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WIP way to support multi multi dimensional latents. (#10456)

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comfyanonymous
2025-10-23 18:21:14 -07:00
committed by GitHub
parent a1864c01f2
commit 1bcda6df98
5 changed files with 158 additions and 15 deletions
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10
comfy/model_base.py
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@@ -197,8 +197,14 @@ class BaseModel(torch.nn.Module):
extra_conds[o] = extra
t = self.process_timestep(t, x=x, **extra_conds)
model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
return self.model_sampling.calculate_denoised(sigma, model_output, x)
if "latent_shapes" in extra_conds:
xc = utils.unpack_latents(xc, extra_conds.pop("latent_shapes"))
model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds)
if len(model_output) > 1 and not torch.is_tensor(model_output):
model_output, _ = utils.pack_latents(model_output)
return self.model_sampling.calculate_denoised(sigma, model_output.float(), x)
def process_timestep(self, timestep, **kwargs):
return timestep