Create app.py

app.py

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+# float16 +32 
+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
+available_models = {
+    "sd3-medium": "stabilityai/stable-diffusion-3-medium-diffusers",
+    "sd2-base": "stabilityai/stable-diffusion-2-1-base"
+}
+
+# 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
+
+# Modell laden Funktion
+def load_model(model_name, precision):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    repo = available_models.get(model_name, available_models["sd3-medium"])
+    
+    try:
+        # Wähle Präzision basierend auf Auswahl
+        if precision == "float16":
+            torch_dtype = torch.float16
+        else:  # float32
+            torch_dtype = torch.float32
+            
+        pipe = StableDiffusionPipeline.from_pretrained(
+            repo, 
+            torch_dtype=torch_dtype
+        ).to(device)
+        
+        # Wenn auf CPU und Speicheroptimierung gewünscht
+        if device == "cpu":
+            pipe.enable_sequential_cpu_offload()
+            
+        return pipe
+    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
+
+# Globale Pipe-Variable
+pipe = None
+
+# Inferenz-Funktion
+def infer(prompt, width, height, guidance_scale, num_inference_steps, seed, randomize_seed, model_name, precision):
+    global pipe
+    
+    # Prüfe, ob Modell neu geladen werden muss
+    if pipe is None:
+        pipe = load_model(model_name, precision)
+    
+    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
+
+# Modell neu laden
+def reload_model(model_name, precision):
+    global pipe
+    pipe = load_model(model_name, precision)
+    return f"Model loaded: {model_name} with {precision} precision"
+
+# Gradio-App
+with gr.Blocks() as demo:
+    gr.Markdown("### Stable Diffusion - Test App")
+    
+    with gr.Row():
+        with gr.Column():
+            # Modell Auswahl
+            model_name = gr.Radio(
+                choices=list(available_models.keys()),
+                value="sd3-medium",
+                label="Model"
+            )
+            
+            # Präzision Auswahl
+            precision = gr.Radio(
+                choices=["float16", "float32"],
+                value="float16",
+                label="Precision"
+            )
+            
+            reload_button = gr.Button("Load/Reload Model")
+            model_status = gr.Textbox(label="Model Status")
+            
+            # Modell laden Button
+            reload_button.click(
+                reload_model,
+                inputs=[model_name, precision],
+                outputs=[model_status]
+            )
+    
+    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,
+            model_name, precision
+        ],
+        outputs=[output, seed]
+    )
+
+demo.launch()