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on
L40S
Running
on
L40S
File size: 4,739 Bytes
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import gradio as gr
from urllib.parse import urlparse
import requests
import time
import os
from utils.gradio_helpers import parse_outputs, process_outputs
names = ['prompt', 'negative_prompt', 'subject', 'number_of_outputs', 'number_of_images_per_pose', 'randomise_poses', 'output_format', 'output_quality', 'seed']
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)
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"
with gr.Blocks() 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():
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'''
)
number_of_outputs = gr.Slider(
label="Number Of Outputs", info='''The number of images to generate.''', value=3,
minimum=1, maximum=20, 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,
)
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"
)
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():
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]
app.queue().launch(share=False)
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