Create app.py

app.py

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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-Optionen - können angepasst werden
+MODEL_REPO = os.getenv('MODEL_REPO', 'stabilityai/stable-diffusion-2-1-base')  # Standard-Modell
+TORCH_DTYPE = os.getenv('TORCH_DTYPE', 'float16')  # Standard-Präzision
+
+# Modell laden
+device = "cuda" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if TORCH_DTYPE == 'float16' else torch.float32
+
+try:
+    pipe = StableDiffusionPipeline.from_pretrained(
+        MODEL_REPO,
+        torch_dtype=torch_dtype
+    ).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):
+    """Versucht, eine Datei auf einen SFTP-Server hochzuladen."""
+    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()
+        return True
+    except Exception as e:
+        return f"Error during SFTP upload: {e}"
+
+# Inferenz-Funktion
+def infer(prompt, width, height, guidance_scale, num_inference_steps, seed, randomize_seed):
+    """Generiert ein Bild basierend auf dem Eingabe-Prompt und lädt es auf einen SFTP-Server hoch."""
+    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"
+    upload_result = upload_to_sftp(local_file, remote_path)
+    
+    # Entferne das lokale Bild, wenn der Upload erfolgreich war
+    if upload_result == True:
+        os.remove(local_file)
+        return f"Image successfully uploaded to {remote_path}", seed
+    else:
+        return upload_result, seed
+
+# App-Titel mit Modell- und Präzisionsinformationen
+APP_TITLE = f"### Stable Diffusion - {os.path.basename(MODEL_REPO)} ({TORCH_DTYPE} auf {device})"
+
+# Gradio-App
+with gr.Blocks() as demo:
+    gr.Markdown(APP_TITLE)
+    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")
+    
+    # Klick-Event für die Generierung
+    generate_button.click(
+        infer,
+        inputs=[prompt, width, height, guidance_scale, num_inference_steps, seed, randomize_seed],
+        outputs=[output, seed]
+    )
+
+demo.launch()