Spaces:
Running
on
A10G
Running
on
A10G
File size: 5,948 Bytes
0a4b1cc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
import gradio as gr
from urllib.parse import urlparse
import requests
import time
import os
from utils.gradio_helpers import parse_outputs, process_outputs
inputs = []
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 = ['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 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"
app = gr.Interface(
fn=predict,
inputs=inputs,
outputs=outputs,
title=title,
description=model_description,
allow_flagging="never",
)
app.launch(share=True)
|