| import gradio as gr |
| import numpy as np |
| import random |
| import torch |
| from diffusers import DiffusionPipeline |
|
|
| |
| MODEL_REPO_ID = "stabilityai/sdxl-turbo" |
| MAX_SEED = np.iinfo(np.int32).max |
| MAX_IMAGE_SIZE = 1024 |
|
|
| def get_torch_dtype(): |
| return torch.float16 if torch.cuda.is_available() else torch.float32 |
|
|
| def get_device(): |
| return "cuda" if torch.cuda.is_available() else "cpu" |
|
|
| |
| def get_pipe(): |
| if not hasattr(get_pipe, "pipe"): |
| pipe = DiffusionPipeline.from_pretrained(MODEL_REPO_ID, torch_dtype=get_torch_dtype()).to(get_device()) |
| get_pipe.pipe = pipe |
| return get_pipe.pipe |
|
|
| |
| def build_prompt(word): |
| return ( |
| f"Create a powerful, emotionally resonant image that vividly illustrates the meaning of the word '{word}', " |
| f"so that even someone who doesn’t speak English can understand it instantly. " |
| f"The visual should be sharp, symbolic, and universally relatable. " |
| f"Seamlessly weave the word '{word}' into the scene—clearly spelled but not overpowering—" |
| f"so it supports the concept without drawing attention away. " |
| f"Format: 1080x1080 pixels (square) for Instagram in a (.png) format." |
| ) |
|
|
| |
| def generate_image(prompt, negative_prompt, guidance_scale, num_inference_steps, width, height, seed): |
| generator = torch.Generator().manual_seed(seed) |
| with torch.inference_mode(): |
| return get_pipe()( |
| prompt=prompt, |
| negative_prompt=negative_prompt, |
| guidance_scale=guidance_scale, |
| num_inference_steps=num_inference_steps, |
| width=width, |
| height=height, |
| generator=generator, |
| ).images[0] |
|
|
| |
| def infer( |
| word, |
| negative_prompt, |
| seed, |
| randomize_seed, |
| width, |
| height, |
| guidance_scale, |
| num_inference_steps, |
| progress=gr.Progress(track_tqdm=True), |
| ): |
| if randomize_seed: |
| seed = random.randint(0, MAX_SEED) |
|
|
| prompt = build_prompt(word) |
| image = generate_image(prompt, negative_prompt, guidance_scale, num_inference_steps, width, height, seed) |
| return image, seed |
|
|
| css = """ |
| #col-container { |
| margin: 0 auto; |
| max-width: 640px; |
| } |
| """ |
|
|
| with gr.Blocks(css=css) as demo: |
| with gr.Column(elem_id="col-container"): |
| gr.Markdown(" # Word-to-Image Generator for Instagram 🎨") |
|
|
| with gr.Row(): |
| word = gr.Text( |
| label="Vocabulary Word", |
| show_label=False, |
| max_lines=1, |
| placeholder="Enter a vocabulary word", |
| container=False, |
| ) |
| run_button = gr.Button("Generate Image", scale=0, variant="primary") |
|
|
| result = gr.Image(label="Generated Image", show_label=False) |
|
|
| with gr.Accordion("Advanced Settings", open=False): |
| negative_prompt = gr.Text( |
| label="Negative prompt", |
| max_lines=1, |
| placeholder="Enter a negative prompt", |
| visible=False, |
| ) |
| seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0) |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
|
|
| with gr.Row(): |
| width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1080) |
| height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1080) |
|
|
| with gr.Row(): |
| guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=3.5) |
| num_inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=4) |
|
|
| run_button.click( |
| fn=infer, |
| inputs=[word, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], |
| outputs=[result, seed], |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
