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Merge 661e1043d1 into 791e30ff50
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@ -538,7 +538,7 @@ class VAE:
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self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
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self.memory_used_encode = lambda shape, dtype: 3300 * shape[3] * shape[4] * model_management.dtype_size(dtype)
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self.memory_used_decode = lambda shape, dtype: 8000 * shape[3] * shape[4] * (16 * 16) * model_management.dtype_size(dtype)
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else: # Wan 2.1 VAE
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else: # Wan 2.1 VAE
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dim = sd["decoder.head.0.gamma"].shape[0]
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self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
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self.upscale_index_formula = (4, 8, 8)
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@ -549,8 +549,10 @@ class VAE:
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ddconfig = {"dim": dim, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
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self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
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self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
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self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
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self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
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self.memory_used_encode = lambda shape, dtype: (1500 if shape[2]<=4 else 6000) * shape[3] * shape[4] * model_management.dtype_size(dtype)
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self.memory_used_decode = lambda shape, dtype: (2200 if shape[2]<=4 else 7000) * shape[3] * shape[4] * (8*8) * model_management.dtype_size(dtype)
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# Hunyuan 3d v2 2.0 & 2.1
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elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd:
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