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Merge branch 'main' into wan-itv-train
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@@ -9,6 +9,10 @@ snapshot_download("Wan-AI/Wan2.1-I2V-14B-480P", local_dir="models/Wan-AI/Wan2.1-
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# Load models
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model_manager = ModelManager(device="cpu")
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model_manager.load_models(
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["models/Wan-AI/Wan2.1-I2V-14B-480P/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"],
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torch_dtype=torch.float32, # Image Encoder is loaded with float32
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)
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model_manager.load_models(
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[
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[
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@@ -20,14 +24,13 @@ model_manager.load_models(
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"models/Wan-AI/Wan2.1-I2V-14B-480P/diffusion_pytorch_model-00006-of-00007.safetensors",
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"models/Wan-AI/Wan2.1-I2V-14B-480P/diffusion_pytorch_model-00007-of-00007.safetensors",
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],
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"models/Wan-AI/Wan2.1-I2V-14B-480P/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth",
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"models/Wan-AI/Wan2.1-I2V-14B-480P/models_t5_umt5-xxl-enc-bf16.pth",
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"models/Wan-AI/Wan2.1-I2V-14B-480P/Wan2.1_VAE.pth",
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],
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torch_dtype=torch.float8_e4m3fn, # You can set `torch_dtype=torch.bfloat16` to disable FP8 quantization.
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torch_dtype=torch.bfloat16, # You can set `torch_dtype=torch.float8_e4m3fn` to enable FP8 quantization.
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)
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pipe = WanVideoPipeline.from_model_manager(model_manager, torch_dtype=torch.bfloat16, device="cuda")
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pipe.enable_vram_management(num_persistent_param_in_dit=None) # You can set `num_persistent_param_in_dit` to a small number to reduce VRAM required.
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pipe.enable_vram_management(num_persistent_param_in_dit=6*10**9) # You can set `num_persistent_param_in_dit` to a small number to reduce VRAM required.
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# Download example image
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dataset_snapshot_download(
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