lllyasviel/stable-diffusion-webui-forge
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rework sd1.5 and sdxl from scratch

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layerdiffusion
2024-08-04 20:23:01 -07:00
parent e28e11fa97
commit 0863765173
25 changed files with 440 additions and 162 deletions
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23
modules/processing.py
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@@ -100,7 +100,7 @@ def create_binary_mask(image, round=True):
return image
def txt2img_image_conditioning(sd_model, x, width, height):
if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
if sd_model.is_inpaint: # Inpainting models
# The "masked-image" in this case will just be all 0.5 since the entire image is masked.
image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
@@ -111,24 +111,7 @@ def txt2img_image_conditioning(sd_model, x, width, height):
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:
if sd_model.is_sdxl_inpaint:
# The "masked-image" in this case will just be all 0.5 since the entire image is masked.
image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
image_conditioning = images_tensor_to_samples(image_conditioning,
approximation_indexes.get(opts.sd_vae_encode_method))
# 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
# 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.
@@ -307,7 +290,7 @@ class StableDiffusionProcessing:
self.comments[text] = 1
def txt2img_image_conditioning(self, x, width=None, height=None):
self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
self.is_using_inpainting_conditioning = self.sd_model.is_inpaint
return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
@@ -497,6 +480,8 @@ class StableDiffusionProcessing:
cache = caches[0]
with devices.autocast():
shared.sd_model.set_clip_skip(opts.CLIP_stop_at_last_layers)
cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)
import backend.text_processing.classic_engine