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import spaces # Import spaces FIRST, before any CUDA-related packages
import torch
from diffusers import Flux2Pipeline
from huggingface_hub import get_token
import requests
import io
import gradio as gr
from PIL import Image
import os
# Configuration
repo_id = "diffusers/FLUX.2-dev-bnb-4bit"
torch_dtype = torch.bfloat16
print("Starting Flux2 Image Generator...")
# Global variable to hold the pipeline
pipe = None
def load_pipeline():
"""Lazy load the pipeline when needed."""
global pipe
if pipe is None:
print("Loading Flux2 pipeline...")
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
try:
pipe = Flux2Pipeline.from_pretrained(
repo_id,
text_encoder=None,
torch_dtype=torch_dtype,
device_map="cuda"
)
print("Pipeline loaded successfully!")
except Exception as e:
print(f"Error loading pipeline: {e}")
raise
return pipe
def remote_text_encoder(prompts):
"""Encode prompts using remote text encoder API."""
try:
token = get_token()
if not token:
raise ValueError("HuggingFace token not found. Please login using 'huggingface-cli login'")
response = requests.post(
"https://remote-text-encoder-flux-2.huggingface.co/predict",
json={"prompt": prompts},
headers={
"Authorization": f"Bearer {token}",
"Content-Type": "application/json"
},
timeout=60
)
response.raise_for_status()
prompt_embeds = torch.load(io.BytesIO(response.content))
device = "cuda" if torch.cuda.is_available() else "cpu"
return prompt_embeds.to(device)
except Exception as e:
raise Exception(f"Failed to encode prompt: {str(e)}")
def get_duration(prompt: str, input_image: Image.Image = None, num_inference_steps: int = 28, guidance_scale: float = 4.0, seed: int = 42, progress=None):
"""Calculate dynamic GPU duration based on inference steps and input image."""
num_images = 0 if input_image is None else 1
step_duration = 1 + 0.7 * num_images
return max(65, num_inference_steps * step_duration + 10)
@spaces.GPU(duration=get_duration) # Dynamic GPU allocation
def generate_image(
prompt: str,
input_image: Image.Image = None,
num_inference_steps: int = 28,
guidance_scale: float = 4.0,
seed: int = 42,
progress=gr.Progress()
):
"""
Generate an image using Flux2 based on text prompt and optional input image.
Args:
prompt: Text description of the desired image
input_image: Optional input image for image-to-image generation
num_inference_steps: Number of denoising steps (higher = better quality but slower)
guidance_scale: How closely to follow the prompt (higher = more strict)
seed: Random seed for reproducibility (-1 for random)
"""
print(f"=== Starting generation ===")
print(f"Prompt: {prompt[:100]}...")
print(f"CUDA available: {torch.cuda.is_available()}")
if not prompt or prompt.strip() == "":
raise gr.Error("Please enter a prompt!")
progress(0, desc="Loading model...")
try:
# Load pipeline (lazy loading)
print("Loading pipeline...")
pipeline = load_pipeline()
print("Pipeline loaded successfully")
progress(0.1, desc="Encoding prompt...")
print("Encoding prompt...")
# Get prompt embeddings from remote encoder
try:
prompt_embeds = remote_text_encoder(prompt)
print(f"Prompt embeds shape: {prompt_embeds.shape}")
except Exception as e:
print(f"Error encoding prompt: {str(e)}")
raise gr.Error(f"Failed to encode prompt. Please check your HuggingFace token. Error: {str(e)}")
progress(0.3, desc="Generating image...")
# Set up generator
generator_device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Generator device: {generator_device}")
if seed == -1:
import random
seed = random.randint(0, 2**32 - 1)
print(f"Using seed: {seed}")
generator = torch.Generator(device=generator_device).manual_seed(int(seed))
# Prepare pipeline arguments
pipe_kwargs = {
"prompt_embeds": prompt_embeds,
"generator": generator,
"num_inference_steps": int(num_inference_steps),
"guidance_scale": float(guidance_scale),
}
# Add input image if provided
if input_image is not None:
pipe_kwargs["image"] = input_image
progress(0.4, desc="Processing input image...")
print("Processing with input image")
print(f"Starting generation with {num_inference_steps} steps...")
# Generate image
with torch.inference_mode():
result = pipeline(**pipe_kwargs)
image = result.images[0]
print("Generation complete!")
progress(1.0, desc="Done!")
return image
except gr.Error:
# Re-raise Gradio errors as-is
raise
except Exception as e:
import traceback
error_msg = f"Error generating image: {str(e)}\n{traceback.format_exc()}"
print(error_msg)
# Provide more helpful error messages
if "CUDA" in str(e):
raise gr.Error(f"GPU Error: {str(e)}. The model requires GPU to run.")
elif "token" in str(e).lower() or "401" in str(e):
raise gr.Error("Authentication failed. Please ensure your HuggingFace token is set correctly.")
elif "timeout" in str(e).lower():
raise gr.Error("Request timed out. Please try again.")
else:
raise gr.Error(f"Error: {str(e)}")
# Create Gradio interface
with gr.Blocks(
title="Flux2 Image Generator",
) as demo:
gr.Markdown(
"""
# 🎨 Flux2 Image Generator
Generate stunning images using **FLUX.2-dev** with 4-bit quantization for efficient inference.
Supports both **text-to-image** and **image-to-image** generation.
⚡ **Powered by Hugging Face Zero GPU** - Automatic GPU allocation on demand!
"""
)
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### 📝 Input")
prompt_input = gr.Textbox(
label="Prompt",
placeholder="Describe the image you want to generate...",
lines=4,
value="Realistic macro photograph of a hermit crab using a soda can as its shell, partially emerging from the can, captured with sharp detail and natural colors, on a sunlit beach with soft shadows and a shallow depth of field, with blurred ocean waves in the background."
)
image_input = gr.Image(
label="Input Image (Optional)",
type="pil",
sources=["upload", "clipboard"],
height=300
)
gr.Markdown("### ⚙️ Parameters")
with gr.Row():
num_steps = gr.Slider(
minimum=1,
maximum=100,
value=28,
step=1,
label="Inference Steps",
info="More steps = better quality but slower"
)
guidance = gr.Slider(
minimum=1.0,
maximum=15.0,
value=4.0,
step=0.5,
label="Guidance Scale",
info="How closely to follow the prompt"
)
seed_input = gr.Number(
label="Seed",
value=42,
precision=0,
info="Use -1 for random seed"
)
generate_btn = gr.Button(
"🚀 Generate Image",
variant="primary",
size="lg",
)
gr.Markdown(
"""
### 💡 Tips
- **Text-to-Image**: Just enter a prompt and click generate
- **Image-to-Image**: Upload an image and describe the changes
- Start with 28 steps for a good balance of quality and speed
- Higher guidance scale follows your prompt more strictly
- Use the same seed to reproduce results
- First generation may take longer as the model loads
"""
)
with gr.Column(scale=1):
gr.Markdown("### 🖼️ Output")
output_image = gr.Image(
label="Generated Image",
type="pil",
height=600
)
gr.Markdown(
"""
### 📊 Examples
Try these prompts for inspiration!
"""
)
# Examples
gr.Examples(
examples=[
[
"A serene landscape with mountains at sunset, vibrant orange and pink sky, reflected in a calm lake, photorealistic",
None,
28,
4.0,
42
],
[
"A futuristic cityscape at night, neon lights, flying cars, cyberpunk style, highly detailed",
None,
28,
4.0,
123
],
[
"A cute robot reading a book in a cozy library, warm lighting, digital art style",
None,
28,
4.0,
456
],
[
"Macro photography of a dew drop on a leaf, morning light, sharp focus, bokeh background",
None,
28,
4.0,
789
],
],
inputs=[prompt_input, image_input, num_steps, guidance, seed_input],
outputs=output_image,
cache_examples=False,
)
# Connect the generate button
generate_btn.click(
fn=generate_image,
inputs=[prompt_input, image_input, num_steps, guidance, seed_input],
outputs=output_image,
)
if __name__ == "__main__":
print("Launching Gradio interface...")
demo.queue(max_size=20).launch() |