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refactor upscalers

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Vladimir Mandic
2023-09-20 13:07:29 -04:00
parent be70c0c576
commit 8ca79ad9b5
29 changed files with 328 additions and 418 deletions
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modules/processing.py
+12 -12
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@@ -965,16 +965,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if (self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height) or self.hr_upscaler is None or self.hr_upscaler == 'None':
self.is_hr_pass = False
return
if self.denoising_strength == 0:
self.is_hr_pass = False
return
self.is_hr_pass = True
if not shared.state.processing_has_refined_job_count:
if shared.state.job_count == -1:
shared.state.job_count = self.n_iter
shared.state.job_count = shared.state.job_count * 2
shared.state.processing_has_refined_job_count = True
shared.log.debug(f'Init hires: upscaler={self.hr_upscaler} sampler={self.latent_sampler} resize={self.hr_resize_x}x{self.hr_resize_y} upscale={self.hr_upscale_to_x}x{self.hr_upscale_to_y}')
shared.log.debug(f'Init hires: upscaler="{self.hr_upscaler}" sampler="{self.latent_sampler}" resize={self.hr_resize_x}x{self.hr_resize_y} upscale={self.hr_upscale_to_x}x{self.hr_upscale_to_y}')
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
@@ -1013,7 +1010,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.init_hr()
if self.is_hr_pass:
self.ops.append('hires')
target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y
for i in range(samples.shape[0]):
@@ -1051,13 +1047,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if self.latent_sampler == "PLMS":
self.latent_sampler = 'UniPC'
if self.hr_force or latent_scale_mode is not None:
devices.torch_gc() # GC now before running the next img2img to prevent running out of memory
self.sampler = modules.sd_samplers.create_sampler(self.latent_sampler or self.sampler_name, self.sd_model)
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
if self.denoising_strength > 0:
self.ops.append('hires')
devices.torch_gc() # GC now before running the next img2img to prevent running out of memory
self.sampler = modules.sd_samplers.create_sampler(self.latent_sampler or self.sampler_name, self.sd_model)
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
else:
self.ops.append('upscale')
x = None
shared.state.nextjob()
self.is_hr_pass = False