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import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline
# Define available models and their corresponding Hugging Face repositories
MODEL_REPOS = {
"Stable Diffusion XL Base 1.0": "stabilityai/stable-diffusion-xl-base-1.0",
"SDXL-Turbo": "stabilityai/sdxl-turbo",
"Playground v2 1024px Aesthetic": "playgroundai/playground-v2-1024px-aesthetic",
"Segmind Vega": "segmind/Segmind-Vega",
"SSD-1B": "segmind/SSD-1B",
"Kandinsky 3": "kandinsky-community/kandinsky-3",
"PixArt-LCM-XL-2-1024-MS": "PixArt-alpha/PixArt-LCM-XL-2-1024-MS",
"BLIP Diffusion": "salesforce/blipdiffusion",
"Muse-512-Finetuned": "amused/muse-512-finetuned",
"Flux 1 Dev": "black-forest-labs/FLUX.1-dev"
}
# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
# Cache for loaded pipelines
loaded_pipelines = {}
# Maximum seed value
MAX_SEED = np.iinfo(np.int32).max
def load_pipeline(model_name):
"""Load and cache the pipeline for the selected model."""
if model_name in loaded_pipelines:
return loaded_pipelines[model_name]
repo_id = MODEL_REPOS[model_name]
try:
pipeline = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch_dtype)
pipeline.to(device)
loaded_pipelines[model_name] = pipeline
return pipeline
except Exception as e:
raise RuntimeError(f"Failed to load model '{model_name}': {e}")
def generate_image(prompt, model_name, width, height, guidance_scale, num_inference_steps, seed, randomize_seed):
"""Generate an image using the selected model and parameters."""
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device=device).manual_seed(seed)
pipeline = load_pipeline(model_name)
try:
image = pipeline(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator
).images[0]
return image, seed
except Exception as e:
raise RuntimeError(f"Image generation failed: {e}")
# Define the Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# 🖼️ Text-to-Image Generator with Multiple Models")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
model_name = gr.Dropdown(label="Select Model", choices=list(MODEL_REPOS.keys()), value="Stable Diffusion XL Base 1.0")
width = gr.Slider(label="Width", minimum=256, maximum=1024, step=64, value=512)
height = gr.Slider(label="Height", minimum=256, maximum=1024, step=64, value=512)
guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=20.0, step=0.5, value=7.5)
num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, step=1, value=50)
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
generate_button = gr.Button("Generate Image")
with gr.Column():
output_image = gr.Image(label="Generated Image")
output_seed = gr.Textbox(label="Used Seed", interactive=False)
generate_button.click(
fn=generate_image,
inputs=[prompt, model_name, width, height, guidance_scale, num_inference_steps, seed, randomize_seed],
outputs=[output_image, output_seed]
)
if __name__ == "__main__":
demo.launch()
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