app.py

import os
import random
import numpy as np
import torch
import gradio as gr
from diffusers import StableDiffusionPipeline
import paramiko
from huggingface_hub import login

# Hugging Face Token
HF_TOKEN = os.getenv('HF_TOKEN', '').strip()
if not HF_TOKEN:
    raise ValueError("HUGGING_TOKEN is not set. Please set the token as an environment variable.")

# Hugging Face Login
login(token=HF_TOKEN)

# Konfiguration
STORAGE_DOMAIN = os.getenv('STORAGE_DOMAIN', '').strip()  # SFTP Server Domain
STORAGE_USER = os.getenv('STORAGE_USER', '').strip()  # SFTP User
STORAGE_PSWD = os.getenv('STORAGE_PSWD', '').strip()  # SFTP Passwort
STORAGE_PORT = int(os.getenv('STORAGE_PORT', '22').strip())  # SFTP Port
STORAGE_SECRET = os.getenv('STORAGE_SECRET', '').strip()  # Secret Token

# Modell-Konfiguration und Device-Setup
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

# Stelle fest, ob auf CPU oder GPU-System
is_gpu_available = torch.cuda.is_available()

# Modell laden - passend zur Hardware
repo = "stabilityai/stable-diffusion-3-medium-diffusers"

# Die Standard-Präzision basiert auf verfügbarer Hardware
DEFAULT_PRECISION = "float16" if is_gpu_available else "float32"
print(f"Default precision: {DEFAULT_PRECISION}")

# Modell beim Start laden
try:
    # Wähle Präzision basierend auf Hardware
    if DEFAULT_PRECISION == "float16":
        pipe = StableDiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16).to(device)
    else:  # float32 für CPU
        pipe = StableDiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float32).to(device)
    
    print("Model loaded successfully")
except Exception as e:
    raise RuntimeError(f"Failed to load the model. Ensure the token has access to the repo. Error: {e}")

# Maximalwerte
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1344

# SFTP-Funktion
def upload_to_sftp(local_file, remote_path):
    try:
        transport = paramiko.Transport((STORAGE_DOMAIN, STORAGE_PORT))
        transport.connect(username=STORAGE_USER, password=STORAGE_PSWD)
        sftp = paramiko.SFTPClient.from_transport(transport)
        sftp.put(local_file, remote_path)
        sftp.close()
        transport.close()
        print(f"File {local_file} successfully uploaded to {remote_path}")
        return True
    except Exception as e:
        print(f"Error during SFTP upload: {e}")
        return False

# Inferenz-Funktion
def infer(prompt, width, height, guidance_scale, num_inference_steps, seed, randomize_seed):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    
    generator = torch.manual_seed(seed)
    image = pipe(
        prompt, 
        guidance_scale=guidance_scale, 
        num_inference_steps=num_inference_steps, 
        width=width, 
        height=height, 
        generator=generator
    ).images[0]
    
    # Speichere Bild lokal
    local_file = f"/tmp/generated_image_{seed}.png"
    image.save(local_file)
    
    # Hochladen zu SFTP
    remote_path = f"/uploads/generated_image_{seed}.png"
    if upload_to_sftp(local_file, remote_path):
        os.remove(local_file)
        return f"Image uploaded to {remote_path}", seed
    else:
        return "Failed to upload image", seed

# Gradio-App
with gr.Blocks() as demo:
    gr.Markdown(f"### Stable Diffusion 3 - Test App (Running on {device.upper()} with {DEFAULT_PRECISION})")
    
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
            width = gr.Slider(256, MAX_IMAGE_SIZE, step=64, value=512, label="Width")
            height = gr.Slider(256, MAX_IMAGE_SIZE, step=64, value=512, label="Height")
            guidance_scale = gr.Slider(0.0, 10.0, step=0.1, value=7.5, label="Guidance Scale")
            num_inference_steps = gr.Slider(1, 50, step=1, value=25, label="Inference Steps")
            seed = gr.Number(value=42, label="Seed")
            randomize_seed = gr.Checkbox(value=False, label="Randomize Seed")
            generate_button = gr.Button("Generate Image")
            output = gr.Text(label="Output")
    
    generate_button.click(
        infer,
        inputs=[
            prompt, width, height, guidance_scale, 
            num_inference_steps, seed, randomize_seed
        ],
        outputs=[output, seed]
    )

demo.launch()