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Running
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Zero
| import spaces | |
| import os | |
| import torch | |
| import gradio as gr | |
| import sys | |
| sys.path.append('./VideoLLaMA2') | |
| from videollama2 import model_init, mm_infer | |
| from videollama2.utils import disable_torch_init | |
| title_markdown = (""" | |
| <div style="display: flex; justify-content: center; align-items: center; text-align: center;"> | |
| <a href="https://github.com/DAMO-NLP-SG/VideoLLaMA2" style="margin-right: 20px; text-decoration: none; display: flex; align-items: center;"> | |
| <img src="https://s2.loli.net/2024/06/03/D3NeXHWy5az9tmT.png" alt="VideoLLaMA 2 🔥🚀🔥" style="max-width: 120px; height: auto;"> | |
| </a> | |
| <div> | |
| <h1 >VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs</h1> | |
| <h5 style="margin: 0;">If this demo please you, please give us a star ⭐ on Github or 💖 on this space.</h5> | |
| <h6 style="margin: 0;">Note that the current demo only supports <b>vision input</b> and <b>single-turn conversation</b>. More features will be available soon.</h6> | |
| </div> | |
| </div> | |
| <div align="center"> | |
| <div style="display:flex; gap: 0.25rem; margin-top: 10px;" align="center"> | |
| <a href="https://github.com/DAMO-NLP-SG/VideoLLaMA2"><img src='https://img.shields.io/badge/Github-VideoLLaMA2-9C276A'></a> | |
| <a href="https://arxiv.org/pdf/2406.07476.pdf"><img src="https://img.shields.io/badge/Arxiv-2406.07476-AD1C18"></a> | |
| <a href="https://github.com/DAMO-NLP-SG/VideoLLaMA2/stargazers"><img src="https://img.shields.io/github/stars/DAMO-NLP-SG/VideoLLaMA2.svg?style=social"></a> | |
| </div> | |
| </div> | |
| """) | |
| block_css = """ | |
| #buttons button { | |
| min-width: min(120px,100%); | |
| color: #9C276A | |
| } | |
| """ | |
| tos_markdown = (""" | |
| ### Terms of use | |
| By using this service, users are required to agree to the following terms: | |
| The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research. | |
| Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator. | |
| For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality. | |
| """) | |
| learn_more_markdown = (""" | |
| ### License | |
| This project is released under the Apache 2.0 license as found in the LICENSE file. The service is a research preview intended for non-commercial use ONLY, subject to the model Licenses of LLaMA and Mistral, Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please get in touch with us if you find any potential violations. | |
| """) | |
| plum_color = gr.themes.colors.Color( | |
| name='plum', | |
| c50='#F8E4EF', | |
| c100='#E9D0DE', | |
| c200='#DABCCD', | |
| c300='#CBA8BC', | |
| c400='#BC94AB', | |
| c500='#AD809A', | |
| c600='#9E6C89', | |
| c700='#8F5878', | |
| c800='#804467', | |
| c900='#713056', | |
| c950='#662647', | |
| ) | |
| class Chat: | |
| def __init__(self, model_path, load_8bit=False, load_4bit=False): | |
| disable_torch_init() | |
| self.model, self.processor, self.tokenizer = model_init(model_path, load_8bit=load_8bit, load_4bit=load_4bit) | |
| def generate(self, data: list, message, temperature, top_p, max_output_tokens): | |
| # TODO: support multiple turns of conversation. | |
| assert len(data) == 1 | |
| tensor, modal = data[0] | |
| response = mm_infer(tensor, message, self.model, self.tokenizer, modal=modal.strip('<>'), | |
| do_sample=True if temperature > 0.0 else False, | |
| temperature=temperature, | |
| top_p=top_p, | |
| max_new_tokens=max_output_tokens) | |
| return response | |
| def generate(image, video, message, chatbot, textbox_in, temperature, top_p, max_output_tokens, dtype=torch.float16): | |
| data = [] | |
| image = image if image else "none" | |
| video = video if video else "none" | |
| assert not (os.path.exists(image) and os.path.exists(video)) | |
| processor = handler.processor | |
| if os.path.exists(image) and not os.path.exists(video): | |
| data.append((processor['image'](image).to(handler.model.device, dtype=dtype), '<image>')) | |
| if not os.path.exists(image) and os.path.exists(video): | |
| data.append((processor['video'](video).to(handler.model.device, dtype=dtype), '<video>')) | |
| if os.path.exists(image) and os.path.exists(video): | |
| raise NotImplementedError("Not support image and video at the same time") | |
| assert len(message) % 2 == 0, "The message should be a pair of user and system message." | |
| message.append({'role': 'user', 'content': textbox_in}) | |
| text_en_out = handler.generate(data, message, temperature=temperature, top_p=top_p, max_output_tokens=max_output_tokens) | |
| message.append({'role': 'assistant', 'content': text_en_out}) | |
| show_images = "" | |
| if os.path.exists(image): | |
| show_images += f'<img src="./file={image}" style="display: inline-block;width: 250px;max-height: 400px;">' | |
| if os.path.exists(video): | |
| show_images += f'<video controls playsinline width="500" style="display: inline-block;" src="./file={video}"></video>' | |
| chatbot.append([textbox_in + "\n" + show_images, text_en_out]) | |
| return ( | |
| gr.update(value=image if os.path.exists(image) else None, interactive=True), | |
| gr.update(value=video if os.path.exists(video) else None, interactive=True), | |
| message, | |
| chatbot) | |
| def regenerate(message, chatbot): | |
| message.pop(-1), message.pop(-1) | |
| chatbot.pop(-1) | |
| return message, chatbot | |
| def clear_history(message, chatbot): | |
| message.clear(), chatbot.clear() | |
| return (gr.update(value=None, interactive=True), | |
| gr.update(value=None, interactive=True), | |
| message, chatbot, | |
| gr.update(value=None, interactive=True)) | |
| # BUG of Zero Environment | |
| # 1. The environment is fixed to torch==2.0.1+cu117, gradio>=4.x.x | |
| # 2. The operation or tensor which requires cuda are limited in those functions wrapped via spaces.GPU | |
| # 3. The function can't return tensor or other cuda objects. | |
| conv_mode = "llama_2" | |
| model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B-16F' | |
| device = torch.device("cuda") | |
| handler = Chat(model_path, conv_mode=conv_mode, load_8bit=False, load_4bit=True) | |
| textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False) | |
| theme = gr.themes.Default(primary_hue=plum_color) | |
| theme.set(slider_color="#9C276A") | |
| theme.set(block_title_text_color="#9C276A") | |
| theme.set(block_label_text_color="#9C276A") | |
| theme.set(button_primary_text_color="#9C276A") | |
| # theme.set(button_secondary_text_color="*neutral_800") | |
| with gr.Blocks(title='VideoLLaMA 2 🔥🚀🔥', theme=theme, css=block_css) as demo: | |
| gr.Markdown(title_markdown) | |
| message = gr.State([]) | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| image = gr.Image(label="Input Image", type="filepath") | |
| video = gr.Video(label="Input Video") | |
| with gr.Accordion("Parameters", open=True) as parameter_row: | |
| # num_beams = gr.Slider( | |
| # minimum=1, | |
| # maximum=10, | |
| # value=1, | |
| # step=1, | |
| # interactive=True, | |
| # label="beam search numbers", | |
| # ) | |
| temperature = gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.2, | |
| step=0.1, | |
| interactive=True, | |
| label="Temperature", | |
| ) | |
| top_p = gr.Slider( | |
| minimum=0.0, | |
| maximum=1.0, | |
| value=0.7, | |
| step=0.1, | |
| interactive=True, | |
| label="Top P", | |
| ) | |
| max_output_tokens = gr.Slider( | |
| minimum=64, | |
| maximum=1024, | |
| value=512, | |
| step=64, | |
| interactive=True, | |
| label="Max output tokens", | |
| ) | |
| with gr.Column(scale=7): | |
| chatbot = gr.Chatbot(label="VideoLLaMA 2", bubble_full_width=True, height=750) | |
| with gr.Row(): | |
| with gr.Column(scale=8): | |
| textbox.render() | |
| with gr.Column(scale=1, min_width=50): | |
| submit_btn = gr.Button(value="Send", variant="primary", interactive=True) | |
| with gr.Row(elem_id="buttons") as button_row: | |
| upvote_btn = gr.Button(value="👍 Upvote", interactive=True) | |
| downvote_btn = gr.Button(value="👎 Downvote", interactive=True) | |
| # flag_btn = gr.Button(value="⚠️ Flag", interactive=True) | |
| # stop_btn = gr.Button(value="⏹️ Stop Generation", interactive=False) | |
| regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=True) | |
| clear_btn = gr.Button(value="🗑️ Clear history", interactive=True) | |
| with gr.Row(): | |
| with gr.Column(): | |
| cur_dir = os.path.dirname(os.path.abspath(__file__)) | |
| gr.Examples( | |
| examples=[ | |
| [ | |
| f"{cur_dir}/examples/sora.png", | |
| "What happens in this image?", | |
| ], | |
| [ | |
| f"{cur_dir}/examples/waterview.jpg", | |
| "What should I be cautious about when I visit the scenic area in this image?", | |
| ], | |
| [ | |
| f"{cur_dir}/examples/desert.jpg", | |
| "If there are factual errors in the questions, point them out; if not, proceed to answer the following question. What’s happening in the desert?", | |
| ], | |
| ], | |
| inputs=[image, textbox], | |
| ) | |
| with gr.Column(): | |
| gr.Examples( | |
| examples=[ | |
| [ | |
| f"{cur_dir}/examples/rap.mp4", | |
| "What happens in this video?", | |
| ], | |
| [ | |
| f"{cur_dir}/examples/demo2.mp4", | |
| "Do you think it's morning or night in this video? Why?", | |
| ], | |
| [ | |
| f"{cur_dir}/examples/demo3.mp4", | |
| "At the intersection, in which direction does the red car turn?", | |
| ], | |
| ], | |
| inputs=[video, textbox], | |
| ) | |
| # gr.Markdown(tos_markdown) | |
| gr.Markdown(learn_more_markdown) | |
| submit_btn.click( | |
| generate, | |
| [image, video, message, chatbot, textbox, temperature, top_p, max_output_tokens], | |
| [image, video, message, chatbot]) | |
| regenerate_btn.click( | |
| regenerate, | |
| [message, chatbot], | |
| [message, chatbot]).then( | |
| generate, | |
| [image, video, message, chatbot, textbox, temperature, top_p, max_output_tokens], | |
| [image, video, message, chatbot]) | |
| clear_btn.click( | |
| clear_history, | |
| [message, chatbot], | |
| [image, video, message, chatbot, textbox]) | |
| demo.launch() | |