update sv4d sampling script and readme

scripts/demo/sv4d_helpers.py

@@ -36,6 +36,20 @@ from sgm.modules.diffusionmodules.sampling import (
 from sgm.util import default, instantiate_from_config
 
 
+def load_module_gpu(model):
+    model.cuda()
+
+
+def unload_module_gpu(model):
+    model.cpu()
+    torch.cuda.empty_cache()
+
+
+def initial_model_load(model):
+    model.model.half()
+    return model
+
+
 def get_resizing_factor(
     desired_shape: Tuple[int, int], current_shape: Tuple[int, int]
 ) -> float:
@@ -60,75 +74,11 @@ def get_resizing_factor(
     return factor
 
 
-def load_img_for_prediction_no_st(
-    image_path: str,
-    mask_path: str,
-    W: int,
-    H: int,
-    crop_h: int,
-    crop_w: int,
-    device="cuda",
-) -> torch.Tensor:
-    image = Image.open(image_path)
-    if image is None:
-        return None
-    image = np.array(image).astype(np.float32) / 255
-    h, w = image.shape[:2]
-    rotated = 0
-
-    mask = None
-    if mask_path is not None:
-        mask = Image.open(mask_path)
-        mask = np.array(mask).astype(np.float32) / 255
-        mask = np.any(mask.reshape(h, w, -1) > 0, axis=2, keepdims=True).astype(
-            np.float32
-        )
-    elif image.shape[-1] == 4:
-        mask = image[:, :, 3:]
-
-    if mask is not None:
-        image = image[:, :, :3] * mask + (1 - mask)
-        # if "DAVIS" in image_path:
-        #     y, x, _ = np.where(mask > 0)
-        #     x_mean, y_mean = np.mean(x), np.mean(y)
-        # else:
-        #     x_mean, y_mean = w//2, h//2
-        # h_new = int(max(crop_h, crop_w) * 1.33)
-        # x_min = max(int(x_mean - h_new//2), 0)
-        # y_min = max(int(y_mean - h_new//2), 0)
-        # image_cropped = image[y_min : y_min + h_new, x_min : x_min + h_new]
-        # h_crop, w_crop = image_cropped.shape[:2]
-        # h_new = max(h_crop, w_crop)
-        # top = max((h_new - h_crop) // 2, 0)
-        # left = max((h_new - w_crop) // 2, 0)
-        # image_padded = np.ones((h_new, h_new, 3)).astype(np.float32)
-        # image_padded[top : top + h_crop, left : left + w_crop, :] = image_cropped
-        # image = image_padded
-        # h, w = image.shape[:2]
-
-    image = image.transpose(2, 0, 1)
-    image = torch.from_numpy(image).to(dtype=torch.float32)
-    image = image.unsqueeze(0)
-
-    rfs = get_resizing_factor((H, W), (h, w))
-    resize_size = [int(np.ceil(rfs * s)) for s in (h, w)]
-    top = (resize_size[0] - H) // 2
-    left = (resize_size[1] - W) // 2
-
-    image = torch.nn.functional.interpolate(
-        image, resize_size, mode="area", antialias=False
-    )
-    image = TT.functional.crop(image, top=top, left=left, height=H, width=W)
-    return image.to(device) * 2.0 - 1.0, rotated
-
-
 def read_gif(input_path, n_frames):
     frames = []
     video = Image.open(input_path)
-    if video.n_frames < n_frames:
-        return frames
     for img in ImageSequence.Iterator(video):
-        frames.append(img.convert("RGB"))
+        frames.append(img.convert("RGBA"))
         if len(frames) == n_frames:
             break
     return frames
@@ -206,16 +156,17 @@ def read_video(
         print(f"Loading {len(all_img_paths)} video frames...")
         images = [Image.open(img_path) for img_path in all_img_paths]
 
+    if len(images) < n_frames:
+        images = (images + images[::-1])[:n_frames]
+        
     if len(images) != n_frames:
-        raise ValueError("Input video contains fewer than {n_frames} frames.")
+        raise ValueError(f"Input video contains fewer than {n_frames} frames.")
 
     # Remove background and crop video frames
     images_v0 = []
-    for image in images:
+    for t, image in enumerate(images):
         if remove_bg:
-            if image.mode == "RGBA":
-                pass
-            else:
+            if image.mode != "RGBA":
                 image.thumbnail([W, H], Image.Resampling.LANCZOS)
                 image = remove(image.convert("RGBA"), alpha_matting=True)
             image_arr = np.array(image)
@@ -225,11 +176,12 @@ def read_video(
             )
             x, y, w, h = cv2.boundingRect(mask)
             max_size = max(w, h)
-            side_len = (
-                int(max_size / image_frame_ratio)
-                if image_frame_ratio is not None
-                else in_w
-            )
+            if t == 0:
+                side_len = (
+                    int(max_size / image_frame_ratio)
+                    if image_frame_ratio is not None
+                    else in_w
+                )
             padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8)
             center = side_len // 2
             padded_image[
@@ -239,7 +191,9 @@ def read_video(
             rgba = Image.fromarray(padded_image).resize((W, H), Image.LANCZOS)
             rgba_arr = np.array(rgba) / 255.0
             rgb = rgba_arr[..., :3] * rgba_arr[..., -1:] + (1 - rgba_arr[..., -1:])
-            images = Image.fromarray((rgb * 255).astype(np.uint8))
+            image = Image.fromarray((rgb * 255).astype(np.uint8))
+        else:
+            image = image.convert("RGB").resize((W, H), Image.LANCZOS)
         image = ToTensor()(image).unsqueeze(0).to(device)
         images_v0.append(image * 2.0 - 1.0)
     return images_v0
@@ -341,11 +295,13 @@ def sample_sv3d(
 
 
 def decode_latents(model, samples_z, timesteps):
+    load_module_gpu(model.first_stage_model)
     if isinstance(model.first_stage_model.decoder, VideoDecoder):
         samples_x = model.decode_first_stage(samples_z, timesteps=timesteps)
     else:
         samples_x = model.decode_first_stage(samples_z)
     samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+    unload_module_gpu(model.first_stage_model)
     return samples
 
 
@@ -751,6 +707,7 @@ def do_sample(
                 else:
                     num_samples = [num_samples]
 
+                load_module_gpu(model.conditioner)
                 batch, batch_uc = get_batch(
                     get_unique_embedder_keys_from_conditioner(model.conditioner),
                     value_dict,
@@ -758,13 +715,13 @@ def do_sample(
                     T=T,
                     additional_batch_uc_fields=additional_batch_uc_fields,
                 )
-
                 c, uc = model.conditioner.get_unconditional_conditioning(
                     batch,
                     batch_uc=batch_uc,
                     force_uc_zero_embeddings=force_uc_zero_embeddings,
                     force_cond_zero_embeddings=force_cond_zero_embeddings,
                 )
+                unload_module_gpu(model.conditioner)
 
                 for k in c:
                     if not k == "crossattn":
@@ -805,8 +762,13 @@ def do_sample(
                         model.model, input, sigma, c, **additional_model_inputs
                     )
 
+                load_module_gpu(model.model)
+                load_module_gpu(model.denoiser)
                 samples_z = sampler(denoiser, randn, cond=c, uc=uc)
+                unload_module_gpu(model.model)
+                unload_module_gpu(model.denoiser)
 
+                load_module_gpu(model.first_stage_model)
                 if isinstance(model.first_stage_model.decoder, VideoDecoder):
                     samples_x = model.decode_first_stage(
                         samples_z, timesteps=default(decoding_t, T)
@@ -814,6 +776,7 @@ def do_sample(
                 else:
                     samples_x = model.decode_first_stage(samples_z)
                 samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+                unload_module_gpu(model.first_stage_model)
 
                 if filter is not None:
                     samples = filter(samples)
@@ -850,6 +813,7 @@ def do_sample_per_step(
                 else:
                     num_samples = [num_samples]
 
+                load_module_gpu(model.conditioner)
                 batch, batch_uc = get_batch(
                     get_unique_embedder_keys_from_conditioner(model.conditioner),
                     value_dict,
@@ -857,13 +821,13 @@ def do_sample_per_step(
                     T=T,
                     additional_batch_uc_fields=additional_batch_uc_fields,
                 )
-
                 c, uc = model.conditioner.get_unconditional_conditioning(
                     batch,
                     batch_uc=batch_uc,
                     force_uc_zero_embeddings=force_uc_zero_embeddings,
                     force_cond_zero_embeddings=force_cond_zero_embeddings,
                 )
+                unload_module_gpu(model.conditioner)
 
                 for k in c:
                     if not k == "crossattn":
@@ -917,6 +881,9 @@ def do_sample_per_step(
                     if sampler.s_tmin <= sigmas[step] <= sampler.s_tmax
                     else 0.0
                 )
+
+                load_module_gpu(model.model)
+                load_module_gpu(model.denoiser)
                 samples_z = sampler.sampler_step(
                     s_in * sigmas[step],
                     s_in * sigmas[step + 1],
@@ -926,6 +893,8 @@ def do_sample_per_step(
                     uc,
                     gamma,
                 )
+                unload_module_gpu(model.model)
+                unload_module_gpu(model.denoiser)
 
     return samples_z
 

scripts/sampling/configs/sv4d.yaml

@@ -93,12 +93,6 @@ model:
             sigma_sampler_config:
               target: sgm.modules.diffusionmodules.sigma_sampling.ZeroSampler
 
-        # - input_key: cond_aug
-        #   is_trainable: False
-        #   target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
-        #   params:
-        #     outdim: 256
-
         - input_key: polar_rad
           is_trainable: False
           target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND

scripts/sampling/simple_video_sample_4d.py

@@ -13,6 +13,7 @@ from fire import Fire
 from scripts.demo.sv4d_helpers import (
     decode_latents,
     load_model,
+    initial_model_load,
     read_video,
     run_img2vid,
     run_img2vid_per_step,
@@ -26,6 +27,7 @@ def sample(
     output_folder: Optional[str] = "outputs/sv4d",
     num_steps: Optional[int] = 20,
     sv3d_version: str = "sv3d_u",  # sv3d_u or sv3d_p
+    img_size: int = 576, # image resolution
     fps_id: int = 6,
     motion_bucket_id: int = 127,
     cond_aug: float = 1e-5,
@@ -47,7 +49,7 @@ def sample(
     V = 8  # number of views per sample
     F = 8  # vae factor to downsize image->latent
     C = 4
-    H, W = 576, 576
+    H, W = img_size, img_size
     n_frames = 21  # number of input and output video frames
     n_views = V + 1  # number of output video views (1 input view + 8 novel views)
     n_views_sv3d = 21
@@ -64,7 +66,7 @@ def sample(
         "f": F,
         "options": {
             "discretization": 1,
-            "cfg": 2.5,
+            "cfg": 3.0,
             "sigma_min": 0.002,
             "sigma_max": 700.0,
             "rho": 7.0,
@@ -137,7 +139,7 @@ def sample(
     for t in range(n_frames):
         img_matrix[t][0] = images_v0[t]
 
-    base_count = len(glob(os.path.join(output_folder, "*.mp4"))) // 10
+    base_count = len(glob(os.path.join(output_folder, "*.mp4"))) // 11
     save_video(
         os.path.join(output_folder, f"{base_count:06d}_t000.mp4"),
         img_matrix[0],
@@ -155,6 +157,7 @@ def sample(
         num_steps,
         verbose,
     )
+    model = initial_model_load(model)
 
     # Interleaved sampling for anchor frames
     t0, v0 = 0, 0