do not add VAE Encoder/Decoder to infotext if it's the default (3f451f30) · Commits · github_fork / Stable Diffusion Webui

modules/processing.py

+9 −3

Original line number	Diff line number	Diff line
		@@ -795,7 +795,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		if getattr(samples_ddim, 'already_decoded', False):
		x_samples_ddim = samples_ddim
		else:
		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()
		@@ -1138,6 +1140,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
		decoded_samples = torch.from_numpy(np.array(batch_images))
		decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae)

		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))

		@@ -1375,7 +1378,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):

		image = torch.from_numpy(batch_images)
		image = image.to(shared.device, dtype=devices.dtype_vae)

		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()