Modify the model to svd

app.py

@@ -22,11 +22,11 @@ mpl.use('agg')
 
 #### Description ####
 title = r"""<h1 align="center">CameraCtrl: Enabling Camera Control for Video Diffusion Models</h1>"""
-subtitle = r"""<h2 align="center">CameraCtrl Image2Video with <a href='https://arxiv.org/abs/2311.15127' target='_blank'> <b>Stable Video Diffusion (SVD)</b> </a>-xt <a href='https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt' target='_blank'> <b> model </b> </a> </h2>"""
+subtitle = r"""<h2 align="center">CameraCtrl Image2Video with <a href='https://arxiv.org/abs/2311.15127' target='_blank'> <b>Stable Video Diffusion (SVD)</b> </a> <a href='https://huggingface.co/stabilityai/stable-video-diffusion-img2vid' target='_blank'> <b> model </b> </a> </h2>"""
 description = r"""
 <b>Official Gradio demo</b> for <a href='https://github.com/hehao13/CameraCtrl' target='_blank'><b>CameraCtrl: Enabling Camera Control for Video Diffusion Models</b></a>.<br>
 CameraCtrl is capable of precisely controlling the camera trajectory during the video generation process.<br>
-Note that, with SVD-xt, CameraCtrl only support Image2Video now.<br>
+Note that, with SVD, CameraCtrl only support Image2Video now.<br>
 """
 
 closing_words = r"""
@@ -65,16 +65,16 @@ RESIZE_MODES = ['Resize then Center Crop', 'Directly resize']
 CAMERA_TRAJECTORY_MODES = ["Provided Camera Trajectories", "Custom Camera Trajectories"]
 height = 320
 width = 576
-num_frames = 25
+num_frames = 14
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-config = "configs/train_cameractrl/svdxt_320_576_cameractrl.yaml"
-model_id = "stabilityai/stable-video-diffusion-img2vid-xt"
+config = "configs/train_cameractrl/svd_320_576_cameractrl.yaml"
+model_id = "stabilityai/stable-video-diffusion-img2vid"
 ckpt = "checkpoints/CameraCtrl_svdxt.ckpt"
 if not os.path.exists(ckpt):
     os.makedirs("checkpoints", exist_ok=True)
-    os.system("wget -c https://huggingface.co/hehao13/CameraCtrl_SVD_ckpts/resolve/main/CameraCtrl_svdxt.ckpt?download=true")
-    os.system("mv CameraCtrl_svdxt.ckpt?download=true checkpoints/CameraCtrl_svdxt.ckpt")
+    os.system("wget -c https://huggingface.co/hehao13/CameraCtrl_SVD_ckpts/resolve/main/CameraCtrl_svd.ckpt?download=true")
+    os.system("mv CameraCtrl_svd.ckpt?download=true checkpoints/CameraCtrl_svdxt.ckpt")
 model_config = OmegaConf.load(config)
 
 
@@ -333,8 +333,31 @@ def update_buttons_for_example(example_image, example_traj_path, provided_traj_n
            gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), \
            gr.update(visible=True)
 
-@spaces.GPU
-@torch.inference_mode()
+
+# @torch.inference_mode()
+# @spaces.GPU(duration=150)
+# def sample(condition_image, plucker_embedding, height, width, num_frames, num_inference_step, min_guidance_scale, max_guidance_scale, fps_id, generator):
+#     res = pipeline(
+#         image=condition_image,
+#         pose_embedding=plucker_embedding,
+#         height=height,
+#         width=width,
+#         num_frames=num_frames,
+#         num_inference_steps=num_inference_step,
+#         min_guidance_scale=min_guidance_scale,
+#         max_guidance_scale=max_guidance_scale,
+#         fps=fps_id,
+#         do_image_process=True,
+#         generator=generator,
+#         output_type='pt'
+#     ).frames[0].transpose(0, 1).cpu()
+#
+#     temporal_video_path = tempfile.NamedTemporaryFile(suffix='.mp4').name
+#     save_videos_grid(res[None], temporal_video_path, rescale=False)
+#     return temporal_video_path
+
+
+@spaces.GPU(duration=120)
 def sample_video(condition_image, trajectory_file, num_inference_step, min_guidance_scale, max_guidance_scale, fps_id, seed):
     global height, width, num_frames, device, pipeline
     with open(trajectory_file, 'r') as f:
@@ -384,8 +407,9 @@ def sample_video(condition_image, trajectory_file, num_inference_step, min_guida
 
     temporal_video_path = tempfile.NamedTemporaryFile(suffix='.mp4').name
     save_videos_grid(sample[None], temporal_video_path, rescale=False)
-
     return temporal_video_path
+    # return sample(condition_image, plucker_embedding, height, width, num_frames, num_inference_step, min_guidance_scale, max_guidance_scale, fps_id, generator)
+
 
 
 def main(args):

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