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added a button to run hires fix on selected image in the gallery

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AUTOMATIC1111
2024-01-01 19:31:06 +03:00
parent 5d7d1823af
commit 501993ebf2
6 changed files with 160 additions and 86 deletions
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46
modules/processing.py
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@@ -179,6 +179,7 @@ class StableDiffusionProcessing:
token_merging_ratio = 0
token_merging_ratio_hr = 0
disable_extra_networks: bool = False
firstpass_image: Image = None
scripts_value: scripts.ScriptRunner = field(default=None, init=False)
script_args_value: list = field(default=None, init=False)
@@ -1238,18 +1239,45 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
x = self.rng.next()
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
del x
if self.firstpass_image is not None and self.enable_hr:
# here we don't need to generate image, we just take self.firstpass_image and prepare it for hires fix
if not self.enable_hr:
return samples
devices.torch_gc()
if self.latent_scale_mode is None:
image = np.array(self.firstpass_image).astype(np.float32) / 255.0 * 2.0 - 1.0
image = np.moveaxis(image, 2, 0)
samples = None
decoded_samples = torch.asarray(np.expand_dims(image, 0))
else:
image = np.array(self.firstpass_image).astype(np.float32) / 255.0
image = np.moveaxis(image, 2, 0)
image = torch.from_numpy(np.expand_dims(image, axis=0))
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
samples = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
decoded_samples = None
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)
else:
decoded_samples = None
# here we generate an image normally
x = self.rng.next()
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
del x
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)
else:
decoded_samples = None
with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=self.hr_checkpoint_info)