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("

FLUX.1-Krea-dev

") gr.HTML("

High-quality image generation via Model Context Protocol

") # 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)