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Fixes and longer prompts

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This commit is contained in:
Jaret Burkett
2023-10-22 08:57:37 -06:00
parent 0e9fc42816
commit 9905a1e205
4 changed files with 177 additions and 54 deletions
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25
toolkit/stable_diffusion_model.py
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@@ -327,7 +327,6 @@ class StableDiffusion:
scheduler=noise_scheduler,
**extra_args
).to(self.device_torch)
# force turn that (ruin your images with obvious green and red dots) the #$@@ off!!!
pipeline.watermark = None
else:
pipeline = Pipe(
@@ -372,7 +371,8 @@ class StableDiffusion:
extra['adapter_conditioning_scale'] = gen_config.adapter_conditioning_scale
if isinstance(self.adapter, IPAdapter):
transform = transforms.Compose([
transforms.Resize(gen_config.width, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.Resize(gen_config.width,
interpolation=transforms.InterpolationMode.BILINEAR),
transforms.PILToTensor(),
])
validation_image = transform(validation_image)
@@ -395,14 +395,15 @@ class StableDiffusion:
unconditional_embeds,
)
if self.adapter is not None and isinstance(self.adapter, IPAdapter) and gen_config.adapter_image_path is not None:
if self.adapter is not None and isinstance(self.adapter,
IPAdapter) and gen_config.adapter_image_path is not None:
# apply the image projection
conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(validation_image)
unconditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(validation_image, True)
unconditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(validation_image,
True)
conditional_embeds = self.adapter(conditional_embeds, conditional_clip_embeds)
unconditional_embeds = self.adapter(unconditional_embeds, unconditional_clip_embeds)
# todo do we disable text encoder here as well if disabled for model, or only do that for training?
if self.is_xl:
# fix guidance rescale for sdxl
@@ -668,7 +669,15 @@ class StableDiffusion:
# return latents_steps
return latents
def encode_prompt(self, prompt, prompt2=None, num_images_per_prompt=1, force_all=False) -> PromptEmbeds:
def encode_prompt(
self,
prompt,
prompt2=None,
num_images_per_prompt=1,
force_all=False,
long_prompts=False,
max_length=None
) -> PromptEmbeds:
# sd1.5 embeddings are (bs, 77, 768)
prompt = prompt
# if it is not a list, make it one
@@ -695,12 +704,14 @@ class StableDiffusion:
num_images_per_prompt=num_images_per_prompt,
use_text_encoder_1=use_encoder_1,
use_text_encoder_2=use_encoder_2,
truncate=not long_prompts,
max_length=max_length,
)
)
else:
return PromptEmbeds(
train_tools.encode_prompts(
self.tokenizer, self.text_encoder, prompt
self.tokenizer, self.text_encoder, prompt, truncate=not long_prompts, max_length=max_length
)
)