mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
467 lines
17 KiB
Python
467 lines
17 KiB
Python
from __future__ import annotations
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import math
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import os
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import re
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import sys
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import traceback
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import shutil
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import numpy as np
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from PIL import Image
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import torch
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import tqdm
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from typing import Callable, List, OrderedDict, Tuple
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from functools import partial
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from dataclasses import dataclass
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from modules import processing, shared, images, devices, sd_models, sd_samplers, sd_vae
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from modules.shared import opts
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import modules.gfpgan_model
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from modules.ui import plaintext_to_html
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import modules.codeformer_model
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import gradio as gr
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import safetensors.torch
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class LruCache(OrderedDict):
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@dataclass(frozen=True)
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class Key:
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image_hash: int
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info_hash: int
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args_hash: int
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@dataclass
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class Value:
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image: Image.Image
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info: str
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def __init__(self, max_size: int = 5, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._max_size = max_size
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def get(self, key: LruCache.Key) -> LruCache.Value:
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ret = super().get(key)
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if ret is not None:
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self.move_to_end(key) # Move to end of eviction list
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return ret
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def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
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self[key] = value
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while len(self) > self._max_size:
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self.popitem(last=False)
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cached_images: LruCache = LruCache(max_size=5)
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def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
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devices.torch_gc()
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shared.state.begin()
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shared.state.job = 'extras'
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imageArr = []
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# Also keep track of original file names
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imageNameArr = []
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outputs = []
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if extras_mode == 1:
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#convert file to pillow image
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for img in image_folder:
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image = Image.open(img)
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imageArr.append(image)
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imageNameArr.append(os.path.splitext(img.orig_name)[0])
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elif extras_mode == 2:
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assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
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if input_dir == '':
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return outputs, "Please select an input directory.", ''
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image_list = shared.listfiles(input_dir)
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for img in image_list:
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try:
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image = Image.open(img)
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except Exception:
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continue
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imageArr.append(image)
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imageNameArr.append(img)
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else:
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imageArr.append(image)
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imageNameArr.append(None)
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if extras_mode == 2 and output_dir != '':
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outpath = output_dir
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else:
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outpath = opts.outdir_samples or opts.outdir_extras_samples
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# Extra operation definitions
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def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
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shared.state.job = 'extras-gfpgan'
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restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
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res = Image.fromarray(restored_img)
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if gfpgan_visibility < 1.0:
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res = Image.blend(image, res, gfpgan_visibility)
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info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
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return (res, info)
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def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
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shared.state.job = 'extras-codeformer'
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restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
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res = Image.fromarray(restored_img)
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if codeformer_visibility < 1.0:
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res = Image.blend(image, res, codeformer_visibility)
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info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
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return (res, info)
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def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
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shared.state.job = 'extras-upscale'
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upscaler = shared.sd_upscalers[scaler_index]
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res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
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if mode == 1 and crop:
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cropped = Image.new("RGB", (resize_w, resize_h))
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cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))
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res = cropped
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return res
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def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
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# Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
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nonlocal upscaling_resize
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if resize_mode == 1:
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upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
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crop_info = " (crop)" if upscaling_crop else ""
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info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
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return (image, info)
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@dataclass
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class UpscaleParams:
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upscaler_idx: int
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blend_alpha: float
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def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
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blended_result: Image.Image = None
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image_hash: str = hash(np.array(image.getdata()).tobytes())
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for upscaler in params:
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upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
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upscaling_resize_w, upscaling_resize_h, upscaling_crop)
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cache_key = LruCache.Key(image_hash=image_hash,
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info_hash=hash(info),
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args_hash=hash(upscale_args))
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cached_entry = cached_images.get(cache_key)
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if cached_entry is None:
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res = upscale(image, *upscale_args)
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info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
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cached_images.put(cache_key, LruCache.Value(image=res, info=info))
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else:
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res, info = cached_entry.image, cached_entry.info
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if blended_result is None:
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blended_result = res
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else:
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blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)
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return (blended_result, info)
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# Build a list of operations to run
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facefix_ops: List[Callable] = []
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facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []
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facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []
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upscale_ops: List[Callable] = []
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upscale_ops += [run_prepare_crop] if resize_mode == 1 else []
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if upscaling_resize != 0:
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step_params: List[UpscaleParams] = []
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step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))
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if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
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step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))
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upscale_ops.append(partial(run_upscalers_blend, step_params))
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extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
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for image, image_name in zip(imageArr, imageNameArr):
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if image is None:
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return outputs, "Please select an input image.", ''
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shared.state.textinfo = f'Processing image {image_name}'
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existing_pnginfo = image.info or {}
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image = image.convert("RGB")
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info = ""
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# Run each operation on each image
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for op in extras_ops:
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image, info = op(image, info)
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if opts.use_original_name_batch and image_name is not None:
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basename = os.path.splitext(os.path.basename(image_name))[0]
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else:
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basename = ''
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if opts.enable_pnginfo: # append info before save
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image.info = existing_pnginfo
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image.info["extras"] = info
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if save_output:
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# Add upscaler name as a suffix.
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suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
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# Add second upscaler if applicable.
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if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:
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suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
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images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
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no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
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if extras_mode != 2 or show_extras_results :
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outputs.append(image)
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devices.torch_gc()
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return outputs, plaintext_to_html(info), ''
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def clear_cache():
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cached_images.clear()
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def run_pnginfo(image):
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if image is None:
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return '', '', ''
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geninfo, items = images.read_info_from_image(image)
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items = {**{'parameters': geninfo}, **items}
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info = ''
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for key, text in items.items():
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info += f"""
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<div>
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<p><b>{plaintext_to_html(str(key))}</b></p>
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<p>{plaintext_to_html(str(text))}</p>
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</div>
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""".strip()+"\n"
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if len(info) == 0:
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message = "Nothing found in the image."
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info = f"<div><p>{message}<p></div>"
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return '', geninfo, info
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def create_config(ckpt_result, config_source, a, b, c):
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def config(x):
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res = sd_models.find_checkpoint_config(x) if x else None
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return res if res != shared.sd_default_config else None
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if config_source == 0:
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cfg = config(a) or config(b) or config(c)
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elif config_source == 1:
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cfg = config(b)
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elif config_source == 2:
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cfg = config(c)
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else:
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cfg = None
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if cfg is None:
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return
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filename, _ = os.path.splitext(ckpt_result)
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checkpoint_filename = filename + ".yaml"
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print("Copying config:")
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print(" from:", cfg)
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print(" to:", checkpoint_filename)
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shutil.copyfile(cfg, checkpoint_filename)
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checkpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"]
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def to_half(tensor, enable):
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if enable and tensor.dtype == torch.float:
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return tensor.half()
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return tensor
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def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights):
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shared.state.begin()
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shared.state.job = 'model-merge'
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def fail(message):
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shared.state.textinfo = message
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shared.state.end()
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return [*[gr.update() for _ in range(4)], message]
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def weighted_sum(theta0, theta1, alpha):
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return ((1 - alpha) * theta0) + (alpha * theta1)
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def get_difference(theta1, theta2):
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return theta1 - theta2
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def add_difference(theta0, theta1_2_diff, alpha):
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return theta0 + (alpha * theta1_2_diff)
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def filename_weighted_sum():
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a = primary_model_info.model_name
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b = secondary_model_info.model_name
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Ma = round(1 - multiplier, 2)
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Mb = round(multiplier, 2)
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return f"{Ma}({a}) + {Mb}({b})"
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def filename_add_difference():
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a = primary_model_info.model_name
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b = secondary_model_info.model_name
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c = tertiary_model_info.model_name
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M = round(multiplier, 2)
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return f"{a} + {M}({b} - {c})"
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def filename_nothing():
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return primary_model_info.model_name
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theta_funcs = {
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"Weighted sum": (filename_weighted_sum, None, weighted_sum),
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"Add difference": (filename_add_difference, get_difference, add_difference),
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"No interpolation": (filename_nothing, None, None),
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}
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filename_generator, theta_func1, theta_func2 = theta_funcs[interp_method]
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shared.state.job_count = (1 if theta_func1 else 0) + (1 if theta_func2 else 0)
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if not primary_model_name:
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return fail("Failed: Merging requires a primary model.")
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primary_model_info = sd_models.checkpoints_list[primary_model_name]
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if theta_func2 and not secondary_model_name:
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return fail("Failed: Merging requires a secondary model.")
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secondary_model_info = sd_models.checkpoints_list[secondary_model_name] if theta_func2 else None
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if theta_func1 and not tertiary_model_name:
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return fail(f"Failed: Interpolation method ({interp_method}) requires a tertiary model.")
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tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None
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result_is_inpainting_model = False
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if theta_func2:
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shared.state.textinfo = f"Loading B"
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print(f"Loading {secondary_model_info.filename}...")
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theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
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else:
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theta_1 = None
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if theta_func1:
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shared.state.textinfo = f"Loading C"
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print(f"Loading {tertiary_model_info.filename}...")
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theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
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shared.state.textinfo = 'Merging B and C'
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shared.state.sampling_steps = len(theta_1.keys())
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for key in tqdm.tqdm(theta_1.keys()):
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if key in checkpoint_dict_skip_on_merge:
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continue
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if 'model' in key:
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if key in theta_2:
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t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
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theta_1[key] = theta_func1(theta_1[key], t2)
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else:
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theta_1[key] = torch.zeros_like(theta_1[key])
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shared.state.sampling_step += 1
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del theta_2
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shared.state.nextjob()
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shared.state.textinfo = f"Loading {primary_model_info.filename}..."
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print(f"Loading {primary_model_info.filename}...")
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theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
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print("Merging...")
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shared.state.textinfo = 'Merging A and B'
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shared.state.sampling_steps = len(theta_0.keys())
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for key in tqdm.tqdm(theta_0.keys()):
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if theta_1 and 'model' in key and key in theta_1:
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if key in checkpoint_dict_skip_on_merge:
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continue
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a = theta_0[key]
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b = theta_1[key]
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# this enables merging an inpainting model (A) with another one (B);
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# where normal model would have 4 channels, for latenst space, inpainting model would
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# have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
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if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]:
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if a.shape[1] == 4 and b.shape[1] == 9:
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raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.")
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assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
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theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
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result_is_inpainting_model = True
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else:
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theta_0[key] = theta_func2(a, b, multiplier)
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theta_0[key] = to_half(theta_0[key], save_as_half)
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shared.state.sampling_step += 1
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del theta_1
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bake_in_vae_filename = sd_vae.vae_dict.get(bake_in_vae, None)
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if bake_in_vae_filename is not None:
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print(f"Baking in VAE from {bake_in_vae_filename}")
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shared.state.textinfo = 'Baking in VAE'
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vae_dict = sd_vae.load_vae_dict(bake_in_vae_filename, map_location='cpu')
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for key in vae_dict.keys():
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theta_0_key = 'first_stage_model.' + key
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if theta_0_key in theta_0:
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theta_0[theta_0_key] = to_half(vae_dict[key], save_as_half)
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del vae_dict
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if save_as_half and not theta_func2:
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for key in theta_0.keys():
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theta_0[key] = to_half(theta_0[key], save_as_half)
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if discard_weights:
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regex = re.compile(discard_weights)
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for key in list(theta_0):
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if re.search(regex, key):
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theta_0.pop(key, None)
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ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
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filename = filename_generator() if custom_name == '' else custom_name
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filename += ".inpainting" if result_is_inpainting_model else ""
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filename += "." + checkpoint_format
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output_modelname = os.path.join(ckpt_dir, filename)
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shared.state.nextjob()
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shared.state.textinfo = "Saving"
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print(f"Saving to {output_modelname}...")
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_, extension = os.path.splitext(output_modelname)
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if extension.lower() == ".safetensors":
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safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
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else:
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torch.save(theta_0, output_modelname)
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sd_models.list_models()
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create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
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print(f"Checkpoint saved to {output_modelname}.")
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shared.state.textinfo = "Checkpoint saved"
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shared.state.end()
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return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], "Checkpoint saved to " + output_modelname]
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