#!/usr/bin/env python3 """ FLUX.1 Space App Template - Enhanced with Model and LoRA Management """ import gradio as gr import torch import numpy as np from PIL import Image import os import json from typing import Dict, List, Optional # Import our managers from flux_space_model_manager import FluxModelManager from flux_space_lora_manager import FluxLoRAManager class FluxSpaceApp: """ Enhanced FLUX.1 Space application with model and LoRA management """ def __init__(self): self.model_manager = FluxModelManager() self.lora_manager = FluxLoRAManager() self.current_model = None def create_interface(self): """ Create the Gradio interface """ with gr.Blocks(title="FLUX.1 Enhanced Space", theme=gr.themes.Default()) as demo: # Header gr.Markdown(""" # FLUX.1 Enhanced Space **Multiple Models + LoRA Support** Choose your base model and load custom LoRAs for enhanced image generation. """) with gr.Row(): with gr.Column(scale=1): # Model Selection gr.Markdown("### Model Selection") model_selector = gr.Dropdown( choices=list(self.model_manager.models.keys()), value="flux1-dev", label="Base Model", info="Select the base model for generation" ) model_info = gr.Markdown("**Model Info:** Select a model to see details") # Load Model Button load_model_btn = gr.Button("Load Model", variant="primary") # Model Status model_status = gr.Markdown("**Status:** No model loaded") with gr.Column(scale=1): # LoRA Management gr.Markdown("### LoRA Management") # Pre-loaded LoRAs gr.Markdown("#### Pre-loaded LoRAs") preloaded_lora_selector = gr.Dropdown( choices=["T11-Ultra-Portrait-E04"], value=None, label="Select Pre-loaded LoRA", info="Load LoRAs directly from Hugging Face" ) preloaded_lora_strength = gr.Slider( minimum=0.0, maximum=2.0, value=1.0, step=0.1, label="Pre-loaded LoRA Strength" ) load_preloaded_lora_btn = gr.Button("Load Pre-loaded LoRA", variant="secondary") # Custom LoRA Upload gr.Markdown("#### Custom LoRA Upload") lora_upload = gr.File( label="Upload LoRA (.safetensors)", file_types=[".safetensors"], file_count="single" ) lora_name = gr.Textbox( label="LoRA Name (optional)", placeholder="Custom name for the LoRA" ) lora_strength = gr.Slider( minimum=0.0, maximum=2.0, value=1.0, step=0.1, label="Custom LoRA Strength" ) with gr.Row(): load_lora_btn = gr.Button("Load Custom LoRA", variant="secondary") unload_lora_btn = gr.Button("Unload LoRA", variant="stop") # LoRA Status lora_status = gr.Markdown("**LoRAs:** None loaded") # Generation Parameters with gr.Row(): with gr.Column(scale=2): gr.Markdown("### Generation") prompt = gr.Textbox( label="Prompt", placeholder="Enter your prompt here...", lines=3 ) negative_prompt = gr.Textbox( label="Negative Prompt", placeholder="Enter negative prompt...", lines=2 ) with gr.Row(): with gr.Column(): steps = gr.Slider( minimum=10, maximum=100, value=50, step=1, label="Inference Steps" ) guidance_scale = gr.Slider( minimum=1.0, maximum=20.0, value=7.5, step=0.1, label="Guidance Scale" ) with gr.Column(): width = gr.Slider( minimum=512, maximum=2048, value=1024, step=64, label="Width" ) height = gr.Slider( minimum=512, maximum=2048, value=1024, step=64, label="Height" ) seed = gr.Number( label="Seed", value=-1, info="Use -1 for random seed" ) generate_btn = gr.Button("Generate Image", variant="primary", size="lg") with gr.Column(scale=1): # Advanced Options gr.Markdown("### Advanced") # LoRA Blending gr.Markdown("#### LoRA Blending") lora_list = gr.Dropdown( choices=[], label="Select LoRAs to Blend", multiselect=True ) blend_weights = gr.Textbox( label="Blend Weights (comma-separated)", placeholder="1.0, 0.5, 0.3", info="Weights for each LoRA in order" ) blend_btn = gr.Button("Blend LoRAs", variant="secondary") # Generation Info gr.Markdown("#### Generation Info") generation_info = gr.JSON(label="Last Generation Details") # Output with gr.Row(): output_image = gr.Image( label="Generated Image", type="pil" ) with gr.Column(): gr.Markdown("### Generation Log") generation_log = gr.Textbox( label="Log", lines=10, max_lines=20, interactive=False ) # Event Handlers def load_model_handler(model_name): """Handle model loading""" try: success = self.model_manager.load_model(model_name) if success: model_info = self.model_manager.get_model_info() status_text = f"Model Loaded: {model_name}" info_text = f""" **Current Model:** {model_info['current_model']} **Description:** {model_info['model_description']} **Device:** {model_info['device']} """ self.current_model = model_name else: status_text = f"Failed to load: {model_name}" info_text = "Error: Model loading failed" return status_text, info_text except Exception as e: return f"Error: {str(e)}", "Error: Model loading failed" def load_preloaded_lora_handler(lora_name, strength): """Handle pre-loaded LoRA loading""" try: if not lora_name: return "Error: No LoRA selected", "LoRAs: None loaded", [] # Load pre-loaded LoRA success = self.model_manager.load_preloaded_lora(lora_name, strength) if success: # Get trigger words lora_info = self.model_manager.get_preloaded_loras().get(lora_name, {}) trigger_words = lora_info.get('trigger_words', '') status_text = f"Pre-loaded LoRA Loaded: {lora_name}" if trigger_words: status_text += f" (Trigger: {trigger_words})" lora_status_text = f"LoRAs: {lora_name}" lora_list = [lora_name] return status_text, lora_status_text, lora_list else: return f"Error: Failed to load pre-loaded LoRA", "LoRAs: None loaded", [] except Exception as e: return f"Error: {str(e)}", "LoRAs: None loaded", [] def load_lora_handler(file, name, strength): """Handle custom LoRA loading""" try: if file is None: return "Error: No file uploaded", "LoRAs: None loaded", [] file_path = file.name lora_name = name if name else os.path.splitext(os.path.basename(file_path))[0] # Load LoRA result = self.lora_manager.load_lora_file(file_path, lora_name) if result['success']: # Apply to current model if available if self.model_manager.current_pipeline is not None: self.lora_manager.apply_lora_to_model( lora_name, self.model_manager.current_pipeline, strength ) # Update LoRA list lora_list = list(self.lora_manager.loaded_loras.keys()) status_text = f"Custom LoRA Loaded: {lora_name}" lora_status_text = f"LoRAs: {', '.join(lora_list)}" return status_text, lora_status_text, lora_list else: return f"Error: {result.get('error', 'Unknown error')}", "LoRAs: None loaded", [] except Exception as e: return f"Error: {str(e)}", "LoRAs: None loaded", [] def generate_handler(prompt, negative_prompt, steps, guidance_scale, width, height, seed): """Handle image generation""" try: if self.model_manager.current_pipeline is None: return None, "Error: No model loaded", {} # Set seed if seed == -1: seed = torch.randint(0, 2**32, (1,)).item() # Generate image image, gen_info = self.model_manager.generate_image( prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=guidance_scale, width=width, height=height, seed=seed ) # Convert to PIL if isinstance(image, torch.Tensor): image = image.cpu().numpy() if image.shape[0] == 3: # CHW format image = np.transpose(image, (1, 2, 0)) image = (image * 255).astype(np.uint8) image = Image.fromarray(image) # Create log entry log_entry = f""" Generation Complete Prompt: {prompt} Negative: {negative_prompt} Steps: {steps}, Guidance: {guidance_scale} Size: {width}x{height} Seed: {seed} Model: {gen_info['model']} LoRAs: {', '.join(gen_info['loras']) if gen_info['loras'] else 'None'} """.strip() return image, log_entry, gen_info except Exception as e: return None, f"Error: {str(e)}", {} # Connect events load_model_btn.click( fn=load_model_handler, inputs=[model_selector], outputs=[model_status, model_info] ) load_preloaded_lora_btn.click( fn=load_preloaded_lora_handler, inputs=[preloaded_lora_selector, preloaded_lora_strength], outputs=[lora_status, lora_status, lora_list] ) load_lora_btn.click( fn=load_lora_handler, inputs=[lora_upload, lora_name, lora_strength], outputs=[lora_status, lora_status, lora_list] ) generate_btn.click( fn=generate_handler, inputs=[prompt, negative_prompt, steps, guidance_scale, width, height, seed], outputs=[output_image, generation_log, generation_info] ) # Auto-load model when selected model_selector.change( fn=load_model_handler, inputs=[model_selector], outputs=[model_status, model_info] ) return demo # Main execution if __name__ == "__main__": app = FluxSpaceApp() demo = app.create_interface() demo.launch(share=True, debug=True)