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import gradio as gr
import torch
from model_handler import ModelHandler
from utils import get_random_seed
# Initialize the model handler
# We initialize it here to load the model when the app starts
model_handler = ModelHandler()
def generate(
prompt,
negative_prompt,
width,
height,
steps,
guidance_scale,
seed,
progress=gr.Progress()
):
"""
Wrapper function to call the model inference.
"""
if seed < 0:
seed = get_random_seed()
try:
image = model_handler.infer(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
num_inference_steps=steps,
guidance_scale=guidance_scale,
seed=seed,
progress_callback=progress
)
return image, seed
except Exception as e:
raise gr.Error(f"Generation failed: {str(e)}")
# CSS for custom styling
css = """
.container { max-width: 900px; margin: auto; }
.header { text-align: center; margin-bottom: 20px; }
.header h1 { font-size: 2.5rem; font-weight: bold; color: #333; }
.header p { font-size: 1.1rem; color: #666; }
.footer { text-align: center; margin-top: 20px; font-size: 0.9rem; }
"""
# Create the Gradio Interface
with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
with gr.Column(elem_classes="container"):
# Header
with gr.Column(elem_classes="header"):
gr.Markdown(
"""
# Kandinsky 5.0 Lite T2I (SFT)
### Text-to-Image Generation
"""
)
gr.Markdown("[Built with anycoder](https://huggingface.co/spaces/akhaliq/anycoder)")
# Status info for hardware
device_info = "Running on **GPU** 🚀" if torch.cuda.is_available() else "Running on **CPU** ⚠️ (Inference will be slow)"
gr.Markdown(device_info)
with gr.Row():
# Left Column: Inputs
with gr.Column(scale=1):
prompt = gr.Textbox(
label="Prompt",
placeholder="Describe the image you want to generate...",
lines=3,
autofocus=True
)
negative_prompt = gr.Textbox(
label="Negative Prompt",
placeholder="Low quality, bad anatomy, blurry...",
lines=2,
value="low quality, bad anatomy, worst quality, deformed, disfigured"
)
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
width = gr.Slider(label="Width", minimum=256, maximum=1024, step=64, value=1024)
height = gr.Slider(label="Height", minimum=256, maximum=1024, step=64, value=1024)
steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=25)
guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=20.0, step=0.5, value=7.5)
with gr.Row():
seed = gr.Number(label="Seed", value=-1, precision=0, info="Set to -1 for random")
random_btn = gr.Button("🎲 Randomize", size="sm", variant="secondary")
run_btn = gr.Button("Generate Image", variant="primary", size="lg")
# Right Column: Output
with gr.Column(scale=1):
result_image = gr.Image(label="Generated Image", type="pil", interactive=False)
used_seed = gr.Number(label="Seed Used", interactive=False)
# Event Handlers
run_btn.click(
fn=generate,
inputs=[prompt, negative_prompt, width, height, steps, guidance_scale, seed],
outputs=[result_image, used_seed]
)
# Helper to randomize seed input visually
random_btn.click(lambda: -1, outputs=seed)
# Examples
gr.Examples(
examples=[
["A futuristic cityscape with neon lights and flying cars, cyberpunk style, high detail", "low quality, blurry", 1024, 1024, 25, 7.5],
["A cute red panda drinking coffee in a cozy cafe, digital art", "deformed, ugly", 1024, 1024, 25, 7.0],
["Portrait of a warrior princess, intricate armor, dramatic lighting, photorealistic", "cartoon, sketch, monochrome", 1024, 1024, 30, 8.0]
],
inputs=[prompt, negative_prompt, width, height, steps, guidance_scale],
fn=generate,
outputs=[result_image, used_seed],
cache_examples=False
)
if __name__ == "__main__":
demo.launch() |