196 lines
7.3 KiB
Python
196 lines
7.3 KiB
Python
import math
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import cv2
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import numpy as np
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from PIL import Image, ImageOps, ImageChops
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from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
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from modules.shared import opts, state
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import modules.shared as shared
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import modules.processing as processing
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from modules.ui import plaintext_to_html
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import modules.images as images
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import modules.scripts
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def img2img(prompt: str, negative_prompt: str, prompt_style: str, init_img, init_img_with_mask, init_mask, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, denoising_strength_change_factor: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, height: int, width: int, resize_mode: int, upscaler_index: str, upscale_overlap: int, inpaint_full_res: bool, inpainting_mask_invert: int, *args):
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is_inpaint = mode == 1
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is_loopback = mode == 2
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is_upscale = mode == 3
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if is_inpaint:
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if mask_mode == 0:
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image = init_img_with_mask['image']
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mask = init_img_with_mask['mask']
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alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
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mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
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image = image.convert('RGB')
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else:
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image = init_img
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mask = init_mask
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else:
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image = init_img
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mask = None
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assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
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p = StableDiffusionProcessingImg2Img(
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sd_model=shared.sd_model,
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outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
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outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
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prompt=prompt,
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negative_prompt=negative_prompt,
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prompt_style=prompt_style,
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seed=seed,
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subseed=subseed,
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subseed_strength=subseed_strength,
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seed_resize_from_h=seed_resize_from_h,
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seed_resize_from_w=seed_resize_from_w,
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sampler_index=sampler_index,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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restore_faces=restore_faces,
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tiling=tiling,
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init_images=[image],
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mask=mask,
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mask_blur=mask_blur,
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inpainting_fill=inpainting_fill,
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resize_mode=resize_mode,
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denoising_strength=denoising_strength,
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inpaint_full_res=inpaint_full_res,
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inpainting_mask_invert=inpainting_mask_invert,
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extra_generation_params={
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"Denoising strength": denoising_strength,
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"Denoising strength change factor": (denoising_strength_change_factor if is_loopback else None)
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}
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)
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print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
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if is_loopback:
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output_images, info = None, None
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history = []
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initial_seed = None
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initial_info = None
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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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state.job = f"Batch {i + 1} out of {n_iter}"
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processed = process_images(p)
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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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if do_color_correction and correction_target is not None:
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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 = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1)
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history.append(processed.images[0])
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grid = images.image_grid(history, batch_size, rows=1)
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images.save_image(grid, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
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processed = Processed(p, history, initial_seed, initial_info)
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elif is_upscale:
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initial_info = None
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processing.fix_seed(p)
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seed = p.seed
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upscaler = shared.sd_upscalers[upscaler_index]
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img = upscaler.upscale(init_img, init_img.width * 2, init_img.height * 2)
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processing.torch_gc()
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grid = images.split_grid(img, tile_w=width, tile_h=height, overlap=upscale_overlap)
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upscale_count = p.n_iter
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p.n_iter = 1
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p.do_not_save_grid = True
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p.do_not_save_samples = True
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work = []
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for y, h, row in grid.tiles:
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for tiledata in row:
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work.append(tiledata[2])
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batch_count = math.ceil(len(work) / p.batch_size)
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state.job_count = batch_count * upscale_count
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print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")
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result_images = []
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for n in range(upscale_count):
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start_seed = seed + n
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p.seed = start_seed
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work_results = []
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for i in range(batch_count):
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p.init_images = work[i*p.batch_size:(i+1)*p.batch_size]
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state.job = f"Batch {i + 1} out of {state.job_count}"
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processed = process_images(p)
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if initial_info is None:
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initial_info = processed.info
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p.seed = processed.seed + 1
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work_results += processed.images
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image_index = 0
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for y, h, row in grid.tiles:
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for tiledata in row:
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tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
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image_index += 1
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combined_image = images.combine_grid(grid)
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result_images.append(combined_image)
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if opts.samples_save:
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images.save_image(combined_image, p.outpath_samples, "", start_seed, prompt, opts.grid_format, info=initial_info)
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processed = Processed(p, result_images, seed, initial_info)
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else:
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processed = modules.scripts.scripts_img2img.run(p, *args)
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if processed is None:
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processed = process_images(p)
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shared.total_tqdm.clear()
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return processed.images, processed.js(), plaintext_to_html(processed.info)
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