import os import re import random import numpy as np # !!! spaces must be imported before torch/CUDA import spaces from huggingface_hub import login from diffusers import DiffusionPipeline import gradio as gr import torch from utils import QPipeline device = "cuda" if torch.cuda.is_available() else "cpu" login(token=os.environ["HF_TOKEN"]) model_repo_id = os.environ["MODEL_ID"] torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 pipe = QPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype).to(device) MAX_SEED = 65535 MAX_IMAGE_SIZE = 128 @spaces.GPU # Enable ZeroGPU if needed def infer( prompt, negative_prompt, seed, randomize_seed, num_inference_steps=10, progress=gr.Progress(track_tqdm=True), ): if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator().manual_seed(seed) image = pipe( [prompt], batch_size=1, generator=generator, num_inference_steps=num_inference_steps ).images[0] return image, seed examples = [ "Structure: (LR 文 英). Style: style001", "Structure: (TL 广 東). Style: style028", "Structure: (TB 艹 (LR 禾 魚)). Style: style015", "Structure: (TB 敬 音). Style: style013", "Structure: (LR 釒 馬). Style: style018", "Structure: (BL 走 羽). Style: style022", "Structure: (LR 羊 大). Style: style005", "Structure: (LR 鹿 孚). Style: style017", "Structure: (OI 口 也). Style: style002", ] # Map style images to style names (use real image files later) style_options = { "images/style001.png": "style001", "images/style002.png": "style002", "images/style003.png": "style003", "images/style004.png": "style004", "images/style005.png": "style005", "images/style006.png": "style006", "images/style007.png": "style007", "images/style008.png": "style008", "images/style009.png": "style009", "images/style010.png": "style010", "images/style011.png": "style011", "images/style012.png": "style012", "images/style013.png": "style013", "images/style014.png": "style014", "images/style015.png": "style015", # "images/style016.png": "style016", very similar to 002 "images/style017.png": "style017", "images/style018.png": "style018", "images/style019.png": "style019", "images/style020.png": "style020", "images/style021.png": "style021", "images/style022.png": "style022", "images/style023.png": "style023", "images/style024.png": "style024", "images/style025.png": "style025", "images/style026.png": "style026", "images/style027.png": "style027", "images/style028.png": "style028", "images/style029.png": "style029", } def apply_style_on_click(evt: gr.SelectData, prompt_text): index = evt.index style_label = list(style_options.values())[index] if re.search(r"Style: [^\n]+", prompt_text): return re.sub(r"Style: [^\n]+", f"Style: {style_label}", prompt_text) else: return prompt_text.strip() + f" Style: {style_label}" # CSS for fixing Gallery layout css = """ #col-container { margin: 0 auto; max-width: 800px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(" # NeoChar ") gr.Markdown(""" - Generate New Chineses Characters (Hanzi/Kanji) - Combine components in a creative way - Write them in style - A Gen-AI's implementation of [Lin Yutang's Ming-Kwai Typewriter](https://thereader.mitpress.mit.edu/the-uncanny-keyboard/) - [README](https://huggingface.co./spaces/lqume/neochar/blob/main/README.md) for more""") gr.Markdown(" ## QuickStart: select an example, edit components, pick a style, then 'generate'") gr.HTML(""" """) gallery = gr.Gallery( value=list(style_options.keys()), label="Click any image", columns=7, allow_preview=False, height=None, elem_classes=["gallery-container"] ) with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) run_button = gr.Button("Generate", scale=0, variant="primary") gallery.select( fn=apply_style_on_click, inputs=[prompt], outputs=prompt ) result = gr.Image(label="Result", 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(): num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=20, step=1, value=10, ) gr.Examples(examples=examples, inputs=[prompt]) gr.on( triggers=[run_button.click, prompt.submit], fn=infer, inputs=[ prompt, negative_prompt, seed, randomize_seed, num_inference_steps, ], outputs=[result, seed], ) if __name__ == "__main__": demo.launch()