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| 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 laden | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| repo = "stabilityai/stable-diffusion-3-medium-diffusers" | |
| try: | |
| pipe = StableDiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16).to(device) | |
| 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("### Stable Diffusion 3 - Test App") | |
| 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() | |