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[do not merge] test dynamic offload string #10221

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8 changes: 6 additions & 2 deletions src/diffusers/pipelines/pipeline_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -994,7 +994,7 @@ def remove_all_hooks(self):
accelerate.hooks.remove_hook_from_module(model, recurse=True)
self._all_hooks = []

def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda", model_cpu_offload_seq: Optional[str] = None):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
Expand Down Expand Up @@ -1051,7 +1051,11 @@ def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[t

self._all_hooks = []
hook = None
for model_str in self.model_cpu_offload_seq.split("->"):

if model_cpu_offload_seq is None:
model_cpu_offload_seq = self.model_cpu_offload_seq

for model_str in model_cpu_offload_seq.split("->"):
model = all_model_components.pop(model_str, None)

if not isinstance(model, torch.nn.Module):
Expand Down
42 changes: 42 additions & 0 deletions src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -417,13 +417,27 @@ def encode_prompt(
clip_skip=clip_skip,
clip_model_index=0,
)
print(f" ")
print(f" after get_clip_prompt_embeds(1):")
print(f" text_encoder: {self.text_encoder.device if self.text_encoder is not None else 'None'}")
print(f" text_encoder_2: {self.text_encoder_2.device if self.text_encoder_2 is not None else 'None'}")
print(f" text_encoder_3: {self.text_encoder_3.device if self.text_encoder_3 is not None else 'None'}")
print(f" transformer: {self.transformer.device if self.transformer is not None else 'None'}")
print(f" vae: {self.vae.device if self.vae is not None else 'None'}")
prompt_2_embed, pooled_prompt_2_embed = self._get_clip_prompt_embeds(
prompt=prompt_2,
device=device,
num_images_per_prompt=num_images_per_prompt,
clip_skip=clip_skip,
clip_model_index=1,
)
print(f" ")
print(f" after get_clip_prompt_embeds(2):")
print(f" text_encoder: {self.text_encoder.device if self.text_encoder is not None else 'None'}")
print(f" text_encoder_2: {self.text_encoder_2.device if self.text_encoder_2 is not None else 'None'}")
print(f" text_encoder_3: {self.text_encoder_3.device if self.text_encoder_3 is not None else 'None'}")
print(f" transformer: {self.transformer.device if self.transformer is not None else 'None'}")
print(f" vae: {self.vae.device if self.vae is not None else 'None'}")
clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)

t5_prompt_embed = self._get_t5_prompt_embeds(
Expand All @@ -432,6 +446,13 @@ def encode_prompt(
max_sequence_length=max_sequence_length,
device=device,
)
print(f" ")
print(f" after get_t5_prompt_embeds:")
print(f" text_encoder: {self.text_encoder.device if self.text_encoder is not None else 'None'}")
print(f" text_encoder_2: {self.text_encoder_2.device if self.text_encoder_2 is not None else 'None'}")
print(f" text_encoder_3: {self.text_encoder_3.device if self.text_encoder_3 is not None else 'None'}")
print(f" transformer: {self.transformer.device if self.transformer is not None else 'None'}")
print(f" vae: {self.vae.device if self.vae is not None else 'None'}")

clip_prompt_embeds = torch.nn.functional.pad(
clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])
Expand Down Expand Up @@ -899,6 +920,13 @@ def __call__(
generator,
latents,
)
print(f" ")
print(f" before denoising loop:")
print(f" text_encoder: {self.text_encoder.device if self.text_encoder is not None else 'None'}")
print(f" text_encoder_2: {self.text_encoder_2.device if self.text_encoder_2 is not None else 'None'}")
print(f" text_encoder_3: {self.text_encoder_3.device if self.text_encoder_3 is not None else 'None'}")
print(f" transformer: {self.transformer.device if self.transformer is not None else 'None'}")
print(f" vae: {self.vae.device if self.vae is not None else 'None'}")

# 6. Denoising loop
with self.progress_bar(total=num_inference_steps) as progress_bar:
Expand Down Expand Up @@ -974,6 +1002,13 @@ def __call__(

if XLA_AVAILABLE:
xm.mark_step()
print(f" ")
print(f" after denoising loop:")
print(f" text_encoder: {self.text_encoder.device if self.text_encoder is not None else 'None'}")
print(f" text_encoder_2: {self.text_encoder_2.device if self.text_encoder_2 is not None else 'None'}")
print(f" text_encoder_3: {self.text_encoder_3.device if self.text_encoder_3 is not None else 'None'}")
print(f" transformer: {self.transformer.device if self.transformer is not None else 'None'}")
print(f" vae: {self.vae.device if self.vae is not None else 'None'}")

if output_type == "latent":
image = latents
Expand All @@ -983,6 +1018,13 @@ def __call__(

image = self.vae.decode(latents, return_dict=False)[0]
image = self.image_processor.postprocess(image, output_type=output_type)
print(f" ")
print(f" after decode:")
print(f" text_encoder: {self.text_encoder.device if self.text_encoder is not None else 'None'}")
print(f" text_encoder_2: {self.text_encoder_2.device if self.text_encoder_2 is not None else 'None'}")
print(f" text_encoder_3: {self.text_encoder_3.device if self.text_encoder_3 is not None else 'None'}")
print(f" transformer: {self.transformer.device if self.transformer is not None else 'None'}")
print(f" vae: {self.vae.device if self.vae is not None else 'None'}")

# Offload all models
self.maybe_free_model_hooks()
Expand Down
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