refactor: always use format width, height
This commit is contained in:
Manuel Schmid
parent
c79f6bd461
commit
bb72938261
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@ -41,7 +41,7 @@ class Censor:
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model_management.load_model_gpu(self.safety_checker_model)
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single = False
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if not isinstance(images, list) or isinstance(images, np.ndarray):
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if not isinstance(images, (list, np.ndarray)):
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images = [images]
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single = True
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@ -274,7 +274,7 @@ def worker():
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yield_result(async_task, img_paths, async_task.black_out_nsfw, False,
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do_not_show_finished_images=len(tasks) == 1 or async_task.disable_intermediate_results)
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return imgs
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return imgs, img_paths
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def apply_patch_settings(async_task):
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patch_settings[pid] = PatchSettings(
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@ -430,7 +430,65 @@ def worker():
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return initial_latent, width, height
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def apply_inpaint(async_task, initial_latent, inpaint_head_model_path, inpaint_image,
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inpaint_mask, inpaint_parameterized, switch):
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inpaint_mask, inpaint_parameterized, denoising_strength, switch, skip_apply_outpaint=False,
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step_from=11):
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if not skip_apply_outpaint:
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inpaint_image, inpaint_mask = apply_outpaint(async_task, inpaint_image, inpaint_mask)
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inpaint_worker.current_task = inpaint_worker.InpaintWorker(
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image=inpaint_image,
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mask=inpaint_mask,
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use_fill=denoising_strength > 0.99,
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k=async_task.inpaint_respective_field
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)
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if async_task.debugging_inpaint_preprocessor:
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yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), async_task.black_out_nsfw,
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do_not_show_finished_images=True)
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raise EarlyReturnException
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progressbar(async_task, step_from, 'VAE Inpaint encoding ...')
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inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)
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inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
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inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask)
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candidate_vae, candidate_vae_swap = pipeline.get_candidate_vae(
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steps=async_task.steps,
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switch=switch,
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denoise=denoising_strength,
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refiner_swap_method=async_task.refiner_swap_method
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)
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latent_inpaint, latent_mask = core.encode_vae_inpaint(
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mask=inpaint_pixel_mask,
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vae=candidate_vae,
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pixels=inpaint_pixel_image)
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latent_swap = None
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if candidate_vae_swap is not None:
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progressbar(async_task, step_from + 1, 'VAE SD15 encoding ...')
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latent_swap = core.encode_vae(
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vae=candidate_vae_swap,
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pixels=inpaint_pixel_fill)['samples']
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progressbar(async_task, step_from + 2, 'VAE encoding ...')
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latent_fill = core.encode_vae(
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vae=candidate_vae,
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pixels=inpaint_pixel_fill)['samples']
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inpaint_worker.current_task.load_latent(
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latent_fill=latent_fill, latent_mask=latent_mask, latent_swap=latent_swap)
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if inpaint_parameterized:
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pipeline.final_unet = inpaint_worker.current_task.patch(
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inpaint_head_model_path=inpaint_head_model_path,
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inpaint_latent=latent_inpaint,
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inpaint_latent_mask=latent_mask,
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model=pipeline.final_unet
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)
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if not async_task.inpaint_disable_initial_latent:
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initial_latent = {'samples': latent_fill}
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B, C, H, W = latent_fill.shape
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height, width = H * 8, W * 8
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final_height, final_width = inpaint_worker.current_task.image.shape[:2]
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print(f'Final resolution is {str((final_width, final_height))}, latent is {str((width, height))}.')
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return denoising_strength, initial_latent, width, height
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def apply_outpaint(async_task, inpaint_image, inpaint_mask):
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if len(async_task.outpaint_selections) > 0:
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H, W, C = inpaint_image.shape
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if 'top' in async_task.outpaint_selections:
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@ -456,59 +514,7 @@ def worker():
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inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
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async_task.inpaint_strength = 1.0
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async_task.inpaint_respective_field = 1.0
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denoising_strength = async_task.inpaint_strength
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inpaint_worker.current_task = inpaint_worker.InpaintWorker(
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image=inpaint_image,
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mask=inpaint_mask,
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use_fill=denoising_strength > 0.99,
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k=async_task.inpaint_respective_field
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)
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if async_task.debugging_inpaint_preprocessor:
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yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), async_task.black_out_nsfw,
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do_not_show_finished_images=True)
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raise EarlyReturnException
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progressbar(async_task, 11, 'VAE Inpaint encoding ...')
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inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)
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inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
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inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask)
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candidate_vae, candidate_vae_swap = pipeline.get_candidate_vae(
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steps=async_task.steps,
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switch=switch,
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denoise=denoising_strength,
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refiner_swap_method=async_task.refiner_swap_method
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)
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latent_inpaint, latent_mask = core.encode_vae_inpaint(
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mask=inpaint_pixel_mask,
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vae=candidate_vae,
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pixels=inpaint_pixel_image)
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latent_swap = None
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if candidate_vae_swap is not None:
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progressbar(async_task, 12, 'VAE SD15 encoding ...')
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latent_swap = core.encode_vae(
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vae=candidate_vae_swap,
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pixels=inpaint_pixel_fill)['samples']
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progressbar(async_task, 13, 'VAE encoding ...')
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latent_fill = core.encode_vae(
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vae=candidate_vae,
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pixels=inpaint_pixel_fill)['samples']
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inpaint_worker.current_task.load_latent(
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latent_fill=latent_fill, latent_mask=latent_mask, latent_swap=latent_swap)
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if inpaint_parameterized:
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pipeline.final_unet = inpaint_worker.current_task.patch(
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inpaint_head_model_path=inpaint_head_model_path,
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inpaint_latent=latent_inpaint,
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inpaint_latent_mask=latent_mask,
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model=pipeline.final_unet
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)
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if not async_task.inpaint_disable_initial_latent:
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initial_latent = {'samples': latent_fill}
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B, C, H, W = latent_fill.shape
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height, width = H * 8, W * 8
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final_height, final_width = inpaint_worker.current_task.image.shape[:2]
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print(f'Final resolution is {str((final_height, final_width))}, latent is {str((width, height))}.')
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return denoising_strength, initial_latent, width, height
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return inpaint_image, inpaint_mask
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def apply_upscale(async_task, switch):
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H, W, C = async_task.uov_input_image.shape
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@ -568,7 +574,7 @@ def worker():
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width = W * 8
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height = H * 8
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print(f'Final resolution is {str((width, height))}.')
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return denoising_strength, height, initial_latent, tiled, width
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return denoising_strength, initial_latent, tiled, width, height
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def apply_overrides(async_task, height, width):
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if async_task.overwrite_step > 0:
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@ -941,19 +947,20 @@ def worker():
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progressbar(async_task, 7, 'Image processing ...')
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if 'vary' in goals:
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height, initial_latent, width = apply_vary(async_task, denoising_strength, switch)
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initial_latent, width, height = apply_vary(async_task, denoising_strength, switch)
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if 'upscale' in goals:
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try:
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denoising_strength, height, initial_latent, tiled, width = apply_upscale(async_task, switch)
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denoising_strength, initial_latent, tiled, width, height = apply_upscale(async_task, switch)
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except EarlyReturnException:
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return
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if 'inpaint' in goals:
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try:
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denoising_strength, initial_latent, height, width = apply_inpaint(async_task, initial_latent,
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denoising_strength, initial_latent, width, height = apply_inpaint(async_task, initial_latent,
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inpaint_head_model_path, inpaint_image,
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inpaint_mask, inpaint_parameterized,
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switch)
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async_task.inpaint_strength, switch)
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except EarlyReturnException:
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return
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