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Added v2 of dfp

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This commit is contained in:
Jaret Burkett
2025-01-22 16:32:13 -07:00
parent e1549ad54d
commit bbfba0c188
3 changed files with 175 additions and 38 deletions
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61
toolkit/pipelines.py
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@@ -1285,20 +1285,21 @@ class FluxWithCFGPipeline(FluxPipeline):
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
)
(
negative_prompt_embeds,
negative_pooled_prompt_embeds,
negative_text_ids,
) = self.encode_prompt(
prompt=negative_prompt,
prompt_2=negative_prompt_2,
prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=negative_pooled_prompt_embeds,
device=device,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
)
if guidance_scale > 1.00001:
(
negative_prompt_embeds,
negative_pooled_prompt_embeds,
negative_text_ids,
) = self.encode_prompt(
prompt=negative_prompt,
prompt_2=negative_prompt_2,
prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=negative_pooled_prompt_embeds,
device=device,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
)
# 4. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels // 4
@@ -1361,21 +1362,25 @@ class FluxWithCFGPipeline(FluxPipeline):
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
if guidance_scale > 1.00001:
# todo combine these
noise_pred_uncond = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=negative_pooled_prompt_embeds,
encoder_hidden_states=negative_prompt_embeds,
txt_ids=negative_text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
# todo combine these
noise_pred_uncond = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=negative_pooled_prompt_embeds,
encoder_hidden_states=negative_prompt_embeds,
txt_ids=negative_text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
else:
noise_pred = noise_pred_text
# compute the previous noisy sample x_t -> x_t-1
latents_dtype = latents.dtype