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| import subprocess | |
| import os | |
| import gradio as gr | |
| import torch | |
| import numpy as np | |
| from PIL import Image, ImageEnhance | |
| import spaces | |
| from pymongo import MongoClient | |
| from pymongo.errors import ConnectionError | |
| # MongoDB connection | |
| mongo_client = None | |
| try: | |
| mongo_client = MongoClient("mongodb+srv://skandanv:[email protected]/?retryWrites=true&w=majority&appName=cluster1") | |
| db = mongo_client['minecraft_skin_generator'] # Replace with your database name | |
| collection = db['generated_skins'] # Collection to store generated skins | |
| connection_message = "Connected to MineSkin Server" | |
| except ConnectionError: | |
| connection_message = "Failed to connect to MineSkin Server" | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| print("Using GPU") | |
| else: | |
| device = "cpu" | |
| print("Using CPU") | |
| MAX_SEED = np.iinfo(np.int32).max | |
| subprocess.run(["git", "clone", "https://github.com/Nick088Official/Stable_Diffusion_Finetuned_Minecraft_Skin_Generator.git"]) | |
| os.chdir("Stable_Diffusion_Finetuned_Minecraft_Skin_Generator") | |
| def run_inference(prompt, stable_diffusion_model, num_inference_steps, guidance_scale, model_precision_type, seed, filename, model_3d, verbose): | |
| # Inference | |
| if stable_diffusion_model == '2': | |
| sd_model = "minecraft-skins" | |
| elif stable_diffusion_model == 'xl': | |
| sd_model = "minecraft-skins-sdxl" | |
| inference_command = f"python Scripts/{sd_model}.py '{prompt}' {num_inference_steps} {guidance_scale} {model_precision_type} {seed} {filename} {'--model_3d' if model_3d else ''} {'--verbose' if verbose else ''}" | |
| os.system(inference_command) | |
| # File paths for generated assets | |
| image_path = os.path.join(f"output_minecraft_skins/{filename}") | |
| model_path = os.path.join(f"output_minecraft_skins/{filename}_3d_model.glb") if model_3d else None | |
| # Prepare data for MongoDB | |
| skin_data = { | |
| 'prompt': prompt, | |
| 'filename': filename, | |
| 'image_path': image_path, | |
| 'model_path': model_path, | |
| 'num_inference_steps': num_inference_steps, | |
| 'guidance_scale': guidance_scale, | |
| 'model_precision_type': model_precision_type, | |
| 'seed': seed, | |
| 'model_3d': model_3d, | |
| 'verbose': verbose | |
| } | |
| # Insert generated skin data into MongoDB and show alert if successful | |
| try: | |
| collection.insert_one(skin_data) | |
| success_message = "The Skin has been pushed to MineSkin Server" | |
| alert_type = "success" # Gradio Alert type for success | |
| except Exception as e: | |
| success_message = f"Failed to push skin to database: {e}" | |
| alert_type = "error" | |
| return image_path, model_path, success_message, alert_type | |
| # Define Gradio UI components | |
| prompt = gr.Textbox(label="Your Prompt", info="What the Minecraft Skin should look like") | |
| stable_diffusion_model = gr.Dropdown(['2', 'xl'], value="xl", label="Stable Diffusion Model", info="Choose which Stable Diffusion Model to use, xl understands prompts better") | |
| num_inference_steps = gr.Slider(label="Number of Inference Steps", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference", minimum=1, maximum=50, value=25, step=1) | |
| guidance_scale = gr.Slider(label="Guidance Scale", info="Controls how much the image generation process follows the text prompt. Higher values make the image stick more closely to the input text.", minimum=0.0, maximum=10.0, value=7.5, step=0.1) | |
| model_precision_type = gr.Dropdown(["fp16", "fp32"], value="fp16", label="Model Precision Type", info="The precision type to load the model, like fp16 which is faster, or fp32 which is more precise but more resource consuming") | |
| seed = gr.Slider(value=42, minimum=0, maximum=MAX_SEED, step=1, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one") | |
| filename = gr.Textbox(label="Output Image Name", info="The name of the file of the output image skin, keep the .png", value="output-skin.png") | |
| model_3d = gr.Checkbox(label="See as 3D Model too", info="View the generated skin as a 3D Model too", value=True) | |
| verbose = gr.Checkbox(label="Verbose Output", info="Produce more detailed output while running", value=False) | |
| # Create the Gradio interface | |
| output_image = gr.Image(label="Generated Minecraft Skin Image Asset", elem_classes="pixelated checkered") | |
| output_model = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model View of the Skin") | |
| output_message = gr.Alert() | |
| gr.Interface( | |
| fn=run_inference, | |
| inputs=[ | |
| prompt, | |
| stable_diffusion_model, | |
| num_inference_steps, | |
| guidance_scale, | |
| model_precision_type, | |
| seed, | |
| filename, | |
| model_3d, | |
| verbose | |
| ], | |
| outputs=[ | |
| output_image, | |
| output_model, | |
| output_message | |
| ], | |
| title="Minecraft Skin Generator", | |
| description="Make AI generated Minecraft Skins by a Finetuned Stable Diffusion Version!<br>Github Repository & Model used: https://github.com/Nick088Official/Stable_Diffusion_Finetuned_Minecraft_Skin_Generator<br>Credits: [Monadical-SAS](https://github.com/Monadical-SAS/minecraft_skin_generator) (Creators of the model), [Nick088](https://linktr.ee/Nick088) (Improving usage of the model), daroche (helping me fix the 3d model texture issue), [Brottweiler](https://gist.github.com/Brottweiler/483d0856c6692ef70cf90bf1a85ce364)(script to fix the 3d model texture), [not-holar](https://huggingface.co/not-holar) (made the rendering of the image asset in the web ui look pixelated like minecraft and have a checkered background),[meew](https://huggingface.co/spaces/meeww/Minecraft_Skin_Generator/blob/main/models/player_model.glb) (Minecraft Player 3d model) <br> [](https://discord.gg/AQsmBmgEPy)", | |
| css=".pixelated {image-rendering: pixelated} .checkered img {background-image: url('data:image/svg+xml,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"2\" height=\"2\" fill-opacity=\".15\"><rect x=\"1\" width=\"1\" height=\"1\"/><rect y=\"1\" width=\"1\" height=\"1\"/></svg>');background-size: 16px;}" | |
| ).launch(show_api=True, share=True) | |
| # Show connection message as alert when the app starts | |
| if mongo_client: | |
| gr.Interface().launch() | |
| output_message.update(value=connection_message, type="info") | |