Merge branch 'AUTOMATIC1111:master' into master

1 files changed, 10 insertions, 218 deletions

diff --git a/modules/extras.py b/modules/extras.py
index 1218f88f..36123aa5 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -1,230 +1,16 @@
-from __future__ import annotations

-import math

 import os

-import sys

-import traceback

+import re

 import shutil

 

-import numpy as np

-from PIL import Image

 

 import torch

 import tqdm

 

-from typing import Callable, List, OrderedDict, Tuple

-from functools import partial

-from dataclasses import dataclass

-

-from modules import processing, shared, images, devices, sd_models, sd_samplers, sd_vae

-from modules.shared import opts

-import modules.gfpgan_model

-from modules.ui import plaintext_to_html

-import modules.codeformer_model

+from modules import shared, images, sd_models, sd_vae

+from modules.ui_common import plaintext_to_html

 import gradio as gr

 import safetensors.torch

 

-class LruCache(OrderedDict):

-    @dataclass(frozen=True)

-    class Key:

-        image_hash: int

-        info_hash: int

-        args_hash: int

-

-    @dataclass

-    class Value:

-        image: Image.Image

-        info: str

-

-    def __init__(self, max_size: int = 5, *args, **kwargs):

-        super().__init__(*args, **kwargs)

-        self._max_size = max_size

-

-    def get(self, key: LruCache.Key) -> LruCache.Value:

-        ret = super().get(key)

-        if ret is not None:

-            self.move_to_end(key)  # Move to end of eviction list

-        return ret

-

-    def put(self, key: LruCache.Key, value: LruCache.Value) -> None:

-        self[key] = value

-        while len(self) > self._max_size:

-            self.popitem(last=False)

-

-

-cached_images: LruCache = LruCache(max_size=5)

-

-

-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):

-    devices.torch_gc()

-

-    shared.state.begin()

-    shared.state.job = 'extras'

-

-    imageArr = []

-    # Also keep track of original file names

-    imageNameArr = []

-    outputs = []

-

-    if extras_mode == 1:

-        #convert file to pillow image

-        for img in image_folder:

-            image = Image.open(img)

-            imageArr.append(image)

-            imageNameArr.append(os.path.splitext(img.orig_name)[0])

-    elif extras_mode == 2:

-        assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'

-

-        if input_dir == '':

-            return outputs, "Please select an input directory.", ''

-        image_list = shared.listfiles(input_dir)

-        for img in image_list:

-            try:

-                image = Image.open(img)

-            except Exception:

-                continue

-            imageArr.append(image)

-            imageNameArr.append(img)

-    else:

-        imageArr.append(image)

-        imageNameArr.append(None)

-

-    if extras_mode == 2 and output_dir != '':

-        outpath = output_dir

-    else:

-        outpath = opts.outdir_samples or opts.outdir_extras_samples

-

-    # Extra operation definitions

-

-    def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:

-        shared.state.job = 'extras-gfpgan'

-        restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))

-        res = Image.fromarray(restored_img)

-

-        if gfpgan_visibility < 1.0:

-            res = Image.blend(image, res, gfpgan_visibility)

-

-        info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"

-        return (res, info)

-

-    def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:

-        shared.state.job = 'extras-codeformer'

-        restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)

-        res = Image.fromarray(restored_img)

-

-        if codeformer_visibility < 1.0:

-            res = Image.blend(image, res, codeformer_visibility)

-

-        info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"

-        return (res, info)

-

-    def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):

-        shared.state.job = 'extras-upscale'

-        upscaler = shared.sd_upscalers[scaler_index]

-        res = upscaler.scaler.upscale(image, resize, upscaler.data_path)

-        if mode == 1 and crop:

-            cropped = Image.new("RGB", (resize_w, resize_h))

-            cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))

-            res = cropped

-        return res

-

-    def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:

-        # Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text

-        nonlocal upscaling_resize

-        if resize_mode == 1:

-            upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)

-            crop_info = " (crop)" if upscaling_crop else ""

-            info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"

-        return (image, info)

-

-    @dataclass

-    class UpscaleParams:

-        upscaler_idx: int

-        blend_alpha: float

-

-    def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:

-        blended_result: Image.Image = None

-        image_hash: str = hash(np.array(image.getdata()).tobytes())

-        for upscaler in params:

-            upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,

-                            upscaling_resize_w, upscaling_resize_h, upscaling_crop)

-            cache_key = LruCache.Key(image_hash=image_hash,

-                                     info_hash=hash(info),

-                                     args_hash=hash(upscale_args))

-            cached_entry = cached_images.get(cache_key)

-            if cached_entry is None:

-                res = upscale(image, *upscale_args)

-                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"

-                cached_images.put(cache_key, LruCache.Value(image=res, info=info))

-            else:

-                res, info = cached_entry.image, cached_entry.info

-

-            if blended_result is None:

-                blended_result = res

-            else:

-                blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)

-        return (blended_result, info)

-

-    # Build a list of operations to run

-    facefix_ops: List[Callable] = []

-    facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []

-    facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []

-

-    upscale_ops: List[Callable] = []

-    upscale_ops += [run_prepare_crop] if resize_mode == 1 else []

-

-    if upscaling_resize != 0:

-        step_params: List[UpscaleParams] = []

-        step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))

-        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:

-            step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))

-

-        upscale_ops.append(partial(run_upscalers_blend, step_params))

-

-    extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)

-

-    for image, image_name in zip(imageArr, imageNameArr):

-        if image is None:

-            return outputs, "Please select an input image.", ''

-

-        shared.state.textinfo = f'Processing image {image_name}'

-        

-        existing_pnginfo = image.info or {}

-

-        image = image.convert("RGB")

-        info = ""

-        # Run each operation on each image

-        for op in extras_ops:

-            image, info = op(image, info)

-

-        if opts.use_original_name_batch and image_name is not None:

-            basename = os.path.splitext(os.path.basename(image_name))[0]

-        else:

-            basename = ''

-

-        if opts.enable_pnginfo: # append info before save

-            image.info = existing_pnginfo

-            image.info["extras"] = info

-

-        if save_output:

-            # Add upscaler name as a suffix.

-            suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""

-            # Add second upscaler if applicable.

-            if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:

-                suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"

-

-            images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,

-                            no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)

-

-        if extras_mode != 2 or show_extras_results :

-            outputs.append(image)

-

-    devices.torch_gc()

-

-    return outputs, plaintext_to_html(info), ''

-

-def clear_cache():

-    cached_images.clear()

-

 

 def run_pnginfo(image):

     if image is None:

@@ -285,7 +71,7 @@ def to_half(tensor, enable):
     return tensor

 

 

-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):

+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):

     shared.state.begin()

     shared.state.job = 'model-merge'

 

@@ -430,6 +216,12 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
         for key in theta_0.keys():

             theta_0[key] = to_half(theta_0[key], save_as_half)

 

+    if discard_weights:

+        regex = re.compile(discard_weights)

+        for key in list(theta_0):

+            if re.search(regex, key):

+                theta_0.pop(key, None)

+

     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path

 

     filename = filename_generator() if custom_name == '' else custom_name