optimize

app.py

@@ -224,8 +224,8 @@ def optimize_stage_2(image_block: Image.Image, seed_slider: int):
     process_dg4d(os.path.join("configs", "4d_demo.yaml"), os.path.join("tmp_data", f"{img_hash}_rgba.png"), guidance_zero123)
     # os.rename(os.path.join('logs', f'{img_hash}_rgba_frames'), os.path.join('logs', f'{img_hash}_{seed_slider:03d}_rgba_frames'))
     image_dir = os.path.join('logs', f'{img_hash}_rgba_frames')
-    # return os.path.join('vis_data', f'{img_hash}_rgba.mp4'), [image_dir+f'/{t:03d}.ply' for t in range(28)]
-    return [image_dir+f'/{t:03d}.ply' for t in range(28)]
+    return os.path.join('vis_data', f'{img_hash}_rgba.mp4'), [image_dir+f'/{t:03d}.ply' for t in range(28)]
+    # return [image_dir+f'/{t:03d}.ply' for t in range(28)]
 
 
 if __name__ == "__main__":
@@ -287,8 +287,8 @@ if __name__ == "__main__":
                         dirving_video = gr.Video(label="video",height=290)
                     with gr.Column(scale=5):
                         obj3d = gr.Video(label="3D Model",height=290)
-                # video4d =  gr.Video(label="4D video",height=290)
-                obj4d = Model4DGS(label="4D Model", height=500, fps=14)
+                video4d =  gr.Video(label="4D video",height=290)
+                obj4d = Model4DGS(label="4D Model", height=500, fps=28)
 
             
             img_run_btn.click(check_img_input, inputs=[image_block], queue=False).success(optimize_stage_0,
@@ -304,7 +304,7 @@ if __name__ == "__main__":
                                                                                                   seed_slider2],
                                                                                           outputs=[
                                                                                               obj3d])
-            fourd_run_btn.click(check_video_3d_input, inputs=[image_block], queue=False).success(optimize_stage_2, inputs=[image_block, seed_slider], outputs=[obj4d])
+            fourd_run_btn.click(check_video_3d_input, inputs=[image_block], queue=False).success(optimize_stage_2, inputs=[image_block, seed_slider], outputs=[video4d, obj4d])
 
     # demo.queue().launch(share=True)
     demo.queue(max_size=10)  # <-- Sets up a queue with default parameters

configs/4d_demo.yaml

@@ -30,7 +30,7 @@ lambda_svd: 0
 # training batch size per iter
 batch_size: 7
 # training iterations for stage 1
-iters: 400
+iters: 300
 # training iterations for stage 2
 iters_refine: 50
 # training camera radius

lgm/core/models.py

@@ -155,7 +155,8 @@ class LGM(nn.Module):
         gaussians_orig_res = torch.cat([pos, opacity, scale, rotation, rgbs], dim=-1) # [B, N, 14]
         
         
-        return gaussians, gaussians_orig_res
+        # return gaussians, gaussians_orig_res
+        return gaussians_orig_res, gaussians_orig_res
 
     
     def forward(self, data, step_ratio=1):