app.py

import subprocess
import os
import gradio as gr
import torch

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"])
os.chdir("Stable_Diffusion_Finetuned_Minecraft_Skin_Generator")


def generate(
    prompt,
    stable_diffusion_model,
    num_inference_steps,
    guidance_scale,
    num_images_per_prompt,
    model_precision_type,
    seed
):
    
    if verbose:
        verbose_opt = '--verbose'
    else:
        verbose_opt = ''
        
    if stable_diffusion_model == '2':
        sd_model = "minecraft-skins"
    else:
        sd_model = "minecraft-skins-sdxl"
    
    command = f"Python_Scripts/{sd_model}.py '{prompt}' {num_inference_steps} {guidance_scale} {num_images_per_prompt} {model_precision_type} {output_image_name} {verbose_opt}"
    
    subprocess.run(["python", "$command"])
    
    return os.path.join(f"output_minecraft_skins/{output_image_name}")


prompt = gr.Textbox(label="Prompt", interactive=True)

stable_diffusion_model = gr.Dropdown(["2", "xl"], interactive=True, label="Stable Diffusion Model", value="xl", info="Choose which Stable Diffusion Model to use, xl understands prompts better")

num_inference_steps = gr.Number(value=50, minimum=1, precision=0, interactive=True, label="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")

guidance_scale = gr.Number(value=7.5, minimum=0.1, interactive=True, label="Guidance Scale", info="How closely the generated image adheres to the prompt")
    
num_images_per_prompt = gr.Number(value=1, minimum=1, precision=0, interactive=True, label="Images Per Prompt", info="The number of images to make with the prompt")

model_precision_type = gr.Dropdown(["fp16", "fp32"], value="fp16", interactive=True, 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, interactive=True, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")

output_image_name = gr.Textbox(label="Name of Generated Skin Output", interactive=Trie, value="output.png")

verbose = gr.Checkbox(label="Verbose Output", interactive=True, value=False, info="Produce verbose output while running")

examples = [
    [
        "A man in a purple suit wearing a tophat.",
        "xl",
        25,
        7.5,
        1,
        "fp16",
        42,
    ]
]

gr.Interface(
    fn=generate,
    inputs=[prompt, stable_diffusion_model, num_inference_steps, guidance_scale, num_images_per_prompt, model_precision_type, output_image_name, verbose, seed],
    outputs=gr.Image(label="Generated Minecraft Skin"),
    title="Stable Diffusion Finetuned Minecraft Skin Generator",
    description="Make your prompts more detailed!<br>Model used: https://huggingface.co/roborovski/superprompt-v1<br>Hugging Face Space made by [Nick088](https://linktr.ee/Nick088)",
    examples=examples,
    concurrency_limit=20,
).launch(show_api=False)