diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/api/api.py | 104 | ||||
| -rw-r--r-- | modules/api/models.py | 55 | ||||
| -rw-r--r-- | modules/extras.py | 176 | ||||
| -rw-r--r-- | modules/hypernetworks/hypernetwork.py | 1 | ||||
| -rw-r--r-- | modules/hypernetworks/ui.py | 3 | ||||
| -rw-r--r-- | modules/images.py | 9 | ||||
| -rw-r--r-- | modules/ui.py | 11 |
7 files changed, 242 insertions, 117 deletions
diff --git a/modules/api/api.py b/modules/api/api.py index 6e9d6097..49c213ea 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -1,46 +1,37 @@ -from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI +import uvicorn +from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image +from fastapi import APIRouter, HTTPException +import modules.shared as shared +from modules.api.models import * from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.sd_samplers import all_samplers -from modules.extras import run_pnginfo -import modules.shared as shared -import uvicorn -from fastapi import Body, APIRouter, HTTPException -from fastapi.responses import JSONResponse -from pydantic import BaseModel, Field, Json -from typing import List -import json -import io -import base64 -from PIL import Image - -sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None) +from modules.extras import run_extras -class TextToImageResponse(BaseModel): - images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") - parameters: Json - info: Json +def upscaler_to_index(name: str): + try: + return [x.name.lower() for x in shared.sd_upscalers].index(name.lower()) + except: + raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}") -class ImageToImageResponse(BaseModel): - images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") - parameters: Json - info: Json +sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None) +def setUpscalers(req: dict): + reqDict = vars(req) + reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1) + reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2) + reqDict.pop('upscaler_1') + reqDict.pop('upscaler_2') + return reqDict class Api: def __init__(self, app, queue_lock): self.router = APIRouter() self.app = app self.queue_lock = queue_lock - self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"]) - self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"]) - - def __base64_to_image(self, base64_string): - # if has a comma, deal with prefix - if "," in base64_string: - base64_string = base64_string.split(",")[1] - imgdata = base64.b64decode(base64_string) - # convert base64 to PIL image - return Image.open(io.BytesIO(imgdata)) + self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse) + self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse) + self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse) + self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse) def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): sampler_index = sampler_to_index(txt2imgreq.sampler_index) @@ -60,15 +51,9 @@ class Api: with self.queue_lock: processed = process_images(p) - b64images = [] - for i in processed.images: - buffer = io.BytesIO() - i.save(buffer, format="png") - b64images.append(base64.b64encode(buffer.getvalue())) - - return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js()) - + b64images = list(map(encode_pil_to_base64, processed.images)) + return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js()) def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI): sampler_index = sampler_to_index(img2imgreq.sampler_index) @@ -83,7 +68,7 @@ class Api: mask = img2imgreq.mask if mask: - mask = self.__base64_to_image(mask) + mask = decode_base64_to_image(mask) populate = img2imgreq.copy(update={ # Override __init__ params @@ -98,7 +83,7 @@ class Api: imgs = [] for img in init_images: - img = self.__base64_to_image(img) + img = decode_base64_to_image(img) imgs = [img] * p.batch_size p.init_images = imgs @@ -106,21 +91,40 @@ class Api: with self.queue_lock: processed = process_images(p) - b64images = [] - for i in processed.images: - buffer = io.BytesIO() - i.save(buffer, format="png") - b64images.append(base64.b64encode(buffer.getvalue())) + b64images = list(map(encode_pil_to_base64, processed.images)) if (not img2imgreq.include_init_images): img2imgreq.init_images = None img2imgreq.mask = None + + return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js()) - return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js()) + def extras_single_image_api(self, req: ExtrasSingleImageRequest): + reqDict = setUpscalers(req) - def extrasapi(self): - raise NotImplementedError + reqDict['image'] = decode_base64_to_image(reqDict['image']) + + with self.queue_lock: + result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", **reqDict) + + return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1]) + + def extras_batch_images_api(self, req: ExtrasBatchImagesRequest): + reqDict = setUpscalers(req) + + def prepareFiles(file): + file = decode_base64_to_file(file.data, file_path=file.name) + file.orig_name = file.name + return file + + reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList'])) + reqDict.pop('imageList') + + with self.queue_lock: + result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict) + return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) + def pnginfoapi(self): raise NotImplementedError diff --git a/modules/api/models.py b/modules/api/models.py index 079e33d9..dd122321 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -1,10 +1,10 @@ -from array import array -from inflection import underscore -from typing import Any, Dict, Optional +import inspect from pydantic import BaseModel, Field, create_model +from typing import Any, Optional +from typing_extensions import Literal +from inflection import underscore from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img -import inspect - +from modules.shared import sd_upscalers API_NOT_ALLOWED = [ "self", @@ -105,4 +105,47 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( "StableDiffusionProcessingImg2Img", StableDiffusionProcessingImg2Img, [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}] -).generate_model()
\ No newline at end of file +).generate_model() + +class TextToImageResponse(BaseModel): + images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + parameters: dict + info: str + +class ImageToImageResponse(BaseModel): + images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + parameters: dict + info: str + +class ExtrasBaseRequest(BaseModel): + resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.") + show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?") + gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.") + codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.") + codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.") + upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.") + upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.") + upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.") + upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?") + upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") + upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") + extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.") + +class ExtraBaseResponse(BaseModel): + html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.") + +class ExtrasSingleImageRequest(ExtrasBaseRequest): + image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.") + +class ExtrasSingleImageResponse(ExtraBaseResponse): + image: str = Field(default=None, title="Image", description="The generated image in base64 format.") + +class FileData(BaseModel): + data: str = Field(title="File data", description="Base64 representation of the file") + name: str = Field(title="File name") + +class ExtrasBatchImagesRequest(ExtrasBaseRequest): + imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings") + +class ExtrasBatchImagesResponse(ExtraBaseResponse): + images: list[str] = Field(title="Images", description="The generated images in base64 format.")
\ No newline at end of file diff --git a/modules/extras.py b/modules/extras.py index 22c5a1c1..681d8d5a 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -1,3 +1,4 @@ +from __future__ import annotations
import math
import os
@@ -7,6 +8,10 @@ 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
from modules.shared import opts
import modules.gfpgan_model
@@ -17,10 +22,38 @@ import piexif.helper import gradio as gr
-cached_images = {}
+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):
+
+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):
devices.torch_gc()
imageArr = []
@@ -56,72 +89,102 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ else:
outpath = opts.outdir_samples or opts.outdir_extras_samples
-
- for image, image_name in zip(imageArr, imageNameArr):
- if image is None:
- return outputs, "Please select an input image.", ''
- existing_pnginfo = image.info or {}
+ # Extra operation definitions
- image = image.convert("RGB")
- info = ""
+ def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+ restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
+ res = Image.fromarray(restored_img)
- if gfpgan_visibility > 0:
- 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)
- if gfpgan_visibility < 1.0:
- res = Image.blend(image, res, gfpgan_visibility)
+ info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
+ return (res, info)
- info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
- image = res
+ def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+ restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
+ res = Image.fromarray(restored_img)
- if codeformer_visibility > 0:
- 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)
- 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)
- info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
- image = res
+ def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
+ 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
+ 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=hash(np.array(image.getdata()).tobytes()),
+ 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.", ''
+ 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 upscaling_resize != 1.0:
- def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
- small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
- pixels = tuple(np.array(small).flatten().tolist())
- key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight,
- resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
-
- c = cached_images.get(key)
- if c is None:
- upscaler = shared.sd_upscalers[scaler_index]
- c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
- if mode == 1 and crop:
- cropped = Image.new("RGB", (resize_w, resize_h))
- cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
- c = cropped
- cached_images[key] = c
-
- return c
-
- info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
- res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-
- if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
- res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
- info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
- res = Image.blend(res, res2, extras_upscaler_2_visibility)
-
- image = res
-
- while len(cached_images) > 2:
- del cached_images[next(iter(cached_images.keys()))]
-
if opts.use_original_name_batch and image_name != None:
basename = os.path.splitext(os.path.basename(image_name))[0]
else:
@@ -141,6 +204,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ return outputs, plaintext_to_html(info), ''
+def clear_cache():
+ cached_images.clear()
+
def run_pnginfo(image):
if image is None:
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 8113b35b..87cf3cf3 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -25,6 +25,7 @@ from statistics import stdev, mean class HypernetworkModule(torch.nn.Module):
multiplier = 1.0
activation_dict = {
+ "linear": torch.nn.Identity,
"relu": torch.nn.ReLU,
"leakyrelu": torch.nn.LeakyReLU,
"elu": torch.nn.ELU,
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 2c6c0470..aad09ffc 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -8,7 +8,8 @@ import modules.textual_inversion.textual_inversion from modules import devices, sd_hijack, shared
from modules.hypernetworks import hypernetwork
-keys = list(hypernetwork.HypernetworkModule.activation_dict.keys())
+not_available = ["hardswish", "multiheadattention"]
+keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
# Remove illegal characters from name.
diff --git a/modules/images.py b/modules/images.py index 7870b5b7..a0728553 100644 --- a/modules/images.py +++ b/modules/images.py @@ -300,8 +300,8 @@ class FilenameGenerator: 'seed': lambda self: self.seed if self.seed is not None else '',
'steps': lambda self: self.p and self.p.steps,
'cfg': lambda self: self.p and self.p.cfg_scale,
- 'width': lambda self: self.p and self.p.width,
- 'height': lambda self: self.p and self.p.height,
+ 'width': lambda self: self.image.width,
+ 'height': lambda self: self.image.height,
'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
@@ -315,10 +315,11 @@ class FilenameGenerator: }
default_time_format = '%Y%m%d%H%M%S'
- def __init__(self, p, seed, prompt):
+ def __init__(self, p, seed, prompt, image):
self.p = p
self.seed = seed
self.prompt = prompt
+ self.image = image
def prompt_no_style(self):
if self.p is None or self.prompt is None:
@@ -449,7 +450,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i txt_fullfn (`str` or None):
If a text file is saved for this image, this will be its full path. Otherwise None.
"""
- namegen = FilenameGenerator(p, seed, prompt)
+ namegen = FilenameGenerator(p, seed, prompt, image)
if save_to_dirs is None:
save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
diff --git a/modules/ui.py b/modules/ui.py index 0a63e357..7ddfe07e 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1119,6 +1119,9 @@ def create_ui(wrap_gradio_gpu_call): codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
+ with gr.Group():
+ upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
+
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
with gr.Column(variant='panel'):
@@ -1152,6 +1155,7 @@ def create_ui(wrap_gradio_gpu_call): extras_upscaler_1,
extras_upscaler_2,
extras_upscaler_2_visibility,
+ upscale_before_face_fix,
],
outputs=[
result_images,
@@ -1174,6 +1178,11 @@ def create_ui(wrap_gradio_gpu_call): outputs=[init_img_with_mask],
)
+ extras_image.change(
+ fn=modules.extras.clear_cache,
+ inputs=[], outputs=[]
+ )
+
with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
with gr.Row().style(equal_height=False):
with gr.Column(variant='panel'):
@@ -1238,7 +1247,7 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_name = gr.Textbox(label="Name")
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
- new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
+ new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
|