "
|
|
"LightX2V build supports runtime LoRA updates for "
|
|
f"{self.model_cls}.")
|
|
|
|
self._applied_loras = list(specs)
|
|
|
|
def unload_loras(self) -> None:
|
|
"""Remove all currently applied LoRAs."""
|
|
if not self._applied_loras:
|
|
return
|
|
lazy = self._config.get("lazy_load", False)
|
|
if lazy:
|
|
self._runner.set_config({
|
|
"lora_configs": None,
|
|
"lora_dynamic_apply": False,
|
|
})
|
|
# If models were materialised, drop them so the next inference
|
|
# recreates them without LoRAs.
|
|
model_struct = getattr(self._runner, "model", None)
|
|
if model_struct is not None and hasattr(model_struct, "model"):
|
|
for i in range(len(model_struct.model)):
|
|
model_struct.model[i] = None
|
|
else:
|
|
self._runner.switch_lora("", 0.0)
|
|
self._applied_loras = []
|
|
|
|
@staticmethod
|
|
def _resolve_lora_path(path: str) -> str:
|
|
"""Resolve a LoRA path. Supports:
|
|
- Absolute/relative local paths (returned as-is if the file exists)
|
|
- ``repo_id:filename`` HuggingFace references
|
|
"""
|
|
if os.path.isfile(path):
|
|
return path
|
|
if ":" in path and not path.startswith(("/", "./")):
|
|
repo_id, filename = path.split(":", 1)
|
|
from huggingface_hub import hf_hub_download
|
|
return hf_hub_download(repo_id=repo_id, filename=filename)
|
|
return path
|
|
|
|
# --- Inference ---------------------------------------------------------
|
|
|
|
def generate_i2v(
|
|
self,
|
|
image_path: str,
|
|
prompt: str,
|
|
seconds: int,
|
|
seed: int | None = None,
|
|
negative_prompt: str = "",
|
|
) -> np.ndarray:
|
|
"""Run image-to-video inference and return decoded frames.
|
|
|
|
Returns ``np.ndarray`` shape ``[T, H, W, 3]`` dtype uint8 in RGB.
|
|
"""
|
|
if seed is None:
|
|
seed = random.randint(0, 2**31 - 1)
|
|
|
|
# Wan2.2 target_video_length is "frames including the conditioning
|
|
# frame", so N seconds → N*fps + 1.
|
|
target_frames = seconds * self.fps + 1
|
|
|
|
from lightx2v.utils.input_info import update_input_info_from_dict # type: ignore[import-not-found]
|
|
|
|
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tf:
|
|
out_path = tf.name
|
|
try:
|
|
log.info("Wan2.2 generate: prompt=%r seconds=%d seed=%d → %s",
|
|
prompt[:80], seconds, seed, out_path)
|
|
update_input_info_from_dict(
|
|
self._input_info_template,
|
|
{
|
|
"seed": seed,
|
|
"prompt": prompt,
|
|
"negative_prompt": negative_prompt,
|
|
"image_path": image_path,
|
|
"save_result_path": out_path,
|
|
"target_video_length": target_frames,
|
|
"return_result_tensor": False,
|
|
},
|
|
)
|
|
self._runner.run_pipeline(self._input_info_template)
|
|
return _read_mp4_to_frames(out_path)
|
|
finally:
|
|
try:
|
|
os.remove(out_path)
|
|
except OSError:
|
|
pass
|
|
|
|
|
|
# --- MP4 decoding helper ------------------------------------------------------
|
|
|
|
def _read_mp4_to_frames(path: str) -> np.ndarray:
|
|
"""Decode an MP4 into an RGB uint8 frame array ``[T, H, W, 3]``."""
|
|
try:
|
|
import imageio.v3 as iio # type: ignore[import-not-found]
|
|
frames = iio.imread(path, plugin="pyav")
|
|
arr = np.asarray(frames)
|
|
if arr.ndim == 3:
|
|
arr = arr[None, ...]
|
|
return arr.astype(np.uint8)
|
|
except Exception as e: # pragma: no cover - fallback path
|
|
log.warning("imageio decode failed (%s); falling back to cv2", e)
|
|
import cv2 # type: ignore[import-not-found]
|
|
cap = cv2.VideoCapture(path)
|
|
frames: list[np.ndarray] = []
|
|
while True:
|
|
ok, frame = cap.read()
|
|
if not ok:
|
|
break
|
|
frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
|
|
cap.release()
|
|
if not frames:
|
|
raise RuntimeError(f"Failed to decode any frames from {path}")
|
|
return np.stack(frames, axis=0).astype(np.uint8)
|