Spaces:
Sleeping
Sleeping
File size: 3,872 Bytes
8f3793b e57b5e3 8f3793b e57b5e3 0f35108 e57b5e3 0f35108 e57b5e3 0f35108 8f3793b 0f35108 8f3793b e57b5e3 8f3793b e57b5e3 8f3793b e57b5e3 8f3793b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
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):
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
# 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")
generate_button.click(
infer,
inputs=[prompt, width, height, guidance_scale, num_inference_steps, seed, randomize_seed],
outputs=[output, seed]
)
demo.launch() |