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Running
on
Zero
| import gradio as gr | |
| from transformers import AutoProcessor, Idefics3ForConditionalGeneration | |
| import re | |
| import time | |
| from PIL import Image | |
| import torch | |
| import spaces | |
| import subprocess | |
| subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) | |
| processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM_converted_4") | |
| model = Idefics3ForConditionalGeneration.from_pretrained("HuggingFaceTB/SmolVLM_converted_4", | |
| torch_dtype=torch.bfloat16, | |
| _attn_implementation="flash_attention_2").to("cuda") | |
| def model_inference( | |
| images, text, assistant_prefix, decoding_strategy, temperature, max_new_tokens, | |
| repetition_penalty, top_p | |
| ): | |
| if text == "" and not images: | |
| gr.Error("Please input a query and optionally image(s).") | |
| if text == "" and images: | |
| gr.Error("Please input a text query along the image(s).") | |
| if isinstance(images, Image.Image): | |
| images = [images] | |
| resulting_messages = [ | |
| { | |
| "role": "user", | |
| "content": [{"type": "image"}] + [ | |
| {"type": "text", "text": text} | |
| ] | |
| } | |
| ] | |
| if assistant_prefix: | |
| text = f"{assistant_prefix} {text}" | |
| prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True) | |
| inputs = processor(text=prompt, images=[images], return_tensors="pt") | |
| inputs = {k: v.to("cuda") for k, v in inputs.items()} | |
| generation_args = { | |
| "max_new_tokens": max_new_tokens, | |
| "repetition_penalty": repetition_penalty, | |
| } | |
| assert decoding_strategy in [ | |
| "Greedy", | |
| "Top P Sampling", | |
| ] | |
| if decoding_strategy == "Greedy": | |
| generation_args["do_sample"] = False | |
| elif decoding_strategy == "Top P Sampling": | |
| generation_args["temperature"] = temperature | |
| generation_args["do_sample"] = True | |
| generation_args["top_p"] = top_p | |
| generation_args.update(inputs) | |
| # Generate | |
| generated_ids = model.generate(**generation_args) | |
| generated_texts = processor.batch_decode(generated_ids[:, generation_args["input_ids"].size(1):], skip_special_tokens=True) | |
| return generated_texts[0] | |
| with gr.Blocks(fill_height=True) as demo: | |
| gr.Markdown("## SmolVLM 🐶") | |
| 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.") | |
| 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.") | |
| with gr.Column(): | |
| image_input = gr.Image(label="Upload your Image", type="pil", scale=1) | |
| query_input = gr.Textbox(label="Prompt") | |
| assistant_prefix = gr.Textbox(label="Assistant Prefix", placeholder="Let's think step by step.") | |
| submit_btn = gr.Button("Submit") | |
| output = gr.Textbox(label="Output") | |
| with gr.Accordion(label="Example Inputs and Advanced Generation Parameters"): | |
| examples=[ | |
| ["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], | |
| ["example_images/rococo_1.jpg", "What art era is this?", None, "Greedy", 0.4, 512, 1.2, 0.8], | |
| ["example_images/paper_with_text.png", "Read what's written on the paper", None, "Greedy", 0.4, 512, 1.2, 0.8], | |
| ["example_images/dragons_playing.png","What's unusual about this image?",None, "Greedy", 0.4, 512, 1.2, 0.8], | |
| ["example_images/example_images_ai2d_example_2.jpeg", "What happens to fish if pelicans increase?", None, "Greedy", 0.4, 512, 1.2, 0.8], | |
| ["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], | |
| ["example_images/dummy_pdf.png", "How much percent is the order status?", None, "Greedy", 0.4, 512, 1.2, 0.8], | |
| ["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], | |
| ["example_images/s2w_example.png", "What is this UI about?", None,"Greedy", 0.4, 512, 1.2, 0.8]] | |
| # Hyper-parameters for generation | |
| max_new_tokens = gr.Slider( | |
| minimum=8, | |
| maximum=1024, | |
| value=512, | |
| step=1, | |
| interactive=True, | |
| label="Maximum number of new tokens to generate", | |
| ) | |
| repetition_penalty = gr.Slider( | |
| minimum=0.01, | |
| maximum=5.0, | |
| value=1.2, | |
| step=0.01, | |
| interactive=True, | |
| label="Repetition penalty", | |
| info="1.0 is equivalent to no penalty", | |
| ) | |
| temperature = gr.Slider( | |
| minimum=0.0, | |
| maximum=5.0, | |
| value=0.4, | |
| step=0.1, | |
| interactive=True, | |
| label="Sampling temperature", | |
| info="Higher values will produce more diverse outputs.", | |
| ) | |
| top_p = gr.Slider( | |
| minimum=0.01, | |
| maximum=0.99, | |
| value=0.8, | |
| step=0.01, | |
| interactive=True, | |
| label="Top P", | |
| info="Higher values is equivalent to sampling more low-probability tokens.", | |
| ) | |
| decoding_strategy = gr.Radio( | |
| [ | |
| "Greedy", | |
| "Top P Sampling", | |
| ], | |
| value="Greedy", | |
| label="Decoding strategy", | |
| interactive=True, | |
| info="Higher values is equivalent to sampling more low-probability tokens.", | |
| ) | |
| decoding_strategy.change( | |
| fn=lambda selection: gr.Slider( | |
| visible=( | |
| selection in ["contrastive_sampling", "beam_sampling", "Top P Sampling", "sampling_top_k"] | |
| ) | |
| ), | |
| inputs=decoding_strategy, | |
| outputs=temperature, | |
| ) | |
| decoding_strategy.change( | |
| fn=lambda selection: gr.Slider( | |
| visible=( | |
| selection in ["contrastive_sampling", "beam_sampling", "Top P Sampling", "sampling_top_k"] | |
| ) | |
| ), | |
| inputs=decoding_strategy, | |
| outputs=repetition_penalty, | |
| ) | |
| decoding_strategy.change( | |
| fn=lambda selection: gr.Slider(visible=(selection in ["Top P Sampling"])), | |
| inputs=decoding_strategy, | |
| outputs=top_p, | |
| ) | |
| gr.Examples( | |
| examples = examples, | |
| inputs=[image_input, query_input, assistant_prefix, decoding_strategy, temperature, | |
| max_new_tokens, repetition_penalty, top_p], | |
| outputs=output, | |
| fn=model_inference | |
| ) | |
| submit_btn.click(model_inference, inputs = [image_input, query_input, assistant_prefix, decoding_strategy, temperature, | |
| max_new_tokens, repetition_penalty, top_p], outputs=output) | |
| demo.launch(debug=True) |