Paper99 xianbao HF staff commited on
Commit
666fad6
1 Parent(s): 0eab3a1

Temporary disable tqdm (#11)

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- Temporary disable tqdm (e5bb05b6b9153186edf9918db2d04483ce9d9c3b)


Co-authored-by: Tiezhen WANG <xianbao@users.noreply.huggingface.co>

Files changed (1) hide show
  1. pipeline.py +43 -44
pipeline.py CHANGED
@@ -398,51 +398,50 @@ class PhotoMakerStableDiffusionXLPipeline(StableDiffusionXLPipeline):
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  # 11. Denoising loop
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  num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
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- with self.progress_bar(total=num_inference_steps) as progress_bar:
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- for i, t in enumerate(timesteps):
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- latent_model_input = (
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- torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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- )
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- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
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- if i <= start_merge_step:
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- current_prompt_embeds = torch.cat(
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- [negative_prompt_embeds, prompt_embeds_text_only], dim=0
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- )
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- add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds_text_only], dim=0)
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- else:
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- current_prompt_embeds = torch.cat(
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- [negative_prompt_embeds, prompt_embeds], dim=0
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- )
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- add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
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- # predict the noise residual
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- added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
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- noise_pred = self.unet(
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- latent_model_input,
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- t,
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- encoder_hidden_states=current_prompt_embeds,
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- cross_attention_kwargs=cross_attention_kwargs,
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- added_cond_kwargs=added_cond_kwargs,
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- return_dict=False,
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- )[0]
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-
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- # perform guidance
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- if do_classifier_free_guidance:
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- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
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-
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- if do_classifier_free_guidance and guidance_rescale > 0.0:
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- # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
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- noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
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-
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- # compute the previous noisy sample x_t -> x_t-1
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- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
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-
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- # call the callback, if provided
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- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
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- progress_bar.update()
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- if callback is not None and i % callback_steps == 0:
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- callback(i, t, latents)
 
 
 
 
 
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  # make sure the VAE is in float32 mode, as it overflows in float16
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  if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
 
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  # 11. Denoising loop
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  num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
 
 
 
 
 
 
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+ for i, t in enumerate(timesteps):
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+ latent_model_input = (
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+ torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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+ )
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+ latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
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+
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+ if i <= start_merge_step:
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+ current_prompt_embeds = torch.cat(
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+ [negative_prompt_embeds, prompt_embeds_text_only], dim=0
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+ )
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+ add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds_text_only], dim=0)
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+ else:
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+ current_prompt_embeds = torch.cat(
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+ [negative_prompt_embeds, prompt_embeds], dim=0
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+ )
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+ add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
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+ # predict the noise residual
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+ added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
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+ noise_pred = self.unet(
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+ latent_model_input,
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+ t,
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+ encoder_hidden_states=current_prompt_embeds,
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+ cross_attention_kwargs=cross_attention_kwargs,
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+ added_cond_kwargs=added_cond_kwargs,
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+ return_dict=False,
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+ )[0]
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+
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+ # perform guidance
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+ if do_classifier_free_guidance:
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+ noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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+ noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
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+
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+ if do_classifier_free_guidance and guidance_rescale > 0.0:
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+ # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
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+ noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
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+
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+ # compute the previous noisy sample x_t -> x_t-1
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+ latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
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+
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+ # call the callback, if provided
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+ if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
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+ if callback is not None and i % callback_steps == 0:
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+ callback(i, t, latents)
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446
  # make sure the VAE is in float32 mode, as it overflows in float16
447
  if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: