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