Final pipeline fixes

README.md

@@ -69,12 +69,12 @@ quantize(
 freeze(pipe.transformer)
 pipe.enable_model_cpu_offload()
 
-# If you are still running out of memory, add do_batch_cfg=False below.
 images = pipe(
  prompt=prompt,
  negative_prompt=negative_prompt,
  device=None,
  return_dict=False,
+ do_batch_cfg=False, # https://github.com/huggingface/optimum-quanto/issues/327
 )
 images[0][0].save('chalkboard.png')
 ```

pipeline.py

@@ -1614,14 +1614,14 @@ class CustomPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
  if guidance_scale_real > 1.0 and i >= no_cfg_until_timestep:
  progress_bar.set_postfix(
  {
- 'ts': t / 1000.0,
+ 'ts': t.detach().item() / 1000.0,
  'cfg': self._guidance_scale_real,
  },
  )
  else:
  progress_bar.set_postfix(
  {
- 'ts': t / 1000.0,
+ 'ts': t.detach().item() / 1000.0,
  'cfg': 'N/A',
  },
  )
@@ -1658,17 +1658,17 @@ class CustomPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
  # Prepare extra transformer arguments
  extra_transformer_args = {}
  if prompt_mask is not None:
- extra_transformer_args["attention_mask"] = prompt_mask_input.to(device=self.transformer.device).contiguous()
+ extra_transformer_args["attention_mask"] = prompt_mask_input.to(device=self.transformer.device)
 
  # Forward pass through the transformer
  noise_pred = self.transformer(
- hidden_states=latent_model_input.to(device=self.transformer.device).contiguous() ,
+ hidden_states=latent_model_input.to(device=self.transformer.device),
  timestep=timestep / 1000,
  guidance=guidance,
- pooled_projections=pooled_prompt_embeds_input.to(device=self.transformer.device).contiguous() ,
- encoder_hidden_states=prompt_embeds_input.to(device=self.transformer.device).contiguous() ,
- txt_ids=text_ids_input.to(device=self.transformer.device).contiguous() if text_ids is not None else None,
- img_ids=latent_image_ids_input.to(device=self.transformer.device).contiguous() if latent_image_ids is not None else None,
+ pooled_projections=pooled_prompt_embeds_input.to(device=self.transformer.device),
+ encoder_hidden_states=prompt_embeds_input.to(device=self.transformer.device),
+ txt_ids=text_ids_input.to(device=self.transformer.device) if text_ids is not None else None,
+ img_ids=latent_image_ids_input.to(device=self.transformer.device) if latent_image_ids is not None else None,
  joint_attention_kwargs=self.joint_attention_kwargs,
  return_dict=False,
  **extra_transformer_args,