| 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( | |
| system_prompt, | |
| prompt, | |
| max_new_tokens, | |
| repetition_penalty, | |
| temperature, | |
| top_p, | |
| top_k, | |
| seed | |
| ): | |
| input_text = f"{system_prompt}, {prompt}" | |
| input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device) | |
| if seed == 0: | |
| seed = random.randint(1, 100000) | |
| torch.manual_seed(seed) | |
| else: | |
| torch.manual_seed(seed) | |
| outputs = model.generate( | |
| input_ids, | |
| max_new_tokens=max_new_tokens, | |
| repetition_penalty=repetition_penalty, | |
| do_sample=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| top_k=top_k, | |
| ) | |
| better_prompt = tokenizer.decode(outputs[0]) | |
| better_prompt = better_prompt.replace("<pad>", "").replace("</s>", "") | |
| return better_prompt | |
| prompt = gr.Textbox(label="Prompt", interactive=True) | |
| stable_diffusion_model = gr.Dropdown(["2", "xl"], interactive=True, label="Stable Diffusion Model", value="xl", type=value, 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") | |
| 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, seed], | |
| outputs=gr.Textbox(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) | |