#import spaces import os import torch import gradio as gr import sys sys.path.append('./') from videollama2.constants import MMODAL_TOKEN_INDEX, DEFAULT_MMODAL_TOKEN from videollama2.conversation import conv_templates, SeparatorStyle, Conversation from videollama2.model.builder import load_pretrained_model from videollama2.mm_utils import KeywordsStoppingCriteria, tokenizer_MMODAL_token, get_model_name_from_path, process_image, process_video import devicetorch title_markdown = ("""
VideoLLaMA 2 πŸ”₯πŸš€πŸ”₯

VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

If this demo please you, please give us a star ⭐ on Github or πŸ’– on this space.
""") 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, conv_mode, model_base=None, load_8bit=False, load_4bit=False): # disable_torch_init() model_name = get_model_name_from_path(model_path) self.tokenizer, self.model, processor, context_len = load_pretrained_model( model_path, model_base, model_name, load_8bit, load_4bit, offload_folder="save_folder") self.processor = processor self.conv_mode = conv_mode self.conv = conv_templates[conv_mode].copy() def get_prompt(self, qs, state): state.append_message(state.roles[0], qs) state.append_message(state.roles[1], None) return state # @spaces.GPU(duration=120) @torch.inference_mode() def generate(self, tensor: list, modals: list, prompt: str, first_run: bool, state, temperature, top_p, max_output_tokens): # TODO: support multiple turns of conversation. assert len(tensor) == len(modals) # 1. prepare model, tokenizer, and processor. tokenizer, model, processor = self.tokenizer, self.model, self.processor # 2. text preprocess (tag process & generate prompt). state = self.get_prompt(prompt, state) prompt = state.get_prompt() input_ids = tokenizer_MMODAL_token(prompt, tokenizer, MMODAL_TOKEN_INDEX[modals[0]], return_tensors='pt') input_ids = input_ids.unsqueeze(0).to(self.model.device) # 3. generate response according to visual signals and prompts. stop_str = self.conv.sep if self.conv.sep_style in [SeparatorStyle.SINGLE] else self.conv.sep2 # keywords = ["", ""] keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, images_or_videos=tensor, modal_list=modals, do_sample=True, temperature=temperature, top_p=top_p, max_new_tokens=max_output_tokens, use_cache=True, stopping_criteria=[stopping_criteria], ) outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] print(outputs) return outputs, state #@spaces.GPU(duration=120) def generate(image, video, state, state_, textbox_in, temperature, top_p, max_output_tokens, dtype=torch.float16): if not textbox_in: if len(state_.messages) > 0: textbox_in = state_.messages[-1][1] state_.messages.pop(-1) else: assert "Please enter instruction" image = image if image else "none" video = video if video else "none" assert not (os.path.exists(image) and os.path.exists(video)) tensor = [] modals = [] if type(state) is not Conversation: state = conv_templates[conv_mode].copy() state_ = conv_templates[conv_mode].copy() first_run = False if len(state.messages) > 0 else True text_en_in = textbox_in.replace("picture", "image") processor = handler.processor if os.path.exists(image) and not os.path.exists(video): tensor.append(process_image(image, processor).to(handler.model.device, dtype=dtype)) modals.append('IMAGE') if not os.path.exists(image) and os.path.exists(video): tensor.append(process_video(video, processor).to(handler.model.device, dtype=dtype)) modals.append('VIDEO') if os.path.exists(image) and os.path.exists(video): raise NotImplementedError("Not support image and video at the same time") # BUG: Only support single video and image inference now. if os.path.exists(image) and not os.path.exists(video): text_en_in = text_en_in.replace(DEFAULT_MMODAL_TOKEN['IMAGE'], '').strip() text_en_in = DEFAULT_MMODAL_TOKEN['IMAGE'] + '\n' + text_en_in if not os.path.exists(image) and os.path.exists(video): text_en_in = text_en_in.replace(DEFAULT_MMODAL_TOKEN['VIDEO'], '').strip() text_en_in = DEFAULT_MMODAL_TOKEN['VIDEO'] + '\n' + text_en_in if os.path.exists(image) and os.path.exists(video): text_en_in = text_en_in.replace(DEFAULT_MMODAL_TOKEN['VIDEO'], '').strip() text_en_in = DEFAULT_MMODAL_TOKEN['VIDEO'] + '\n' + text_en_in text_en_out, state_ = handler.generate(tensor, modals, text_en_in, first_run=first_run, state=state_, temperature=temperature, top_p=top_p, max_output_tokens=max_output_tokens) state_.messages[-1] = (state_.roles[1], text_en_out) text_en_out = text_en_out.split('#')[0] textbox_out = text_en_out show_images = "" if os.path.exists(image): show_images += f'' if os.path.exists(video): show_images += f'' state.append_message(state.roles[0], textbox_in + "\n" + show_images) state.append_message(state.roles[1], textbox_out) # BUG: only support single turn conversation now. state_.messages.pop(-1) state_.messages.pop(-1) 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), state.to_gradio_chatbot(), state, state_) def regenerate(state, state_): state.messages.pop(-1) state.messages.pop(-1) if len(state.messages) > 0: return state.to_gradio_chatbot(), state, state_ return state.to_gradio_chatbot(), state, state_ def clear_history(state, state_): state = conv_templates[conv_mode].copy() state_ = conv_templates[conv_mode].copy() return (gr.update(value=None, interactive=True), gr.update(value=None, interactive=True), state.to_gradio_chatbot(), state, state_, 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' device = devicetorch.get(torch) #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 πŸ”₯πŸš€πŸ”₯', theme=theme, css=block_css) as demo: gr.Markdown(title_markdown) state = gr.State() state_ = 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="πŸ‘ Upvote", interactive=True) downvote_btn = gr.Button(value="πŸ‘Ž 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="πŸ”„ Regenerate", interactive=True) clear_btn = gr.Button(value="πŸ—‘οΈ 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 are the things I should be cautious about when I visit here?", ], [ f"{cur_dir}/examples/desert.jpg", "If there are factual errors in the questions, point it out; if not, proceed answering the 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, state, state_, textbox, temperature, top_p, max_output_tokens], [image, video, chatbot, state, state_]) regenerate_btn.click( regenerate, [state, state_], [chatbot, state, state_]).then( generate, [image, video, state, state_, textbox, temperature, top_p, max_output_tokens], [image, video, chatbot, state, state_]) clear_btn.click( clear_history, [state, state_], [image, video, chatbot, state, state_, textbox]) demo.launch()