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Let chroma TE work on regular flux. (#8429)

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comfyanonymous 2025-06-05 07:07:17 -07:00 committed by GitHub
parent 866f6cdab4
commit 4248b1618f
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2 changed files with 9 additions and 2 deletions
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5
comfy/ldm/flux/controlnet.py
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@ -121,6 +121,9 @@ class ControlNetFlux(Flux):
if img.ndim != 3 or txt.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
if y is None:
y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
# running on sequences img
img = self.img_in(img)
@ -174,7 +177,7 @@ class ControlNetFlux(Flux):
out["output"] = out_output[:self.main_model_single]
return out
def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
def forward(self, x, timesteps, context, y=None, guidance=None, hint=None, **kwargs):
patch_size = 2
if self.latent_input:
hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))

6
comfy/ldm/flux/model.py
View File

@ -101,6 +101,10 @@ class Flux(nn.Module):
transformer_options={},
attn_mask: Tensor = None,
) -> Tensor:
if y is None:
y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
patches_replace = transformer_options.get("patches_replace", {})
if img.ndim != 3 or txt.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
@ -188,7 +192,7 @@ class Flux(nn.Module):
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
return img
def forward(self, x, timestep, context, y, guidance=None, control=None, transformer_options={}, **kwargs):
def forward(self, x, timestep, context, y=None, guidance=None, control=None, transformer_options={}, **kwargs):
bs, c, h, w = x.shape
patch_size = self.patch_size
x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))