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fix controlnet to work with gradual latent

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Kohya S
2023-12-12 08:03:27 +09:00
parent 9278031e60
commit 07ef03d340
1 changed files with 14 additions and 1 deletions
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15
tools/original_control_net.py
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@@ -179,8 +179,21 @@ def call_unet_and_control_net(
return original_unet(sample, timestep, encoder_hidden_states)
guided_hint = guided_hints[cnet_idx]
# gradual latent support: match the size of guided_hint to the size of sample
if guided_hint.shape[-2:] != sample.shape[-2:]:
# print(f"guided_hint.shape={guided_hint.shape}, sample.shape={sample.shape}")
org_dtype = guided_hint.dtype
if org_dtype == torch.bfloat16:
guided_hint = guided_hint.to(torch.float32)
guided_hint = torch.nn.functional.interpolate(guided_hint, size=sample.shape[-2:], mode="bicubic")
if org_dtype == torch.bfloat16:
guided_hint = guided_hint.to(org_dtype)
guided_hint = guided_hint.repeat((num_latent_input, 1, 1, 1))
outs = unet_forward(True, cnet_info.net, cnet_info.unet, guided_hint, None, sample, timestep, encoder_hidden_states_for_control_net)
outs = unet_forward(
True, cnet_info.net, cnet_info.unet, guided_hint, None, sample, timestep, encoder_hidden_states_for_control_net
)
outs = [o * cnet_info.weight for o in outs]
# U-Net