mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 22:53:25 +03:00
fix for GPFGAN RGB/BGR (thanks deggua)
experimental support for negative prompts (without UI) option to do inpainting at full resolution Tooltips for UI elements
This commit is contained in:
AUTOMATIC
parent
a8c002587e
commit
757bb7c46b
53
script.js
Normal file
53
script.js
Normal file
@ -0,0 +1,53 @@
|
||||
console.log("running")
|
||||
|
||||
titles = {
|
||||
"Sampling steps": "How many times to imptove the generated image itratively; higher values take longer; very low values can produce bad results",
|
||||
"Sampling method": "Which algorithm to use to produce the image",
|
||||
"GFPGAN": "Restore low quality faces using GFPGAN neural network",
|
||||
"Euler a": "Euler Ancestral - very creative, each can get acompletely different pictures depending on step count, setting seps tohigher than 30-40 does not help",
|
||||
"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
|
||||
"Prompt matrix": "Separate prompts into part using vertical pipe character (|) and the script will create a picture for every combination of them (except for first part, which will be present in all combinations)",
|
||||
"Batch count": "How many batches of images to create",
|
||||
"Batch size": "How many image to create in a single batch",
|
||||
"CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
|
||||
"Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
|
||||
|
||||
"Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
|
||||
"Loopback": "Process an image, use it as an input, repeat. Batch count determings number of iterations.",
|
||||
"SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
|
||||
|
||||
"Just resize": "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.",
|
||||
"Crop and resize": "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.",
|
||||
"Resize and fill": "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.",
|
||||
|
||||
"Mask blur": "How much to blur the mask before processing, in pixels.",
|
||||
"Masked content": "What to put inside the masked area before processing it with Stable Diffusion.",
|
||||
"fill": "fill it with colors of the image",
|
||||
"original": "keep whatever was there originally",
|
||||
"latent noise": "fill it with latent space noise",
|
||||
"latent nothing": "fill it with latent space zeroes",
|
||||
"Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
|
||||
|
||||
"Denoising Strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image.",
|
||||
}
|
||||
|
||||
function gradioApp(){
|
||||
return document.getElementsByTagName('gradio-app')[0];
|
||||
}
|
||||
|
||||
function addTitles(root){
|
||||
root.querySelectorAll('span').forEach(function(span){
|
||||
tooltip = titles[span.textContent];
|
||||
if(tooltip){
|
||||
span.title = tooltip;
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
document.addEventListener("DOMContentLoaded", function() {
|
||||
var mutationObserver = new MutationObserver(function(m){
|
||||
addTitles(gradioApp().shadowRoot);
|
||||
});
|
||||
mutationObserver.observe( gradioApp().shadowRoot, { childList:true, subtree:true })
|
||||
|
||||
});
|
||||
@ -1,3 +1,5 @@
|
||||
.output-html p {margin: 0 0.5em;}
|
||||
.performance { font-size: 0.85em; color: #444; }
|
||||
|
||||
button{
|
||||
align-self: stretch !important;
|
||||
|
||||
174
webui.py
174
webui.py
@ -149,6 +149,12 @@ def gfpgan_model_path():
|
||||
def gfpgan():
|
||||
return GFPGANer(model_path=gfpgan_model_path(), upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
|
||||
|
||||
def gfpgan_fix_faces(gfpgan_model, np_image):
|
||||
np_image_bgr = np_image[:, :, ::-1]
|
||||
cropped_faces, restored_faces, gfpgan_output_bgr = gfpgan_model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
|
||||
np_image = gfpgan_output_bgr[:, :, ::-1]
|
||||
|
||||
return np_image
|
||||
|
||||
have_gfpgan = False
|
||||
try:
|
||||
@ -808,9 +814,10 @@ class EmbeddingsWithFixes(nn.Module):
|
||||
|
||||
|
||||
class StableDiffusionProcessing:
|
||||
def __init__(self, outpath=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None):
|
||||
def __init__(self, outpath=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
|
||||
self.outpath: str = outpath
|
||||
self.prompt: str = prompt
|
||||
self.negative_prompt: str = (negative_prompt or "")
|
||||
self.seed: int = seed
|
||||
self.sampler_index: int = sampler_index
|
||||
self.batch_size: int = batch_size
|
||||
@ -825,6 +832,7 @@ class StableDiffusionProcessing:
|
||||
self.do_not_save_grid: bool = do_not_save_grid
|
||||
self.extra_generation_params: dict = extra_generation_params
|
||||
self.overlay_images = overlay_images
|
||||
self.paste_to = None
|
||||
|
||||
def init(self):
|
||||
pass
|
||||
@ -997,7 +1005,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
||||
prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
|
||||
uc = model.get_learned_conditioning(len(prompts) * [""])
|
||||
uc = model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
|
||||
c = model.get_learned_conditioning(prompts)
|
||||
|
||||
if len(model_hijack.comments) > 0:
|
||||
@ -1020,14 +1028,22 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
||||
torch_gc()
|
||||
|
||||
gfpgan_model = gfpgan()
|
||||
cropped_faces, restored_faces, restored_img = gfpgan_model.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
|
||||
x_sample = restored_img
|
||||
x_sample = gfpgan_fix_faces(gfpgan_model, x_sample)
|
||||
|
||||
image = Image.fromarray(x_sample)
|
||||
|
||||
if p.overlay_images is not None and i < len(p.overlay_images):
|
||||
overlay = p.overlay_images[i]
|
||||
|
||||
if p.paste_to is not None:
|
||||
x, y, w, h = p.paste_to
|
||||
base_image = Image.new('RGBA', (overlay.width, overlay.height))
|
||||
image = resize_image(1, image, w, h)
|
||||
base_image.paste(image, (x, y))
|
||||
image = base_image
|
||||
|
||||
image = image.convert('RGBA')
|
||||
image.alpha_composite(p.overlay_images[i])
|
||||
image.alpha_composite(overlay)
|
||||
image = image.convert('RGB')
|
||||
|
||||
if not p.do_not_save_samples:
|
||||
@ -1074,12 +1090,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
|
||||
return samples_ddim
|
||||
|
||||
def txt2img(prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
|
||||
def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
|
||||
outpath = opts.outdir or "outputs/txt2img-samples"
|
||||
|
||||
p = StableDiffusionProcessingTxt2Img(
|
||||
outpath=outpath,
|
||||
prompt=prompt,
|
||||
negative_prompt=negative_prompt,
|
||||
seed=seed,
|
||||
sampler_index=sampler_index,
|
||||
batch_size=batch_size,
|
||||
@ -1160,6 +1177,7 @@ class Flagging(gr.FlaggingCallback):
|
||||
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
|
||||
with gr.Row():
|
||||
prompt = gr.Textbox(label="Prompt", elem_id="txt2img_prompt", show_label=False, placeholder="Prompt", lines=1)
|
||||
negative_prompt = gr.Textbox(label="Negative prompt", elem_id="txt2img_negative_prompt", show_label=False, placeholder="Negative prompt", lines=1, visible=False)
|
||||
submit = gr.Button('Generate', variant='primary')
|
||||
|
||||
with gr.Row().style(equal_height=False):
|
||||
@ -1175,7 +1193,7 @@ with gr.Blocks(analytics_enabled=False) as txt2img_interface:
|
||||
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
|
||||
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
|
||||
|
||||
cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0)
|
||||
cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.0)
|
||||
|
||||
with gr.Group():
|
||||
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
|
||||
@ -1195,6 +1213,7 @@ with gr.Blocks(analytics_enabled=False) as txt2img_interface:
|
||||
fn=wrap_gradio_call(txt2img),
|
||||
inputs=[
|
||||
prompt,
|
||||
negative_prompt,
|
||||
steps,
|
||||
sampler_index,
|
||||
use_GFPGAN,
|
||||
@ -1218,6 +1237,41 @@ with gr.Blocks(analytics_enabled=False) as txt2img_interface:
|
||||
submit.click(**txt2img_args)
|
||||
|
||||
|
||||
def get_crop_region(mask, pad=0):
|
||||
h, w = mask.shape
|
||||
|
||||
crop_left = 0
|
||||
for i in range(w):
|
||||
if not (mask[:,i] == 0).all():
|
||||
break
|
||||
crop_left += 1
|
||||
|
||||
crop_right = 0
|
||||
for i in reversed(range(w)):
|
||||
if not (mask[:,i] == 0).all():
|
||||
break
|
||||
crop_right += 1
|
||||
|
||||
|
||||
crop_top = 0
|
||||
for i in range(h):
|
||||
if not (mask[i] == 0).all():
|
||||
break
|
||||
crop_top += 1
|
||||
|
||||
crop_bottom = 0
|
||||
for i in reversed(range(h)):
|
||||
if not (mask[i] == 0).all():
|
||||
break
|
||||
crop_bottom += 1
|
||||
|
||||
return (
|
||||
int(max(crop_left-pad, 0)),
|
||||
int(max(crop_top-pad, 0)),
|
||||
int(min(w - crop_right + pad, w)),
|
||||
int(min(h - crop_bottom + pad, h))
|
||||
)
|
||||
|
||||
|
||||
def fill(image, mask):
|
||||
image_mod = Image.new('RGBA', (image.width, image.height))
|
||||
@ -1238,40 +1292,66 @@ def fill(image, mask):
|
||||
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
||||
sampler = None
|
||||
|
||||
def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, **kwargs):
|
||||
def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.init_images = init_images
|
||||
self.resize_mode: int = resize_mode
|
||||
self.denoising_strength: float = denoising_strength
|
||||
self.init_latent = None
|
||||
self.original_mask = mask
|
||||
self.image_mask = mask
|
||||
self.mask_for_overlay = None
|
||||
self.mask_blur = mask_blur
|
||||
self.inpainting_fill = inpainting_fill
|
||||
self.inpaint_full_res = inpaint_full_res
|
||||
self.mask = None
|
||||
self.nmask = None
|
||||
|
||||
def init(self):
|
||||
self.sampler = samplers_for_img2img[self.sampler_index].constructor()
|
||||
crop_region = None
|
||||
|
||||
if self.image_mask is not None:
|
||||
if self.mask_blur > 0:
|
||||
self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)).convert('L')
|
||||
|
||||
|
||||
if self.inpaint_full_res:
|
||||
self.mask_for_overlay = self.image_mask
|
||||
mask = self.image_mask.convert('L')
|
||||
crop_region = get_crop_region(np.array(mask), 64)
|
||||
x1, y1, x2, y2 = crop_region
|
||||
|
||||
mask = mask.crop(crop_region)
|
||||
self.image_mask = resize_image(2, mask, self.width, self.height)
|
||||
self.paste_to = (x1, y1, x2-x1, y2-y1)
|
||||
else:
|
||||
self.image_mask = resize_image(self.resize_mode, self.image_mask, self.width, self.height)
|
||||
self.mask_for_overlay = self.image_mask
|
||||
|
||||
if self.original_mask is not None:
|
||||
self.original_mask = resize_image(self.resize_mode, self.original_mask, self.width, self.height)
|
||||
self.overlay_images = []
|
||||
|
||||
|
||||
imgs = []
|
||||
for img in self.init_images:
|
||||
image = img.convert("RGB")
|
||||
image = resize_image(self.resize_mode, image, self.width, self.height)
|
||||
|
||||
if self.original_mask is not None:
|
||||
if crop_region is None:
|
||||
image = resize_image(self.resize_mode, image, self.width, self.height)
|
||||
|
||||
if self.image_mask is not None:
|
||||
if self.inpainting_fill != 1:
|
||||
image = fill(image, self.original_mask)
|
||||
image = fill(image, self.mask_for_overlay)
|
||||
|
||||
image_masked = Image.new('RGBa', (image.width, image.height))
|
||||
image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.original_mask.convert('L')))
|
||||
image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
|
||||
|
||||
self.overlay_images.append(image_masked.convert('RGBA'))
|
||||
|
||||
if crop_region is not None:
|
||||
image = image.crop(crop_region)
|
||||
image = resize_image(2, image, self.width, self.height)
|
||||
|
||||
image = np.array(image).astype(np.float32) / 255.0
|
||||
image = np.moveaxis(image, 2, 0)
|
||||
|
||||
@ -1293,11 +1373,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
||||
|
||||
self.init_latent = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image))
|
||||
|
||||
if self.original_mask is not None:
|
||||
if self.mask_blur > 0:
|
||||
self.original_mask = self.original_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)).convert('L')
|
||||
|
||||
latmask = self.original_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
|
||||
if self.image_mask is not None:
|
||||
latmask = self.image_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
|
||||
latmask = np.moveaxis(np.array(latmask, dtype=np.float64), 2, 0) / 255
|
||||
latmask = latmask[0]
|
||||
latmask = np.tile(latmask[None], (4, 1, 1))
|
||||
@ -1314,7 +1391,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
||||
return samples
|
||||
|
||||
|
||||
def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int):
|
||||
def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool):
|
||||
outpath = opts.outdir or "outputs/img2img-samples"
|
||||
|
||||
is_classic = mode == 0
|
||||
@ -1350,6 +1427,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
|
||||
inpainting_fill=inpainting_fill,
|
||||
resize_mode=resize_mode,
|
||||
denoising_strength=denoising_strength,
|
||||
inpaint_full_res=inpaint_full_res,
|
||||
extra_generation_params={"Denoising Strength": denoising_strength}
|
||||
)
|
||||
|
||||
@ -1458,12 +1536,13 @@ with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
|
||||
steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
|
||||
sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
|
||||
mask_blur = gr.Slider(label='Inpainting: mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
|
||||
inpainting_fill = gr.Radio(label='Inpainting: masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
|
||||
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
|
||||
inpainting_fill = gr.Radio(label='Msked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
|
||||
|
||||
with gr.Row():
|
||||
use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=have_gfpgan)
|
||||
prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
|
||||
inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=True, visible=False)
|
||||
|
||||
with gr.Row():
|
||||
sd_upscale_upscaler_name = gr.Radio(label='Upscaler', choices=list(sd_upscalers.keys()), value="RealESRGAN")
|
||||
@ -1474,7 +1553,7 @@ with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
|
||||
|
||||
with gr.Group():
|
||||
cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0)
|
||||
cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.0)
|
||||
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75)
|
||||
|
||||
with gr.Group():
|
||||
@ -1505,6 +1584,7 @@ with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
batch_size: gr.update(visible=not is_loopback),
|
||||
sd_upscale_upscaler_name: gr.update(visible=is_upscale),
|
||||
sd_upscale_overlap: gr.update(visible=is_upscale),
|
||||
inpaint_full_res: gr.update(visible=is_inpaint),
|
||||
}
|
||||
|
||||
switch_mode.change(
|
||||
@ -1520,6 +1600,7 @@ with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
batch_size,
|
||||
sd_upscale_upscaler_name,
|
||||
sd_upscale_overlap,
|
||||
inpaint_full_res,
|
||||
]
|
||||
)
|
||||
|
||||
@ -1546,6 +1627,7 @@ with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
resize_mode,
|
||||
sd_upscale_upscaler_name,
|
||||
sd_upscale_overlap,
|
||||
inpaint_full_res,
|
||||
],
|
||||
outputs=[
|
||||
gallery,
|
||||
@ -1584,7 +1666,8 @@ def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_in
|
||||
|
||||
if have_gfpgan is not None and GFPGAN_strength > 0:
|
||||
gfpgan_model = gfpgan()
|
||||
cropped_faces, restored_faces, restored_img = gfpgan_model.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
|
||||
|
||||
restored_img = gfpgan_fix_faces(gfpgan_model, np.array(image, dtype=np.uint8))
|
||||
res = Image.fromarray(restored_img)
|
||||
|
||||
if GFPGAN_strength < 1.0:
|
||||
@ -1724,7 +1807,6 @@ sd_model = (sd_model if cmd_opts.no_half else sd_model.half())
|
||||
|
||||
if not cmd_opts.lowvram:
|
||||
sd_model = sd_model.to(device)
|
||||
|
||||
else:
|
||||
setup_for_low_vram(sd_model)
|
||||
|
||||
@ -1734,22 +1816,44 @@ model_hijack.hijack(sd_model)
|
||||
with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
|
||||
css = file.read()
|
||||
|
||||
demo = gr.TabbedInterface(
|
||||
interface_list=[x[0] for x in interfaces],
|
||||
tab_names=[x[1] for x in interfaces],
|
||||
css=("" if cmd_opts.no_progressbar_hiding else css_hide_progressbar) + """
|
||||
.output-html p {margin: 0 0.5em;}
|
||||
.performance { font-size: 0.85em; color: #444; }
|
||||
""" + css,
|
||||
analytics_enabled=False,
|
||||
)
|
||||
if not cmd_opts.no_progressbar_hiding:
|
||||
css += css_hide_progressbar
|
||||
|
||||
with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as file:
|
||||
javascript = file.read()
|
||||
|
||||
|
||||
# make the program just exit at ctrl+c without waiting for anything
|
||||
def sigint_handler(signal, frame):
|
||||
print('Interrupted')
|
||||
os._exit(0)
|
||||
|
||||
|
||||
signal.signal(signal.SIGINT, sigint_handler)
|
||||
|
||||
demo = gr.TabbedInterface(
|
||||
interface_list=[x[0] for x in interfaces],
|
||||
tab_names=[x[1] for x in interfaces],
|
||||
analytics_enabled=False,
|
||||
css=css,
|
||||
)
|
||||
|
||||
|
||||
def inject_gradio_html(javascript):
|
||||
import gradio.routes
|
||||
|
||||
def template_response(*args, **kwargs):
|
||||
res = gradio_routes_templates_response(*args, **kwargs)
|
||||
res.body = res.body.replace(b'</head>', f'<script>{javascript}</script></head>'.encode("utf8"))
|
||||
res.init_headers()
|
||||
return res
|
||||
|
||||
gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
|
||||
gradio.routes.templates.TemplateResponse = template_response
|
||||
|
||||
|
||||
inject_gradio_html(javascript)
|
||||
|
||||
demo.queue(concurrency_count=1)
|
||||
demo.launch()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user