Spaces:
Runtime error
Runtime error
File size: 7,180 Bytes
586348d 558490f 586348d 558490f 586348d 558490f 586348d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
import gradio as gr
from urllib.parse import urlparse
import requests
import time
import os
import spaces
import torch
import subprocess
import signal
zero = torch.Tensor([0]).cuda()
print(zero.device) # <-- 'cpu' 🤔
names = ['prompt', 'negative_prompt', 'subject', 'number_of_outputs', 'number_of_images_per_pose', 'randomise_poses', 'output_format', 'output_quality', 'seed']
def check_cog_server():
try:
# Start the Cog server in the background
cog_process = subprocess.Popen(["python3", "-m", "cog.server.http", "--threads=10"], cwd="/src")
# Wait for the Cog server to start on port 5000
counter1 = 0
while True:
try:
requests.get("http://localhost:5000")
print("Cog server is running on port 5000.")
break
except requests.exceptions.ConnectionError:
print("Waiting for Cog server to start on port 5000...")
time.sleep(5)
counter1 += 1
if counter1 >= 250:
raise Exception("Error: Cog server did not start on port 5000 after 250 attempts.")
# Wait for the Cog server to be fully ready
counter2 = 0
while True:
response = requests.get("http://localhost:5000/health-check")
status = response.json().get("status")
if status == "READY":
print("Cog server is fully ready.")
break
else:
print("Waiting for Cog server (models loading) on port 5000...")
time.sleep(5)
counter2 += 1
if counter2 >= 250:
raise Exception("Error: Cog server did not become fully ready after 250 attempts.")
except Exception as e:
print(f"Error: {str(e)}")
cog_process.send_signal(signal.SIGINT) # Send interrupt signal to the Cog process
raise e
@spaces.GPU
def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)):
print(zero.device) # <-- 'cuda:0' 🤗
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)
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 consistent-character cog image by fofr"
description = "Create images of a given character in different poses • running cog image by fofr"
css="""
#col-container{
margin: 0 auto;
max-width: 1400px;
text-align: left;
}
"""
with gr.Blocks(css=css) as app:
with gr.Column(elem_id="col-container"):
gr.HTML(f"""
<h2 style="text-align: center;">Consistent Character Workflow</h2>
<p style="text-align: center;">{description}</p>
""")
with gr.Row():
with gr.Column(scale=1):
prompt = gr.Textbox(
label="Prompt", info='''Describe the subject. Include clothes and hairstyle for more consistency.'''
)
subject = gr.Image(
label="Subject", type="filepath"
)
submit_btn = gr.Button("Submit")
with gr.Accordion(label="Advanced Settings", open=False):
negative_prompt = gr.Textbox(
label="Negative Prompt", info='''Things you do not want to see in your image''',
value="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry"
)
with gr.Row():
number_of_outputs = gr.Slider(
label="Number Of Outputs", info='''The number of images to generate.''', value=2,
minimum=1, maximum=4, step=1,
)
number_of_images_per_pose = gr.Slider(
label="Number Of Images Per Pose", info='''The number of images to generate for each pose.''', value=1,
minimum=1, maximum=4, step=1,
)
with gr.Row():
randomise_poses = gr.Checkbox(
label="Randomise Poses", info='''Randomise the poses used.''', value=True
)
output_format = gr.Dropdown(
choices=['webp', 'jpg', 'png'], label="output_format", info='''Format of the output images''', value="webp"
)
with gr.Row():
output_quality = gr.Number(
label="Output Quality", info='''Quality of the output images, from 0 to 100. 100 is best quality, 0 is lowest quality.''', value=80
)
seed = gr.Number(
label="Seed", info='''Set a seed for reproducibility. Random by default.''', value=None
)
with gr.Column(scale=1.5):
consistent_results = gr.Gallery(label="Consistent Results")
inputs = [prompt, negative_prompt, subject, number_of_outputs, number_of_images_per_pose, randomise_poses, output_format, output_quality, seed]
outputs = [consistent_results]
submit_btn.click(
fn = predict,
inputs = inputs,
outputs = outputs,
show_api = False
)
# Check the Cog server's readiness before launching the Gradio app
check_cog_server()
app.queue(max_size=12, api_open=False).launch(share=False, show_api=False)
|