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| import gradio as gr | |
| from transformers import T5TokenizerFast, CLIPTokenizer | |
| def count_tokens(text): | |
| # Load the common tokenizers | |
| t5_tokenizer = T5TokenizerFast.from_pretrained("google/t5-v1_1-xxl", legacy=False) | |
| clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") | |
| # Get tokens and their IDs | |
| t5_tokens = t5_tokenizer.encode(text, return_tensors="pt")[0].tolist() | |
| clip_tokens = clip_tokenizer.encode(text) | |
| # Decode individual tokens for display, replacing whitespace with visible characters | |
| t5_decoded = [] | |
| for token in t5_tokens: | |
| decoded = t5_tokenizer.decode([token]) | |
| # Replace whitespace with visible characters and empty strings with special markers | |
| if decoded.isspace(): | |
| decoded = "␣" # visible space marker | |
| elif decoded == "": | |
| decoded = "∅" # empty token marker | |
| t5_decoded.append(decoded) | |
| clip_decoded = [] | |
| for token in clip_tokens: | |
| decoded = clip_tokenizer.decode([token]) | |
| if decoded.isspace(): | |
| decoded = "␣" | |
| elif decoded == "": | |
| decoded = "∅" | |
| clip_decoded.append(decoded) | |
| # Create highlighted text tuples (text, label) | |
| t5_highlights = [(token, f"Token {i}") for i, token in enumerate(t5_decoded)] | |
| clip_highlights = [(token, f"Token {i}") for i, token in enumerate(clip_decoded)] | |
| return ( | |
| # T5 outputs | |
| len(t5_tokens), | |
| t5_highlights, | |
| str(t5_tokens), | |
| # CLIP outputs | |
| len(clip_tokens), | |
| clip_highlights, | |
| str(clip_tokens) | |
| ) | |
| # Create a Gradio interface with custom layout | |
| with gr.Blocks(title="Common Diffusion Model Token Counter") as iface: | |
| gr.Markdown("# Common Diffusion Model Token Counter") | |
| gr.Markdown("Enter text to count tokens using T5 and CLIP tokenizers, commonly used in diffusion models.") | |
| with gr.Row(): | |
| text_input = gr.Textbox(label="Diffusion Prompt", placeholder="Enter your prompt here...") | |
| with gr.Row(): | |
| # T5 Column | |
| with gr.Column(): | |
| gr.Markdown("### T5 Tokenizer Results") | |
| t5_count = gr.Number(label="T5 Token Count") | |
| t5_highlights = gr.HighlightedText(label="T5 Tokens", show_legend=True) | |
| t5_ids = gr.Textbox(label="T5 Token IDs", lines=2) | |
| # CLIP Column | |
| with gr.Column(): | |
| gr.Markdown("### CLIP Tokenizer Results") | |
| clip_count = gr.Number(label="CLIP Token Count") | |
| clip_highlights = gr.HighlightedText(label="CLIP Tokens", show_legend=True) | |
| clip_ids = gr.Textbox(label="CLIP Token IDs", lines=2) | |
| text_input.change( | |
| fn=count_tokens, | |
| inputs=[text_input], | |
| outputs=[t5_count, t5_highlights, t5_ids, clip_count, clip_highlights, clip_ids] | |
| ) | |
| # Launch the app | |
| iface.launch(show_error=True) |