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[Flux] Optimize guidance creation in flux pipeline by moving it outside the loop (#9153)

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* optimize guidance creation in flux pipeline by moving it outside the loop

* use torch.full instead of torch.tensor to create a tensor with a single value

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
This commit is contained in:
C
2024-08-16 12:44:05 +08:00
committed by GitHub
parent 39b87b14b5
commit e649678bf5
1 changed files with 7 additions and 7 deletions
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src/diffusers/pipelines/flux/pipeline_flux.py
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@@ -677,6 +677,13 @@ class FluxPipeline(DiffusionPipeline, FluxLoraLoaderMixin):
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps)
# handle guidance
if self.transformer.config.guidance_embeds:
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
guidance = guidance.expand(latents.shape[0])
else:
guidance = None
# 6. Denoising loop
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
@@ -686,13 +693,6 @@ class FluxPipeline(DiffusionPipeline, FluxLoraLoaderMixin):
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latents.shape[0]).to(latents.dtype)
# handle guidance
if self.transformer.config.guidance_embeds:
guidance = torch.tensor([guidance_scale], device=device)
guidance = guidance.expand(latents.shape[0])
else:
guidance = None
noise_pred = self.transformer(
hidden_states=latents,
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)