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Add color correction to img2img loopback to avoid a progressive skew to magenta. Based on codedealer's PR to hlky's repo here: https://github.com/sd-webui/stable-diffusion-webui/pull/698/files.
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@ -1,4 +1,6 @@
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import math
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import cv2
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import numpy as np
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from PIL import Image
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from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
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@ -57,8 +59,19 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
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state.job_count = n_iter
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do_color_correction = False
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try:
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from skimage import exposure
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do_color_correction = True
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except:
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print("Install scikit-image to perform color correction on loopback")
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for i in range(n_iter):
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if do_color_correction and i == 0:
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correction_target = cv2.cvtColor(np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB)
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p.n_iter = 1
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p.batch_size = 1
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p.do_not_save_grid = True
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@ -69,8 +82,21 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
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if initial_seed is None:
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initial_seed = processed.seed
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initial_info = processed.info
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init_img = processed.images[0]
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p.init_images = [processed.images[0]]
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if do_color_correction and correction_target is not None:
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print("Colour correcting input...")
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init_img = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
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cv2.cvtColor(
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np.asarray(init_img),
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cv2.COLOR_RGB2LAB
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),
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correction_target,
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channel_axis=2
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), cv2.COLOR_LAB2RGB).astype("uint8"))
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p.init_images = [init_img]
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p.seed = processed.seed + 1
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p.denoising_strength = max(p.denoising_strength * 0.95, 0.1)
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history.append(processed.images[0])
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@ -10,5 +10,6 @@ omegaconf
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pytorch_lightning
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diffusers
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invisible-watermark
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scikit-image
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git+https://github.com/crowsonkb/k-diffusion.git
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git+https://github.com/TencentARC/GFPGAN.git
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