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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() | |