5b7ddf8b22
* only make stop_button and skip_button interactive when rendering process starts
fix inconsistency in behaviour of stop_button and skip_button as it was possible to skip or stop other users processes while still being in queue
* use AsyncTask for last_stop handling instead of shared
* Revert "only make stop_button and skip_button interactive when rendering process starts"
This reverts commit d3f9156854.
* introduce state for task skipping/stopping
* fix return parameters of stop_clicked
* code cleanup, do not disable skip/stop on stop_clicked
* reset last_stop when skipping for further processing
* fix: replace fcbh with ldm_patched
* fix: use currentTask instead of ctrls after merging upstream
* feat: extract attribute disable_preview
* feat: extract attribute adm_scaler_positive
* feat: extract attribute adm_scaler_negative
* feat: extract attribute adm_scaler_end
* feat: extract attribute adaptive_cfg
* feat: extract attribute sampler_name
* feat: extract attribute scheduler_name
* feat: extract attribute generate_image_grid
* feat: extract attribute overwrite_step
* feat: extract attribute overwrite_switch
* feat: extract attribute overwrite_width
* feat: extract attribute overwrite_height
* feat: extract attribute overwrite_vary_strength
* feat: extract attribute overwrite_upscale_strength
* feat: extract attribute mixing_image_prompt_and_vary_upscale
* feat: extract attribute mixing_image_prompt_and_inpaint
* feat: extract attribute debugging_cn_preprocessor
* feat: extract attribute skipping_cn_preprocessor
* feat: extract attribute canny_low_threshold
* feat: extract attribute canny_high_threshold
* feat: extract attribute refiner_swap_method
* feat: extract freeu_ctrls attributes
freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2
* feat: extract inpaint_ctrls attributes
debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate
* wip: add TODOs
* chore: cleanup code
* feat: extract attribute controlnet_softness
* feat: extract remaining attributes, do not use globals in patch
* fix: resolve circular import, patch_all now in async_worker
* chore: cleanup pid code
82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
import cv2
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import numpy as np
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def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold):
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assert isinstance(x, np.ndarray)
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assert x.ndim == 2 and x.dtype == np.uint8
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y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold))
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y = y.astype(np.float32) / 255.0
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return y
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def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
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assert isinstance(x, np.ndarray)
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assert x.ndim == 3 and x.shape[2] == 3
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result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)]
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result = np.stack(result, axis=2)
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return result
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def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
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assert isinstance(x, np.ndarray)
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assert x.ndim == 3 and x.shape[2] == 3
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H, W, C = x.shape
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acc_edge = None
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for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
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Hs, Ws = int(H * k), int(W * k)
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small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA)
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edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold)
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if acc_edge is None:
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acc_edge = edge
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else:
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acc_edge = cv2.resize(acc_edge, (edge.shape[1], edge.shape[0]), interpolation=cv2.INTER_LINEAR)
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acc_edge = acc_edge * 0.75 + edge * 0.25
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return acc_edge
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def norm255(x, low=4, high=96):
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assert isinstance(x, np.ndarray)
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assert x.ndim == 2 and x.dtype == np.float32
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v_min = np.percentile(x, low)
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v_max = np.percentile(x, high)
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x -= v_min
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x /= v_max - v_min
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return x * 255.0
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def canny_pyramid(x, canny_low_threshold, canny_high_threshold):
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# For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions.
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# Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions.
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color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold)
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result = np.sum(color_canny, axis=2)
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return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8)
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def cpds(x):
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# cv2.decolor is not "decolor", it is Cewu Lu's method
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# See http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html
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# See https://docs.opencv.org/3.0-beta/modules/photo/doc/decolor.html
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raw = cv2.GaussianBlur(x, (0, 0), 0.8)
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density, boost = cv2.decolor(raw)
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raw = raw.astype(np.float32)
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density = density.astype(np.float32)
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boost = boost.astype(np.float32)
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offset = np.sum((raw - boost) ** 2.0, axis=2) ** 0.5
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result = density + offset
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return norm255(result, low=4, high=96).clip(0, 255).astype(np.uint8)
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