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import gradio as gr
from diffusers import AutoPipelineForText2Image
import torch
import os
# Model configuration
MODEL_NAME = "katuni4ka/tiny-random-flex.2-preview"
CACHE_DIR = "./model_cache"
# Create cache directory
os.makedirs(CACHE_DIR, exist_ok=True)
# Load model with optimized settings
pipe = AutoPipelineForText2Image.from_pretrained(
MODEL_NAME,
torch_dtype=torch.float16,
cache_dir=CACHE_DIR
).to("cuda" if torch.cuda.is_available() else "cpu")
# Aspect ratio presets
ASPECT_RATIOS = {
"Square (1:1)": (512, 512),
"Landscape (16:9)": (1024, 576),
"Portrait (9:16)": (576, 1024),
"A4 (3:4)": (864, 1152)
}
def generate_image(prompt, aspect_ratio, num_inference_steps=25, guidance_scale=4.5):
"""Generate image with optimized inference settings"""
width, height = ASPECT_RATIOS[aspect_ratio]
with torch.inference_mode():
image = pipe(
prompt=prompt,
width=width,
height=height,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale
).images[0]
return image
# UI Configuration
with gr.Blocks(theme="huggingface", analytics_enabled=False) as demo:
gr.Markdown("""
# Tiny Random Flex Text-to-Image Generator
Create images from text prompts using the `katuni4ka/tiny-random-flex.2-preview` model
💡 Tip: Try descriptive prompts like "A futuristic cityscape at sunset" or "Abstract watercolor patterns"
""")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(
label="Prompt",
placeholder="Describe your image...",
lines=3
)
aspect_ratio = gr.Dropdown(
label="Aspect Ratio",
choices=list(ASPECT_RATIOS.keys()),
value="Square (1:1)"
)
generate_btn = gr.Button("🎨 Generate Image", variant="primary")
with gr.Column():
output_image = gr.Image(label="Generated Image", interactive=False)
generate_btn.click(
fn=generate_image,
inputs=[prompt, aspect_ratio],
outputs=output_image
)
gr.Examples(
examples=[
["A vibrant neon cityscape at night", "Landscape (16:9)"],
["Abstract geometric patterns in pastel colors", "Square (1:1)"],
["Mystical forest with glowing plants", "Portrait (9:16)"]
],
inputs=[prompt, aspect_ratio]
)
if __name__ == "__main__":
demo.launch() |