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| import subprocess | |
| import os | |
| import gradio as gr | |
| import torch | |
| from PIL import Image, ImageEnhance | |
| from pygltflib import GLTF2 | |
| from pygltflib.utils import ImageFormat, Texture, Material, Image as GLTFImage | |
| import spaces | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| print("Using GPU") | |
| else: | |
| device = "cpu" | |
| print("Using CPU") | |
| subprocess.run(["git", "clone", "https://github.com/Nick088Official/Stable_Diffusion_Finetuned_Minecraft_Skin_Generator.git"]) | |
| def run_inference(prompt, stable_diffusion_model, num_inference_steps, guidance_scale, model_precision_type, seed, output_image_name, verbose): | |
| os.chdir("Stable_Diffusion_Finetuned_Minecraft_Skin_Generator") | |
| if stable_diffusion_model == '2': | |
| sd_model = "minecraft-skins" | |
| else: | |
| sd_model = "minecraft-skins-sdxl" | |
| inference_command = f"python Python_Scripts/{sd_model}.py '{prompt}' {num_inference_steps} {guidance_scale} {model_precision_type} {seed} {output_image_name} {'--verbose' if verbose else ''}" | |
| os.system(inference_command) | |
| os.chdir("..") | |
| to3d_model_command = f"sh 64x32to64x64skin3dmodel.sh Stable_Diffusion_Finetuned_Minecraft_Skin_Generator/output_minecraft_skins/{output_image_name}" | |
| os.system(to3d_model_command) | |
| filename = "3d_model_player.glb" | |
| gltf = GLTF2().load(filename) | |
| # Step 1: Find the index of the existing texture you want to replace | |
| # Let's assume the texture you want to replace is at index 1 (you need to replace 1 with the actual index) | |
| existing_texture_index = 0 | |
| # Check if the existing_texture_index is valid | |
| if existing_texture_index < len(gltf.textures): | |
| # Step 2: Remove the old texture and its associated image from the GLB | |
| # Remove the texture | |
| gltf.textures.pop(existing_texture_index) | |
| # Remove the image associated with the texture | |
| existing_image_index = gltf.materials[0].pbrMetallicRoughness.baseColorTexture.index | |
| gltf.images.pop(existing_image_index) | |
| # Step 3: Add the new image and texture to the GLB | |
| # Create and add a new image to the glTF (same as before) | |
| new_image = GLTFImage() | |
| new_image.uri = os.path.join(f"Stable_Diffusion_Finetuned_Minecraft_Skin_Generator/output_minecraft_skins/{output_image_name}-converted.png") | |
| gltf.images.append(new_image) | |
| # Create a new texture and associate it with the added image | |
| new_texture = Texture() | |
| new_texture.source = len(gltf.images) - 1 # Index of the newly added image | |
| new_texture.sampler = 0 | |
| # set to nearest neighbor | |
| gltf.textures.append(new_texture) | |
| # Step 4: Assign the new texture to the appropriate material(s) or mesh(es) | |
| # Assuming you have a mesh/primitive that was using the old texture and you want to apply the new texture to it, you need to set the material index for that mesh/primitive. | |
| # Replace 0 with the actual index of the mesh/primitive you want to update. | |
| gltf.materials[0].pbrMetallicRoughness.baseColorTexture.index = len(gltf.textures) - 1 | |
| # Now you can convert the images to data URIs and save the updated GLB | |
| gltf.convert_images(ImageFormat.DATAURI) | |
| output_3d_model = "output_3d_model.glb" | |
| gltf.save(output_3d_model) | |
| else: | |
| print("Invalid existing_texture_index") | |
| # Return the generated image and the processed model | |
| return os.path.join(f"Stable_Diffusion_Finetuned_Minecraft_Skin_Generator/output_minecraft_skins/{output_image_name}"), output_3d_model | |
| # 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.Number(label="Number of Inference Steps", precision=0, value=25) | |
| guidance_scale = gr.Number(minimum=0.1, value=7.5, label="Guidance Scale", 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") | |
| 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 gives better results") | |
| seed = gr.Number(value=42, label="Seed", info="A starting point to initiate generation, put 0 for a random one") | |
| output_image_name = gr.Textbox(label="Output Image Name", info="The name of the file of the output image skin, keep the .png", value="output-skin.png") | |
| verbose = gr.Checkbox(label="Verbose Output", info="Produce more detailed output while running", value=False) | |
| # Create the Gradio interface | |
| gr.Interface( | |
| fn=run_inference, | |
| inputs=[ | |
| prompt, | |
| stable_diffusion_model, | |
| num_inference_steps, | |
| guidance_scale, | |
| model_precision_type, | |
| seed, | |
| output_image_name, | |
| verbose | |
| ], | |
| outputs=[ | |
| gr.Image(label="Generated Minecraft Skin Image Asset"), | |
| gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model") | |
| ], | |
| title="Minecraft Skin Generator", | |
| description="Make AI generated Minecraft Skins by a Finetuned Stable Diffusion Version!<br>Model used: https://github.com/Nick088Official/Stable_Diffusion_Finetuned_Minecraft_Skin_Generator<br>Hugging Face Space made by [Nick088](https://linktr.ee/Nick088)", | |
| ).launch(show_api=False, share=True) |