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Reformat

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This commit is contained in:
Kevin Black
2023-11-16 22:36:46 +00:00
parent 378dd18298
commit 1958463f02
5 changed files with 227 additions and 68 deletions
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48
ddpo_pytorch/diffusers_patch/pipeline_with_logprob.py
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@@ -116,7 +116,15 @@ def pipeline_with_logprob(
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
self.check_inputs(
prompt,
height,
width,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
@@ -133,7 +141,11 @@ def pipeline_with_logprob(
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None)
if cross_attention_kwargs is not None
else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
@@ -172,7 +184,9 @@ def pipeline_with_logprob(
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = (
torch.cat([latents] * 2) if do_classifier_free_guidance else latents
)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
@@ -187,27 +201,39 @@ def pipeline_with_logprob(
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = noise_pred_uncond + guidance_scale * (
noise_pred_text - noise_pred_uncond
)
if do_classifier_free_guidance and guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
noise_pred = rescale_noise_cfg(
noise_pred, noise_pred_text, guidance_rescale=guidance_rescale
)
# compute the previous noisy sample x_t -> x_t-1
latents, log_prob = ddim_step_with_logprob(self.scheduler, noise_pred, t, latents, **extra_step_kwargs)
latents, log_prob = ddim_step_with_logprob(
self.scheduler, noise_pred, t, latents, **extra_step_kwargs
)
all_latents.append(latents)
all_log_probs.append(log_prob)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
if i == len(timesteps) - 1 or (
(i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
image = self.vae.decode(
latents / self.vae.config.scaling_factor, return_dict=False
)[0]
image, has_nsfw_concept = self.run_safety_checker(
image, device, prompt_embeds.dtype
)
else:
image = latents
has_nsfw_concept = None
@@ -217,7 +243,9 @@ def pipeline_with_logprob(
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
image = self.image_processor.postprocess(
image, output_type=output_type, do_denormalize=do_denormalize
)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: