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import gradio as gr |
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from transformers import AutoProcessor, Idefics3ForConditionalGeneration |
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import re |
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import time |
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from PIL import Image |
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import torch |
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import spaces |
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import subprocess |
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subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) |
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processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM_converted_4") |
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model = Idefics3ForConditionalGeneration.from_pretrained("HuggingFaceTB/SmolVLM_converted_4", |
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torch_dtype=torch.bfloat16, |
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_attn_implementation="flash_attention_2").to("cuda") |
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@spaces.GPU |
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def model_inference( |
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images, text, assistant_prefix, decoding_strategy, temperature, max_new_tokens, |
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repetition_penalty, top_p |
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): |
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if text == "" and not images: |
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gr.Error("Please input a query and optionally image(s).") |
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if text == "" and images: |
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gr.Error("Please input a text query along the image(s).") |
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if isinstance(images, Image.Image): |
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images = [images] |
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resulting_messages = [ |
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{ |
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"role": "user", |
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"content": [{"type": "image"}] + [ |
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{"type": "text", "text": text} |
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] |
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} |
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] |
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if assistant_prefix: |
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text = f"{assistant_prefix} {text}" |
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prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True) |
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inputs = processor(text=prompt, images=[images], return_tensors="pt") |
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inputs = {k: v.to("cuda") for k, v in inputs.items()} |
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generation_args = { |
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"max_new_tokens": max_new_tokens, |
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"repetition_penalty": repetition_penalty, |
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} |
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assert decoding_strategy in [ |
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"Greedy", |
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"Top P Sampling", |
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] |
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if decoding_strategy == "Greedy": |
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generation_args["do_sample"] = False |
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elif decoding_strategy == "Top P Sampling": |
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generation_args["temperature"] = temperature |
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generation_args["do_sample"] = True |
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generation_args["top_p"] = top_p |
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generation_args.update(inputs) |
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generated_ids = model.generate(**generation_args) |
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generated_texts = processor.batch_decode(generated_ids[:, generation_args["input_ids"].size(1):], skip_special_tokens=True) |
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return generated_texts[0] |
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with gr.Blocks(fill_height=True) as demo: |
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gr.Markdown("## SmolVLM 🐶") |
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gr.Markdown("Play with [HuggingFaceTB/SmolVLM](https://huggingface.co/HuggingFaceTB/SmolVLM) in this demo. To get started, upload an image and text or try one of the examples.") |
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gr.Markdown("**Disclaimer:** SmolVLM does not include an RLHF alignment stage, so it may not consistently follow prompts or handle complex tasks. However, this doesn't mean it is incapable of doing so. Adding a prefix to the assistant's response, such as Let's think step for a reasoning question or `<html>` for HTML code generation, can significantly improve the output in practice. You could also play with the parameters such as the temperature in non-greedy mode.") |
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with gr.Column(): |
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image_input = gr.Image(label="Upload your Image", type="pil", scale=1) |
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query_input = gr.Textbox(label="Prompt") |
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assistant_prefix = gr.Textbox(label="Assistant Prefix", placeholder="Let's think step by step.") |
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submit_btn = gr.Button("Submit") |
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output = gr.Textbox(label="Output") |
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with gr.Accordion(label="Example Inputs and Advanced Generation Parameters"): |
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examples=[ |
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["example_images/mmmu_example.jpeg", "Chase wants to buy 4 kilograms of oval beads and 5 kilograms of star-shaped beads. How much will he spend?", "Let's think step by step.", "Greedy", 0.4, 512, 1.2, 0.8], |
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["example_images/rococo_1.jpg", "What art era is this?", None, "Greedy", 0.4, 512, 1.2, 0.8], |
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["example_images/paper_with_text.png", "Read what's written on the paper", None, "Greedy", 0.4, 512, 1.2, 0.8], |
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["example_images/dragons_playing.png","What's unusual about this image?",None, "Greedy", 0.4, 512, 1.2, 0.8], |
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["example_images/example_images_ai2d_example_2.jpeg", "What happens to fish if pelicans increase?", None, "Greedy", 0.4, 512, 1.2, 0.8], |
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["example_images/travel_tips.jpg", "I want to go somewhere similar to the one in the photo. Give me destinations and travel tips.", None, "Greedy", 0.4, 512, 1.2, 0.8], |
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["example_images/dummy_pdf.png", "How much percent is the order status?", None, "Greedy", 0.4, 512, 1.2, 0.8], |
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["example_images/art_critic.png", "As an art critic AI assistant, could you describe this painting in details and make a thorough critic?.",None, "Greedy", 0.4, 512, 1.2, 0.8], |
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["example_images/s2w_example.png", "What is this UI about?", None,"Greedy", 0.4, 512, 1.2, 0.8]] |
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max_new_tokens = gr.Slider( |
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minimum=8, |
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maximum=1024, |
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value=512, |
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step=1, |
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interactive=True, |
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label="Maximum number of new tokens to generate", |
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) |
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repetition_penalty = gr.Slider( |
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minimum=0.01, |
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maximum=5.0, |
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value=1.2, |
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step=0.01, |
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interactive=True, |
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label="Repetition penalty", |
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info="1.0 is equivalent to no penalty", |
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) |
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temperature = gr.Slider( |
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minimum=0.0, |
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maximum=5.0, |
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value=0.4, |
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step=0.1, |
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interactive=True, |
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label="Sampling temperature", |
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info="Higher values will produce more diverse outputs.", |
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) |
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top_p = gr.Slider( |
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minimum=0.01, |
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maximum=0.99, |
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value=0.8, |
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step=0.01, |
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interactive=True, |
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label="Top P", |
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info="Higher values is equivalent to sampling more low-probability tokens.", |
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) |
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decoding_strategy = gr.Radio( |
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[ |
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"Greedy", |
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"Top P Sampling", |
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], |
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value="Greedy", |
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label="Decoding strategy", |
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interactive=True, |
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info="Higher values is equivalent to sampling more low-probability tokens.", |
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) |
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decoding_strategy.change( |
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fn=lambda selection: gr.Slider( |
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visible=( |
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selection in ["contrastive_sampling", "beam_sampling", "Top P Sampling", "sampling_top_k"] |
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) |
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), |
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inputs=decoding_strategy, |
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outputs=temperature, |
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) |
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decoding_strategy.change( |
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fn=lambda selection: gr.Slider( |
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visible=( |
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selection in ["contrastive_sampling", "beam_sampling", "Top P Sampling", "sampling_top_k"] |
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) |
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), |
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inputs=decoding_strategy, |
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outputs=repetition_penalty, |
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) |
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decoding_strategy.change( |
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fn=lambda selection: gr.Slider(visible=(selection in ["Top P Sampling"])), |
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inputs=decoding_strategy, |
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outputs=top_p, |
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) |
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gr.Examples( |
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examples = examples, |
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inputs=[image_input, query_input, assistant_prefix, decoding_strategy, temperature, |
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max_new_tokens, repetition_penalty, top_p], |
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outputs=output, |
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fn=model_inference |
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) |
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submit_btn.click(model_inference, inputs = [image_input, query_input, assistant_prefix, decoding_strategy, temperature, |
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max_new_tokens, repetition_penalty, top_p], outputs=output) |
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demo.launch(debug=True) |
