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import subprocess |
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import os |
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import gradio as gr |
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import torch |
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from PIL import Image, ImageEnhance |
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from pygltflib import GLTF2 |
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from pygltflib.utils import ImageFormat, Texture, Material, Image as GLTFImage |
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import sys |
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import spaces |
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if torch.cuda.is_available(): |
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device = "cuda" |
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print("Using GPU") |
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else: |
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device = "cpu" |
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print("Using CPU") |
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subprocess.run(["git", "clone", "https://github.com/Nick088Official/Stable_Diffusion_Finetuned_Minecraft_Skin_Generator.git"]) |
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os.chdir("Stable_Diffusion_Finetuned_Minecraft_Skin_Generator") |
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@spaces.GPU() |
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def run_inference(prompt, stable_diffusion_model, num_inference_steps, guidance_scale, model_precision_type, seed, filename, verbose): |
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if stable_diffusion_model == '2': |
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sd_model = "minecraft-skins" |
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else: |
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sd_model = "minecraft-skins-sdxl" |
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inference_command = f"python Scripts/{sd_model}.py '{prompt}' {num_inference_steps} {guidance_scale} {model_precision_type} {seed} {filename} {'--verbose' if verbose else ''}" |
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os.system(inference_command) |
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os.chdir("Scripts") |
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command_3d_model = f"python to_3d_model.py '{filename}'" |
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os.system(command_3d_model) |
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os.chdir("..") |
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glb_path = os.path.join(f"output_minecraft_skins/{filename}_3d_model.glb") |
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return os.path.join(f"output_minecraft_skins/{filename}"), glb_path |
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def custom_output(image_path, glb_path): |
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if glb_path is None: |
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return image_path |
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else: |
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return [image_path, gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model", path=glb_path)] |
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with gr.Blocks() as minecraft_skin_generator: |
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with gr.Row(): |
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prompt = gr.Textbox(label="Your Prompt", info="What the Minecraft Skin should look like") |
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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") |
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num_inference_steps = gr.Number(label="Number of Inference Steps", precision=0, value=25) |
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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") |
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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") |
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seed = gr.Number(value=42, label="Seed", info="A starting point to initiate generation, put 0 for a random one") |
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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") |
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verbose = gr.Checkbox(label="Verbose Output", info="Produce more detailed output while running", value=False) |
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see_in_3d = gr.Checkbox(label="See in 3D", info="View the generated skin in 3D", value=False) |
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image_path, glb_path = run_inference(prompt, stable_diffusion_model, num_inference_steps, guidance_scale, model_precision_type, seed, filename, verbose) |
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with gr.Row(): |
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output = gr.Image(label="Generated Minecraft Skin Image Asset") |
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if see_in_3d: |
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output.style(height=500) |
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output.style(width=500) |
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output.style(display="flex") |
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output.style(justify_content="center") |
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output.style(align_items="center") |
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output.style(flex_wrap="wrap") |
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output.style(grid_template_columns="repeat(auto-fill, minmax(250px, 1fr))") |
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output.style(grid_gap="10px") |
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output.style(overflow="auto") |
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output.style(padding="10px") |
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output.style(box_sizing="border-box") |
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output.style(border="1px solid #ccc") |
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output.style(border_radius="5px") |
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output.style(margin="10px 0") |
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output.style(background_color="#f9f9f9") |
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output.render(custom_output, inputs=[image_path, glb_path]) |
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else: |
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output.render(image_path) |
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minecraft_skin_generator.launch(show_api=False, share=True) |