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Add support for the Variations models (unclip-h and unclip-l)

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MrCheeze
2023-03-24 22:48:16 -04:00
parent a9fed7c364
commit 8a34671fe9
8 changed files with 85 additions and 30 deletions
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41
modules/processing.py
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@@ -78,22 +78,28 @@ def apply_overlay(image, paste_loc, index, overlays):
def txt2img_image_conditioning(sd_model, x, width, height):
if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
# Dummy zero conditioning if we're not using inpainting model.
if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
# The "masked-image" in this case will just be all zeros since the entire image is masked.
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
# Add the fake full 1s mask to the first dimension.
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
image_conditioning = image_conditioning.to(x.dtype)
return image_conditioning
elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
else:
# Dummy zero conditioning if we're not using inpainting or unclip models.
# Still takes up a bit of memory, but no encoder call.
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
# The "masked-image" in this case will just be all zeros since the entire image is masked.
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
# Add the fake full 1s mask to the first dimension.
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
image_conditioning = image_conditioning.to(x.dtype)
return image_conditioning
class StableDiffusionProcessing:
"""
@@ -190,6 +196,14 @@ class StableDiffusionProcessing:
return conditioning_image
def unclip_image_conditioning(self, source_image):
c_adm = self.sd_model.embedder(source_image)
if self.sd_model.noise_augmentor is not None:
noise_level = 0 # TODO: Allow other noise levels?
c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
c_adm = torch.cat((c_adm, noise_level_emb), 1)
return c_adm
def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
self.is_using_inpainting_conditioning = True
@@ -241,6 +255,9 @@ class StableDiffusionProcessing:
if self.sampler.conditioning_key in {'hybrid', 'concat'}:
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
if self.sampler.conditioning_key == "crossattn-adm":
return self.unclip_image_conditioning(source_image)
# Dummy zero conditioning if we're not using inpainting or depth model.
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)