mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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91bfc71261
SD upscale moved to scripts Batch processing script removed Batch processing added to main img2img and now works with scripts img2img page UI reworked to use tabs
94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
import math
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import modules.scripts as scripts
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import gradio as gr
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from PIL import Image
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from modules import processing, shared, sd_samplers, images, devices
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from modules.processing import Processed
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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 "SD upscale"
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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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info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image to twice the dimensions; use width and height sliders to set tile size</p>")
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overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, visible=False)
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upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", visible=False)
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return [info, overlap, upscaler_index]
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def run(self, p, _, overlap, upscaler_index):
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processing.fix_seed(p)
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upscaler = shared.sd_upscalers[upscaler_index]
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p.extra_generation_params["SD upscale overlap"] = overlap
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p.extra_generation_params["SD upscale upscaler"] = upscaler.name
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initial_info = None
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seed = p.seed
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init_img = p.init_images[0]
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img = upscaler.upscale(init_img, init_img.width * 2, init_img.height * 2)
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devices.torch_gc()
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grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=overlap)
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batch_size = p.batch_size
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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) / 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.batch_size = batch_size
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p.init_images = work[i*batch_size:(i+1)*batch_size]
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state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"
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processed = processing.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, p.prompt, opts.samples_format, info=initial_info, p=p)
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processed = Processed(p, result_images, seed, initial_info)
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return processed
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