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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"
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
print(f"Using device: {device}")
print(f"Using dtype: {torch_dtype}")
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))
return prompt_embeds.to(device)
except Exception as e:
raise Exception(f"Failed to encode prompt: {str(e)}")
# Load the pipeline
print("Loading Flux2 pipeline...")
try:
pipe = Flux2Pipeline.from_pretrained(
repo_id,
text_encoder=None,
torch_dtype=torch_dtype,
device_map="cuda"
)
if not torch.cuda.is_available():
pipe = pipe.to(device)
print("Pipeline loaded successfully!")
except Exception as e:
print(f"Error loading pipeline: {e}")
raise
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)
"""
if not prompt or prompt.strip() == "":
raise gr.Error("Please enter a prompt!")
if not torch.cuda.is_available():
raise gr.Error("This Space requires a GPU to run. Please try a different Space or upgrade your hardware.")
progress(0, desc="Encoding prompt...")
try:
# Get prompt embeddings from remote encoder
prompt_embeds = remote_text_encoder(prompt)
progress(0.3, desc="Generating image...")
# Set up generator
generator_device = "cuda" if torch.cuda.is_available() else "cpu"
if seed == -1:
import random
seed = random.randint(0, 2**32 - 1)
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...")
# Generate image
with torch.inference_mode():
image = pipe(**pipe_kwargs).images[0]
progress(1.0, desc="Done!")
return image
except Exception as e:
import traceback
error_msg = f"Error generating image: {str(e)}\n{traceback.format_exc()}"
print(error_msg)
raise gr.Error(f"Error generating image: {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.
⚠️ **Note**: This Space requires a GPU to run.
"""
)
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",
elem_classes="generate-btn"
)
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
"""
)
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__":
demo.queue(max_size=20).launch()
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