app.py

import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline, StableDiffusionXLBaseModel, StableDiffusionTrainer
from transformers import CLIPTextModel, CLIPTokenizer, TrainingArguments
from datasets import load_dataset
from huggingface_hub import HfApi, HfFolder, Repository

device = "cuda" if torch.cuda.is_available() else "cpu"

if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
else:
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
    pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)
    
    image = pipe(
        prompt=prompt, 
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale, 
        num_inference_steps=num_inference_steps, 
        width=width, 
        height=height,
        generator=generator
    ).images[0]
    
    return image

def get_latest_version(repo_id):
    api = HfApi()
    repo_info = api.repo_info(repo_id)
    versions = [tag.name for tag in repo_info.tags]
    if not versions:
        return "v_0.0"
    latest_version = sorted(versions)[-1]
    return latest_version

def increment_version(version):
    major, minor = map(int, version.split('_')[1:])
    minor += 1
    return f"v_{major}.{minor}"

def train_model(train_data_path, output_dir, num_train_epochs, per_device_train_batch_size, learning_rate):
    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
    text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")

    base_model = StableDiffusionXLBaseModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")

    dataset = load_dataset('imagefolder', data_dir=train_data_path)

    training_args = TrainingArguments(
        output_dir=output_dir,
        num_train_epochs=num_train_epochs,
        per_device_train_batch_size=per_device_train_batch_size,
        learning_rate=learning_rate,
        logging_dir="./logs",
        logging_steps=10,
    )

    trainer = StableDiffusionTrainer(
        model=base_model,
        args=training_args,
        train_dataset=dataset['train'],
        tokenizer=tokenizer,
    )

    trainer.train()
    base_model.save_pretrained(output_dir)

    # Publish the model
    repo_id = "ZennyKenny/stable-diffusion-xl-base-1.0_NatalieDiffusion"
    latest_version = get_latest_version(repo_id)
    new_version = increment_version(latest_version)

    api = HfApi()
    token = HfFolder.get_token()
    repo = Repository(output_dir, clone_from=repo_id, token=token)
    repo.git_tag(new_version)
    repo.push_tag(new_version)
    
    return f"Training complete. Model saved to {output_dir} and published as version {new_version}."

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Text-to-Image Gradio Template
        Currently running on {power_device}.
        """)
        
        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            
            run_button = gr.Button("Run", scale=0)
        
        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=False,
            )
            
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            
            with gr.Row():
                
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
            
            with gr.Row():
                
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,
                )
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=12,
                    step=1,
                    value=2,
                )
        
        gr.Examples(
            examples=examples,
            inputs=[prompt]
        )
    
    # Add new section for training the model
    with gr.Accordion("Training Settings", open=False):
        train_data_path = gr.Text(
            label="Training Data Path",
            placeholder="Enter the path to your training data",
        )
        output_dir = gr.Text(
            label="Output Directory",
            placeholder="Enter the output directory for the trained model",
        )
        num_train_epochs = gr.Slider(
            label="Number of Training Epochs",
            minimum=1,
            maximum=10,
            step=1,
            value=3,
        )
        per_device_train_batch_size = gr.Slider(
            label="Batch Size per Device",
            minimum=1,
            maximum=16,
            step=1,
            value=4,
        )
        learning_rate = gr.Slider(
            label="Learning Rate",
            minimum=1e-5,
            maximum=1e-3,
            step=1e-5,
            value=5e-5,
        )
        train_button = gr.Button("Train Model")
        train_result = gr.Text(label="Training Result", show_label=False)

    train_button.click(
        fn=train_model,
        inputs=[train_data_path, output_dir, num_train_epochs, per_device_train_batch_size, learning_rate],
        outputs=[train_result],
    )

    run_button.click(
        fn=infer,
        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result]
    )

demo.queue().launch()