diff --git a/modules/processing.py b/modules/processing.py index 43cb763fe..a9d660056 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -795,7 +795,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if getattr(samples_ddim, 'already_decoded', False): x_samples_ddim = samples_ddim else: - p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method + if opts.sd_vae_decode_method != 'Full': + p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method + x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True) x_samples_ddim = torch.stack(x_samples_ddim).float() @@ -1138,7 +1140,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): decoded_samples = torch.from_numpy(np.array(batch_images)) decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae) - self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method + if opts.sd_vae_encode_method != 'Full': + self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method)) image_conditioning = self.img2img_image_conditioning(decoded_samples, samples) @@ -1375,7 +1378,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image = torch.from_numpy(batch_images) image = image.to(shared.device, dtype=devices.dtype_vae) - self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method + + if opts.sd_vae_encode_method != 'Full': + self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method + self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model) devices.torch_gc()