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Revert "Merge branch 'dev' into master"

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This reverts commit 4b91ee0044, reversing
changes made to fc7e3c5721.
This commit is contained in:
Vladimir Mandic
2023-10-26 07:17:40 -04:00
parent 90d2197e04
commit 5219daa7fb
282 changed files with 288 additions and 43168 deletions
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modules/processing.py
+11 -12
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@@ -442,8 +442,6 @@ def decode_first_stage(model, x, full_quality=True):
shared.log.debug(f'Decode VAE: skipped={shared.state.skipped} interrupted={shared.state.interrupted}')
x_sample = torch.zeros((len(x), 3, x.shape[2] * 8, x.shape[3] * 8), dtype=devices.dtype_vae, device=devices.device)
return x_sample
prev_job = shared.state.job
shared.state.job = 'vae'
with devices.autocast(disable = x.dtype==devices.dtype_vae):
try:
if full_quality:
@@ -461,7 +459,6 @@ def decode_first_stage(model, x, full_quality=True):
except Exception as e:
x_sample = x
shared.log.error(f'Decode VAE: {e}')
shared.state.job = prev_job
return x_sample
@@ -772,11 +769,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
return ''
ema_scope_context = p.sd_model.ema_scope if shared.backend == shared.Backend.ORIGINAL else nullcontext
shared.state.job_count = p.n_iter
with devices.inference_context(), ema_scope_context():
t0 = time.time()
with devices.autocast():
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
if shared.state.job_count == -1:
shared.state.job_count = p.n_iter
extra_network_data = None
for n in range(p.n_iter):
p.iteration = n
@@ -808,6 +806,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
step_multiplier = 1
sampler_config = modules.sd_samplers.find_sampler_config(p.sampler_name)
step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
if shared.backend == shared.Backend.ORIGINAL:
uc = get_conds_with_caching(modules.prompt_parser.get_learned_conditioning, p.negative_prompts, p.steps * step_multiplier, cached_uc)
@@ -913,6 +913,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
output_images.append(image_mask_composite)
del x_samples_ddim
devices.torch_gc()
shared.state.nextjob()
t1 = time.time()
shared.log.info(f'Processed: images={len(output_images)} time={t1 - t0:.2f}s its={(p.steps * len(output_images)) / (t1 - t0):.2f} memory={modules.memstats.memory_stats()}')
@@ -1035,8 +1036,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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
hypertile_set(self, hr=True)
shared.state.job_count = 2 * self.n_iter
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):
@@ -1056,13 +1061,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.sampler.initialize(self)
x = create_random_tensors([4, self.height // 8, self.width // 8], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
shared.state.nextjob()
if not self.enable_hr or shared.state.interrupted or shared.state.skipped:
return samples
self.init_hr()
if self.is_hr_pass:
prev_job = shared.state.job
target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y
decoded_samples = None
@@ -1080,7 +1083,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.extra_generation_params, self.restore_faces = bak_extra_generation_params, bak_restore_faces
images.save_image(image, self.outpath_samples, "", seeds[i], prompts[i], shared.opts.samples_format, info=info, suffix="-before-hires")
if latent_scale_mode is None or self.hr_force: # non-latent upscaling
shared.state.job = 'upscale'
if decoded_samples is None:
decoded_samples = decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae), self.full_quality)
decoded_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
@@ -1110,7 +1112,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if self.latent_sampler == "PLMS":
self.latent_sampler = 'UniPC'
if self.hr_force or latent_scale_mode is not None:
shared.state.job = 'hires'
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
@@ -1126,9 +1127,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
else:
self.ops.append('upscale')
x = None
self.is_hr_pass = False
shared.state.job = prev_job
shared.state.nextjob()
self.is_hr_pass = False
return samples
@@ -1293,7 +1293,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
samples = samples * self.nmask + self.init_latent * self.mask
del x
devices.torch_gc()
shared.state.nextjob()
return samples
def get_token_merging_ratio(self, for_hr=False):