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fix control batch and hires

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This commit is contained in:
Vladimir Mandic
2024-03-17 14:53:15 -04:00
parent 2508f4f245
commit d0e35a7a6b
4 changed files with 13 additions and 11 deletions
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modules/processing_diffusers.py
+7 -6
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@@ -298,7 +298,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
clean['generator'] = generator_device
clean['parser'] = parser
for k, v in clean.items():
if isinstance(v, torch.Tensor):
if isinstance(v, torch.Tensor) or isinstance(v, np.ndarray) or (isinstance(v, list) and len(v) > 0 and (isinstance(v[0], torch.Tensor) or isinstance(v[0], np.ndarray))):
clean[k] = v.shape
shared.log.debug(f'Diffuser pipeline: {model.__class__.__name__} task={sd_models.get_diffusers_task(model)} set={clean}')
if p.hdr_clamp or p.hdr_maximize or p.hdr_brightness != 0 or p.hdr_color != 0 or p.hdr_sharpen != 0:
@@ -453,8 +453,6 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
if hasattr(shared.sd_model, 'embedding_db') and len(shared.sd_model.embedding_db.embeddings_used) > 0: # register used embeddings
p.extra_generation_params['Embeddings'] = ', '.join(shared.sd_model.embedding_db.embeddings_used)
if hasattr(p, 'task_args') and p.task_args.get('image', None) is not None and output is not None: # replace input with output so it can be used by hires/refine
p.task_args['image'] = output.images
shared.state.nextjob()
if shared.state.interrupted or shared.state.skipped:
@@ -479,8 +477,6 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
save_intermediate(latents=output.images, suffix="-before-hires")
shared.state.job = 'upscale'
output.images = resize_hires(p, latents=output.images)
if hasattr(p, 'task_args') and p.task_args.get('image', None) is not None and output is not None: # replace input with output so it can be used by hires/refine
p.task_args['image'] = output.images
sd_hijack_hypertile.hypertile_set(p, hr=True)
latent_upscale = shared.latent_upscale_modes.get(p.hr_upscaler, None)
@@ -497,6 +493,10 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
update_sampler(shared.sd_model, second_pass=True)
shared.log.info(f'HiRes: class={shared.sd_model.__class__.__name__} sampler="{p.hr_sampler_name}"')
if p.is_control and hasattr(p, 'task_args') and p.task_args.get('image', None) is not None:
if hasattr(shared.sd_model, "vae") and output.images is not None and len(output.images) > 0:
output.images = processing_vae.vae_decode(latents=output.images, model=shared.sd_model, full_quality=p.full_quality, output_type='pil') # controlnet cannnot deal with latent input
p.task_args['image'] = output.images # replace so hires uses new output
sd_models.move_model(shared.sd_model, devices.device)
orig_denoise = p.denoising_strength
p.denoising_strength = getattr(p, 'hr_denoising_strength', p.denoising_strength)
@@ -528,7 +528,6 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
except AssertionError as e:
shared.log.info(e)
p.denoising_strength = orig_denoise
# p.init_images = []
shared.state.job = prev_job
shared.state.nextjob()
p.is_hr_pass = False
@@ -562,6 +561,8 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
image = processing_vae.vae_decode(latents=image, model=shared.sd_model, full_quality=p.full_quality, output_type='pil')
p.extra_generation_params['Noise level'] = noise_level
output_type = 'np'
if hasattr(p, 'task_args') and p.task_args.get('image', None) is not None and output is not None: # replace input with output so it can be used by hires/refine
p.task_args['image'] = image
shared.log.info(f'Refiner: class={shared.sd_refiner.__class__.__name__}')
refiner_args = set_pipeline_args(
model=shared.sd_refiner,