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import gradio as gr
import random
import torch
from diffusers import DiffusionPipeline
# Device setup
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id_turbo = "stabilityai/sdxl-turbo" # Stability AI Model
pipe_turbo = DiffusionPipeline.from_pretrained(model_repo_id_turbo, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32).to(device)
# Placeholder for ZB-Tech model
def load_zb_model():
return gr.Interface.load("models/ZB-Tech/Text-to-Image")
# Inference function
def custom_infer(
model_choice, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps
):
# Load the selected model
if model_choice == "Faster image generation (suitable for CPUs)":
model = load_zb_model()
return model(prompt)
else:
default_negative_prompt = "no watermark, hezzy, blurry"
combined_negative_prompt = f"{default_negative_prompt}, {negative_prompt}" if negative_prompt else default_negative_prompt
if randomize_seed:
seed = random.randint(0, np.iinfo(np.int32).max)
generator = torch.Generator().manual_seed(seed)
image = pipe_turbo(
prompt=prompt,
negative_prompt=combined_negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
# CSS for centering UI
css = """
#col-container {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
text-align: center;
margin: 0 auto;
}
"""
# Gradio app
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
# App name and description
gr.Markdown(
"""
# AI-Powered Text-to-Image Generator
*Generate stunning images from text prompts using advanced AI models.*
"""
)
# Dropdown for model selection
model_choice = gr.Dropdown(
label="Select Model",
choices=[
"Faster image generation (suitable for CPUs)",
"More customizable option (slower, suitable for GPUs)"
],
value="Faster image generation (suitable for CPUs)",
)
# Input section
prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter a negative prompt here...")
seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=0)
randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
width = gr.Slider(label="Width", minimum=256, maximum=1024, step=32, value=512)
height = gr.Slider(label="Height", minimum=256, maximum=1024, step=32, value=512)
guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=7.5)
num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=25)
# Output section
result = gr.Image(label="Generated Image", type="pil")
gr.Button("Generate").click(
custom_infer,
inputs=[model_choice, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=result
)
# Launch app
if __name__ == "__main__":
demo.launch()
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