diff options
Diffstat (limited to 'modules/processing.py')
| -rw-r--r-- | modules/processing.py | 11 |
1 files changed, 3 insertions, 8 deletions
diff --git a/modules/processing.py b/modules/processing.py index b0e240a4..5ab6ddde 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -679,8 +679,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Size": f"{p.width}x{p.height}",
"Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
"Model": p.sd_model_name if opts.add_model_name_to_info else None,
- "VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
- "VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
+ "VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
+ "VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
"Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@@ -799,7 +799,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: infotexts = []
output_images = []
-
with torch.no_grad(), p.sd_model.ema_scope():
with devices.autocast():
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
@@ -873,7 +872,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: 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()
@@ -1147,6 +1145,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if not self.enable_hr:
return samples
+ devices.torch_gc()
if self.latent_scale_mode is None:
decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
@@ -1156,8 +1155,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=self.hr_checkpoint_info)
- devices.torch_gc()
-
return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
@@ -1165,7 +1162,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return samples
self.is_hr_pass = True
-
target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y
@@ -1254,7 +1250,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
self.is_hr_pass = False
-
return decoded_samples
def close(self):
|