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support bf16 for stable diffusion #792

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Merged
patil-suraj merged 3 commits into from
Oct 11, 2022
Merged

support bf16 for stable diffusion #792

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2c86a94
support bf16 for stable diffusion
patil-suraj Oct 10, 2022
90385bb
fix typo
patil-suraj Oct 10, 2022
880f9f9
address review comments
patil-suraj Oct 11, 2022
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11 changes: 11 additions & 0 deletions src/diffusers/models/resnet.py
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Expand Up @@ -40,13 +40,24 @@ def forward(self, hidden_states, output_size=None):
if self.use_conv_transpose:
return self.conv(hidden_states)

# Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
# TODO(Suraj): Remove this cast once the issue is fixed in PyTorch
# https://github.com/pytorch/pytorch/issues/86679
dtype = hidden_states.dtype
if dtype == torch.bfloat16:
hidden_states = hidden_states.to(torch.float32)

# if `output_size` is passed we force the interpolation output
# size and do not make use of `scale_factor=2`
if output_size is None:
hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
else:
hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")

# If the input is bfloat16, we cast back to bfloat16
if dtype == torch.bfloat16:
hidden_states = hidden_states.to(dtype)

# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if self.use_conv:
if self.name == "conv":
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4 changes: 3 additions & 1 deletion src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
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Expand Up @@ -327,7 +327,9 @@ def __call__(
image = self.vae.decode(latents).sample

image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()

# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()

safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
image, has_nsfw_concept = self.safety_checker(
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5 changes: 3 additions & 2 deletions src/diffusers/pipelines/stable_diffusion/safety_checker.py
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Expand Up @@ -36,8 +36,9 @@ def forward(self, clip_input, images):
pooled_output = self.vision_model(clip_input)[1] # pooled_output
image_embeds = self.visual_projection(pooled_output)

special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().numpy()
cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().numpy()
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()
cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()

result = []
batch_size = image_embeds.shape[0]
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