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debugging

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This commit is contained in:
sayakpaul
2023-08-23 16:25:35 +05:30
parent 5c98d93767
commit 66955beba6
1 changed files with 2 additions and 1 deletions
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3
src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py
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@@ -711,6 +711,7 @@ class StableDiffusionAdapterPipeline(DiffusionPipeline):
# 7. Denoising loop
adapter_state = self.adapter(adapter_input)
print(f"From pipeline (before rejigging): {len(adapter_state)}.")
for k, v in enumerate(adapter_state):
adapter_state[k] = v * adapter_conditioning_scale
if num_images_per_prompt > 1:
@@ -719,6 +720,7 @@ class StableDiffusionAdapterPipeline(DiffusionPipeline):
if do_classifier_free_guidance:
for k, v in enumerate(adapter_state):
adapter_state[k] = torch.cat([v] * 2, dim=0)
print(f"From pipeline (after rejigging): {len(adapter_state)}.")
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
@@ -728,7 +730,6 @@ class StableDiffusionAdapterPipeline(DiffusionPipeline):
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
print(f"From pipeline: {len(adapter_state)}.")
noise_pred = self.unet(
latent_model_input,
t,