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allow generation to be started with any dimensions specified
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@ -75,3 +75,6 @@ class SD3Inferencer(torch.nn.Module):
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def add_noise_to_latent(self, x, noise, amount):
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return x * (1 - amount) + noise * amount
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def fix_dimensions(self, width, height):
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return width // 16 * 16, height // 16 * 16
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@ -884,6 +884,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if p.refiner_checkpoint_info is None:
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raise Exception(f'Could not find checkpoint with name {p.refiner_checkpoint}')
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if hasattr(shared.sd_model, 'fix_dimensions'):
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p.width, p.height = shared.sd_model.fix_dimensions(p.width, p.height)
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p.sd_model_name = shared.sd_model.sd_checkpoint_info.name_for_extra
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p.sd_model_hash = shared.sd_model.sd_model_hash
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p.sd_vae_name = sd_vae.get_loaded_vae_name()
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