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| import os | |
| import io | |
| #import IPython.display | |
| from PIL import Image | |
| import base64 | |
| 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 | |
| import gradio as gr | |
| #A helper function to convert the PIL image to base64 | |
| # so you can send it to the API | |
| def base64_to_pil(img_base64): | |
| base64_decoded = base64.b64decode(img_base64) | |
| byte_stream = io.BytesIO(base64_decoded) | |
| pil_image = Image.open(byte_stream) | |
| return pil_image | |
| 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 | |
| } | |
| end_point_url="https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5" | |
| output = get_completion(prompt, params,end_point_url) | |
| pil_image = base64_to_pil(output) | |
| return pil_image | |
| #Text-to-image endpoint | |
| def get_completion(inputs, parameters=None, end_point_url=None): | |
| 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", | |
| end_point_url, | |
| headers=headers, | |
| data=json.dumps(data)) | |
| print ("response:",response) | |
| print("response.content:",response.content) | |
| return json.loads(response.content.decode("utf-8")) | |
| API_URL = "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5" | |
| headers = {"Authorization": "Bearer "+hf_api_key} | |
| #nel corso usa chiamata a generate che puntava ad un endpoit diverso. se usa quella chiamata non va (incompatibilità endpoint-funzione?) | |
| # questa è identica a quella suggerita dal sito Use this model with the Inference API. profata in simple_api_call.py | |
| def my_generate(payload, negative_prompt, steps, guidance, width, height): | |
| payload_input={"inputs": payload} | |
| #print("payload:",payload_input) | |
| response= requests.post(API_URL, headers=headers, json=payload_input) | |
| #print("response.content:",response.content) | |
| image = Image.open(io.BytesIO(response.content)) | |
| return image | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Image Generation with Stable Diffusion") | |
| with gr.Row(): | |
| with gr.Column(scale=4): | |
| prompt = gr.Textbox(label="Your prompt") #Give prompt some real estate | |
| with gr.Column(scale=1, min_width=50): | |
| btn = gr.Button("Submit") #Submit button side by side! | |
| with gr.Accordion("Advanced options", open=False): #Let's hide the advanced options! | |
| negative_prompt = gr.Textbox(label="Negative prompt") | |
| with gr.Row(): | |
| with gr.Column(): | |
| steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=25, | |
| info="In many steps will the denoiser denoise the image?") | |
| guidance = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, value=7, | |
| info="Controls how much the text prompt influences the result") | |
| with gr.Column(): | |
| width = gr.Slider(label="Width", minimum=64, maximum=512, step=64, value=512) | |
| height = gr.Slider(label="Height", minimum=64, maximum=512, step=64, value=512) | |
| output = gr.Image(label="Result") #Move the output up too | |
| btn.click(fn=my_generate, inputs=[prompt,negative_prompt,steps,guidance,width,height], outputs=[output]) | |
| demo.launch(share=False, server_port=8081) |