mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-11-04 12:03:36 +00:00
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
import math
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import os
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import sys
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import traceback
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import modules.scripts as scripts
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import gradio as gr
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from modules.processing import Processed, process_images
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from PIL import Image
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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 "Batch processing"
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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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input_dir = gr.Textbox(label="Input directory", lines=1)
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output_dir = gr.Textbox(label="Output directory", lines=1)
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return [input_dir, output_dir]
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def run(self, p, input_dir, output_dir):
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images = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
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batch_count = math.ceil(len(images) / p.batch_size)
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print(f"Will process {len(images)} images in {batch_count} batches.")
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p.batch_count = 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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state.job_count = batch_count
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for batch_no in range(batch_count):
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batch_images = []
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for path in images[batch_no*p.batch_size:(batch_no+1)*p.batch_size]:
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try:
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img = Image.open(path)
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batch_images.append((img, path))
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except:
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print(f"Error processing {path}:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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if len(batch_images) == 0:
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continue
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state.job = f"{batch_no} out of {batch_count}: {batch_images[0][1]}"
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p.init_images = [x[0] for x in batch_images]
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proc = process_images(p)
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for image, (_, path) in zip(proc.images, batch_images):
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filename = os.path.basename(path)
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image.save(os.path.join(output_dir, filename))
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return Processed(p, [], p.seed, "")
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