Create app.py

app.py

@@ -0,0 +1,309 @@
+import os
+
+os.system("git clone https://github.com/showlab/Show-1.git")
+
+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(
+            """
+            <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")
+
+        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],
+                    api_name="show-1")
+    
+demo.queue(max_size=12).launch(show_api=True)