mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-10-31 01:54:44 +00:00
4b460fcb1a
Before a new batch would use the last image from the previous batch. Now each batch will use the original image for the init image at the start of the batch.
88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
import numpy as np
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from tqdm import trange
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import modules.scripts as scripts
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import gradio as gr
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from modules import processing, shared, sd_samplers, images
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from modules.processing import Processed
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from modules.sd_samplers import samplers
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from modules.shared import opts, cmd_opts, state
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class Script(scripts.Script):
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def title(self):
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return "Loopback"
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def show(self, is_img2img):
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return is_img2img
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def ui(self, is_img2img):
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loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4)
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denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1)
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return [loops, denoising_strength_change_factor]
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def run(self, p, loops, denoising_strength_change_factor):
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processing.fix_seed(p)
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batch_count = p.n_iter
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p.extra_generation_params = {
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"Denoising strength change factor": denoising_strength_change_factor,
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}
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p.batch_size = 1
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p.n_iter = 1
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output_images, info = None, None
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initial_seed = None
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initial_info = None
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grids = []
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all_images = []
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original_init_image = p.init_images
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state.job_count = loops * batch_count
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initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
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for n in range(batch_count):
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history = []
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# Reset to original init image at the start of each batch
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p.init_images = original_init_image
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for i in range(loops):
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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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if opts.img2img_color_correction:
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p.color_corrections = initial_color_corrections
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state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
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processed = processing.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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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, rows=1)
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if opts.grid_save:
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images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
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grids.append(grid)
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all_images += history
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if opts.return_grid:
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all_images = grids + all_images
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processed = Processed(p, all_images, initial_seed, initial_info)
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return processed
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