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fix: Handle ControlNet conditioning args correctly in do_controlnet_step (closes #9444) #9469

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fix: Handle ControlNet conditioning args correctly in do_controlnet_step (closes #9444) #9469

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25 changes: 18 additions & 7 deletions invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,8 @@ def do_controlnet_step(
# control_data should be type List[ControlNetData]
# this loop covers both ControlNet (one ControlNetData in list)
# and MultiControlNet (multiple ControlNetData in list)
if control_data is None:
return sample
for _i, control_datum in enumerate(control_data):
control_mode = control_datum.control_mode
# soft_injection and cfg_injection are the two ControlNet control_mode booleans
Expand All @@ -83,13 +85,22 @@ def do_controlnet_step(
last_control_step = math.ceil(control_datum.end_step_percent * total_step_count)
# only apply controlnet if current step is within the controlnet's begin/end step range
if step_index >= first_control_step and step_index <= last_control_step:
if cfg_injection:
sample_model_input = sample
else:
# expand the latents input to control model if doing classifier free guidance
# (which I think for now is always true, there is conditional elsewhere that stops execution if
# classifier_free_guidance is <= 1.0 ?)
sample_model_input = torch.cat([sample] * 2)
# Build cross_attention_kwargs with controlnet residuals
cross_attention_kwargs = {
"down_block_additional_residuals": down_block_res_samples,
"mid_block_additional_residual": mid_block_res_sample,
"controlnet_conditioning_scale": control_datum.control_weight,
"soft_injection": soft_injection,
"cfg_injection": cfg_injection,
}
# Call the model forward callback with the correct signature
# The callback may accept either (x, t, conditioning) or (x, t, conditioning, cross_attention_kwargs)
try:
sample = self.model_forward_callback(sample, timestep, conditioning_data, cross_attention_kwargs)
except TypeError:
# Fallback for older callback signature without cross_attention_kwargs
sample = self.model_forward_callback(sample, timestep, conditioning_data)
return samplet = torch.cat([sample] * 2)

added_cond_kwargs = None

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