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Add batch processing to Extras tab

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This commit is contained in:
ArrowM
2022-09-15 22:23:37 -05:00
committed by AUTOMATIC1111
parent deea9f4d70
commit 3763837003
2 changed files with 68 additions and 44 deletions
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99
modules/extras.py
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@@ -13,66 +13,85 @@ import piexif.helper
cached_images = {}
def run_extras(image, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
def run_extras(image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
devices.torch_gc()
existing_pnginfo = image.info or {}
imageArr = []
image = image.convert("RGB")
info = ""
if image_folder != None:
if image != None:
print("Batch detected and single image detected, please only use one of the two. Aborting.")
return None
#convert file to pillow image
for img in image_folder:
image = Image.fromarray(np.array(Image.open(img)))
imageArr.append(image)
elif image != None:
if image_folder != None:
print("Batch detected and single image detected, please only use one of the two. Aborting.")
return None
else:
imageArr.append(image)
outpath = opts.outdir_samples or opts.outdir_extras_samples
if gfpgan_visibility > 0:
restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
res = Image.fromarray(restored_img)
for image in imageArr:
existing_pnginfo = image.info or {}
if gfpgan_visibility < 1.0:
res = Image.blend(image, res, gfpgan_visibility)
image = image.convert("RGB")
info = ""
info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
image = res
if gfpgan_visibility > 0:
restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
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 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)}\n"
image = res
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 upscaling_resize != 1.0:
def upscale(image, scaler_index, resize):
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) + pixels
if codeformer_visibility < 1.0:
res = Image.blend(image, res, codeformer_visibility)
c = cached_images.get(key)
if c is None:
upscaler = shared.sd_upscalers[scaler_index]
c = upscaler.upscale(image, image.width * resize, image.height * resize)
cached_images[key] = c
info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility)}\n"
image = res
return c
if upscaling_resize != 1.0:
def upscale(image, scaler_index, resize):
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) + pixels
info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
res = upscale(image, extras_upscaler_1, upscaling_resize)
c = cached_images.get(key)
if c is None:
upscaler = shared.sd_upscalers[scaler_index]
c = upscaler.upscale(image, image.width * resize, image.height * resize)
cached_images[key] = c
if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
res2 = upscale(image, extras_upscaler_2, upscaling_resize)
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)
return c
image = res
info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
res = upscale(image, extras_upscaler_1, upscaling_resize)
while len(cached_images) > 2:
del cached_images[next(iter(cached_images.keys()))]
if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
res2 = upscale(image, extras_upscaler_2, upscaling_resize)
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)
images.save_image(image, path=outpath, 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)
image = res
return image, plaintext_to_html(info), ''
while len(cached_images) > 2:
del cached_images[next(iter(cached_images.keys()))]
images.save_image(image, path=outpath, 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)
return imageArr, plaintext_to_html(info), ''
def run_pnginfo(image):