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refactor img2img processing

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This commit is contained in:
Vladimir Mandic
2024-01-04 14:46:11 -05:00
parent d4196b8185
commit 43d68ca784
5 changed files with 82 additions and 114 deletions
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modules/processing_diffusers.py
+25 -24
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@@ -35,18 +35,21 @@ def process_diffusers(p: StableDiffusionProcessing):
def is_refiner_enabled():
return p.enable_hr and p.refiner_steps > 0 and p.refiner_start > 0 and p.refiner_start < 1 and shared.sd_refiner is not None
if getattr(p, 'init_images', None) is not None and len(p.init_images) > 0:
tgt_width, tgt_height = 8 * math.ceil(p.init_images[0].width / 8), 8 * math.ceil(p.init_images[0].height / 8)
if p.init_images[0].width != tgt_width or p.init_images[0].height != tgt_height:
shared.log.debug(f'Resizing init images: original={p.init_images[0].width}x{p.init_images[0].height} target={tgt_width}x{tgt_height}')
p.init_images = [images.resize_image(1, image, tgt_width, tgt_height, upscaler_name=None) for image in p.init_images]
p.height = tgt_height
p.width = tgt_width
hypertile_set(p)
if getattr(p, 'mask', None) is not None and p.mask.size != (tgt_width, tgt_height):
p.mask = images.resize_image(1, p.mask, tgt_width, tgt_height, upscaler_name=None)
if getattr(p, 'mask_for_overlay', None) is not None and p.mask_for_overlay.size != (tgt_width, tgt_height):
p.mask_for_overlay = images.resize_image(1, p.mask_for_overlay, tgt_width, tgt_height, upscaler_name=None)
def resize_images():
if getattr(p, 'init_images', None) is not None and len(p.init_images) > 0:
tgt_width, tgt_height = 8 * math.ceil(p.init_images[0].width / 8), 8 * math.ceil(p.init_images[0].height / 8)
if p.init_images[0].size != (tgt_width, tgt_height):
shared.log.debug(f'Resizing init images: original={p.init_images[0].width}x{p.init_images[0].height} target={tgt_width}x{tgt_height}')
p.init_images = [images.resize_image(1, image, tgt_width, tgt_height, upscaler_name=None) for image in p.init_images]
p.height = tgt_height
p.width = tgt_width
hypertile_set(p)
if getattr(p, 'mask', None) is not None and p.mask.size != (tgt_width, tgt_height):
p.mask = images.resize_image(1, p.mask, tgt_width, tgt_height, upscaler_name=None)
if getattr(p, 'mask_for_overlay', None) is not None and p.mask_for_overlay.size != (tgt_width, tgt_height):
p.mask_for_overlay = images.resize_image(1, p.mask_for_overlay, tgt_width, tgt_height, upscaler_name=None)
return tgt_width, tgt_height
return p.width, p.height
def hires_resize(latents): # input=latents output=pil
if not torch.is_tensor(latents):
@@ -165,13 +168,15 @@ def process_diffusers(p: StableDiffusionProcessing):
}
elif (sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.INPAINTING or is_img2img_model) and len(getattr(p, 'init_images' ,[])) > 0:
p.ops.append('inpaint')
if getattr(p, 'mask', None) is None:
if getattr(p, 'image_mask', None) is not None:
p.mask = p.image_mask
else:
p.mask = TF.to_pil_image(torch.ones_like(TF.to_tensor(p.init_images[0]))).convert("L")
width = 8 * math.ceil(p.init_images[0].width / 8)
height = 8 * math.ceil(p.init_images[0].height / 8)
if p.task_args.get('mask_image', None) is not None: # provided as override by a module
p.mask = shared.sd_model.mask_processor.blur(p.task_args['mask_image'], blur_factor=p.mask_blur) if p.mask_blur > 0 else p.task_args['mask_image']
elif getattr(p, 'image_mask', None) is not None: # standard
p.mask = p.image_mask
elif getattr(p, 'mask', None) is not None: # backward compatibility
pass
else: # fallback
p.mask = TF.to_pil_image(torch.ones_like(TF.to_tensor(p.init_images[0]))).convert("L")
width, height = resize_images()
task_args = {
'image': p.init_images,
'mask_image': p.mask,
@@ -180,10 +185,6 @@ def process_diffusers(p: StableDiffusionProcessing):
'width': width,
# 'padding_mask_crop': p.inpaint_full_res_padding # done back in main processing method
}
if p.task_args.get('mask_image', None) is None:
if p.mask_blur > 0:
p.mask = shared.sd_model.mask_processor.blur(p.mask, blur_factor=p.mask_blur)
task_args['mask_image'] = p.mask
if model.__class__.__name__ == 'LatentConsistencyModelPipeline' and hasattr(p, 'init_images') and len(p.init_images) > 0:
p.ops.append('lcm')
init_latents = [vae_encode(image, model=shared.sd_model, full_quality=p.full_quality).squeeze(dim=0) for image in p.init_images]
@@ -465,7 +466,7 @@ def process_diffusers(p: StableDiffusionProcessing):
try:
t0 = time.time()
output = shared.sd_model(**base_args) # pylint: disable=not-callable
downcast_openvino(op="base")
downcast_openvino(op="base") # only executes on compiled vino models
if shared.cmd_opts.profile:
t1 = time.time()
shared.log.debug(f'Profile: pipeline call: {t1-t0:.2f}')