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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() | |