diff options
Diffstat (limited to 'modules/extras.py')
| -rw-r--r-- | modules/extras.py | 145 |
1 files changed, 95 insertions, 50 deletions
diff --git a/modules/extras.py b/modules/extras.py index 22c5a1c1..79047f3a 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -7,6 +7,10 @@ from PIL import Image import torch
import tqdm
+from typing import Callable, List, 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
@@ -20,7 +24,7 @@ import gradio as gr cached_images = {}
-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 = []
@@ -56,68 +60,109 @@ 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 {}
- image = image.convert("RGB")
- info = ""
+ # Extra operation definitions
+ 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 < 1.0:
+ res = Image.blend(image, res, gfpgan_visibility)
+
+ info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
+ return (res, info)
- if gfpgan_visibility > 0:
- restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
- res = Image.fromarray(restored_img)
+ 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 gfpgan_visibility < 1.0:
- res = Image.blend(image, res, gfpgan_visibility)
+ if codeformer_visibility < 1.0:
+ res = Image.blend(image, res, codeformer_visibility)
- info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
- image = res
+ info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
+ return (res, info)
- 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)
+ 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
- info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
- image = res
+ 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
+
+ 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:
+ res = upscale(image, upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
+ info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
+ 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] = []
+ if gfpgan_visibility > 0:
+ facefix_ops.append(run_gfpgan)
+ if codeformer_visibility > 0:
+ facefix_ops.append(run_codeformer)
+
+ upscale_ops: List[Callable] = []
+ if resize_mode == 1:
+ upscale_ops.append(run_prepare_crop)
+
+ 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] = []
+ if upscale_first:
+ extras_ops = upscale_ops + facefix_ops
+ else:
+ extras_ops = 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 {}
- 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
+ image = image.convert("RGB")
+ info = ""
+ # Run each operation on each image
+ for op in extras_ops:
+ image, info = op(image, info)
while len(cached_images) > 2:
del cached_images[next(iter(cached_images.keys()))]
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