OPENGIT/stable-diffusion-webui
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add some codes for robust

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wangqyqq 2023-12-27 10:20:56 +08:00
parent 9feb034e34
commit bfe418a58d
2 changed files with 16 additions and 13 deletions
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2
modules/processing.py
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@ -108,6 +108,7 @@ def txt2img_image_conditioning(sd_model, x, width, height):
else: else:
sd = sd_model.model.state_dict() sd = sd_model.model.state_dict()
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None) diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9: if diffusion_model_input.shape[1] == 9:
# The "masked-image" in this case will just be all 0.5 since the entire image is masked. # 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 = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
@ -378,6 +379,7 @@ class StableDiffusionProcessing:
sd = self.sampler.model_wrap.inner_model.model.state_dict() sd = self.sampler.model_wrap.inner_model.model.state_dict()
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None) diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9: if diffusion_model_input.shape[1] == 9:
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask) return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)

1
modules/sd_models_xl.py
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@ -36,6 +36,7 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond): def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
sd = self.model.state_dict() sd = self.model.state_dict()
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None) diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9: if diffusion_model_input.shape[1] == 9:
x = torch.cat([x] + cond['c_concat'], dim=1) x = torch.cat([x] + cond['c_concat'], dim=1)