Add bf16 model
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@ -127,7 +127,6 @@ class AsymmetricAttention(nn.Module):
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):
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):
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# Pre-norm for visual features
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# Pre-norm for visual features
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x = modulated_rmsnorm(x, scale_x) # (B, M, dim_x) where M = N / cp_group_size
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x = modulated_rmsnorm(x, scale_x) # (B, M, dim_x) where M = N / cp_group_size
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#print("x in attn", x.dtype, x.device)
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# Process visual features
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# Process visual features
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qkv_x = self.qkv_x(x) # (B, M, 3 * dim_x)
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qkv_x = self.qkv_x(x) # (B, M, 3 * dim_x)
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@ -139,13 +138,6 @@ class AsymmetricAttention(nn.Module):
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# Process text features
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# Process text features
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y = modulated_rmsnorm(y, scale_y) # (B, L, dim_y)
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y = modulated_rmsnorm(y, scale_y) # (B, L, dim_y)
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q_y, k_y, v_y = self.run_qkv_y(y) # (B, L, local_heads, head_dim)
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q_y, k_y, v_y = self.run_qkv_y(y) # (B, L, local_heads, head_dim)
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#print("y in attn", y.dtype, y.device)
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#print(q_y.dtype, q_y.device)
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#print(self.q_norm_y.weight.dtype, self.q_norm_y.weight.device)
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# self.q_norm_y.weight = self.q_norm_y.weight.to(q_y.dtype)
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# self.q_norm_y.bias = self.q_norm_y.bias.to(q_y.dtype)
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# self.k_norm_y.weight = self.k_norm_y.weight.to(k_y.dtype)
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# self.k_norm_y.bias = self.k_norm_y.bias.to(k_y.dtype)
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q_y = self.q_norm_y(q_y)
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q_y = self.q_norm_y(q_y)
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k_y = self.k_norm_y(k_y)
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k_y = self.k_norm_y(k_y)
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2
nodes.py
2
nodes.py
@ -48,6 +48,8 @@ class DownloadAndLoadMochiModel:
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"model": (
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"model": (
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[
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[
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"mochi_preview_dit_fp8_e4m3fn.safetensors",
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"mochi_preview_dit_fp8_e4m3fn.safetensors",
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"mochi_preview_dit_bf16.safetensors",
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],
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],
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{"tooltip": "Downloads from 'https://huggingface.co/Kijai/Mochi_preview_comfy' to 'models/diffusion_models/mochi'", },
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{"tooltip": "Downloads from 'https://huggingface.co/Kijai/Mochi_preview_comfy' to 'models/diffusion_models/mochi'", },
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),
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),
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