import gradio as gr 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 # 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", ) 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() output_dir = "./outputs" os.makedirs(output_dir, exist_ok=True) 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, f"{output_dir}/{prompt[:200]}.mp4") print(video_path) return video_path 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( """
A text-to-video generation model that marries the strength and alleviates the weakness of pixel-based and latent-based VDMs.
Paper | Project Page | Github
""" ) 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") gr.HTML(""" """) submit_btn.click(fn=infer, inputs=[prompt_in], outputs=[video_result], api_name="show-1") demo.queue(max_size=12).launch(show_api=True)