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do not add VAE Encoder/Decoder to infotext if it's the default

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AUTOMATIC1111 2023-08-05 10:36:26 +03:00
parent c980dca234
commit 3f451f3042
1 changed files with 9 additions and 3 deletions
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12
modules/processing.py
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@ -795,7 +795,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if getattr(samples_ddim, 'already_decoded', False): if getattr(samples_ddim, 'already_decoded', False):
x_samples_ddim = samples_ddim x_samples_ddim = samples_ddim
else: 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 = 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() 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 = torch.from_numpy(np.array(batch_images))
decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae) 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)) samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
image_conditioning = self.img2img_image_conditioning(decoded_samples, samples) image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
@ -1375,7 +1378,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
image = torch.from_numpy(batch_images) image = torch.from_numpy(batch_images)
image = image.to(shared.device, dtype=devices.dtype_vae) 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) self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
devices.torch_gc() devices.torch_gc()