mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
58 lines
2.5 KiB
Python
58 lines
2.5 KiB
Python
import math
|
|
import os
|
|
import sys
|
|
import traceback
|
|
|
|
import modules.scripts as scripts
|
|
import gradio as gr
|
|
|
|
from modules.processing import Processed, process_images
|
|
from PIL import Image
|
|
from modules.shared import opts, cmd_opts, state
|
|
|
|
class Script(scripts.Script):
|
|
def title(self):
|
|
return "Prompts from file or textbox"
|
|
|
|
def ui(self, is_img2img):
|
|
# This checkbox would look nicer as two tabs, but there are two problems:
|
|
# 1) There is a bug in Gradio 3.3 that prevents visibility from working on Tabs
|
|
# 2) Even with Gradio 3.3.1, returning a control (like Tabs) that can't be used as input
|
|
# causes a AttributeError: 'Tabs' object has no attribute 'preprocess' assert,
|
|
# due to the way Script assumes all controls returned can be used as inputs.
|
|
# Therefore, there's no good way to use grouping components right now,
|
|
# so we will use a checkbox! :)
|
|
checkbox_txt = gr.Checkbox(label="Show Textbox", value=False)
|
|
file = gr.File(label="File with inputs", type='bytes')
|
|
prompt_txt = gr.TextArea(label="Prompts")
|
|
checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
|
|
return [checkbox_txt, file, prompt_txt]
|
|
|
|
def on_show(self, checkbox_txt, file, prompt_txt):
|
|
return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
|
|
|
|
def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
|
|
if (checkbox_txt):
|
|
lines = [x.strip() for x in prompt_txt.splitlines()]
|
|
else:
|
|
lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
|
|
lines = [x for x in lines if len(x) > 0]
|
|
|
|
img_count = len(lines) * p.n_iter
|
|
batch_count = math.ceil(img_count / p.batch_size)
|
|
loop_count = math.ceil(batch_count / p.n_iter)
|
|
print(f"Will process {img_count} images in {batch_count} batches.")
|
|
|
|
p.do_not_save_grid = True
|
|
|
|
state.job_count = batch_count
|
|
|
|
images = []
|
|
for loop_no in range(loop_count):
|
|
state.job = f"{loop_no + 1} out of {loop_count}"
|
|
p.prompt = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
|
|
proc = process_images(p)
|
|
images += proc.images
|
|
|
|
return Processed(p, images, p.seed, "")
|