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reset pipeline and handle hypertile errors

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This commit is contained in:
Vladimir Mandic
2023-10-14 17:03:49 -04:00
parent 9124352911
commit a29b740cee
4 changed files with 28 additions and 21 deletions
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modules/processing_diffusers.py
+14 -11
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@@ -32,9 +32,9 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
p.height = tgt_height
p.width = tgt_width
hypertile_set(p)
if p.mask is not None:
if getattr(p, 'mask', None) is not None:
p.mask = images.resize_image(1, p.mask, tgt_width, tgt_height, upscaler_name=None)
if p.mask_for_overlay is not None:
if getattr(p, 'mask_for_overlay', None) is not None:
p.mask_for_overlay = images.resize_image(1, p.mask_for_overlay, tgt_width, tgt_height, upscaler_name=None)
def hires_resize(latents): # input=latents output=pil
@@ -44,11 +44,11 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
latents = torch.nn.functional.interpolate(latents, size=(p.hr_upscale_to_y // 8, p.hr_upscale_to_x // 8), mode=latent_upscaler["mode"], antialias=latent_upscaler["antialias"])
first_pass_images = vae_decode(latents=latents, model=shared.sd_model, full_quality=p.full_quality, output_type='pil')
p.init_images = []
for first_pass_image in first_pass_images:
for img in first_pass_images:
if latent_upscaler is None:
init_image = images.resize_image(1, first_pass_image, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler)
init_image = images.resize_image(1, img, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler)
else:
init_image = first_pass_image
init_image = img
# if is_refiner_enabled:
# init_image = vae_encode(init_image, model=shared.sd_model, full_quality=p.full_quality)
p.init_images.append(init_image)
@@ -185,13 +185,13 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE:
p.ops.append('txt2img')
task_args = {"height": 8 * math.ceil(p.height / 8), "width": 8 * math.ceil(p.width / 8)}
elif sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE:
elif sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE and len(getattr(p, 'init_images' ,[])) > 0:
p.ops.append('img2img')
task_args = {"image": p.init_images, "strength": p.denoising_strength}
elif sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.INSTRUCT:
elif sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.INSTRUCT and len(getattr(p, 'init_images' ,[])) > 0:
p.ops.append('instruct')
task_args = {"height": 8 * math.ceil(p.height / 8), "width": 8 * math.ceil(p.width / 8), "image": p.init_images, "strength": p.denoising_strength}
elif sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.INPAINTING:
elif sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.INPAINTING and len(getattr(p, 'init_images' ,[])) > 0:
p.ops.append('inpaint')
if getattr(p, 'mask', None) is None:
p.mask = TF.to_pil_image(torch.ones_like(TF.to_tensor(p.init_images[0]))).convert("L")
@@ -201,6 +201,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
return task_args
def set_pipeline_args(model, prompts: list, negative_prompts: list, prompts_2: typing.Optional[list]=None, negative_prompts_2: typing.Optional[list]=None, desc:str='', **kwargs):
if hasattr(model, "set_progress_bar_config"):
model.set_progress_bar_config(bar_format='Progress {rate_fmt}{postfix} {bar} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining} ' + '\x1b[38;5;71m' + desc, ncols=80, colour='#327fba')
args = {}
@@ -261,7 +262,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
pass
# shared.log.debug(f'Diffuser not supported: pipeline={pipeline.__class__.__name__} task={sd_models.get_diffusers_task(model)} arg={arg}')
# shared.log.debug(f'Diffuser pipeline: {pipeline.__class__.__name__} possible={possible}')
hypertile_set(p, hr=hasattr(p, 'init_images') and len(p.init_images) > 0)
hypertile_set(p, hr=len(getattr(p, 'init_images', [])))
clean = args.copy()
clean.pop('callback', None)
clean.pop('callback_steps', None)
@@ -351,7 +352,9 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
else:
return p.steps
# pipeline type is set earlier in processing.py
# pipeline type is set earlier in processing, but check for sanity
if sd_models.get_diffusers_task(shared.sd_model) != sd_models.DiffusersTaskType.TEXT_2_IMAGE and len(getattr(p, 'init_images' ,[])) == 0: # reset pipeline
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
base_args = set_pipeline_args(
model=shared.sd_model,
prompts=prompts,
@@ -420,11 +423,11 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
strength=p.denoising_strength,
desc='Hires',
)
# p.steps += hires_args['num_inference_steps']
try:
output = shared.sd_model(**hires_args) # pylint: disable=not-callable
except AssertionError as e:
shared.log.info(e)
p.init_images = []
# optional refiner pass or decode
if is_refiner_enabled: