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import gradio as gr
import torch
from diffusers import FluxPipeline
from huggingface_hub import HfApi
import spaces
import random
@spaces.GPU(duration=70)
def initialize_model():
model_id = "Freepik/flux.1-lite-8B-alpha"
pipe = FluxPipeline.from_pretrained(
model_id,
torch_dtype=torch.bfloat16
).to("cuda")
return pipe
@spaces.GPU(duration=70)
def generate_image(
prompt,
guidance_scale=3.5,
width=1024,
height=1024
):
try:
# Initialize model within the GPU context
pipe = initialize_model()
# Generate random seed
seed = random.randint(1, 1000000)
with torch.inference_mode():
image = pipe(
prompt=prompt,
generator=torch.Generator(device="cuda").manual_seed(seed),
num_inference_steps=25, # Fixed steps
guidance_scale=guidance_scale,
height=height,
width=width,
).images[0]
return image
except Exception as e:
print(f"Error during image generation: {str(e)}")
raise e
# Create the Gradio interface
demo = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(
label="Prompt",
placeholder="Enter your image description here...",
value="A serene landscape with mountains at sunset"
),
gr.Slider(
minimum=1,
maximum=20,
value=3.5,
label="Guidance Scale",
step=0.5
),
gr.Slider(
minimum=128,
maximum=1024,
value=1024,
label="Width",
step=64
),
gr.Slider(
minimum=128,
maximum=1024,
value=1024,
label="Height",
step=64
)
],
outputs=gr.Image(type="pil", label="Generated Image"),
title="Flux Image Generator (Zero-GPU)",
description="Generate images using Freepik's Flux model with Zero-GPU allocation. Using 25 fixed steps and random seed for each generation.",
examples=[
["A close-up image of a green alien with fluorescent skin in the middle of a dark purple forest", 3.5, 1024, 1024],
["A serene landscape with mountains at sunset", 3.5, 1024, 1024]
]
)
# Launch the app
if __name__ == "__main__":
demo.launch() |