cleanup code
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5ec01cbff4
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@ -62,28 +62,6 @@ class TimestepEmbedder(nn.Module):
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return t_emb
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class PooledCaptionEmbedder(nn.Module):
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def __init__(
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self,
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caption_feature_dim: int,
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hidden_size: int,
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*,
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bias: bool = True,
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device: Optional[torch.device] = None,
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):
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super().__init__()
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self.caption_feature_dim = caption_feature_dim
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self.hidden_size = hidden_size
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self.mlp = nn.Sequential(
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nn.Linear(caption_feature_dim, hidden_size, bias=bias, device=device),
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nn.SiLU(),
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nn.Linear(hidden_size, hidden_size, bias=bias, device=device),
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)
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def forward(self, x):
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return self.mlp(x)
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class FeedForward(nn.Module):
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def __init__(
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self,
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@ -286,25 +286,24 @@ class T2VSynthMochiModel:
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sample_null["y_mask"], **latent_dims
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)
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def model_fn(*, z, sigma, cfg_scale):
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self.dit.to(self.device)
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if hasattr(self.dit, "cublas_half_matmul") and self.dit.cublas_half_matmul:
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autocast_dtype = torch.float16
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else:
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autocast_dtype = torch.bfloat16
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def model_fn(*, z, sigma, cfg_scale):
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nonlocal sample, sample_null
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with torch.autocast(mm.get_autocast_device(self.device), dtype=autocast_dtype):
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if cfg_scale > 1.0:
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out_cond = self.dit(z, sigma, **sample)
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out_uncond = self.dit(z, sigma, **sample_null)
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else:
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out_cond = self.dit(z, sigma, **sample)
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return out_cond
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return out_uncond + cfg_scale * (out_cond - out_uncond)
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comfy_pbar = ProgressBar(sample_steps)
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with torch.autocast(mm.get_autocast_device(self.device), dtype=autocast_dtype):
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for i in tqdm(range(0, sample_steps), desc="Processing Samples", total=sample_steps):
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sigma = sigma_schedule[i]
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dsigma = sigma - sigma_schedule[i + 1]
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