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| import gradio as gr | |
| from share_btn import community_icon_html, loading_icon_html, share_js | |
| import torch | |
| from diffusers.utils import export_to_video | |
| import os | |
| from PIL import Image | |
| import torch.nn.functional as F | |
| from diffusers import IFSuperResolutionPipeline, VideoToVideoSDPipeline | |
| from diffusers.utils import export_to_video | |
| from diffusers.utils.torch_utils import randn_tensor | |
| from showone.pipelines import TextToVideoIFPipeline, TextToVideoIFInterpPipeline, TextToVideoIFSuperResolutionPipeline | |
| from showone.pipelines.pipeline_t2v_base_pixel import tensor2vid | |
| from showone.pipelines.pipeline_t2v_sr_pixel_cond import TextToVideoIFSuperResolutionPipeline_Cond | |
| HF_TOKEN = os.getenv('HF_TOKEN') | |
| # Base Model | |
| pretrained_model_path = "showlab/show-1-base" | |
| pipe_base = TextToVideoIFPipeline.from_pretrained( | |
| pretrained_model_path, | |
| torch_dtype=torch.float16, | |
| variant="fp16" | |
| ) | |
| pipe_base.enable_model_cpu_offload() | |
| # Interpolation Model | |
| pretrained_model_path = "showlab/show-1-interpolation" | |
| pipe_interp_1 = TextToVideoIFInterpPipeline.from_pretrained( | |
| pretrained_model_path, | |
| text_encoder=None, | |
| torch_dtype=torch.float16, | |
| variant="fp16" | |
| ) | |
| pipe_interp_1.enable_model_cpu_offload() | |
| # Super-Resolution Model 1 | |
| # Image super-resolution model from DeepFloyd https://huggingface.co/DeepFloyd/IF-II-L-v1.0 | |
| pretrained_model_path = "DeepFloyd/IF-II-L-v1.0" | |
| pipe_sr_1_image = IFSuperResolutionPipeline.from_pretrained( | |
| pretrained_model_path, | |
| text_encoder=None, | |
| torch_dtype=torch.float16, | |
| variant="fp16", | |
| use_auth_token=HF_TOKEN | |
| ) | |
| pipe_sr_1_image.enable_model_cpu_offload() | |
| pretrained_model_path = "showlab/show-1-sr1" | |
| pipe_sr_1_cond = TextToVideoIFSuperResolutionPipeline_Cond.from_pretrained( | |
| pretrained_model_path, | |
| text_encoder=None, | |
| torch_dtype=torch.float16 | |
| ) | |
| pipe_sr_1_cond.enable_model_cpu_offload() | |
| # Super-Resolution Model 2 | |
| pretrained_model_path = "showlab/show-1-sr2" | |
| pipe_sr_2 = VideoToVideoSDPipeline.from_pretrained( | |
| pretrained_model_path, | |
| torch_dtype=torch.float16 | |
| ) | |
| pipe_sr_2.enable_model_cpu_offload() | |
| pipe_sr_2.enable_vae_slicing() | |
| def infer(prompt): | |
| print(prompt) | |
| negative_prompt = "low resolution, blur" | |
| # Text embeds | |
| prompt_embeds, negative_embeds = pipe_base.encode_prompt(prompt) | |
| # Keyframes generation (8x64x40, 2fps) | |
| video_frames = pipe_base( | |
| prompt_embeds=prompt_embeds, | |
| negative_prompt_embeds=negative_embeds, | |
| num_frames=8, | |
| height=40, | |
| width=64, | |
| num_inference_steps=75, | |
| guidance_scale=9.0, | |
| output_type="pt" | |
| ).frames | |
| # Frame interpolation (8x64x40, 2fps -> 29x64x40, 7.5fps) | |
| bsz, channel, num_frames, height, width = video_frames.shape | |
| new_num_frames = 3 * (num_frames - 1) + num_frames | |
| new_video_frames = torch.zeros((bsz, channel, new_num_frames, height, width), | |
| dtype=video_frames.dtype, device=video_frames.device) | |
| new_video_frames[:, :, torch.arange(0, new_num_frames, 4), ...] = video_frames | |
| init_noise = randn_tensor((bsz, channel, 5, height, width), dtype=video_frames.dtype, | |
| device=video_frames.device) | |
| for i in range(num_frames - 1): | |
| batch_i = torch.zeros((bsz, channel, 5, height, width), dtype=video_frames.dtype, device=video_frames.device) | |
| batch_i[:, :, 0, ...] = video_frames[:, :, i, ...] | |
| batch_i[:, :, -1, ...] = video_frames[:, :, i + 1, ...] | |
| batch_i = pipe_interp_1( | |
| pixel_values=batch_i, | |
| prompt_embeds=prompt_embeds, | |
| negative_prompt_embeds=negative_embeds, | |
| num_frames=batch_i.shape[2], | |
| height=40, | |
| width=64, | |
| num_inference_steps=50, | |
| guidance_scale=4.0, | |
| output_type="pt", | |
| init_noise=init_noise, | |
| cond_interpolation=True, | |
| ).frames | |
| new_video_frames[:, :, i * 4:i * 4 + 5, ...] = batch_i | |
| video_frames = new_video_frames | |
| # Super-resolution 1 (29x64x40 -> 29x256x160) | |
| bsz, channel, num_frames, height, width = video_frames.shape | |
| window_size, stride = 8, 7 | |
| new_video_frames = torch.zeros( | |
| (bsz, channel, num_frames, height * 4, width * 4), | |
| dtype=video_frames.dtype, | |
| device=video_frames.device) | |
| for i in range(0, num_frames - window_size + 1, stride): | |
| batch_i = video_frames[:, :, i:i + window_size, ...] | |
| if i == 0: | |
| first_frame_cond = pipe_sr_1_image( | |
| image=video_frames[:, :, 0, ...], | |
| prompt_embeds=prompt_embeds, | |
| negative_prompt_embeds=negative_embeds, | |
| height=height * 4, | |
| width=width * 4, | |
| num_inference_steps=50, | |
| guidance_scale=4.0, | |
| noise_level=150, | |
| output_type="pt" | |
| ).images | |
| first_frame_cond = first_frame_cond.unsqueeze(2) | |
| else: | |
| first_frame_cond = new_video_frames[:, :, i:i + 1, ...] | |
| batch_i = pipe_sr_1_cond( | |
| image=batch_i, | |
| prompt_embeds=prompt_embeds, | |
| negative_prompt_embeds=negative_embeds, | |
| first_frame_cond=first_frame_cond, | |
| height=height * 4, | |
| width=width * 4, | |
| num_inference_steps=50, | |
| guidance_scale=7.0, | |
| noise_level=250, | |
| output_type="pt" | |
| ).frames | |
| new_video_frames[:, :, i:i + window_size, ...] = batch_i | |
| video_frames = new_video_frames | |
| # Super-resolution 2 (29x256x160 -> 29x576x320) | |
| video_frames = [Image.fromarray(frame).resize((576, 320)) for frame in tensor2vid(video_frames.clone())] | |
| video_frames = pipe_sr_2( | |
| prompt, | |
| negative_prompt=negative_prompt, | |
| video=video_frames, | |
| strength=0.8, | |
| num_inference_steps=50, | |
| ).frames | |
| video_path = export_to_video(video_frames) | |
| print(video_path) | |
| return video_path, gr.Group.update(visible=True) | |
| css = """ | |
| #col-container {max-width: 510px; margin-left: auto; margin-right: auto;} | |
| a {text-decoration-line: underline; font-weight: 600;} | |
| .animate-spin { | |
| animation: spin 1s linear infinite; | |
| } | |
| @keyframes spin { | |
| from { | |
| transform: rotate(0deg); | |
| } | |
| to { | |
| transform: rotate(360deg); | |
| } | |
| } | |
| #share-btn-container { | |
| display: flex; | |
| padding-left: 0.5rem !important; | |
| padding-right: 0.5rem !important; | |
| background-color: #000000; | |
| justify-content: center; | |
| align-items: center; | |
| border-radius: 9999px !important; | |
| max-width: 15rem; | |
| height: 36px; | |
| } | |
| div#share-btn-container > div { | |
| flex-direction: row; | |
| background: black; | |
| align-items: center; | |
| } | |
| #share-btn-container:hover { | |
| background-color: #060606; | |
| } | |
| #share-btn { | |
| all: initial; | |
| color: #ffffff; | |
| font-weight: 600; | |
| cursor:pointer; | |
| font-family: 'IBM Plex Sans', sans-serif; | |
| margin-left: 0.5rem !important; | |
| padding-top: 0.5rem !important; | |
| padding-bottom: 0.5rem !important; | |
| right:0; | |
| } | |
| #share-btn * { | |
| all: unset; | |
| } | |
| #share-btn-container div:nth-child(-n+2){ | |
| width: auto !important; | |
| min-height: 0px !important; | |
| } | |
| #share-btn-container .wrap { | |
| display: none !important; | |
| } | |
| #share-btn-container.hidden { | |
| display: none!important; | |
| } | |
| img[src*='#center'] { | |
| display: inline-block; | |
| margin: unset; | |
| } | |
| .footer { | |
| margin-bottom: 45px; | |
| margin-top: 10px; | |
| text-align: center; | |
| border-bottom: 1px solid #e5e5e5; | |
| } | |
| .footer>p { | |
| font-size: .8rem; | |
| display: inline-block; | |
| padding: 0 10px; | |
| transform: translateY(10px); | |
| background: white; | |
| } | |
| .dark .footer { | |
| border-color: #303030; | |
| } | |
| .dark .footer>p { | |
| background: #0b0f19; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown( | |
| """ | |
| <h1 style="text-align: center;">Show-1 Text-to-Video</h1> | |
| <p style="text-align: center;"> | |
| A text-to-video generation model that marries the strength and alleviates the weakness of pixel-based and latent-based VDMs. <br /> | |
| </p> | |
| <p style="text-align: center;"> | |
| <a href="https://arxiv.org/abs/2309.15818" target="_blank">Paper</a> | | |
| <a href="https://showlab.github.io/Show-1" target="_blank">Project Page</a> | | |
| <a href="https://github.com/showlab/Show-1" target="_blank">Github</a> | |
| </p> | |
| """ | |
| ) | |
| prompt_in = gr.Textbox(label="Prompt", placeholder="A panda taking a selfie", elem_id="prompt-in") | |
| #neg_prompt = gr.Textbox(label="Negative prompt", value="text, watermark, copyright, blurry, nsfw", elem_id="neg-prompt-in") | |
| #inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False) | |
| submit_btn = gr.Button("Submit") | |
| video_result = gr.Video(label="Video Output", elem_id="video-output") | |
| with gr.Row(): | |
| with gr.Group(elem_id="share-btn-container", visible=False) as share_group: | |
| community_icon = gr.HTML(community_icon_html) | |
| loading_icon = gr.HTML(loading_icon_html) | |
| share_button = gr.Button("Share with Community", elem_id="share-btn") | |
| gr.Markdown(""" | |
| [](https://huggingface.co/spaces/showlab/Show-1?duplicate=true) | |
| """) | |
| gr.HTML(""" | |
| <div class="footer"> | |
| <p> | |
| Demo adapted from <a href="https://huggingface.co/spaces/fffiloni/zeroscope" target="_blank">zeroscope</a> | |
| by 🤗 <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> | |
| </p> | |
| </div> | |
| """) | |
| submit_btn.click(fn=infer, | |
| inputs=[prompt_in], | |
| outputs=[video_result, share_group], | |
| api_name="show-1") | |
| share_button.click(None, [], [], _js=share_js) | |
| demo.queue(max_size=12).launch(show_api=True) |