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| import gradio as gr | |
| import random | |
| import os | |
| from PIL import Image | |
| from typing import Optional | |
| from huggingface_hub import InferenceClient | |
| # Project by Nymbo | |
| API_TOKEN = os.getenv("HF_READ_TOKEN") | |
| timeout = 100 | |
| # Function to query the API and return the generated image | |
| def flux_krea_generate( | |
| prompt: str, | |
| negative_prompt: str = "(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", | |
| steps: int = 35, | |
| cfg_scale: float = 7.0, | |
| sampler: str = "DPM++ 2M Karras", | |
| seed: int = -1, | |
| strength: float = 0.7, | |
| width: int = 1024, | |
| height: int = 1024 | |
| ) -> Optional[Image.Image]: | |
| """ | |
| Text-to-image generation with FLUX.1-Krea-dev (no input image required). | |
| This tool generates a single image from a text prompt using the | |
| black-forest-labs/FLUX.1-Krea-dev model on Hugging Face Inference. | |
| Args: | |
| prompt: Text description of the image to generate. | |
| negative_prompt: What should NOT appear in the image. | |
| steps: Number of denoising steps (1-100). Higher is slower but can improve quality. | |
| cfg_scale: Classifier-free guidance scale (1-20). Higher = follow the prompt more closely. | |
| sampler: Sampling method to use. One of: "DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM". | |
| seed: Random seed for reproducible results. Use -1 for a random seed per call. | |
| strength: Generation strength (0-1). Kept for parity; not an input image strength. | |
| width: Output width in pixels (64-1216, multiple of 32 recommended). | |
| height: Output height in pixels (64-1216, multiple of 32 recommended). | |
| Returns: | |
| A PIL.Image of the generated result. No input image is expected or required. | |
| """ | |
| if prompt == "" or prompt is None: | |
| return None | |
| key = random.randint(0, 999) | |
| # Add some extra flair to the prompt | |
| enhanced_prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect." | |
| print(f'\033[1mGeneration {key}:\033[0m {enhanced_prompt}') | |
| try: | |
| # Initialize the Hugging Face Inference Client | |
| # Try different providers in order of preference | |
| providers = ["auto", "replicate", "fal-ai"] | |
| for provider in providers: | |
| try: | |
| client = InferenceClient( | |
| api_key=API_TOKEN, | |
| provider=provider | |
| ) | |
| # Generate the image using the proper client | |
| image = client.text_to_image( | |
| prompt=enhanced_prompt, | |
| negative_prompt=negative_prompt, | |
| model="black-forest-labs/FLUX.1-Krea-dev", | |
| width=width, | |
| height=height, | |
| num_inference_steps=steps, | |
| guidance_scale=cfg_scale, | |
| seed=seed if seed != -1 else random.randint(1, 1000000000) | |
| ) | |
| print(f'\033[1mGeneration {key} completed with {provider}!\033[0m ({enhanced_prompt})') | |
| return image | |
| except Exception as provider_error: | |
| print(f"Provider {provider} failed: {provider_error}") | |
| if provider == providers[-1]: # Last provider | |
| raise provider_error | |
| continue | |
| except Exception as e: | |
| print(f"Error during image generation: {e}") | |
| if "404" in str(e): | |
| raise gr.Error("Model not found. Please ensure the FLUX.1-Krea-dev model is accessible with your API token.") | |
| elif "503" in str(e): | |
| raise gr.Error("The model is currently being loaded. Please try again in a moment.") | |
| elif "401" in str(e) or "403" in str(e): | |
| raise gr.Error("Authentication failed. Please check your HF_READ_TOKEN environment variable.") | |
| else: | |
| raise gr.Error(f"Image generation failed: {str(e)}") | |
| return None | |
| # CSS to style the app | |
| css = """ | |
| #app-container { | |
| max-width: 800px; | |
| margin-left: auto; | |
| margin-right: auto; | |
| } | |
| """ | |
| # Build the Gradio UI with Blocks | |
| with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app: | |
| # Add a title to the app | |
| gr.HTML("<center><h1>FLUX.1-Krea-dev</h1></center>") | |
| gr.HTML("<center><p>High-quality image generation via Model Context Protocol</p></center>") | |
| # 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++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"]) | |
| # 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(): | |
| # Output component only; no input image is required by the tool | |
| image_output = gr.Image(label="Image Output", elem_id="gallery") | |
| # Bind the button to the flux_krea_generate function for the UI only | |
| # Hide this event as an MCP tool to avoid schema confusion (UI wires image output) | |
| text_button.click( | |
| flux_krea_generate, | |
| inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], | |
| outputs=image_output, | |
| show_api=False, | |
| api_description=False, | |
| ) | |
| # Expose a dedicated MCP/API endpoint with a clear schema (text-to-image only) | |
| # This avoids clients misinterpreting the UI event as requiring an input image. | |
| gr.api( | |
| flux_krea_generate, | |
| api_name="generate_image", | |
| api_description=( | |
| "Generate an image from a text prompt using FLUX.1-Krea-dev. " | |
| "Inputs are text and numeric parameters only; no input image is required." | |
| ), | |
| ) | |
| # Launch the Gradio app with MCP server enabled | |
| app.launch(show_api=True, share=False, mcp_server=True) |