Support block replace patches in auraflow.
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@ -437,7 +437,8 @@ class MMDiT(nn.Module):
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pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w]
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return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
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def forward(self, x, timestep, context, **kwargs):
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def forward(self, x, timestep, context, transformer_options={}, **kwargs):
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patches_replace = transformer_options.get("patches_replace", {})
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# patchify x, add PE
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b, c, h, w = x.shape
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@ -458,15 +459,36 @@ class MMDiT(nn.Module):
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global_cond = self.t_embedder(t, x.dtype) # B, D
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blocks_replace = patches_replace.get("dit", {})
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if len(self.double_layers) > 0:
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for layer in self.double_layers:
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c, x = layer(c, x, global_cond, **kwargs)
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for i, layer in enumerate(self.double_layers):
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if ("double_block", i) in blocks_replace:
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def block_wrap(args):
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out = {}
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out["txt"], out["img"] = layer(args["txt"],
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args["img"],
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args["vec"])
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return out
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out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap})
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c = out["txt"]
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x = out["img"]
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else:
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c, x = layer(c, x, global_cond, **kwargs)
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if len(self.single_layers) > 0:
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c_len = c.size(1)
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cx = torch.cat([c, x], dim=1)
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for layer in self.single_layers:
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cx = layer(cx, global_cond, **kwargs)
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for i, layer in enumerate(self.single_layers):
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if ("single_block", i) in blocks_replace:
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def block_wrap(args):
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out = {}
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out["img"] = layer(args["img"], args["vec"])
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return out
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out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap})
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cx = out["img"]
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else:
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cx = layer(cx, global_cond, **kwargs)
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x = cx[:, c_len:]
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