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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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do not add VAE Encoder/Decoder to infotext if it's the default
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c980dca234
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@ -795,7 +795,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if getattr(samples_ddim, 'already_decoded', False):
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x_samples_ddim = samples_ddim
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else:
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p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
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if opts.sd_vae_decode_method != 'Full':
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p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
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x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
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x_samples_ddim = torch.stack(x_samples_ddim).float()
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@ -1138,7 +1140,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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decoded_samples = torch.from_numpy(np.array(batch_images))
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decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae)
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self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
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if opts.sd_vae_encode_method != 'Full':
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self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
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samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
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image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
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@ -1375,7 +1378,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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image = torch.from_numpy(batch_images)
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image = image.to(shared.device, dtype=devices.dtype_vae)
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self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
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if opts.sd_vae_encode_method != 'Full':
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self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
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self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
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devices.torch_gc()
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