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| import subprocess | |
| subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) | |
| import os | |
| import sys | |
| import gradio as gr | |
| # prototyping | |
| # from demo_test import Text2Video, Video2Video | |
| from demo.t2v import Text2Video | |
| t2v_examples = [ | |
| ['walk fast clean',16,], | |
| ['run fast clean',16,], | |
| ['standing up',16], | |
| ['doing the splits',16], | |
| ['doing backflips',16], | |
| ['a headstand',16], | |
| ['karate kick',16], | |
| ['crunch abs',16], | |
| ['doing push ups',16], | |
| ] | |
| def do_nothing(): | |
| return | |
| def videocrafter_demo(result_dir='./tmp/'): | |
| text2video = Text2Video(result_dir) | |
| # video2video = Video2Video(result_dir) | |
| # tex | |
| with gr.Blocks(analytics_enabled=False) as videocrafter_iface: | |
| gr.Markdown("<div align='center'> \ | |
| <h2> GenRL: Multimodal foundation world models for generalist embodied agents </h2> \ | |
| <a style='font-size:18px;' href='https://github.com/mazpie/genrl'> [Github] </a> \ | |
| \ | |
| <a style='font-size:18px;' href='https://huggingface.co/mazpie/genrl_models'> [Models] </a> \ | |
| \ | |
| <a style='font-size:18px;' href='https://huggingface.co/datasets/mazpie/genrl_datasets'> [Datasets] </a> \ | |
| </div>") | |
| gr.Markdown("<b> Notes: </b>") | |
| gr.Markdown("<b> - Low quality of the videos generated is expected, as the work focuses on visual-language alignment for behavior learning, not on video generation quality.</b>") | |
| gr.Markdown("<b> - The model is trained on small 64x64 images, and the videos are generated only from a small 512-dimensional embedding. </b>") | |
| gr.Markdown("<b> - Some prompts require styling instructions, e.g. fast, clean, in order to work well. See some of the examples. </b>") | |
| #######t2v####### | |
| with gr.Tab(label="Text2Video"): | |
| with gr.Column(): | |
| with gr.Row(): # .style(equal_height=False) | |
| with gr.Column(): | |
| input_text = gr.Text(label='prompt') | |
| duration = gr.Slider(minimum=8, maximum=32, elem_id=f"duration", label="duration", value=16, step=8) | |
| send_btn = gr.Button("Send") | |
| with gr.Column(): # label='result', | |
| pass | |
| with gr.Column(): # label='result', | |
| output_video_1 = gr.Video(autoplay=True, width=256, height=256) | |
| with gr.Row(): | |
| gr.Examples(examples=t2v_examples, | |
| inputs=[input_text,duration], | |
| outputs=[output_video_1], | |
| fn=text2video.get_prompt, | |
| cache_examples=False) | |
| #cache_examples=os.getenv('SYSTEM') == 'spaces') | |
| send_btn.click( | |
| fn=text2video.get_prompt, | |
| inputs=[input_text,duration], | |
| outputs=[output_video_1], | |
| ) | |
| input_text.submit( | |
| fn=text2video.get_prompt, | |
| inputs=[input_text,duration], | |
| outputs=[output_video_1], | |
| ) | |
| return videocrafter_iface | |
| if __name__ == "__main__": | |
| result_dir = os.path.join('./', 'results') | |
| videocrafter_iface = videocrafter_demo(result_dir) | |
| videocrafter_iface.queue() # concurrency_count=1, max_size=10 | |
| videocrafter_iface.launch() | |
| # videocrafter_iface.launch(server_name='0.0.0.0', server_port=80) |