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Add logic for applying T2I-Adapter weights and accumulating.

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This commit is contained in:
Ryan Dick
2023-09-20 10:05:19 -04:00
parent 0902d5ce59
commit a979836f67
2 changed files with 29 additions and 7 deletions
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4
invokeai/app/invocations/latent.py
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@ -509,8 +509,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
adapter_state = t2i_adapter_model(t2i_image)
if do_classifier_free_guidance:
for k, v in enumerate(adapter_state):
adapter_state[k] = torch.cat([v] * 2, dim=0)
for idx, value in enumerate(adapter_state):
adapter_state[idx] = torch.cat([value] * 2, dim=0)
t2i_adapter_data.append(
T2IAdapterData(

32
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -559,9 +559,31 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
conditioning_data=conditioning_data,
)
elif t2i_adapter_data is not None:
# TODO(ryand): Finish this.
pass
# down_block_additional_residuals = [state.clone() for state in t2i_adapter_data]
accum_adapter_state = None
for single_t2i_adapter_data in t2i_adapter_data:
# Determine the T2I-Adapter weights for the current denoising step.
first_t2i_adapter_step = math.floor(single_t2i_adapter_data.begin_step_percent * total_step_count)
last_t2i_adapter_step = math.ceil(single_t2i_adapter_data.end_step_percent * total_step_count)
t2i_adapter_weight = (
single_t2i_adapter_data.weight[step_index]
if isinstance(single_t2i_adapter_data.weight, list)
else single_t2i_adapter_data.weight
)
if step_index < first_t2i_adapter_step or step_index > last_t2i_adapter_step:
# If the current step is outside of the T2I-Adapter's begin/end step range, then set its weight to 0
# so it has no effect.
t2i_adapter_weight = 0.0
# Apply the t2i_adapter_weight, and accumulate.
if accum_adapter_state is None:
# Handle the first T2I-Adapter.
accum_adapter_state = [val * t2i_adapter_weight for val in single_t2i_adapter_data.adapter_state]
else:
# Add to the previous adapter states.
for idx, value in enumerate(single_t2i_adapter_data.adapter_state):
accum_adapter_state[idx] += value * t2i_adapter_weight
down_block_additional_residuals = accum_adapter_state
uc_noise_pred, c_noise_pred = self.invokeai_diffuser.do_unet_step(
sample=latent_model_input,
@ -570,8 +592,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
total_step_count=total_step_count,
conditioning_data=conditioning_data,
# extra:
down_block_additional_residuals=down_block_additional_residuals, # from controlnet(s)
mid_block_additional_residual=mid_block_additional_residual, # from controlnet(s)
down_block_additional_residuals=down_block_additional_residuals,
mid_block_additional_residual=mid_block_additional_residual,
)
guidance_scale = conditioning_data.guidance_scale