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Code Activity

Modify FlowMatch Scale Noise (#8678)

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* initial fix

* apply suggestion

* delete step_index line
This commit is contained in:
Álvaro Somoza
2024-06-27 00:36:33 -04:00
committed by GitHub
parent e2a4a46e99
commit 3b01d72a64
2 changed files with 25 additions and 4 deletions
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2
src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py
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@@ -852,7 +852,7 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline):
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_inference_steps)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 5. Prepare latent variables
if latents is None:

27
src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py
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@@ -126,10 +126,31 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
`torch.FloatTensor`:
A scaled input sample.
"""
if self.step_index is None:
self._init_step_index(timestep)
# Make sure sigmas and timesteps have the same device and dtype as original_samples
sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype)
if sample.device.type == "mps" and torch.is_floating_point(timestep):
# mps does not support float64
schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32)
timestep = timestep.to(sample.device, dtype=torch.float32)
else:
schedule_timesteps = self.timesteps.to(sample.device)
timestep = timestep.to(sample.device)
# self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index
if self.begin_index is None:
step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep]
elif self.step_index is not None:
# add_noise is called after first denoising step (for inpainting)
step_indices = [self.step_index] * timestep.shape[0]
else:
# add noise is called before first denoising step to create initial latent(img2img)
step_indices = [self.begin_index] * timestep.shape[0]
sigma = sigmas[step_indices].flatten()
while len(sigma.shape) < len(sample.shape):
sigma = sigma.unsqueeze(-1)
sigma = self.sigmas[self.step_index]
sample = sigma * noise + (1.0 - sigma) * sample
return sample