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import gradio as gr
from urllib.parse import urlparse
import requests
import time
import base64
import os
from io import BytesIO
from PIL import Image
from utils.gradio_helpers import parse_outputs, process_outputs
SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')
def decode_data_uri_to_image(data_uri):
# parse the data uri
header, encoded = data_uri.split(",", 1)
data = base64.b64decode(encoded)
img = Image.open(BytesIO(data))
return img
inputs = []
inputs.append(gr.Textbox(
label="Secret Token", info="Secret Token"
))
inputs.append(gr.Image(
label="Image", type="filepath"
))
inputs.append(gr.Textbox(
label="Prompt", info='''Prompt'''
))
inputs.append(gr.Textbox(
label="Negative Prompt", info='''Negative Prompt'''
))
inputs.append(gr.Number(
label="Scale Factor", info='''Scale factor''', value=2
))
inputs.append(gr.Slider(
label="Dynamic", info='''HDR, try from 3 - 9''', value=6,
minimum=1, maximum=50
))
inputs.append(gr.Number(
label="Creativity", info='''Creativity, try from 0.3 - 0.9''', value=0.35
))
inputs.append(gr.Number(
label="Resemblance", info='''Resemblance, try from 0.3 - 1.6''', value=0.6
))
inputs.append(gr.Dropdown(
choices=[16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256], label="tiling_width", info='''Fractality, set lower tile width for a high Fractality''', value="112"
))
inputs.append(gr.Dropdown(
choices=[16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256], label="tiling_height", info='''Fractality, set lower tile height for a high Fractality''', value="144"
))
inputs.append(gr.Dropdown(
choices=['epicrealism_naturalSinRC1VAE.safetensors [84d76a0328]', 'juggernaut_reborn.safetensors [338b85bc4f]', 'flat2DAnimerge_v45Sharp.safetensors'], label="sd_model", info='''Stable Diffusion model checkpoint''', value="juggernaut_reborn.safetensors [338b85bc4f]"
))
inputs.append(gr.Dropdown(
choices=['DPM++ 2M Karras', 'DPM++ SDE Karras', 'DPM++ 2M SDE Exponential', 'DPM++ 2M SDE Karras', 'Euler a', 'Euler', 'LMS', 'Heun', 'DPM2', 'DPM2 a', 'DPM++ 2S a', 'DPM++ 2M', 'DPM++ SDE', 'DPM++ 2M SDE', 'DPM++ 2M SDE Heun', 'DPM++ 2M SDE Heun Karras', 'DPM++ 2M SDE Heun Exponential', 'DPM++ 3M SDE', 'DPM++ 3M SDE Karras', 'DPM++ 3M SDE Exponential', 'DPM fast', 'DPM adaptive', 'LMS Karras', 'DPM2 Karras', 'DPM2 a Karras', 'DPM++ 2S a Karras', 'Restart', 'DDIM', 'PLMS', 'UniPC'], label="scheduler", info='''scheduler''', value="DPM++ 3M SDE Karras"
))
inputs.append(gr.Slider(
label="Num Inference Steps", info='''Number of denoising steps''', value=18,
minimum=1, maximum=100, step=1,
))
inputs.append(gr.Number(
label="Seed", info='''Random seed. Leave blank to randomize the seed''', value=1337
))
inputs.append(gr.Checkbox(
label="Downscaling", info='''Downscale the image before upscaling. Can improve quality and speed for images with high resolution but lower quality''', value=False
))
inputs.append(gr.Number(
label="Downscaling Resolution", info='''Downscaling resolution''', value=768
))
inputs.append(gr.Textbox(
label="Lora Links", info='''Link to a lora file you want to use in your upscaling. Multiple links possible, seperated by comma'''
))
inputs.append(gr.Textbox(
label="Custom Sd Model", info='''Link to a custom safetensors checkpoint file you want to use in your upscaling. Will overwrite sd_model checkpoint.'''
))
names = ['secret_token', 'image', 'prompt', 'negative_prompt', 'scale_factor', 'dynamic', 'creativity', 'resemblance', 'tiling_width', 'tiling_height', 'sd_model', 'scheduler', 'num_inference_steps', 'seed', 'downscaling', 'downscaling_resolution', 'lora_links', 'custom_sd_model']
outputs = []
outputs.append(gr.Image())
expected_outputs = len(outputs)
def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)):
headers = {'Content-Type': 'application/json'}
payload = {"input": {}}
base_url = "http://0.0.0.0:7860"
for i, key in enumerate(names):
value = args[i]
if name is "secret_token":
if value is not SECRET_TOKEN:
raise gr.Error("Invalid secret token! Please fork this space if you want to use it, and define your own secret token.")
continue
if value and (os.path.exists(str(value))):
value = f"{base_url}/file=" + value
if value is not None and value != "":
payload["input"][key] = value
response = requests.post("http://0.0.0.0:5000/predictions", headers=headers, json=payload)
if response.status_code == 201:
follow_up_url = response.json()["urls"]["get"]
response = requests.get(follow_up_url, headers=headers)
while response.json()["status"] != "succeeded":
if response.json()["status"] == "failed":
raise gr.Error("The submission failed!")
response = requests.get(follow_up_url, headers=headers)
time.sleep(1)
if response.status_code == 200:
json_response = response.json()
#If the output component is JSON return the entire output response
if(outputs[0].get_config()["name"] == "json"):
return json_response["output"]
predict_outputs = parse_outputs(json_response["output"])
processed_outputs = process_outputs(predict_outputs)
difference_outputs = expected_outputs - len(processed_outputs)
# If less outputs than expected, hide the extra ones
if difference_outputs > 0:
extra_outputs = [gr.update(visible=False)] * difference_outputs
processed_outputs.extend(extra_outputs)
# If more outputs than expected, cap the outputs to the expected number
elif difference_outputs < 0:
processed_outputs = processed_outputs[:difference_outputs]
return tuple(processed_outputs) if len(processed_outputs) > 1 else processed_outputs[0]
else:
if(response.status_code == 409):
raise gr.Error(f"Sorry, the Cog image is still processing. Try again in a bit.")
raise gr.Error(f"The submission failed! Error: {response.status_code}")
title = "Demo for clarity-upscaler cog image by philz1337x"
model_description = "High resolution image Upscaler and Enhancer. Use at ClarityAI.cc. A free Magnific alternative. Twitter/X: @philz1337x"
gr.HTML("""
<div style="z-index: 100; position: fixed; top: 0px; right: 0px; left: 0px; bottom: 0px; width: 100%; height: 100%; background: white; display: flex; align-items: center; justify-content: center; color: black;">
<div style="text-align: center; color: black;">
<p style="color: black;">This space is not a normal Gradio space you can use through a UI, but a microservice API designed for automated access.</p>
<p style="color: black;">You can clone the original space here: <a href="https://huggingface.co/spaces/jbilcke-hf/clarity-upscaler" target="_blank"jbilcke-hf/clarity-upscaler</a>.</p>
</div>
</div>""")
app = gr.Interface(
fn=predict,
inputs=inputs,
outputs=outputs,
title=title,
description=model_description,
allow_flagging="never",
)
app.launch(share=True) |