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| import gradio as gr | |
| import requests | |
| import io | |
| import random | |
| import os | |
| import time | |
| from PIL import Image | |
| from deep_translator import GoogleTranslator | |
| import json | |
| from theme import theme | |
| from fastapi import FastAPI | |
| app = FastAPI() | |
| # Project by Nymbo | |
| API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev" | |
| API_TOKEN = os.getenv("HF_READ_TOKEN") | |
| headers = {"Authorization": f"Bearer {API_TOKEN}"} | |
| timeout = 100 | |
| # Function to query the API and return the generated image | |
| def query(prompt, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024): | |
| if prompt == "" or prompt is None: | |
| return None | |
| key = random.randint(0, 999) | |
| API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")]) | |
| headers = {"Authorization": f"Bearer {API_TOKEN}"} | |
| # Translate the prompt from Russian to English if necessary | |
| prompt = GoogleTranslator(source='ru', target='en').translate(prompt) | |
| print(f'\033[1mGeneration {key} translation:\033[0m {prompt}') | |
| # Add some extra flair to the prompt | |
| prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect." | |
| print(f'\033[1mGeneration {key}:\033[0m {prompt}') | |
| # Prepare the payload for the API call, including width and height | |
| payload = { | |
| "inputs": prompt, | |
| "is_negative": is_negative, | |
| "steps": steps, | |
| "cfg_scale": cfg_scale, | |
| "seed": seed if seed != -1 else random.randint(1, 1000000000), | |
| "strength": strength, | |
| "parameters": { | |
| "width": width, # Pass the width to the API | |
| "height": height # Pass the height to the API | |
| } | |
| } | |
| # Send the request to the API and handle the response | |
| response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout) | |
| if response.status_code != 200: | |
| print(f"Error: Failed to get image. Response status: {response.status_code}") | |
| print(f"Response content: {response.text}") | |
| if response.status_code == 503: | |
| raise gr.Error(f"{response.status_code} : The model is being loaded") | |
| raise gr.Error(f"{response.status_code}") | |
| try: | |
| # Convert the response content into an image | |
| image_bytes = response.content | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})') | |
| return image | |
| except Exception as e: | |
| print(f"Error when trying to open the image: {e}") | |
| return None | |
| # CSS to style the app | |
| css = """ | |
| .gradio-container {background-color: MediumAquaMarine} | |
| footer{display:none !important} | |
| #app-container { | |
| max-width: 930px; | |
| margin-left: auto; | |
| margin-right: auto; | |
| } | |
| """ | |
| examples = [ | |
| "a beautiful woman with blonde hair and blue eyes", | |
| "a beautiful woman with brown hair and grey eyes", | |
| "a beautiful woman with black hair and brown eyes", | |
| ] | |
| # Build the Gradio UI with Blocks | |
| with gr.Blocks(theme=theme, css=css) as app: | |
| # Add a title to the app | |
| gr.HTML("<center><h1>FLUX.1-Dev</h1></center>") | |
| with gr.Tabs() as tabs: | |
| with gr.TabItem("✍️ Text to Image 🖼", visible=True): | |
| # Container for all the UI elements | |
| with gr.Column(elem_id="app-container"): | |
| # Add a text input for the main prompt | |
| with gr.Row(): | |
| with gr.Column(elem_id="prompt-container"): | |
| with gr.Row(): | |
| text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input") | |
| # Accordion for advanced settings | |
| with gr.Row(): | |
| with gr.Accordion("Advanced Settings", open=False): | |
| negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input") | |
| with gr.Row(): | |
| width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32) | |
| height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32) | |
| steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1) | |
| cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1) | |
| strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001) | |
| seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) # Setting the seed to -1 will make it random | |
| method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ 2S a Karras", "DPM2 a Karras", "DPM2 Karras", "DPM++ SDE Karras", "DEIS", "LMS", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral", "Euler", "Euler CFG PP", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "DDIM", "LMS Karras", "PLMS", "UniPC", "UniPC BH2"]) | |
| # Add a button to trigger the image generation | |
| with gr.Row(): | |
| text_button = gr.Button("Run", variant='primary', elem_id="gen-button") | |
| # Image output area to display the generated image | |
| with gr.Row(): | |
| image_output = gr.Image(type="pil", label="Image Output", show_share_button=False, elem_id="gallery") | |
| with gr.Row(): | |
| clear_prompt =gr.Button("Clear 🗑️",variant="primary", elem_id="clear_button") | |
| clear_prompt.click(lambda: (None, None), None, [text_prompt, image_output], queue=False, show_api=False) | |
| with gr.Row(): | |
| gr.Examples( | |
| examples = examples, | |
| inputs = [text_prompt], | |
| ) | |
| # Bind the button to the query function with the added width and height inputs | |
| text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output) | |
| app.queue(default_concurrency_limit=200, max_size=200) # <-- Sets up a queue with default parameters | |
| if __name__ == "__main__": | |
| # Launch the Gradio app | |
| app.launch(show_api=False, share=True) | |