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import os
import io
from PIL import Image
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
hf_api_key = os.environ['HF_API_KEY']
# Helper function
import requests, json
# API_URL = "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5"
API_URL = "https://api-inference.huggingface.co/models/cloudqi/cqi_text_to_image_pt_v0"
#Text-to-image endpoint
def get_completion(inputs, parameters=None, ENDPOINT_URL=API_URL):
headers = {
"Authorization": f"Bearer {hf_api_key}",
"Content-Type": "application/json"
}
data = { "inputs": inputs }
if parameters is not None:
data.update({"parameters": parameters})
response = requests.request("POST",ENDPOINT_URL,headers=headers,data=json.dumps(data))
return response.content
import gradio as gr
def generate(prompt):
output = get_completion(prompt)
result_image = Image.open(io.BytesIO(output))
return result_image
# def loadGUI():
# gr.close_all()
# demo = gr.Interface(fn=generate,
# inputs=[gr.Textbox(label="Your prompt")],
# outputs=[gr.Image(label="Result")],
# title="Image Generation with Stable Diffusion",
# description="Generate any image with Stable Diffusion",
# allow_flagging="never",
# examples=["the spirit of a tamagotchi wandering in the city of Vienna","a mecha robot in a favela"])
# demo.launch(share=True)
import gradio as gr
def generate(prompt, negative_prompt, steps, guidance, width, height):
params = {
"negative_prompt": negative_prompt,
"num_inference_steps": steps,
"guidance_scale": guidance,
"width": width,
"height": height
}
output = get_completion(prompt, params)
pil_image = Image.open(io.BytesIO(output))
return pil_image
def loadGUI():
gr.close_all()
demo = gr.Interface(fn=generate,
inputs=[
gr.Textbox(label="Your prompt"),
gr.Textbox(label="Negative prompt"),
gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=25,
info="In how many steps will the denoiser denoise the image?"),
gr.Slider(label="Guidance Scale", minimum=1, maximum=20, value=7,
info="Controls how much the text prompt influences the result"),
gr.Slider(label="Width", minimum=64, maximum=512, step=64, value=512),
gr.Slider(label="Height", minimum=64, maximum=512, step=64, value=512),
],
outputs=[gr.Image(label="Result")],
title="Image Generation with Stable Diffusion",
description="Generate any image with Stable Diffusion",
allow_flagging="never"
)
demo.launch(share=True)
def main():
loadGUI()
if __name__ == "__main__":
main()
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