bhetherman
9debc56137
Benchmarks the dense 5B Turbo model (Q8_0 GGUF + fp8 T5) as a lower-VRAM alternative to the 14B MoE pipeline. Includes dtype patches for dense WanModel, Wan 2.2 VAE config (48 channels, 16x spatial), and Blackwell fp8 workaround. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
"""Stage Wan2.2-TI2V-5B-Turbo GGUF pipeline for LightX2V.
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Downloads:
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1. Base `Wan-AI/Wan2.2-TI2V-5B` snapshot (configs, T5, VAE — skip bf16 DIT shards).
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2. Turbo Q8 GGUF DIT from `hum-ma/Wan2.2-TI2V-5B-Turbo-GGUF`.
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Quant file can be overridden via GGUF_FILE env (default Q8_0).
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Idempotent: huggingface_hub handles caching.
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"""
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from __future__ import annotations
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import os
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from huggingface_hub import hf_hub_download, snapshot_download
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BASE_REPO = "Wan-AI/Wan2.2-TI2V-5B"
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GGUF_REPO = "hum-ma/Wan2.2-TI2V-5B-Turbo-GGUF"
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GGUF_FILE = os.environ.get(
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"GGUF_FILE", "Wan2_2-TI2V-5B-Turbo-Q8_0.gguf"
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)
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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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def main() -> None:
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print(f"\n=== 1/2 Snapshot base pipeline {BASE_REPO} ===", flush=True)
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# The base repo ships bf16 DIT shards we don't need (we use the Turbo GGUF instead).
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base_dir = snapshot_download(
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repo_id=BASE_REPO,
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ignore_patterns=[
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"*.pt",
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"diffusion_pytorch_model*.safetensors",
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],
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)
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print(f"Base pipeline at: {base_dir}")
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print(f"\n=== 2/3 Download {GGUF_FILE} from {GGUF_REPO} ===", flush=True)
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gguf_path = hf_hub_download(repo_id=GGUF_REPO, filename=GGUF_FILE)
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print(f"GGUF DIT at: {gguf_path}")
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print(f"\n=== 3/3 Download fp8 T5 from {T5_FP8_REPO} ===", flush=True)
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t5_path = hf_hub_download(repo_id=T5_FP8_REPO, filename=T5_FP8_FILE)
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print(f"fp8 T5 at: {t5_path}")
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print(f"\n{'=' * 50}")
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print("Ready. Export to test_i2v.py via env:")
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print(f" BASE_DIR={base_dir}")
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print(f" DIT_GGUF={gguf_path}")
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print(f" T5_FP8={t5_path}")
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if __name__ == "__main__":
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main()
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