Add gradio codes for CameraCtrl with SVD-xt model

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README.md

@@ -1,6 +1,6 @@
 ---
 title: CameraCtrl Svd Xt
-emoji: 🚀
+emoji: 🎥
 colorFrom: gray
 colorTo: indigo
 sdk: gradio

app.py

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+import spaces
+import argparse
+import torch
+import tempfile
+import os
+import cv2
+
+import numpy as np
+import gradio as gr
+import torchvision.transforms.functional as F
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+
+
+from omegaconf import OmegaConf
+from mpl_toolkits.mplot3d.art3d import Poly3DCollection
+from inference_cameractrl import get_relative_pose, ray_condition, get_pipeline
+from cameractrl.utils.util import save_videos_grid
+
+cv2.setNumThreads(1)
+mpl.use('agg')
+
+#### Description ####
+title = r"""<h1 align="center">CameraCtrl: Enabling Camera Control for Text-to-Video Generation</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>"""
+description = r"""
+<b>Official Gradio demo</b> for <a href='https://github.com/hehao13/CameraCtrl' target='_blank'><b>CameraCtrl: Enabling Camera Control for Text-to-Video Generation</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>
+"""
+
+closing_words = r"""
+
+---
+
+If you are interested in this demo or CameraCtrl is helpful for you, please give us a ⭐ of the <a href='https://github.com/hehao13/CameraCtrl' target='_blank'> CameraCtrl</a> Github Repo ! 
+[![GitHub Stars](https://img.shields.io/github/stars/hehao13/CameraCtrl
+)](https://github.com/hehao13/CameraCtrl)
+
+---
+
+📝 **Citation**
+<br>
+If you find our paper or code is useful for your research, please consider citing:
+```bibtex
+@article{he2024cameractrl,
+      title={CameraCtrl: Enabling Camera Control for Text-to-Video Generation}, 
+      author={Hao He and Yinghao Xu and Yuwei Guo and Gordon Wetzstein and Bo Dai and Hongsheng Li and Ceyuan Yang},
+      journal={arXiv preprint arXiv:2404.02101},
+      year={2024}
+}
+```
+
+📧 **Contact**
+<br>
+If you have any questions, please feel free to contact me at <b>1155203564@link.cuhk.edu.hk</b>.
+
+**Acknowledgement**
+<br>
+We thank <a href='https://wzhouxiff.github.io/projects/MotionCtrl/' target='_blank'><b>MotionCtrl</b></a> and <a href='https://huggingface.co/spaces/lllyasviel/IC-Light' target='_blank'><b>IC-Light</b></a> for their gradio codes.<br>
+"""
+
+
+RESIZE_MODES = ['Resize then Center Crop', 'Directly resize']
+CAMERA_TRAJECTORY_MODES = ["Provided Camera Trajectories", "Custom Camera Trajectories"]
+height = 320
+width = 576
+num_frames = 25
+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"
+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/resolve/main/CameraCtrl_svdxt.ckpt?download=true")
+    os.system("mv CameraCtrl_svdxt.ckpt?download=true checkpoints/CameraCtrl_svdxt.ckpt")
+model_config = OmegaConf.load(config)
+
+
+pipeline = get_pipeline(model_id, "unet", model_config['down_block_types'], model_config['up_block_types'],
+                        model_config['pose_encoder_kwargs'], model_config['attention_processor_kwargs'],
+                        ckpt, True, device)
+
+
+examples = [
+    [
+        "assets/example_condition_images/A_tiny_finch_on_a_branch_with_spring_flowers_on_background..png",
+        "assets/pose_files/0bf152ef84195293.txt",
+        "Trajectory 1"
+    ],
+    [
+        "assets/example_condition_images/A_beautiful_fluffy_domestic_hen_sitting_on_white_eggs_in_a_brown_nest,_eggs_are_under_the_hen..png",
+        "assets/pose_files/0c9b371cc6225682.txt",
+        "Trajectory 2"
+    ],
+    [
+        "assets/example_condition_images/Rocky_coastline_with_crashing_waves..png",
+        "assets/pose_files/0c11dbe781b1c11c.txt",
+        "Trajectory 3"
+    ],
+    [
+        "assets/example_condition_images/A_lion_standing_on_a_surfboard_in_the_ocean..png",
+        "assets/pose_files/0f47577ab3441480.txt",
+        "Trajectory 4"
+    ],
+    [
+        "assets/example_condition_images/An_exploding_cheese_house..png",
+        "assets/pose_files/0f47577ab3441480.txt",
+        "Trajectory 4"
+    ],
+    [
+        "assets/example_condition_images/Dolphins_leaping_out_of_the_ocean_at_sunset..png",
+        "assets/pose_files/0f68374b76390082.txt",
+        "Trajectory 5"
+    ],
+    [
+        "assets/example_condition_images/Leaves_are_falling_from_trees..png",
+        "assets/pose_files/2c80f9eb0d3b2bb4.txt",
+        "Trajectory 6"
+    ],
+    [
+        "assets/example_condition_images/A_serene_mountain_lake_at_sunrise,_with_mist_hovering_over_the_water..png",
+        "assets/pose_files/2f25826f0d0ef09a.txt",
+        "Trajectory 7"
+    ],
+    [
+        "assets/example_condition_images/Fireworks_display_illuminating_the_night_sky..png",
+        "assets/pose_files/3f79dc32d575bcdc.txt",
+        "Trajectory 8"
+    ],
+    [
+        "assets/example_condition_images/A_car_running_on_Mars..png",
+        "assets/pose_files/4a2d6753676df096.txt",
+        "Trajectory 9"
+    ],
+]
+
+
+class Camera(object):
+    def __init__(self, entry):
+        fx, fy, cx, cy = entry[1:5]
+        self.fx = fx
+        self.fy = fy
+        self.cx = cx
+        self.cy = cy
+        w2c_mat = np.array(entry[7:]).reshape(3, 4)
+        w2c_mat_4x4 = np.eye(4)
+        w2c_mat_4x4[:3, :] = w2c_mat
+        self.w2c_mat = w2c_mat_4x4
+        self.c2w_mat = np.linalg.inv(w2c_mat_4x4)
+
+
+class CameraPoseVisualizer:
+    def __init__(self, xlim, ylim, zlim):
+        self.fig = plt.figure(figsize=(18, 7))
+        self.ax = self.fig.add_subplot(projection='3d')
+        self.plotly_data = None  # plotly data traces
+        self.ax.set_aspect("auto")
+        self.ax.set_xlim(xlim)
+        self.ax.set_ylim(ylim)
+        self.ax.set_zlim(zlim)
+        self.ax.set_xlabel('x')
+        self.ax.set_ylabel('y')
+        self.ax.set_zlabel('z')
+
+    def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=9 / 16, base_xval=1, zval=3):
+        vertex_std = np.array([[0, 0, 0, 1],
+                               [base_xval, -base_xval * hw_ratio, zval, 1],
+                               [base_xval, base_xval * hw_ratio, zval, 1],
+                               [-base_xval, base_xval * hw_ratio, zval, 1],
+                               [-base_xval, -base_xval * hw_ratio, zval, 1]])
+        vertex_transformed = vertex_std @ extrinsic.T
+        meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]],
+                  [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]],
+                  [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]],
+                  [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]],
+                  [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1],
+                   vertex_transformed[4, :-1]]]
+
+        color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map)
+
+        self.ax.add_collection3d(
+            Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.35))
+
+    def colorbar(self, max_frame_length):
+        cmap = mpl.cm.rainbow
+        norm = mpl.colors.Normalize(vmin=0, vmax=max_frame_length)
+        self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical',
+                          label='Frame Indexes')
+
+    def show(self):
+        plt.title('Camera Trajectory')
+        plt.show()
+
+
+def get_c2w(w2cs):
+    target_cam_c2w = np.array([
+        [1, 0, 0, 0],
+        [0, 1, 0, 0],
+        [0, 0, 1, 0],
+        [0, 0, 0, 1]
+    ])
+    abs2rel = target_cam_c2w @ w2cs[0]
+    ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]]
+    camera_positions = np.asarray([c2w[:3, 3] for c2w in ret_poses])  # [n_frame, 3]
+    position_distances = [camera_positions[i] - camera_positions[i - 1] for i in range(1, len(camera_positions))]
+    xyz_max = np.max(camera_positions, axis=0)
+    xyz_min = np.min(camera_positions, axis=0)
+    xyz_ranges = xyz_max - xyz_min  # [3, ]
+    max_range = np.max(xyz_ranges)
+    expected_xyz_ranges = 1
+    scale_ratio = expected_xyz_ranges / max_range
+    scaled_position_distances = [dis * scale_ratio for dis in position_distances]  # [n_frame - 1]
+    scaled_camera_positions = [camera_positions[0], ]
+    scaled_camera_positions.extend([camera_positions[0] + np.sum(np.asarray(scaled_position_distances[:i]), axis=0)
+                                    for i in range(1, len(camera_positions))])
+    ret_poses = [np.concatenate(
+        (np.concatenate((ori_pose[:3, :3], cam_position[:, None]), axis=1), np.asarray([0, 0, 0, 1])[None]), axis=0)
+                 for ori_pose, cam_position in zip(ret_poses, scaled_camera_positions)]
+    transform_matrix = np.asarray([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]).reshape(4, 4)
+    ret_poses = [transform_matrix @ x for x in ret_poses]
+    return np.array(ret_poses, dtype=np.float32)
+
+
+def visualize_trajectory(trajectory_file):
+    with open(trajectory_file, 'r') as f:
+        poses = f.readlines()
+    w2cs = [np.asarray([float(p) for p in pose.strip().split(' ')[7:]]).reshape(3, 4) for pose in poses[1:]]
+    num_frames = len(w2cs)
+    last_row = np.zeros((1, 4))
+    last_row[0, -1] = 1.0
+    w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs]
+    c2ws = get_c2w(w2cs)
+    visualizer = CameraPoseVisualizer([-1.2, 1.2], [-1.2, 1.2], [-1.2, 1.2])
+    for frame_idx, c2w in enumerate(c2ws):
+        visualizer.extrinsic2pyramid(c2w, frame_idx / num_frames, hw_ratio=9 / 16, base_xval=0.02, zval=0.1)
+    visualizer.colorbar(num_frames)
+    return visualizer.fig
+
+
+vis_traj = visualize_trajectory('assets/pose_files/0bf152ef84195293.txt')
+
+
+@torch.inference_mode()
+def process_input_image(input_image, resize_mode):
+    global height, width
+    expected_hw_ratio = height / width
+    inp_w, inp_h = input_image.size
+    inp_hw_ratio = inp_h / inp_w
+
+    if inp_hw_ratio > expected_hw_ratio:
+        resized_height = inp_hw_ratio * width
+        resized_width = width
+    else:
+        resized_height = height
+        resized_width = height / inp_hw_ratio
+    resized_image = F.resize(input_image, size=[resized_height, resized_width])
+
+    if resize_mode == RESIZE_MODES[0]:
+        return_image = F.center_crop(resized_image, output_size=[height, width])
+    else:
+        return_image = resized_image
+
+    return gr.update(visible=True, value=return_image, height=height, width=width), gr.update(visible=True), gr.update(
+        visible=True), gr.update(visible=True), gr.update(visible=True)
+
+
+def update_camera_trajectories(trajectory_mode):
+    if trajectory_mode == CAMERA_TRAJECTORY_MODES[0]:
+        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), \
+               gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), \
+               gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+    elif trajectory_mode == CAMERA_TRAJECTORY_MODES[1]:
+        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), \
+               gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), \
+               gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+
+
+def update_camera_args(trajectory_mode, provided_camera_trajectory, customized_trajectory_file):
+    if trajectory_mode == CAMERA_TRAJECTORY_MODES[0]:
+        res = "Provided " + str(provided_camera_trajectory)
+    else:
+        if customized_trajectory_file is None:
+            res = " "
+        else:
+            res = f"Customized trajectory file {customized_trajectory_file.name.split('/')[-1]}"
+    return res
+
+
+def update_camera_args_reset():
+    return " "
+
+
+def update_trajectory_vis_plot(camera_trajectory_args, provided_camera_trajectory, customized_trajectory_file):
+    if 'Provided' in camera_trajectory_args:
+        if provided_camera_trajectory == "Trajectory 1":
+            trajectory_file_path = "assets/pose_files/0bf152ef84195293.txt"
+        elif provided_camera_trajectory == "Trajectory 2":
+            trajectory_file_path = "assets/pose_files/0c9b371cc6225682.txt"
+        elif provided_camera_trajectory == "Trajectory 3":
+            trajectory_file_path = "assets/pose_files/0c11dbe781b1c11c.txt"
+        elif provided_camera_trajectory == "Trajectory 4":
+            trajectory_file_path = "assets/pose_files/0f47577ab3441480.txt"
+        elif provided_camera_trajectory == "Trajectory 5":
+            trajectory_file_path = "assets/pose_files/0f68374b76390082.txt"
+        elif provided_camera_trajectory == "Trajectory 6":
+            trajectory_file_path = "assets/pose_files/2c80f9eb0d3b2bb4.txt"
+        elif provided_camera_trajectory == "Trajectory 7":
+            trajectory_file_path = "assets/pose_files/2f25826f0d0ef09a.txt"
+        elif provided_camera_trajectory == "Trajectory 8":
+            trajectory_file_path = "assets/pose_files/3f79dc32d575bcdc.txt"
+        else:
+            trajectory_file_path = "assets/pose_files/4a2d6753676df096.txt"
+    else:
+        trajectory_file_path = customized_trajectory_file.name
+    vis_traj = visualize_trajectory(trajectory_file_path)
+    return gr.update(visible=True), vis_traj, gr.update(visible=True), gr.update(visible=True), \
+           gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), \
+           gr.update(visible=True), gr.update(visible=True), trajectory_file_path
+
+
+def update_set_button():
+    return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+
+
+def update_buttons_for_example(example_image, example_traj_path, provided_traj_name):
+    global height, width
+    return_image = example_image
+    return gr.update(visible=True, value=return_image, height=height, width=width), gr.update(visible=True), \
+           gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), \
+           gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), \
+           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()
+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:
+        poses = f.readlines()
+    poses = [pose.strip().split(' ') for pose in poses[1:]]
+    cam_params = [[float(x) for x in pose] for pose in poses]
+    cam_params = [Camera(cam_param) for cam_param in cam_params]
+    sample_wh_ratio = width / height
+    pose_wh_ratio = cam_params[0].fy / cam_params[0].fx
+    if pose_wh_ratio > sample_wh_ratio:
+        resized_ori_w = height * pose_wh_ratio
+        for cam_param in cam_params:
+            cam_param.fx = resized_ori_w * cam_param.fx / width
+    else:
+        resized_ori_h = width / pose_wh_ratio
+        for cam_param in cam_params:
+            cam_param.fy = resized_ori_h * cam_param.fy / height
+    intrinsic = np.asarray([[cam_param.fx * width,
+                             cam_param.fy * height,
+                             cam_param.cx * width,
+                             cam_param.cy * height]
+                            for cam_param in cam_params], dtype=np.float32)
+    K = torch.as_tensor(intrinsic)[None]  # [1, 1, 4]
+    c2ws = get_relative_pose(cam_params, zero_first_frame_scale=True)
+    c2ws = torch.as_tensor(c2ws)[None]  # [1, n_frame, 4, 4]
+    plucker_embedding = ray_condition(K, c2ws, height, width, device='cpu')  # b f h w 6
+    plucker_embedding = plucker_embedding.permute(0, 1, 4, 2, 3).contiguous().to(device=device)
+
+    generator = torch.Generator(device=device)
+    generator.manual_seed(int(seed))
+
+    with torch.no_grad():
+        sample = 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(sample[None], temporal_video_path, rescale=False)
+
+    return temporal_video_path
+
+
+def main(args):
+    demo = gr.Blocks().queue()
+    with demo:
+        gr.Markdown(title)
+        gr.Markdown(subtitle)
+        gr.Markdown(description)
+
+        with gr.Column():
+            # step1: Input condition image
+            step1_title = gr.Markdown("---\n## Step 1: Input an Image", show_label=False, visible=True)
+            step1_dec = gr.Markdown(f"\n 1. Upload an Image by `Drag` or Click `Upload Image`; \
+                                                \n 2. Click `{RESIZE_MODES[0]}` or `{RESIZE_MODES[1]}` to select the image resize mode. \
+                                                \n - `{RESIZE_MODES[0]}`: First resize the input image, then center crop it into the resolution of 320 x 576. \
+                                                \n - `{RESIZE_MODES[1]}`: Only resize the input image, and keep the original aspect ratio.",
+                                    show_label=False, visible=True)
+            with gr.Row(equal_height=True):
+                with gr.Column(scale=2):
+                    input_image = gr.Image(type='pil', interactive=True, elem_id='condition_image',
+                                           elem_classes='image',
+                                           visible=True)
+                    with gr.Row():
+                        resize_crop_button = gr.Button(RESIZE_MODES[0], visible=True)
+                        directly_resize_button = gr.Button(RESIZE_MODES[1], visible=True)
+                with gr.Column(scale=2):
+                    processed_image = gr.Image(type='pil', interactive=False, elem_id='processed_image',
+                                               elem_classes='image', visible=False)
+
+            # step2: Select camera trajectory
+            step2_camera_trajectory = gr.Markdown("---\n## Step 2: Select the camera trajectory", show_label=False,
+                                                  visible=False)
+            step2_camera_trajectory_des = gr.Markdown(f"\n - `{CAMERA_TRAJECTORY_MODES[0]}`: Including 9 camera trajectories extracted from the test set of RealEstate10K dataset, each has 25 frames. \
+                                                                \n - `{CAMERA_TRAJECTORY_MODES[1]}`: You can provide the customized camera trajectories in the txt file.",
+                                                      show_label=False, visible=False)
+            with gr.Row(equal_height=True):
+                provide_trajectory_button = gr.Button(CAMERA_TRAJECTORY_MODES[0], visible=False)
+                customized_trajectory_button = gr.Button(CAMERA_TRAJECTORY_MODES[1], visible=False)
+            with gr.Row():
+                with gr.Column():
+                    provided_camera_trajectory = gr.Markdown(f"---\n### {CAMERA_TRAJECTORY_MODES[0]}", show_label=False,
+                                                             visible=False)
+                    provided_camera_trajectory_des = gr.Markdown(f"\n 1. Click one of the provide camera trajectories, such as `Trajectory 1`; \
+                                                                   \n 2. Click `Visualize Trajectory` to visualize the camera trajectory; \
+                                                                   \n 3. Click `Reset Trajectory` to reset the camera trajectory. ",
+                                                                 show_label=False, visible=False)
+
+                    customized_camera_trajectory = gr.Markdown(f"---\n### {CAMERA_TRAJECTORY_MODES[1]}",
+                                                               show_label=False,
+                                                               visible=False)
+                    customized_run_status = gr.Markdown(f"\n 1. Input the txt file containing camera trajectory. \
+                                                    \n 2. Click `Visualize Trajectory` to visualize the camera trajectory; \
+                                                    \n 3. Click `Reset Trajectory` to reset the camera trajectory. ",
+                                                        show_label=False, visible=False)
+
+                    with gr.Row():
+                        provided_trajectories = gr.Dropdown(
+                            ["Trajectory 1", "Trajectory 2", "Trajectory 3", "Trajectory 4", "Trajectory 5",
+                             "Trajectory 6", "Trajectory 7", "Trajectory 8", "Trajectory 9"],
+                            label="Provided Trajectories", interactive=True, visible=False)
+                    with gr.Row():
+                        customized_camera_trajectory_file = gr.File(
+                            label="Upload customized camera trajectory (in .txt format).", visible=False, interactive=True)
+
+                    with gr.Row():
+                        camera_args = gr.Textbox(value=" ", label="Camera Trajectory Name", visible=False)
+                        camera_trajectory_path = gr.Textbox(value=" ", visible=False)
+
+                    with gr.Row():
+                        camera_trajectory_vis = gr.Button(value="Visualize Camera Trajectory", visible=False)
+                        camera_trajectory_reset = gr.Button(value="Reset Camera Trajectory", visible=False)
+                with gr.Column():
+                    vis_camera_trajectory = gr.Plot(vis_traj, label='Camera Trajectory', visible=False)
+
+            # step3: Set inference parameters
+            with gr.Row():
+                with gr.Column():
+                    step3_title = gr.Markdown(f"---\n## Step3: Setting the inference hyper-parameters.", visible=False)
+                    step3_des = gr.Markdown(
+                        f"\n 1. Set the mumber of inference step; \
+                         \n 2. Set the seed; \
+                         \n 3. Set the minimum guidance scale and the maximum guidance scale; \
+                         \n 4. Set the fps; \
+                          \n - Please refer to the SVD paper for the meaning of the last three parameter",
+                        visible=False)
+                    with gr.Row():
+                        with gr.Column():
+                            num_inference_steps = gr.Number(value=25, label='Number Inference Steps', step=1, interactive=True,
+                                                            visible=False)
+                        with gr.Column():
+                            seed = gr.Number(value=42, label='Seed', minimum=1, interactive=True, visible=False, step=1)
+                        with gr.Column():
+                            min_guidance_scale = gr.Number(value=1.0, label='Minimum Guidance Scale', minimum=1.0, step=0.5,
+                                                           interactive=True, visible=False)
+                        with gr.Column():
+                            max_guidance_scale = gr.Number(value=3.0, label='Maximum Guidance Scale', minimum=1.0, step=0.5,
+                                                           interactive=True, visible=False)
+                        with gr.Column():
+                            fps = gr.Number(value=7, label='FPS', minimum=1, step=1, interactive=True, visible=False)
+                        with gr.Column():
+                            _ = gr.Button("Seed", visible=False)
+                        with gr.Column():
+                            _ = gr.Button("Seed", visible=False)
+                        with gr.Column():
+                            _ = gr.Button("Seed", visible=False)
+            with gr.Row():
+                with gr.Column():
+                    _ = gr.Button("Set", visible=False)
+                with gr.Column():
+                    set_button = gr.Button("Set", visible=False)
+                with gr.Column():
+                    _ = gr.Button("Set", visible=False)
+
+            # step 4: Generate video
+            with gr.Row():
+                with gr.Column():
+                    step4_title = gr.Markdown("---\n## Step4 Generating video", show_label=False, visible=False)
+                    step4_des = gr.Markdown(f"\n - Click the `Start generation !` button to generate the video.; \
+                    \n - If the content of generated video is not very aligned with the condition image, try to increase the `Minimum Guidance Scale` and `Maximum Guidance Scale`. \
+                                         \n - If the generated videos are distored, try to increase `FPS`.",
+                                            visible=False)
+                    start_button = gr.Button(value="Start generation !", visible=False)
+                with gr.Column():
+                    generate_video = gr.Video(value=None, label="Generate Video", visible=False)
+        resize_crop_button.click(fn=process_input_image, inputs=[input_image, resize_crop_button],
+                                 outputs=[processed_image, step2_camera_trajectory, step2_camera_trajectory_des,
+                                          provide_trajectory_button, customized_trajectory_button])
+        directly_resize_button.click(fn=process_input_image, inputs=[input_image, directly_resize_button],
+                                     outputs=[processed_image, step2_camera_trajectory, step2_camera_trajectory_des,
+                                              provide_trajectory_button, customized_trajectory_button])
+        provide_trajectory_button.click(fn=update_camera_trajectories, inputs=[provide_trajectory_button],
+                                        outputs=[provided_camera_trajectory, provided_camera_trajectory_des,
+                                                 provided_trajectories,
+                                                 customized_camera_trajectory, customized_run_status,
+                                                 customized_camera_trajectory_file,
+                                                 camera_args, camera_trajectory_vis, camera_trajectory_reset])
+        customized_trajectory_button.click(fn=update_camera_trajectories, inputs=[customized_trajectory_button],
+                                           outputs=[provided_camera_trajectory, provided_camera_trajectory_des,
+                                                    provided_trajectories,
+                                                    customized_camera_trajectory, customized_run_status,
+                                                    customized_camera_trajectory_file,
+                                                    camera_args, camera_trajectory_vis, camera_trajectory_reset])
+
+        provided_trajectories.change(fn=update_camera_args, inputs=[provide_trajectory_button, provided_trajectories, customized_camera_trajectory_file],
+                                     outputs=[camera_args])
+        customized_camera_trajectory_file.change(fn=update_camera_args, inputs=[customized_trajectory_button, provided_trajectories, customized_camera_trajectory_file],
+                                                 outputs=[camera_args])
+        camera_trajectory_reset.click(fn=update_camera_args_reset, inputs=None, outputs=[camera_args])
+        camera_trajectory_vis.click(fn=update_trajectory_vis_plot, inputs=[camera_args, provided_trajectories, customized_camera_trajectory_file],
+                                    outputs=[vis_camera_trajectory, vis_camera_trajectory, step3_title, step3_des,
+                                             num_inference_steps, min_guidance_scale, max_guidance_scale, fps,
+                                             seed, set_button, camera_trajectory_path])
+        set_button.click(fn=update_set_button, inputs=None, outputs=[step4_title, step4_des, start_button, generate_video])
+        start_button.click(fn=sample_video, inputs=[processed_image, camera_trajectory_path, num_inference_steps,
+                                                    min_guidance_scale, max_guidance_scale, fps, seed],
+                           outputs=[generate_video])
+
+        # set example
+        gr.Markdown("## Examples")
+        gr.Markdown("\n Choosing the one of the following examples to get a quick start, by selecting an example, "
+                    "we will set the condition image and camera trajectory automatically. "
+                    "Then, you can click the `Visualize Camera Trajectory` button to visualize the camera trajectory.")
+        gr.Examples(
+            fn=update_buttons_for_example,
+            run_on_click=True,
+            cache_examples=False,
+            examples=examples,
+            inputs=[input_image, camera_args, provided_trajectories],
+            outputs=[processed_image, step2_camera_trajectory, step2_camera_trajectory_des, provide_trajectory_button,
+                     customized_trajectory_button,
+                     provided_camera_trajectory, provided_camera_trajectory_des, provided_trajectories,
+                     customized_camera_trajectory, customized_run_status, customized_camera_trajectory_file,
+                     camera_args, camera_trajectory_vis, camera_trajectory_reset]
+        )
+        with gr.Row():
+            gr.Markdown(closing_words)
+
+    demo.launch(**args)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--listen', default='0.0.0.0')
+    parser.add_argument('--broswer', action='store_true')
+    parser.add_argument('--share', action='store_true')
+    args = parser.parse_args()
+
+    launch_kwargs = {'server_name': args.listen,
+                     'inbrowser': args.broswer,
+                     'share': args.share}
+    main(launch_kwargs)

assets/pose_files/0bf152ef84195293.txt

@@ -0,0 +1,26 @@
+https://www.youtube.com/watch?v=QShWPZxTDoE
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assets/pose_files/0c11dbe781b1c11c.txt

@@ -0,0 +1,26 @@
+https://www.youtube.com/watch?v=a-Unpcomk5k
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cameractrl/data/dataset.py

@@ -0,0 +1,355 @@
+import os
+import random
+import json
+import torch
+
+import torch.nn as nn
+import torchvision.transforms as transforms
+import torchvision.transforms.functional as F
+import numpy as np
+
+from decord import VideoReader
+from torch.utils.data.dataset import Dataset
+from packaging import version as pver
+
+
+class RandomHorizontalFlipWithPose(nn.Module):
+    def __init__(self, p=0.5):
+        super(RandomHorizontalFlipWithPose, self).__init__()
+        self.p = p
+
+    def get_flip_flag(self, n_image):
+        return torch.rand(n_image) < self.p
+
+    def forward(self, image, flip_flag=None):
+        n_image = image.shape[0]
+        if flip_flag is not None:
+            assert n_image == flip_flag.shape[0]
+        else:
+            flip_flag = self.get_flip_flag(n_image)
+
+        ret_images = []
+        for fflag, img in zip(flip_flag, image):
+            if fflag:
+                ret_images.append(F.hflip(img))
+            else:
+                ret_images.append(img)
+        return torch.stack(ret_images, dim=0)
+
+
+class Camera(object):
+    def __init__(self, entry):
+        fx, fy, cx, cy = entry[1:5]
+        self.fx = fx
+        self.fy = fy
+        self.cx = cx
+        self.cy = cy
+        w2c_mat = np.array(entry[7:]).reshape(3, 4)
+        w2c_mat_4x4 = np.eye(4)
+        w2c_mat_4x4[:3, :] = w2c_mat
+        self.w2c_mat = w2c_mat_4x4
+        self.c2w_mat = np.linalg.inv(w2c_mat_4x4)
+
+
+def custom_meshgrid(*args):
+    # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid
+    if pver.parse(torch.__version__) < pver.parse('1.10'):
+        return torch.meshgrid(*args)
+    else:
+        return torch.meshgrid(*args, indexing='ij')
+
+
+def ray_condition(K, c2w, H, W, device, flip_flag=None):
+    # c2w: B, V, 4, 4
+    # K: B, V, 4
+
+    B, V = K.shape[:2]
+
+    j, i = custom_meshgrid(
+        torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+        torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
+    )
+    i = i.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5          # [B, V, HxW]
+    j = j.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5          # [B, V, HxW]
+
+    n_flip = torch.sum(flip_flag).item() if flip_flag is not None else 0
+    if n_flip > 0:
+        j_flip, i_flip = custom_meshgrid(
+            torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+            torch.linspace(W - 1, 0, W, device=device, dtype=c2w.dtype)
+        )
+        i_flip = i_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5
+        j_flip = j_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5
+        i[:, flip_flag, ...] = i_flip
+        j[:, flip_flag, ...] = j_flip
+
+    fx, fy, cx, cy = K.chunk(4, dim=-1)     # B,V, 1
+
+    zs = torch.ones_like(i)                 # [B, V, HxW]
+    xs = (i - cx) / fx * zs
+    ys = (j - cy) / fy * zs
+    zs = zs.expand_as(ys)
+
+    directions = torch.stack((xs, ys, zs), dim=-1)              # B, V, HW, 3
+    directions = directions / directions.norm(dim=-1, keepdim=True)             # B, V, HW, 3
+
+    rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2)        # B, V, HW, 3
+    rays_o = c2w[..., :3, 3]                                        # B, V, 3
+    rays_o = rays_o[:, :, None].expand_as(rays_d)                   # B, V, HW, 3
+    # c2w @ dirctions
+    rays_dxo = torch.linalg.cross(rays_o, rays_d)                          # B, V, HW, 3
+    plucker = torch.cat([rays_dxo, rays_d], dim=-1)
+    plucker = plucker.reshape(B, c2w.shape[1], H, W, 6)             # B, V, H, W, 6
+    # plucker = plucker.permute(0, 1, 4, 2, 3)
+    return plucker
+
+
+class RealEstate10K(Dataset):
+    def __init__(
+            self,
+            root_path,
+            annotation_json,
+            sample_stride=4,
+            sample_n_frames=16,
+            sample_size=[256, 384],
+            is_image=False,
+    ):
+        self.root_path = root_path
+        self.sample_stride = sample_stride
+        self.sample_n_frames = sample_n_frames
+        self.is_image = is_image
+
+        self.dataset = json.load(open(os.path.join(root_path, annotation_json), 'r'))
+        self.length = len(self.dataset)
+
+        sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size)
+        pixel_transforms = [transforms.Resize(sample_size),
+                            transforms.RandomHorizontalFlip(),
+                            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)]
+
+        self.pixel_transforms = transforms.Compose(pixel_transforms)
+
+    def load_video_reader(self, idx):
+        video_dict = self.dataset[idx]
+
+        video_path = os.path.join(self.root_path, video_dict['clip_path'])
+        video_reader = VideoReader(video_path)
+        return video_reader, video_dict['caption']
+
+    def get_batch(self, idx):
+        video_reader, video_caption = self.load_video_reader(idx)
+        total_frames = len(video_reader)
+
+        if self.is_image:
+            frame_indice = [random.randint(0, total_frames - 1)]
+        else:
+            if isinstance(self.sample_stride, int):
+                current_sample_stride = self.sample_stride
+            else:
+                assert len(self.sample_stride) == 2
+                assert (self.sample_stride[0] >= 1) and (self.sample_stride[1] >= self.sample_stride[0])
+                current_sample_stride = random.randint(self.sample_stride[0], self.sample_stride[1])
+
+            cropped_length = self.sample_n_frames * current_sample_stride
+            start_frame_ind = random.randint(0, max(0, total_frames - cropped_length - 1))
+            end_frame_ind = min(start_frame_ind + cropped_length, total_frames)
+
+            assert end_frame_ind - start_frame_ind >= self.sample_n_frames
+            frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, self.sample_n_frames, dtype=int)
+
+        pixel_values = torch.from_numpy(video_reader.get_batch(frame_indice).asnumpy()).permute(0, 3, 1, 2).contiguous()
+        pixel_values = pixel_values / 255.
+
+        if self.is_image:
+            pixel_values = pixel_values[0]
+
+        return pixel_values, video_caption
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, idx):
+        while True:
+            try:
+                video, video_caption = self.get_batch(idx)
+                break
+
+            except Exception as e:
+                idx = random.randint(0, self.length - 1)
+
+        video = self.pixel_transforms(video)
+        sample = dict(pixel_values=video, caption=video_caption)
+
+        return sample
+
+
+class RealEstate10KPose(Dataset):
+    def __init__(
+            self,
+            root_path,
+            annotation_json,
+            sample_stride=4,
+            minimum_sample_stride=1,
+            sample_n_frames=16,
+            relative_pose=False,
+            zero_t_first_frame=False,
+            sample_size=[256, 384],
+            rescale_fxy=False,
+            shuffle_frames=False,
+            use_flip=False,
+            return_clip_name=False,
+    ):
+        self.root_path = root_path
+        self.relative_pose = relative_pose
+        self.zero_t_first_frame = zero_t_first_frame
+        self.sample_stride = sample_stride
+        self.minimum_sample_stride = minimum_sample_stride
+        self.sample_n_frames = sample_n_frames
+        self.return_clip_name = return_clip_name
+
+        self.dataset = json.load(open(os.path.join(root_path, annotation_json), 'r'))
+        self.length = len(self.dataset)
+
+        sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size)
+        self.sample_size = sample_size
+        if use_flip:
+            pixel_transforms = [transforms.Resize(sample_size),
+                                RandomHorizontalFlipWithPose(),
+                                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)]
+        else:
+            pixel_transforms = [transforms.Resize(sample_size),
+                                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)]
+        self.rescale_fxy = rescale_fxy
+        self.sample_wh_ratio = sample_size[1] / sample_size[0]
+
+        self.pixel_transforms = pixel_transforms
+        self.shuffle_frames = shuffle_frames
+        self.use_flip = use_flip
+
+    def get_relative_pose(self, cam_params):
+        abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
+        abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
+        source_cam_c2w = abs_c2ws[0]
+        if self.zero_t_first_frame:
+            cam_to_origin = 0
+        else:
+            cam_to_origin = np.linalg.norm(source_cam_c2w[:3, 3])
+        target_cam_c2w = np.array([
+            [1, 0, 0, 0],
+            [0, 1, 0, -cam_to_origin],
+            [0, 0, 1, 0],
+            [0, 0, 0, 1]
+        ])
+        abs2rel = target_cam_c2w @ abs_w2cs[0]
+        ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
+        ret_poses = np.array(ret_poses, dtype=np.float32)
+        return ret_poses
+
+    def load_video_reader(self, idx):
+        video_dict = self.dataset[idx]
+
+        video_path = os.path.join(self.root_path, video_dict['clip_path'])
+        video_reader = VideoReader(video_path)
+        return video_dict['clip_name'], video_reader, video_dict['caption']
+
+    def load_cameras(self, idx):
+        video_dict = self.dataset[idx]
+        pose_file = os.path.join(self.root_path, video_dict['pose_file'])
+        with open(pose_file, 'r') as f:
+            poses = f.readlines()
+        poses = [pose.strip().split(' ') for pose in poses[1:]]
+        cam_params = [[float(x) for x in pose] for pose in poses]
+        cam_params = [Camera(cam_param) for cam_param in cam_params]
+        return cam_params
+
+    def get_batch(self, idx):
+        clip_name, video_reader, video_caption = self.load_video_reader(idx)
+        cam_params = self.load_cameras(idx)
+        assert len(cam_params) >= self.sample_n_frames
+        total_frames = len(cam_params)
+
+        current_sample_stride = self.sample_stride
+
+        if total_frames < self.sample_n_frames * current_sample_stride:
+            maximum_sample_stride = int(total_frames // self.sample_n_frames)
+            current_sample_stride = random.randint(self.minimum_sample_stride, maximum_sample_stride)
+
+        cropped_length = self.sample_n_frames * current_sample_stride
+        start_frame_ind = random.randint(0, max(0, total_frames - cropped_length - 1))
+        end_frame_ind = min(start_frame_ind + cropped_length, total_frames)
+
+        assert end_frame_ind - start_frame_ind >= self.sample_n_frames
+        frame_indices = np.linspace(start_frame_ind, end_frame_ind - 1, self.sample_n_frames, dtype=int)
+
+        condition_image_ind = random.sample(list(set(range(total_frames)) - set(frame_indices.tolist())), 1)
+        condition_image = torch.from_numpy(video_reader.get_batch(condition_image_ind).asnumpy()).permute(0, 3, 1, 2).contiguous()
+        condition_image = condition_image / 255.
+
+        if self.shuffle_frames:
+            perm = np.random.permutation(self.sample_n_frames)
+            frame_indices = frame_indices[perm]
+
+        pixel_values = torch.from_numpy(video_reader.get_batch(frame_indices).asnumpy()).permute(0, 3, 1, 2).contiguous()
+        pixel_values = pixel_values / 255.
+
+        cam_params = [cam_params[indice] for indice in frame_indices]
+        if self.rescale_fxy:
+            ori_h, ori_w = pixel_values.shape[-2:]
+            ori_wh_ratio = ori_w / ori_h
+            if ori_wh_ratio > self.sample_wh_ratio:       # rescale fx
+                resized_ori_w = self.sample_size[0] * ori_wh_ratio
+                for cam_param in cam_params:
+                    cam_param.fx = resized_ori_w * cam_param.fx / self.sample_size[1]
+            else:                                          # rescale fy
+                resized_ori_h = self.sample_size[1] / ori_wh_ratio
+                for cam_param in cam_params:
+                    cam_param.fy = resized_ori_h * cam_param.fy / self.sample_size[0]
+        intrinsics = np.asarray([[cam_param.fx * self.sample_size[1],
+                                  cam_param.fy * self.sample_size[0],
+                                  cam_param.cx * self.sample_size[1],
+                                  cam_param.cy * self.sample_size[0]]
+                                 for cam_param in cam_params], dtype=np.float32)
+        intrinsics = torch.as_tensor(intrinsics)[None]                  # [1, n_frame, 4]
+        if self.relative_pose:
+            c2w_poses = self.get_relative_pose(cam_params)
+        else:
+            c2w_poses = np.array([cam_param.c2w_mat for cam_param in cam_params], dtype=np.float32)
+        c2w = torch.as_tensor(c2w_poses)[None]                          # [1, n_frame, 4, 4]
+        if self.use_flip:
+            flip_flag = self.pixel_transforms[1].get_flip_flag(self.sample_n_frames)
+        else:
+            flip_flag = torch.zeros(self.sample_n_frames, dtype=torch.bool, device=c2w.device)
+        plucker_embedding = ray_condition(intrinsics, c2w, self.sample_size[0], self.sample_size[1], device='cpu',
+                                          flip_flag=flip_flag)[0].permute(0, 3, 1, 2).contiguous()
+
+        return pixel_values, condition_image, plucker_embedding, video_caption, flip_flag, clip_name
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, idx):
+        while True:
+            try:
+                video, condition_image, plucker_embedding, video_caption, flip_flag, clip_name = self.get_batch(idx)
+                break
+
+            except Exception as e:
+                idx = random.randint(0, self.length - 1)
+
+        if self.use_flip:
+            video = self.pixel_transforms[0](video)
+            video = self.pixel_transforms[1](video, flip_flag)
+            for transform in self.pixel_transforms[2:]:
+                video = transform(video)
+        else:
+            for transform in self.pixel_transforms:
+                video = transform(video)
+        for transform in self.pixel_transforms:
+            condition_image = transform(condition_image)
+        if self.return_clip_name:
+            sample = dict(pixel_values=video, condition_image=condition_image, plucker_embedding=plucker_embedding, video_caption=video_caption, clip_name=clip_name)
+        else:
+            sample = dict(pixel_values=video, condition_image=condition_image, plucker_embedding=plucker_embedding, video_caption=video_caption)
+
+        return sample
+

cameractrl/models/attention.py

@@ -0,0 +1,65 @@
+import torch
+from typing import Optional
+from diffusers.models.attention import TemporalBasicTransformerBlock, _chunked_feed_forward
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+
+
+@maybe_allow_in_graph
+class TemporalPoseCondTransformerBlock(TemporalBasicTransformerBlock):
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,   # [bs * num_frame, h * w, c]
+        num_frames: int,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,  # [bs * h * w, 1, c]
+        pose_feature: Optional[torch.FloatTensor] = None,       # [bs, c, n_frame, h, w]
+    ) -> torch.FloatTensor:
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Self-Attention
+
+        batch_frames, seq_length, channels = hidden_states.shape
+        batch_size = batch_frames // num_frames
+
+        hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, seq_length, channels)
+        hidden_states = hidden_states.permute(0, 2, 1, 3)
+        hidden_states = hidden_states.reshape(batch_size * seq_length, num_frames, channels)  # [bs * h * w, frame, c]
+
+        residual = hidden_states
+        hidden_states = self.norm_in(hidden_states)
+
+        if self._chunk_size is not None:
+            hidden_states = _chunked_feed_forward(self.ff_in, hidden_states, self._chunk_dim, self._chunk_size)
+        else:
+            hidden_states = self.ff_in(hidden_states)
+
+        if self.is_res:
+            hidden_states = hidden_states + residual
+
+        norm_hidden_states = self.norm1(hidden_states)
+        pose_feature = pose_feature.permute(0, 3, 4, 2, 1).reshape(batch_size * seq_length, num_frames, -1)
+        attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None, pose_feature=pose_feature)
+        hidden_states = attn_output + hidden_states
+
+        # 3. Cross-Attention
+        if self.attn2 is not None:
+            norm_hidden_states = self.norm2(hidden_states)
+            attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states, pose_feature=pose_feature)
+            hidden_states = attn_output + hidden_states
+
+        # 4. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+
+        if self._chunk_size is not None:
+            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
+        else:
+            ff_output = self.ff(norm_hidden_states)
+
+        if self.is_res:
+            hidden_states = ff_output + hidden_states
+        else:
+            hidden_states = ff_output
+
+        hidden_states = hidden_states[None, :].reshape(batch_size, seq_length, num_frames, channels)
+        hidden_states = hidden_states.permute(0, 2, 1, 3)
+        hidden_states = hidden_states.reshape(batch_size * num_frames, seq_length, channels)
+
+        return hidden_states

cameractrl/models/attention_processor.py

@@ -0,0 +1,591 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+import logging
+from diffusers.models.attention import Attention
+from diffusers.utils import USE_PEFT_BACKEND, is_xformers_available
+from typing import Optional, Callable
+
+from einops import rearrange
+
+if is_xformers_available():
+    import xformers
+    import xformers.ops
+else:
+    xformers = None
+
+logger = logging.getLogger(__name__)
+
+
+class AttnProcessor:
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __call__(
+            self,
+            attn: Attention,
+            hidden_states: torch.FloatTensor,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            temb: Optional[torch.FloatTensor] = None,
+            scale: float = 1.0,
+            pose_feature=None
+    ) -> torch.Tensor:
+        residual = hidden_states
+
+        args = () if USE_PEFT_BACKEND else (scale,)
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states, *args)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states, *args)
+        value = attn.to_v(encoder_hidden_states, *args)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states, *args)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class AttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        temb: Optional[torch.FloatTensor] = None,
+        scale: float = 1.0,
+        pose_feature=None
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+
+        args = () if USE_PEFT_BACKEND else (scale,)
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        args = () if USE_PEFT_BACKEND else (scale,)
+        query = attn.to_q(hidden_states, *args)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states, *args)
+        value = attn.to_v(encoder_hidden_states, *args)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states, *args)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class XFormersAttnProcessor:
+    r"""
+    Processor for implementing memory efficient attention using xFormers.
+
+    Args:
+        attention_op (`Callable`, *optional*, defaults to `None`):
+            The base
+            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
+            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
+            operator.
+    """
+
+    def __init__(self, attention_op: Optional[Callable] = None):
+        self.attention_op = attention_op
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        temb: Optional[torch.FloatTensor] = None,
+        scale: float = 1.0,
+        pose_feature=None
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+
+        args = () if USE_PEFT_BACKEND else (scale,)
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, key_tokens, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size)
+        if attention_mask is not None:
+            # expand our mask's singleton query_tokens dimension:
+            #   [batch*heads,            1, key_tokens] ->
+            #   [batch*heads, query_tokens, key_tokens]
+            # so that it can be added as a bias onto the attention scores that xformers computes:
+            #   [batch*heads, query_tokens, key_tokens]
+            # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
+            _, query_tokens, _ = hidden_states.shape
+            attention_mask = attention_mask.expand(-1, query_tokens, -1)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states, *args)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states, *args)
+        value = attn.to_v(encoder_hidden_states, *args)
+
+        query = attn.head_to_batch_dim(query).contiguous()
+        key = attn.head_to_batch_dim(key).contiguous()
+        value = attn.head_to_batch_dim(value).contiguous()
+
+        hidden_states = xformers.ops.memory_efficient_attention(
+            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
+        )
+        hidden_states = hidden_states.to(query.dtype)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states, *args)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class PoseAdaptorAttnProcessor(nn.Module):
+    def __init__(self,
+                 hidden_size,  # dimension of hidden state
+                 pose_feature_dim=None,  # dimension of the pose feature
+                 cross_attention_dim=None,  # dimension of the text embedding
+                 query_condition=False,
+                 key_value_condition=False,
+                 scale=1.0):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.pose_feature_dim = pose_feature_dim
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.query_condition = query_condition
+        self.key_value_condition = key_value_condition
+        assert hidden_size == pose_feature_dim
+        if self.query_condition and self.key_value_condition:
+            self.qkv_merge = nn.Linear(hidden_size, hidden_size)
+            init.zeros_(self.qkv_merge.weight)
+            init.zeros_(self.qkv_merge.bias)
+        elif self.query_condition:
+            self.q_merge = nn.Linear(hidden_size, hidden_size)
+            init.zeros_(self.q_merge.weight)
+            init.zeros_(self.q_merge.bias)
+        else:
+            self.kv_merge = nn.Linear(hidden_size, hidden_size)
+            init.zeros_(self.kv_merge.weight)
+            init.zeros_(self.kv_merge.bias)
+
+    def forward(self,
+                attn,
+                hidden_states,
+                pose_feature,
+                encoder_hidden_states=None,
+                attention_mask=None,
+                temb=None,
+                scale=None,):
+        assert pose_feature is not None
+        pose_embedding_scale = (scale or self.scale)
+
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        assert hidden_states.ndim == 3 and pose_feature.ndim == 3
+
+        if self.query_condition and self.key_value_condition:
+            assert encoder_hidden_states is None
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        assert encoder_hidden_states.ndim == 3
+
+        batch_size, ehs_sequence_length, _ = encoder_hidden_states.shape
+        attention_mask = attn.prepare_attention_mask(attention_mask, ehs_sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        if attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        if self.query_condition and self.key_value_condition:  # only self attention
+            query_hidden_state = self.qkv_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states
+            key_value_hidden_state = query_hidden_state
+        elif self.query_condition:
+            query_hidden_state = self.q_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states
+            key_value_hidden_state = encoder_hidden_states
+        else:
+            key_value_hidden_state = self.kv_merge(encoder_hidden_states + pose_feature) * pose_embedding_scale + encoder_hidden_states
+            query_hidden_state = hidden_states
+
+        # original attention
+        query = attn.to_q(query_hidden_state)
+        key = attn.to_k(key_value_hidden_state)
+        value = attn.to_v(key_value_hidden_state)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class PoseAdaptorAttnProcessor2_0(nn.Module):
+    def __init__(self,
+                 hidden_size,  # dimension of hidden state
+                 pose_feature_dim=None,  # dimension of the pose feature
+                 cross_attention_dim=None,  # dimension of the text embedding
+                 query_condition=False,
+                 key_value_condition=False,
+                 scale=1.0):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.hidden_size = hidden_size
+        self.pose_feature_dim = pose_feature_dim
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.query_condition = query_condition
+        self.key_value_condition = key_value_condition
+        assert hidden_size == pose_feature_dim
+        if self.query_condition and self.key_value_condition:
+            self.qkv_merge = nn.Linear(hidden_size, hidden_size)
+            init.zeros_(self.qkv_merge.weight)
+            init.zeros_(self.qkv_merge.bias)
+        elif self.query_condition:
+            self.q_merge = nn.Linear(hidden_size, hidden_size)
+            init.zeros_(self.q_merge.weight)
+            init.zeros_(self.q_merge.bias)
+        else:
+            self.kv_merge = nn.Linear(hidden_size, hidden_size)
+            init.zeros_(self.kv_merge.weight)
+            init.zeros_(self.kv_merge.bias)
+
+    def forward(self,
+                attn,
+                hidden_states,
+                pose_feature,
+                encoder_hidden_states=None,
+                attention_mask=None,
+                temb=None,
+                scale=None,):
+        assert pose_feature is not None
+        pose_embedding_scale = (scale or self.scale)
+
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        assert hidden_states.ndim == 3 and pose_feature.ndim == 3
+
+        if self.query_condition and self.key_value_condition:
+            assert encoder_hidden_states is None
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        assert encoder_hidden_states.ndim == 3
+
+        batch_size, ehs_sequence_length, _ = encoder_hidden_states.shape
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, ehs_sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        if attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        if self.query_condition and self.key_value_condition:  # only self attention
+            query_hidden_state = self.qkv_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states
+            key_value_hidden_state = query_hidden_state
+        elif self.query_condition:
+            query_hidden_state = self.q_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states
+            key_value_hidden_state = encoder_hidden_states
+        else:
+            key_value_hidden_state = self.kv_merge(encoder_hidden_states + pose_feature) * pose_embedding_scale + encoder_hidden_states
+            query_hidden_state = hidden_states
+
+        # original attention
+        query = attn.to_q(query_hidden_state)
+        key = attn.to_k(key_value_hidden_state)
+        value = attn.to_v(key_value_hidden_state)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)        # [bs, seq_len, nhead, head_dim] -> [bs, nhead, seq_len, head_dim]
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False)  # [bs, nhead, seq_len, head_dim]
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)    # [bs, seq_len, dim]
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class PoseAdaptorXFormersAttnProcessor(nn.Module):
+    def __init__(self,
+                 hidden_size,  # dimension of hidden state
+                 pose_feature_dim=None,  # dimension of the pose feature
+                 cross_attention_dim=None,  # dimension of the text embedding
+                 query_condition=False,
+                 key_value_condition=False,
+                 scale=1.0,
+                 attention_op: Optional[Callable] = None):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.pose_feature_dim = pose_feature_dim
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.query_condition = query_condition
+        self.key_value_condition = key_value_condition
+        self.attention_op = attention_op
+        assert hidden_size == pose_feature_dim
+        if self.query_condition and self.key_value_condition:
+            self.qkv_merge = nn.Linear(hidden_size, hidden_size)
+            init.zeros_(self.qkv_merge.weight)
+            init.zeros_(self.qkv_merge.bias)
+        elif self.query_condition:
+            self.q_merge = nn.Linear(hidden_size, hidden_size)
+            init.zeros_(self.q_merge.weight)
+            init.zeros_(self.q_merge.bias)
+        else:
+            self.kv_merge = nn.Linear(hidden_size, hidden_size)
+            init.zeros_(self.kv_merge.weight)
+            init.zeros_(self.kv_merge.bias)
+
+    def forward(self,
+                attn,
+                hidden_states,
+                pose_feature,
+                encoder_hidden_states=None,
+                attention_mask=None,
+                temb=None,
+                scale=None,):
+        assert pose_feature is not None
+        pose_embedding_scale = (scale or self.scale)
+
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        assert hidden_states.ndim == 3 and pose_feature.ndim == 3
+
+        if self.query_condition and self.key_value_condition:
+            assert encoder_hidden_states is None
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        assert encoder_hidden_states.ndim == 3
+
+        batch_size, ehs_sequence_length, _ = encoder_hidden_states.shape
+        attention_mask = attn.prepare_attention_mask(attention_mask, ehs_sequence_length, batch_size)
+        if attention_mask is not None:
+            # expand our mask's singleton query_tokens dimension:
+            #   [batch*heads,            1, key_tokens] ->
+            #   [batch*heads, query_tokens, key_tokens]
+            # so that it can be added as a bias onto the attention scores that xformers computes:
+            #   [batch*heads, query_tokens, key_tokens]
+            # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
+            _, query_tokens, _ = hidden_states.shape
+            attention_mask = attention_mask.expand(-1, query_tokens, -1)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        if attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        if self.query_condition and self.key_value_condition:  # only self attention
+            query_hidden_state = self.qkv_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states
+            key_value_hidden_state = query_hidden_state
+        elif self.query_condition:
+            query_hidden_state = self.q_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states
+            key_value_hidden_state = encoder_hidden_states
+        else:
+            key_value_hidden_state = self.kv_merge(encoder_hidden_states + pose_feature) * pose_embedding_scale + encoder_hidden_states
+            query_hidden_state = hidden_states
+
+        # original attention
+        query = attn.to_q(query_hidden_state)
+        key = attn.to_k(key_value_hidden_state)
+        value = attn.to_v(key_value_hidden_state)
+
+        query = attn.head_to_batch_dim(query).contiguous()
+        key = attn.head_to_batch_dim(key).contiguous()
+        value = attn.head_to_batch_dim(value).contiguous()
+
+        hidden_states = xformers.ops.memory_efficient_attention(
+            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
+        )
+        hidden_states = hidden_states.to(query.dtype)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states

cameractrl/models/motion_module.py

@@ -0,0 +1,399 @@
+from dataclasses import dataclass
+from typing import Callable, Optional
+
+import torch
+from torch import nn
+
+from diffusers.utils import BaseOutput
+from diffusers.models.attention_processor import Attention
+from diffusers.models.attention import FeedForward
+
+from typing import Dict, Any
+from cameractrl.models.attention_processor import PoseAdaptorAttnProcessor
+
+from einops import rearrange
+import math
+
+
+class InflatedGroupNorm(nn.GroupNorm):
+    def forward(self, x):
+        # return super().forward(x)
+
+        video_length = x.shape[2]
+
+        x = rearrange(x, "b c f h w -> (b f) c h w")
+        x = super().forward(x)
+        x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
+
+        return x
+
+def zero_module(module):
+    # Zero out the parameters of a module and return it.
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+@dataclass
+class TemporalTransformer3DModelOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+def get_motion_module(
+        in_channels,
+        motion_module_type: str,
+        motion_module_kwargs: dict
+):
+    if motion_module_type == "Vanilla":
+        return VanillaTemporalModule(in_channels=in_channels, **motion_module_kwargs)
+    else:
+        raise ValueError
+
+
+class VanillaTemporalModule(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            num_attention_heads=8,
+            num_transformer_block=2,
+            attention_block_types=("Temporal_Self",),
+            temporal_position_encoding=True,
+            temporal_position_encoding_max_len=32,
+            temporal_attention_dim_div=1,
+            cross_attention_dim=320,
+            zero_initialize=True,
+            encoder_hidden_states_query=(False, False),
+            attention_activation_scale=1.0,
+            attention_processor_kwargs: Dict = {},
+            causal_temporal_attention=False,
+            causal_temporal_attention_mask_type="",
+            rescale_output_factor=1.0
+    ):
+        super().__init__()
+
+        self.temporal_transformer = TemporalTransformer3DModel(
+            in_channels=in_channels,
+            num_attention_heads=num_attention_heads,
+            attention_head_dim=in_channels // num_attention_heads // temporal_attention_dim_div,
+            num_layers=num_transformer_block,
+            attention_block_types=attention_block_types,
+            cross_attention_dim=cross_attention_dim,
+            temporal_position_encoding=temporal_position_encoding,
+            temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+            encoder_hidden_states_query=encoder_hidden_states_query,
+            attention_activation_scale=attention_activation_scale,
+            attention_processor_kwargs=attention_processor_kwargs,
+            causal_temporal_attention=causal_temporal_attention,
+            causal_temporal_attention_mask_type=causal_temporal_attention_mask_type,
+            rescale_output_factor=rescale_output_factor
+        )
+
+        if zero_initialize:
+            self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out)
+
+    def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None,
+                cross_attention_kwargs: Dict[str, Any] = {}):
+        hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask, cross_attention_kwargs=cross_attention_kwargs)
+
+        output = hidden_states
+        return output
+
+
+class TemporalTransformer3DModel(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            num_attention_heads,
+            attention_head_dim,
+            num_layers,
+            attention_block_types=("Temporal_Self", "Temporal_Self",),
+            dropout=0.0,
+            norm_num_groups=32,
+            cross_attention_dim=320,
+            activation_fn="geglu",
+            attention_bias=False,
+            upcast_attention=False,
+            temporal_position_encoding=False,
+            temporal_position_encoding_max_len=32,
+            encoder_hidden_states_query=(False, False),
+            attention_activation_scale=1.0,
+            attention_processor_kwargs: Dict = {},
+
+            causal_temporal_attention=None,
+            causal_temporal_attention_mask_type="",
+            rescale_output_factor=1.0
+    ):
+        super().__init__()
+        assert causal_temporal_attention is not None
+        self.causal_temporal_attention = causal_temporal_attention
+
+        assert (not causal_temporal_attention) or (causal_temporal_attention_mask_type != "")
+        self.causal_temporal_attention_mask_type = causal_temporal_attention_mask_type
+        self.causal_temporal_attention_mask = None
+
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = InflatedGroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+        self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                TemporalTransformerBlock(
+                    dim=inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    attention_block_types=attention_block_types,
+                    dropout=dropout,
+                    norm_num_groups=norm_num_groups,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    attention_bias=attention_bias,
+                    upcast_attention=upcast_attention,
+                    temporal_position_encoding=temporal_position_encoding,
+                    temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+                    encoder_hidden_states_query=encoder_hidden_states_query,
+                    attention_activation_scale=attention_activation_scale,
+                    attention_processor_kwargs=attention_processor_kwargs,
+                    rescale_output_factor=rescale_output_factor,
+                )
+                for d in range(num_layers)
+            ]
+        )
+        self.proj_out = nn.Linear(inner_dim, in_channels)
+
+    def get_causal_temporal_attention_mask(self, hidden_states):
+        batch_size, sequence_length, dim = hidden_states.shape
+
+        if self.causal_temporal_attention_mask is None or self.causal_temporal_attention_mask.shape != (
+        batch_size, sequence_length, sequence_length):
+            if self.causal_temporal_attention_mask_type == "causal":
+                # 1. vanilla causal mask
+                mask = torch.tril(torch.ones(sequence_length, sequence_length))
+
+            elif self.causal_temporal_attention_mask_type == "2-seq":
+                # 2. 2-seq
+                mask = torch.zeros(sequence_length, sequence_length)
+                mask[:sequence_length // 2, :sequence_length // 2] = 1
+                mask[-sequence_length // 2:, -sequence_length // 2:] = 1
+
+            elif self.causal_temporal_attention_mask_type == "0-prev":
+                # attn to the previous frame
+                indices = torch.arange(sequence_length)
+                indices_prev = indices - 1
+                indices_prev[0] = 0
+                mask = torch.zeros(sequence_length, sequence_length)
+                mask[:, 0] = 1.
+                mask[indices, indices_prev] = 1.
+
+            elif self.causal_temporal_attention_mask_type == "0":
+                # only attn to first frame
+                mask = torch.zeros(sequence_length, sequence_length)
+                mask[:, 0] = 1
+
+            elif self.causal_temporal_attention_mask_type == "wo-self":
+                indices = torch.arange(sequence_length)
+                mask = torch.ones(sequence_length, sequence_length)
+                mask[indices, indices] = 0
+
+            elif self.causal_temporal_attention_mask_type == "circle":
+                indices = torch.arange(sequence_length)
+                indices_prev = indices - 1
+                indices_prev[0] = 0
+
+                mask = torch.eye(sequence_length)
+                mask[indices, indices_prev] = 1
+                mask[0, -1] = 1
+
+            else:
+                raise ValueError
+
+            # generate attention mask fron binary values
+            mask = mask.masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
+            mask = mask.unsqueeze(0)
+            mask = mask.repeat(batch_size, 1, 1)
+
+            self.causal_temporal_attention_mask = mask.to(hidden_states.device)
+
+        return self.causal_temporal_attention_mask
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None,
+                cross_attention_kwargs: Dict[str, Any] = {},):
+        residual = hidden_states
+
+        assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
+        height, width = hidden_states.shape[-2:]
+
+        hidden_states = self.norm(hidden_states)
+        hidden_states = rearrange(hidden_states, "b c f h w -> (b h w) f c")
+        hidden_states = self.proj_in(hidden_states)
+
+        attention_mask = self.get_causal_temporal_attention_mask(
+            hidden_states) if self.causal_temporal_attention else attention_mask
+
+        # Transformer Blocks
+        for block in self.transformer_blocks:
+            hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states,
+                                  attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs)
+        hidden_states = self.proj_out(hidden_states)
+
+        hidden_states = rearrange(hidden_states, "(b h w) f c -> b c f h w", h=height, w=width)
+
+        output = hidden_states + residual
+
+        return output
+
+
+class TemporalTransformerBlock(nn.Module):
+    def __init__(
+            self,
+            dim,
+            num_attention_heads,
+            attention_head_dim,
+            attention_block_types=("Temporal_Self", "Temporal_Self",),
+            dropout=0.0,
+            norm_num_groups=32,
+            cross_attention_dim=768,
+            activation_fn="geglu",
+            attention_bias=False,
+            upcast_attention=False,
+            temporal_position_encoding=False,
+            temporal_position_encoding_max_len=32,
+            encoder_hidden_states_query=(False, False),
+            attention_activation_scale=1.0,
+            attention_processor_kwargs: Dict = {},
+            rescale_output_factor=1.0
+    ):
+        super().__init__()
+
+        attention_blocks = []
+        norms = []
+        self.attention_block_types = attention_block_types
+
+        for block_idx, block_name in enumerate(attention_block_types):
+            attention_blocks.append(
+                TemporalSelfAttention(
+                    attention_mode=block_name,
+                    cross_attention_dim=cross_attention_dim if block_name in ['Temporal_Cross', 'Temporal_Pose_Adaptor'] else None,
+                    query_dim=dim,
+                    heads=num_attention_heads,
+                    dim_head=attention_head_dim,
+                    dropout=dropout,
+                    bias=attention_bias,
+                    upcast_attention=upcast_attention,
+                    temporal_position_encoding=temporal_position_encoding,
+                    temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+                    rescale_output_factor=rescale_output_factor,
+                )
+            )
+            norms.append(nn.LayerNorm(dim))
+
+        self.attention_blocks = nn.ModuleList(attention_blocks)
+        self.norms = nn.ModuleList(norms)
+
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
+        self.ff_norm = nn.LayerNorm(dim)
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs: Dict[str, Any] = {}):
+        for attention_block, norm, attention_block_type in zip(self.attention_blocks, self.norms, self.attention_block_types):
+            norm_hidden_states = norm(hidden_states)
+            hidden_states = attention_block(
+                norm_hidden_states,
+                encoder_hidden_states=norm_hidden_states if attention_block_type == 'Temporal_Self' else encoder_hidden_states,
+                attention_mask=attention_mask,
+                **cross_attention_kwargs
+            ) + hidden_states
+
+        hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
+
+        output = hidden_states
+        return output
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(
+            self,
+            d_model,
+            dropout=0.,
+            max_len=32,
+    ):
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        position = torch.arange(max_len).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
+        pe = torch.zeros(1, max_len, d_model)
+        pe[0, :, 0::2] = torch.sin(position * div_term)
+        pe[0, :, 1::2] = torch.cos(position * div_term)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[:, :x.size(1)]
+        return self.dropout(x)
+
+
+class TemporalSelfAttention(Attention):
+    def __init__(
+            self,
+            attention_mode=None,
+            temporal_position_encoding=False,
+            temporal_position_encoding_max_len=32,
+            rescale_output_factor=1.0,
+            *args, **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+        assert attention_mode == "Temporal_Self"
+
+        self.pos_encoder = PositionalEncoding(
+            kwargs["query_dim"],
+            max_len=temporal_position_encoding_max_len
+        ) if temporal_position_encoding else None
+        self.rescale_output_factor = rescale_output_factor
+
+    def set_use_memory_efficient_attention_xformers(
+            self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None
+    ):
+        # disable motion module efficient xformers to avoid bad results, don't know why
+        # TODO: fix this bug
+        pass
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, **cross_attention_kwargs):
+        # The `Attention` class can call different attention processors / attention functions
+        # here we simply pass along all tensors to the selected processor class
+        # For standard processors that are defined here, `**cross_attention_kwargs` is empty
+
+        # add position encoding
+        if self.pos_encoder is not None:
+            hidden_states = self.pos_encoder(hidden_states)
+        if "pose_feature" in cross_attention_kwargs:
+            pose_feature = cross_attention_kwargs["pose_feature"]
+            if pose_feature.ndim == 5:
+                pose_feature = rearrange(pose_feature, "b c f h w -> (b h w) f c")
+            else:
+                assert pose_feature.ndim == 3
+            cross_attention_kwargs["pose_feature"] = pose_feature
+
+        if isinstance(self.processor,  PoseAdaptorAttnProcessor):
+            return self.processor(
+                self,
+                hidden_states,
+                cross_attention_kwargs.pop('pose_feature'),
+                encoder_hidden_states=None,
+                attention_mask=attention_mask,
+                **cross_attention_kwargs,
+            )
+        elif hasattr(self.processor, "__call__"):
+            return self.processor.__call__(
+                    self,
+                    hidden_states,
+                    encoder_hidden_states=None,
+                    attention_mask=attention_mask,
+                    **cross_attention_kwargs,
+                )
+        else:
+            return self.processor(
+                self,
+                hidden_states,
+                encoder_hidden_states=None,
+                attention_mask=attention_mask,
+                **cross_attention_kwargs,
+            )
+

cameractrl/models/pose_adaptor.py

@@ -0,0 +1,240 @@
+import math
+import torch
+import torch.nn as nn
+from einops import rearrange
+from typing import List, Tuple
+from cameractrl.models.motion_module import TemporalTransformerBlock
+
+
+def get_parameter_dtype(parameter: torch.nn.Module):
+    try:
+        params = tuple(parameter.parameters())
+        if len(params) > 0:
+            return params[0].dtype
+
+        buffers = tuple(parameter.buffers())
+        if len(buffers) > 0:
+            return buffers[0].dtype
+
+    except StopIteration:
+        # For torch.nn.DataParallel compatibility in PyTorch 1.5
+
+        def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, torch.Tensor]]:
+            tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
+            return tuples
+
+        gen = parameter._named_members(get_members_fn=find_tensor_attributes)
+        first_tuple = next(gen)
+        return first_tuple[1].dtype
+
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class PoseAdaptor(nn.Module):
+    def __init__(self, unet, pose_encoder):
+        super().__init__()
+        self.unet = unet
+        self.pose_encoder = pose_encoder
+
+    def forward(self, noisy_latents, c_noise, encoder_hidden_states, added_time_ids, pose_embedding):
+        assert pose_embedding.ndim == 5
+        pose_embedding_features = self.pose_encoder(pose_embedding)      # b c f h w
+        noise_pred = self.unet(noisy_latents,
+                               c_noise,
+                               encoder_hidden_states,
+                               added_time_ids=added_time_ids,
+                               pose_features=pose_embedding_features).sample
+        return noise_pred
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.op = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=padding)
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+
+class ResnetBlock(nn.Module):
+
+    def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
+        super().__init__()
+        ps = ksize // 2
+        if in_c != out_c or sk == False:
+            self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
+        else:
+            self.in_conv = None
+        self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
+        self.act = nn.ReLU()
+        self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
+        if sk == False:
+            self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
+        else:
+            self.skep = None
+
+        self.down = down
+        if self.down == True:
+            self.down_opt = Downsample(in_c, use_conv=use_conv)
+
+    def forward(self, x):
+        if self.down == True:
+            x = self.down_opt(x)
+        if self.in_conv is not None:  # edit
+            x = self.in_conv(x)
+
+        h = self.block1(x)
+        h = self.act(h)
+        h = self.block2(h)
+        if self.skep is not None:
+            return h + self.skep(x)
+        else:
+            return h + x
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(
+            self,
+            d_model,
+            dropout=0.,
+            max_len=32,
+    ):
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        position = torch.arange(max_len).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
+        pe = torch.zeros(1, max_len, d_model)
+        pe[0, :, 0::2, ...] = torch.sin(position * div_term)
+        pe[0, :, 1::2, ...] = torch.cos(position * div_term)
+        pe.unsqueeze_(-1).unsqueeze_(-1)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[:, :x.size(1), ...]
+        return self.dropout(x)
+
+
+class CameraPoseEncoder(nn.Module):
+
+    def __init__(self,
+                 downscale_factor,
+                 channels=[320, 640, 1280, 1280],
+                 nums_rb=3,
+                 cin=64,
+                 ksize=3,
+                 sk=False,
+                 use_conv=True,
+                 compression_factor=1,
+                 temporal_attention_nhead=8,
+                 attention_block_types=("Temporal_Self", ),
+                 temporal_position_encoding=False,
+                 temporal_position_encoding_max_len=16,
+                 rescale_output_factor=1.0):
+        super(CameraPoseEncoder, self).__init__()
+        self.unshuffle = nn.PixelUnshuffle(downscale_factor)
+        self.channels = channels
+        self.nums_rb = nums_rb
+        self.encoder_down_conv_blocks = nn.ModuleList()
+        self.encoder_down_attention_blocks = nn.ModuleList()
+        for i in range(len(channels)):
+            conv_layers = nn.ModuleList()
+            temporal_attention_layers = nn.ModuleList()
+            for j in range(nums_rb):
+                if j == 0 and i != 0:
+                    in_dim = channels[i - 1]
+                    out_dim = int(channels[i] / compression_factor)
+                    conv_layer = ResnetBlock(in_dim, out_dim, down=True, ksize=ksize, sk=sk, use_conv=use_conv)
+                elif j == 0:
+                    in_dim = channels[0]
+                    out_dim = int(channels[i] / compression_factor)
+                    conv_layer = ResnetBlock(in_dim, out_dim, down=False, ksize=ksize, sk=sk, use_conv=use_conv)
+                elif j == nums_rb - 1:
+                    in_dim = channels[i] / compression_factor
+                    out_dim = channels[i]
+                    conv_layer = ResnetBlock(in_dim, out_dim, down=False, ksize=ksize, sk=sk, use_conv=use_conv)
+                else:
+                    in_dim = int(channels[i] / compression_factor)
+                    out_dim = int(channels[i] / compression_factor)
+                    conv_layer = ResnetBlock(in_dim, out_dim, down=False, ksize=ksize, sk=sk, use_conv=use_conv)
+                temporal_attention_layer = TemporalTransformerBlock(dim=out_dim,
+                                                                    num_attention_heads=temporal_attention_nhead,
+                                                                    attention_head_dim=int(out_dim / temporal_attention_nhead),
+                                                                    attention_block_types=attention_block_types,
+                                                                    dropout=0.0,
+                                                                    cross_attention_dim=None,
+                                                                    temporal_position_encoding=temporal_position_encoding,
+                                                                    temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+                                                                    rescale_output_factor=rescale_output_factor)
+                conv_layers.append(conv_layer)
+                temporal_attention_layers.append(temporal_attention_layer)
+            self.encoder_down_conv_blocks.append(conv_layers)
+            self.encoder_down_attention_blocks.append(temporal_attention_layers)
+
+        self.encoder_conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)
+
+    @property
+    def dtype(self) -> torch.dtype:
+        """
+        `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
+        """
+        return get_parameter_dtype(self)
+
+    def forward(self, x):
+        # unshuffle
+        bs = x.shape[0]
+        x = rearrange(x, "b f c h w -> (b f) c h w")
+        x = self.unshuffle(x)
+        # extract features
+        features = []
+        x = self.encoder_conv_in(x)
+        for res_block, attention_block in zip(self.encoder_down_conv_blocks, self.encoder_down_attention_blocks):
+            for res_layer, attention_layer in zip(res_block, attention_block):
+                x = res_layer(x)
+                h, w = x.shape[-2:]
+                x = rearrange(x, '(b f) c h w -> (b h w) f c', b=bs)
+                x = attention_layer(x)
+                x = rearrange(x, '(b h w) f c -> (b f) c h w', h=h, w=w)
+            features.append(rearrange(x, '(b f) c h w -> b c f h w', b=bs))
+        return features

cameractrl/models/transformer_temporal.py

@@ -0,0 +1,191 @@
+import torch
+import torch.nn as nn
+
+from typing import Optional
+from diffusers.models.transformer_temporal import TransformerTemporalModelOutput
+from diffusers.models.attention import BasicTransformerBlock
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from diffusers.models.resnet import AlphaBlender
+from cameractrl.models.attention import TemporalPoseCondTransformerBlock
+
+
+class TransformerSpatioTemporalModelPoseCond(nn.Module):
+    """
+        A Transformer model for video-like data.
+
+        Parameters:
+            num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
+            attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
+            in_channels (`int`, *optional*):
+                The number of channels in the input and output (specify if the input is **continuous**).
+            out_channels (`int`, *optional*):
+                The number of channels in the output (specify if the input is **continuous**).
+            num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
+            cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+        """
+
+    def __init__(
+            self,
+            num_attention_heads: int = 16,
+            attention_head_dim: int = 88,
+            in_channels: int = 320,
+            out_channels: Optional[int] = None,
+            num_layers: int = 1,
+            cross_attention_dim: Optional[int] = None,
+    ):
+        super().__init__()
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+
+        inner_dim = num_attention_heads * attention_head_dim
+        self.inner_dim = inner_dim
+
+        # 2. Define input layers
+        self.in_channels = in_channels
+        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6)
+        self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        # 3. Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    cross_attention_dim=cross_attention_dim,
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        time_mix_inner_dim = inner_dim
+        self.temporal_transformer_blocks = nn.ModuleList(
+            [
+                TemporalPoseCondTransformerBlock(
+                    inner_dim,
+                    time_mix_inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    cross_attention_dim=cross_attention_dim,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        time_embed_dim = in_channels * 4
+        self.time_pos_embed = TimestepEmbedding(in_channels, time_embed_dim, out_dim=in_channels)
+        self.time_proj = Timesteps(in_channels, True, 0)
+        self.time_mixer = AlphaBlender(alpha=0.5, merge_strategy="learned_with_images")
+
+        # 4. Define output layers
+        self.out_channels = in_channels if out_channels is None else out_channels
+        # TODO: should use out_channels for continuous projections
+        self.proj_out = nn.Linear(inner_dim, in_channels)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,            # [bs * frame, c, h, w]
+        encoder_hidden_states: Optional[torch.Tensor] = None,        # [bs * frame, 1, c]
+        image_only_indicator: Optional[torch.Tensor] = None,         # [bs, frame]
+        pose_feature: Optional[torch.Tensor] = None,                # [bs, c, frame, h, w]
+        return_dict: bool = True,
+    ):
+        """
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+                Input hidden_states.
+            num_frames (`int`):
+                The number of frames to be processed per batch. This is used to reshape the hidden states.
+            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
+                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
+                self-attention.
+            image_only_indicator (`torch.LongTensor` of shape `(batch size, num_frames)`, *optional*):
+                A tensor indicating whether the input contains only images. 1 indicates that the input contains only
+                images, 0 indicates that the input contains video frames.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_temporal.TransformerTemporalModelOutput`] instead of a
+                plain tuple.
+
+        Returns:
+            [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
+                returned, otherwise a `tuple` where the first element is the sample tensor.
+        """
+        # 1. Input
+        batch_frames, _, height, width = hidden_states.shape
+        num_frames = image_only_indicator.shape[-1]
+        batch_size = batch_frames // num_frames
+
+        time_context = encoder_hidden_states        # [bs * frame, 1, c]
+        time_context_first_timestep = time_context[None, :].reshape(
+            batch_size, num_frames, -1, time_context.shape[-1]
+        )[:, 0]     # [bs, frame, c]
+        time_context = time_context_first_timestep[:, None].broadcast_to(
+            batch_size, height * width, time_context.shape[-2], time_context.shape[-1]
+        )           # [bs, h*w, 1, c]
+        time_context = time_context.reshape(batch_size * height * width, -1, time_context.shape[-1])    # [bs * h * w, 1, c]
+
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)        # [bs * frame, c, h, w]
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_frames, height * width, inner_dim)  # [bs * frame, h * w, c]
+        hidden_states = self.proj_in(hidden_states)     # [bs * frame, h * w, c]
+
+        num_frames_emb = torch.arange(num_frames, device=hidden_states.device)
+        num_frames_emb = num_frames_emb.repeat(batch_size, 1)       # [bs, frame]
+        num_frames_emb = num_frames_emb.reshape(-1)     # [bs * frame]
+        t_emb = self.time_proj(num_frames_emb)          # [bs * frame, c]
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=hidden_states.dtype)
+
+        emb = self.time_pos_embed(t_emb)
+        emb = emb[:, None, :]       # [bs * frame, 1, c]
+
+        # 2. Blocks
+        for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks):
+            if self.training and self.gradient_checkpointing:
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    block,
+                    hidden_states,
+                    None,
+                    encoder_hidden_states,
+                    None,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states = block(
+                    hidden_states,          # [bs * frame, h * w, c]
+                    encoder_hidden_states=encoder_hidden_states,    # [bs * frame, 1, c]
+                )               # [bs * frame, h * w, c]
+
+            hidden_states_mix = hidden_states
+            hidden_states_mix = hidden_states_mix + emb
+
+            hidden_states_mix = temporal_block(
+                hidden_states_mix,      # [bs * frame, h * w, c]
+                num_frames=num_frames,
+                encoder_hidden_states=time_context,     # [bs * h * w, 1, c]
+                pose_feature=pose_feature
+            )
+            hidden_states = self.time_mixer(
+                x_spatial=hidden_states,
+                x_temporal=hidden_states_mix,
+                image_only_indicator=image_only_indicator,
+            )
+
+        # 3. Output
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = hidden_states.reshape(batch_frames, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+
+        output = hidden_states + residual
+
+        if not return_dict:
+            return (output,)
+
+        return TransformerTemporalModelOutput(sample=output)

cameractrl/models/unet.py

@@ -0,0 +1,587 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+
+from typing import List, Optional, Tuple, Union, Dict
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor, CROSS_ATTENTION_PROCESSORS
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from diffusers.loaders import UNet2DConditionLoadersMixin
+from diffusers.models.unet_spatio_temporal_condition import UNetSpatioTemporalConditionOutput
+
+from cameractrl.models.unet_3d_blocks import (
+    get_down_block,
+    get_up_block,
+    UNetMidBlockSpatioTemporalPoseCond
+)
+from cameractrl.models.attention_processor import XFormersAttnProcessor as CustomizedXFormerAttnProcessor
+from cameractrl.models.attention_processor import PoseAdaptorXFormersAttnProcessor
+
+if hasattr(F, "scaled_dot_product_attention"):
+    from cameractrl.models.attention_processor import PoseAdaptorAttnProcessor2_0 as PoseAdaptorAttnProcessor
+    from cameractrl.models.attention_processor import AttnProcessor2_0 as CustomizedAttnProcessor
+else:
+    from cameractrl.models.attention_processor import PoseAdaptorAttnProcessor
+    from cameractrl.models.attention_processor import AttnProcessor as CustomizedAttnProcessor
+
+class UNetSpatioTemporalConditionModelPoseCond(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
+    r"""
+    A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample
+    shaped output.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+            Height and width of input/output sample.
+        in_channels (`int`, *optional*, defaults to 8): Number of channels in the input sample.
+        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal")`):
+            The tuple of downsample blocks to use.
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal")`):
+            The tuple of upsample blocks to use.
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
+            The tuple of output channels for each block.
+        addition_time_embed_dim: (`int`, defaults to 256):
+            Dimension to to encode the additional time ids.
+        projection_class_embeddings_input_dim (`int`, defaults to 768):
+            The dimension of the projection of encoded `added_time_ids`.
+        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
+        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
+            The dimension of the cross attention features.
+        transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
+            [`~models.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`], [`~models.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`],
+            [`~models.unet_3d_blocks.UNetMidBlockSpatioTemporal`].
+        num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`):
+            The number of attention heads.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        sample_size: Optional[int] = None,
+        in_channels: int = 8,
+        out_channels: int = 4,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlockSpatioTemporalPoseCond",
+            "CrossAttnDownBlockSpatioTemporalPoseCond",
+            "CrossAttnDownBlockSpatioTemporalPoseCond",
+            "DownBlockSpatioTemporal",
+        ),
+        up_block_types: Tuple[str] = (
+            "UpBlockSpatioTemporal",
+            "CrossAttnUpBlockSpatioTemporalPoseCond",
+            "CrossAttnUpBlockSpatioTemporalPoseCond",
+            "CrossAttnUpBlockSpatioTemporalPoseCond",
+        ),
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        addition_time_embed_dim: int = 256,
+        projection_class_embeddings_input_dim: int = 768,
+        layers_per_block: Union[int, Tuple[int]] = 2,
+        cross_attention_dim: Union[int, Tuple[int]] = 1024,
+        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
+        num_attention_heads: Union[int, Tuple[int]] = (5, 10, 10, 20),
+        num_frames: int = 25,
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+
+        # input
+        self.conv_in = nn.Conv2d(
+            in_channels,
+            block_out_channels[0],
+            kernel_size=3,
+            padding=1,
+        )
+
+        # time
+        time_embed_dim = block_out_channels[0] * 4
+
+        self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0)
+        timestep_input_dim = block_out_channels[0]
+
+        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+
+        self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0)
+        self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+
+        self.down_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(num_attention_heads, int):
+            num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+        if isinstance(layers_per_block, int):
+            layers_per_block = [layers_per_block] * len(down_block_types)
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
+        blocks_time_embed_dim = time_embed_dim
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block[i],
+                transformer_layers_per_block=transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=1e-5,
+                cross_attention_dim=cross_attention_dim[i],
+                num_attention_heads=num_attention_heads[i],
+                resnet_act_fn="silu",
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        self.mid_block = UNetMidBlockSpatioTemporalPoseCond(
+            block_out_channels[-1],
+            temb_channels=blocks_time_embed_dim,
+            transformer_layers_per_block=transformer_layers_per_block[-1],
+            cross_attention_dim=cross_attention_dim[-1],
+            num_attention_heads=num_attention_heads[-1],
+        )
+
+        # count how many layers upsample the images
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_num_attention_heads = list(reversed(num_attention_heads))
+        reversed_layers_per_block = list(reversed(layers_per_block))
+        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
+
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=reversed_layers_per_block[i] + 1,
+                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=1e-5,
+                resolution_idx=i,
+                cross_attention_dim=reversed_cross_attention_dim[i],
+                num_attention_heads=reversed_num_attention_heads[i],
+                resnet_act_fn="silu",
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5)
+        self.conv_act = nn.SiLU()
+
+        self.conv_out = nn.Conv2d(
+            block_out_channels[0],
+            out_channels,
+            kernel_size=3,
+            padding=1,
+        )
+
+    @property
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(
+            name: str,
+            module: torch.nn.Module,
+            processors: Dict[str, AttentionProcessor],
+        ):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnProcessor()
+        else:
+            raise ValueError(
+                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+            )
+
+        self.set_attn_processor(processor)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    # Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
+    def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
+        """
+        Sets the attention processor to use [feed forward
+        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
+
+        Parameters:
+            chunk_size (`int`, *optional*):
+                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
+                over each tensor of dim=`dim`.
+            dim (`int`, *optional*, defaults to `0`):
+                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
+                or dim=1 (sequence length).
+        """
+        if dim not in [0, 1]:
+            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
+
+        # By default chunk size is 1
+        chunk_size = chunk_size or 1
+
+        def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
+            if hasattr(module, "set_chunk_feed_forward"):
+                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
+
+            for child in module.children():
+                fn_recursive_feed_forward(child, chunk_size, dim)
+
+        for module in self.children():
+            fn_recursive_feed_forward(module, chunk_size, dim)
+
+    def set_pose_cond_attn_processor(self,
+                                     add_spatial=False,
+                                     add_temporal=False,
+                                     enable_xformers=False,
+                                     attn_processor_name='attn1',
+                                     pose_feature_dimensions=[320, 640, 1280, 1280],
+                                     **attention_processor_kwargs):
+        all_attn_processors = {}
+        set_processor_names = attn_processor_name.split(',')
+        if add_spatial:
+            for processor_key in self.attn_processors.keys():
+                if 'temporal' in processor_key:
+                    continue
+                processor_name = processor_key.split('.')[-2]
+                cross_attention_dim = None if processor_name == 'attn1' else self.config.cross_attention_dim
+                if processor_key.startswith("mid_block"):
+                    hidden_size = self.config.block_out_channels[-1]
+                    block_id = -1
+                    add_pose_adaptor = processor_name in set_processor_names
+                    pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None
+                elif processor_key.startswith("up_blocks"):
+                    block_id = int(processor_key[len("up_blocks.")])
+                    hidden_size = list(reversed(self.config.block_out_channels))[block_id]
+                    add_pose_adaptor = processor_name in set_processor_names
+                    pose_feature_dim = list(reversed(pose_feature_dimensions))[block_id] if add_pose_adaptor else None
+                else:
+                    block_id = int(processor_key[len("down_blocks.")])
+                    hidden_size = self.config.block_out_channels[block_id]
+                    add_pose_adaptor = processor_name in set_processor_names
+                    pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None
+                if add_pose_adaptor and enable_xformers:
+                    all_attn_processors[processor_key] = PoseAdaptorXFormersAttnProcessor(hidden_size=hidden_size,
+                                                                                  pose_feature_dim=pose_feature_dim,
+                                                                                  cross_attention_dim=cross_attention_dim,
+                                                                                  **attention_processor_kwargs)
+                elif add_pose_adaptor:
+                    all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size,
+                                                                                  pose_feature_dim=pose_feature_dim,
+                                                                                  cross_attention_dim=cross_attention_dim,
+                                                                                  **attention_processor_kwargs)
+                elif enable_xformers:
+                    all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor()
+                else:
+                    all_attn_processors[processor_key] = CustomizedAttnProcessor()
+        else:
+            for processor_key in self.attn_processors.keys():
+                if 'temporal' not in processor_key and enable_xformers:
+                    all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor()
+                elif 'temporal' not in processor_key:
+                    all_attn_processors[processor_key] = CustomizedAttnProcessor()
+
+        if add_temporal:
+            for processor_key in self.attn_processors.keys():
+                if 'temporal' not in processor_key:
+                    continue
+                processor_name = processor_key.split('.')[-2]
+                cross_attention_dim = None if processor_name == 'attn1' else self.config.cross_attention_dim
+                if processor_key.startswith("mid_block"):
+                    hidden_size = self.config.block_out_channels[-1]
+                    block_id = -1
+                    add_pose_adaptor = processor_name in set_processor_names
+                    pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None
+                elif processor_key.startswith("up_blocks"):
+                    block_id = int(processor_key[len("up_blocks.")])
+                    hidden_size = list(reversed(self.config.block_out_channels))[block_id]
+                    add_pose_adaptor = (processor_name in set_processor_names)
+                    pose_feature_dim = list(reversed(pose_feature_dimensions))[block_id] if add_pose_adaptor else None
+                else:
+                    block_id = int(processor_key[len("down_blocks.")])
+                    hidden_size = self.config.block_out_channels[block_id]
+                    add_pose_adaptor = processor_name in set_processor_names
+                    pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None
+                if add_pose_adaptor and enable_xformers:
+                    all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size,
+                                                                                          pose_feature_dim=pose_feature_dim,
+                                                                                          cross_attention_dim=cross_attention_dim,
+                                                                                          **attention_processor_kwargs)
+                elif add_pose_adaptor:
+                    all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size,
+                                                                                  pose_feature_dim=pose_feature_dim,
+                                                                                  cross_attention_dim=cross_attention_dim,
+                                                                                  **attention_processor_kwargs)
+                elif enable_xformers:
+                    all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor()
+                else:
+                    all_attn_processors[processor_key] = CustomizedAttnProcessor()
+        else:
+            for processor_key in self.attn_processors.keys():
+                if 'temporal' in processor_key and enable_xformers:
+                    all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor()
+                elif 'temporal' in processor_key:
+                    all_attn_processors[processor_key] = CustomizedAttnProcessor()
+
+        self.set_attn_processor(all_attn_processors)
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        added_time_ids: torch.Tensor,
+        pose_features: List[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNetSpatioTemporalConditionOutput, Tuple]:
+        r"""
+        The [`UNetSpatioTemporalConditionModel`] forward method.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`.
+            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.FloatTensor`):
+                The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`.
+            added_time_ids: (`torch.FloatTensor`):
+                The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal
+                embeddings and added to the time embeddings.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] instead of a plain
+                tuple.
+        Returns:
+            [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is returned, otherwise
+                a `tuple` is returned where the first element is the sample tensor.
+        """
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        batch_size, num_frames = sample.shape[:2]
+        timesteps = timesteps.expand(batch_size)
+
+        t_emb = self.time_proj(timesteps)
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+
+        emb = self.time_embedding(t_emb)
+
+        time_embeds = self.add_time_proj(added_time_ids.flatten())
+        time_embeds = time_embeds.reshape((batch_size, -1))
+        time_embeds = time_embeds.to(emb.dtype)
+        aug_emb = self.add_embedding(time_embeds)
+        emb = emb + aug_emb
+
+        # Flatten the batch and frames dimensions
+        # sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width]
+        sample = sample.flatten(0, 1)
+        # Repeat the embeddings num_video_frames times
+        # emb: [batch, channels] -> [batch * frames, channels]
+        emb = emb.repeat_interleave(num_frames, dim=0)
+        # encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels]
+        encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0)
+
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device)
+
+        down_block_res_samples = (sample,)
+        for block_idx, downsample_block in enumerate(self.down_blocks):
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_only_indicator=image_only_indicator,
+                    pose_feature=pose_features[block_idx]
+                )
+            else:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    image_only_indicator=image_only_indicator,
+                )
+
+            down_block_res_samples += res_samples
+
+        # 4. mid
+        sample = self.mid_block(
+            hidden_states=sample,
+            temb=emb,
+            encoder_hidden_states=encoder_hidden_states,
+            image_only_indicator=image_only_indicator,
+            pose_feature=pose_features[-1]
+        )
+
+        # 5. up
+        for block_idx, upsample_block in enumerate(self.up_blocks):
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_only_indicator=image_only_indicator,
+                    pose_feature=pose_features[-(block_idx + 1)]
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    image_only_indicator=image_only_indicator,
+                )
+
+        # 6. post-process
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        # 7. Reshape back to original shape
+        sample = sample.reshape(batch_size, num_frames, *sample.shape[1:])
+
+        if not return_dict:
+            return (sample,)
+
+        return UNetSpatioTemporalConditionOutput(sample=sample)

cameractrl/models/unet_3d_blocks.py

@@ -0,0 +1,461 @@
+import torch
+import torch.nn as nn
+from typing import Union, Tuple, Optional, Dict, Any
+from diffusers.utils import is_torch_version
+from diffusers.models.resnet import (
+    Downsample2D,
+    SpatioTemporalResBlock,
+    Upsample2D
+)
+from diffusers.models.unet_3d_blocks import (
+    DownBlockSpatioTemporal,
+    UpBlockSpatioTemporal,
+)
+
+from cameractrl.models.transformer_temporal import TransformerSpatioTemporalModelPoseCond
+
+
+def get_down_block(
+        down_block_type: str,
+        num_layers: int,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        add_downsample: bool,
+        num_attention_heads: int,
+        cross_attention_dim: Optional[int] = None,
+        transformer_layers_per_block: int = 1,
+        **kwargs,
+) -> Union[
+    "DownBlockSpatioTemporal",
+    "CrossAttnDownBlockSpatioTemporalPoseCond",
+]:
+    if down_block_type == "DownBlockSpatioTemporal":
+        # added for SDV
+        return DownBlockSpatioTemporal(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+        )
+    elif down_block_type == "CrossAttnDownBlockSpatioTemporalPoseCond":
+        # added for SDV
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockSpatioTemporal")
+        return CrossAttnDownBlockSpatioTemporalPoseCond(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
+            add_downsample=add_downsample,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads,
+        )
+
+    raise ValueError(f"{down_block_type} does not exist.")
+
+
+def get_up_block(
+        up_block_type: str,
+        num_layers: int,
+        in_channels: int,
+        out_channels: int,
+        prev_output_channel: int,
+        temb_channels: int,
+        add_upsample: bool,
+        num_attention_heads: int,
+        resolution_idx: Optional[int] = None,
+        cross_attention_dim: Optional[int] = None,
+        transformer_layers_per_block: int = 1,
+        **kwargs,
+) -> Union[
+    "UpBlockSpatioTemporal",
+    "CrossAttnUpBlockSpatioTemporalPoseCond",
+]:
+    if up_block_type == "UpBlockSpatioTemporal":
+        # added for SDV
+        return UpBlockSpatioTemporal(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            resolution_idx=resolution_idx,
+            add_upsample=add_upsample,
+        )
+    elif up_block_type == "CrossAttnUpBlockSpatioTemporalPoseCond":
+        # added for SDV
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockSpatioTemporal")
+        return CrossAttnUpBlockSpatioTemporalPoseCond(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
+            add_upsample=add_upsample,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads,
+            resolution_idx=resolution_idx,
+        )
+
+    raise ValueError(f"{up_block_type} does not exist.")
+
+
+class CrossAttnDownBlockSpatioTemporalPoseCond(nn.Module):
+    def __init__(
+            self,
+            in_channels: int,
+            out_channels: int,
+            temb_channels: int,
+            num_layers: int = 1,
+            transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+            num_attention_heads: int = 1,
+            cross_attention_dim: int = 1280,
+            add_downsample: bool = True,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                SpatioTemporalResBlock(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=1e-6,
+                )
+            )
+            attentions.append(
+                TransformerSpatioTemporalModelPoseCond(
+                    num_attention_heads,
+                    out_channels // num_attention_heads,
+                    in_channels=out_channels,
+                    num_layers=transformer_layers_per_block[i],
+                    cross_attention_dim=cross_attention_dim,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample2D(
+                        out_channels,
+                        use_conv=True,
+                        out_channels=out_channels,
+                        padding=1,
+                        name="op",
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+            self,
+            hidden_states: torch.FloatTensor,  # [bs * frame, c, h, w]
+            temb: Optional[torch.FloatTensor] = None,  # [bs * frame, c]
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,  # [bs * frame, 1, c]
+            image_only_indicator: Optional[torch.Tensor] = None,  # [bs, frame]
+            pose_feature: Optional[torch.Tensor] = None  # [bs, c, frame, h, w]
+    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
+        output_states = ()
+
+        blocks = list(zip(self.resnets, self.attentions))
+        for resnet, attn in blocks:
+            if self.training and self.gradient_checkpointing:  # TODO
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    image_only_indicator,
+                    **ckpt_kwargs,
+                )
+
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_only_indicator=image_only_indicator,
+                    return_dict=False,
+                )[0]
+            else:
+                hidden_states = resnet(
+                    hidden_states,
+                    temb,
+                    image_only_indicator=image_only_indicator,
+                )  # [bs * frame, c, h, w]
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_only_indicator=image_only_indicator,
+                    pose_feature=pose_feature,
+                    return_dict=False,
+                )[0]
+
+            output_states = output_states + (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states = output_states + (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class UNetMidBlockSpatioTemporalPoseCond(nn.Module):
+    def __init__(
+            self,
+            in_channels: int,
+            temb_channels: int,
+            num_layers: int = 1,
+            transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+            num_attention_heads: int = 1,
+            cross_attention_dim: int = 1280,
+    ):
+        super().__init__()
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+
+        # support for variable transformer layers per block
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
+
+        # there is always at least one resnet
+        resnets = [
+            SpatioTemporalResBlock(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                temb_channels=temb_channels,
+                eps=1e-5,
+            )
+        ]
+        attentions = []
+
+        for i in range(num_layers):
+            attentions.append(
+                TransformerSpatioTemporalModelPoseCond(
+                    num_attention_heads,
+                    in_channels // num_attention_heads,
+                    in_channels=in_channels,
+                    num_layers=transformer_layers_per_block[i],
+                    cross_attention_dim=cross_attention_dim,
+                )
+            )
+
+            resnets.append(
+                SpatioTemporalResBlock(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=1e-5,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            temb: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            image_only_indicator: Optional[torch.Tensor] = None,
+            pose_feature: Optional[torch.Tensor] = None  # [bs, c, frame, h, w]
+    ) -> torch.FloatTensor:
+        hidden_states = self.resnets[0](
+            hidden_states,
+            temb,
+            image_only_indicator=image_only_indicator,
+        )
+
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            if self.training and self.gradient_checkpointing:  # TODO
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_only_indicator=image_only_indicator,
+                    return_dict=False,
+                )[0]
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    image_only_indicator,
+                    **ckpt_kwargs,
+                )
+            else:
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_only_indicator=image_only_indicator,
+                    pose_feature=pose_feature,
+                    return_dict=False,
+                )[0]
+                hidden_states = resnet(
+                    hidden_states,
+                    temb,
+                    image_only_indicator=image_only_indicator,
+                )
+
+        return hidden_states
+
+
+class CrossAttnUpBlockSpatioTemporalPoseCond(nn.Module):
+    def __init__(
+            self,
+            in_channels: int,
+            out_channels: int,
+            prev_output_channel: int,
+            temb_channels: int,
+            resolution_idx: Optional[int] = None,
+            num_layers: int = 1,
+            transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+            resnet_eps: float = 1e-6,
+            num_attention_heads: int = 1,
+            cross_attention_dim: int = 1280,
+            add_upsample: bool = True,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                SpatioTemporalResBlock(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                )
+            )
+            attentions.append(
+                TransformerSpatioTemporalModelPoseCond(
+                    num_attention_heads,
+                    out_channels // num_attention_heads,
+                    in_channels=out_channels,
+                    num_layers=transformer_layers_per_block[i],
+                    cross_attention_dim=cross_attention_dim,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+        self.resolution_idx = resolution_idx
+
+    def forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+            temb: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            image_only_indicator: Optional[torch.Tensor] = None,
+            pose_feature: Optional[torch.Tensor] = None  # [bs, c, frame, h, w]
+    ) -> torch.FloatTensor:
+        for resnet, attn in zip(self.resnets, self.attentions):
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if self.training and self.gradient_checkpointing:  # TODO
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    image_only_indicator,
+                    **ckpt_kwargs,
+                )
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_only_indicator=image_only_indicator,
+                    return_dict=False,
+                )[0]
+            else:
+                hidden_states = resnet(
+                    hidden_states,
+                    temb,
+                    image_only_indicator=image_only_indicator,
+                )
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_only_indicator=image_only_indicator,
+                    pose_feature=pose_feature,
+                    return_dict=False,
+                )[0]
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states)
+
+        return hidden_states

cameractrl/pipelines/pipeline_animation.py

@@ -0,0 +1,523 @@
+# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
+
+import inspect
+
+import pandas as pd
+import torch
+import PIL.Image
+
+from typing import Callable, List, Optional, Union, Dict
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+from diffusers.models import AutoencoderKLTemporalDecoder
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import EulerDiscreteScheduler
+from diffusers.utils import logging
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.stable_video_diffusion.pipeline_stable_video_diffusion import (
+    _resize_with_antialiasing,
+    _append_dims,
+    tensor2vid,
+    StableVideoDiffusionPipelineOutput
+)
+
+from cameractrl.models.pose_adaptor import CameraPoseEncoder
+from cameractrl.models.unet import UNetSpatioTemporalConditionModelPoseCond
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class StableVideoDiffusionPipelinePoseCond(DiffusionPipeline):
+    r"""
+    Pipeline to generate video from an input image using Stable Video Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
+        image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
+            Frozen CLIP image-encoder ([laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)).
+        unet ([`UNetSpatioTemporalConditionModel`]):
+            A `UNetSpatioTemporalConditionModel` to denoise the encoded image latents.
+        scheduler ([`EulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents.
+        feature_extractor ([`~transformers.CLIPImageProcessor`]):
+            A `CLIPImageProcessor` to extract features from generated images.
+    """
+
+    model_cpu_offload_seq = "image_encoder->unet->vae"
+    _callback_tensor_inputs = ["latents"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKLTemporalDecoder,
+        image_encoder: CLIPVisionModelWithProjection,
+        unet: UNetSpatioTemporalConditionModelPoseCond,
+        scheduler: EulerDiscreteScheduler,
+        feature_extractor: CLIPImageProcessor,
+        pose_encoder: CameraPoseEncoder
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            image_encoder=image_encoder,
+            unet=unet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+            pose_encoder=pose_encoder
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+    def _encode_image(self, image, device, num_videos_per_prompt, do_classifier_free_guidance, do_resize_normalize):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        if not isinstance(image, torch.Tensor):
+            image = self.image_processor.pil_to_numpy(image)
+            image = self.image_processor.numpy_to_pt(image)
+
+            # We normalize the image before resizing to match with the original implementation.
+            # Then we unnormalize it after resizing.
+            image = image * 2.0 - 1.0
+            image = _resize_with_antialiasing(image, (224, 224))
+            image = (image + 1.0) / 2.0
+
+            # Normalize the image with for CLIP input
+            image = self.feature_extractor(
+                images=image,
+                do_normalize=True,
+                do_center_crop=False,
+                do_resize=False,
+                do_rescale=False,
+                return_tensors="pt",
+            ).pixel_values
+        elif do_resize_normalize:
+            image = _resize_with_antialiasing(image, (224, 224))
+            image = (image + 1.0) / 2.0
+            # Normalize the image with for CLIP input
+            image = self.feature_extractor(
+                images=image,
+                do_normalize=True,
+                do_center_crop=False,
+                do_resize=False,
+                do_rescale=False,
+                return_tensors="pt",
+            ).pixel_values
+
+        image = image.to(device=device, dtype=dtype)
+        image_embeddings = self.image_encoder(image).image_embeds
+        image_embeddings = image_embeddings.unsqueeze(1)
+
+        # duplicate image embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = image_embeddings.shape
+        image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1)
+        image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            negative_image_embeddings = torch.zeros_like(image_embeddings)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])
+
+        return image_embeddings
+
+    def _encode_vae_image(
+        self,
+        image: torch.Tensor,
+        device,
+        num_videos_per_prompt,
+        do_classifier_free_guidance,
+    ):
+        image = image.to(device=device)
+        image_latents = self.vae.encode(image).latent_dist.mode()
+
+        if do_classifier_free_guidance:
+            negative_image_latents = torch.zeros_like(image_latents)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            image_latents = torch.cat([negative_image_latents, image_latents])
+
+        # duplicate image_latents for each generation per prompt, using mps friendly method
+        image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1)
+
+        return image_latents
+
+    def _get_add_time_ids(
+        self,
+        fps,
+        motion_bucket_id,
+        noise_aug_strength,
+        dtype,
+        batch_size,
+        num_videos_per_prompt,
+        do_classifier_free_guidance,
+    ):
+        add_time_ids = [fps, motion_bucket_id, noise_aug_strength]
+
+        passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids)
+        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
+
+        if expected_add_embed_dim != passed_add_embed_dim:
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+            )
+
+        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+        add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)
+
+        if do_classifier_free_guidance:
+            add_time_ids = torch.cat([add_time_ids, add_time_ids])
+
+        return add_time_ids
+
+    def decode_latents(self, latents, num_frames, decode_chunk_size=14):
+        # [batch, frames, channels, height, width] -> [batch*frames, channels, height, width]
+        latents = latents.flatten(0, 1)
+
+        latents = 1 / self.vae.config.scaling_factor * latents
+
+        accepts_num_frames = "num_frames" in set(inspect.signature(self.vae.forward).parameters.keys())
+
+        # decode decode_chunk_size frames at a time to avoid OOM
+        frames = []
+        for i in range(0, latents.shape[0], decode_chunk_size):
+            num_frames_in = latents[i : i + decode_chunk_size].shape[0]
+            decode_kwargs = {}
+            if accepts_num_frames:
+                # we only pass num_frames_in if it's expected
+                decode_kwargs["num_frames"] = num_frames_in
+
+            frame = self.vae.decode(latents[i : i + decode_chunk_size], **decode_kwargs).sample
+            frames.append(frame)
+        frames = torch.cat(frames, dim=0)
+
+        # [batch*frames, channels, height, width] -> [batch, channels, frames, height, width]
+        frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        frames = frames.float()
+        return frames
+
+    def check_inputs(self, image, height, width):
+        if (
+            not isinstance(image, torch.Tensor)
+            and not isinstance(image, PIL.Image.Image)
+            and not isinstance(image, list)
+        ):
+            raise ValueError(
+                "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
+                f" {type(image)}"
+            )
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_frames,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        shape = (
+            batch_size,
+            num_frames,
+            num_channels_latents // 2,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+    # corresponds to doing no classifier free guidance.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor],
+        pose_embedding: torch.FloatTensor,
+        height: int = 576,
+        width: int = 1024,
+        num_frames: Optional[int] = None,
+        num_inference_steps: int = 25,
+        min_guidance_scale: float = 1.0,
+        max_guidance_scale: float = 3.0,
+        fps: int = 7,
+        motion_bucket_id: int = 127,
+        noise_aug_strength: int = 0.02,
+        do_resize_normalize: bool = True,
+        do_image_process: bool = False,
+        decode_chunk_size: Optional[int] = None,
+        num_videos_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        return_dict: bool = True,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
+                Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
+                [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
+            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The width in pixels of the generated image.
+            num_frames (`int`, *optional*):
+                The number of video frames to generate. Defaults to 14 for `stable-video-diffusion-img2vid` and to 25 for `stable-video-diffusion-img2vid-xt`
+            num_inference_steps (`int`, *optional*, defaults to 25):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference. This parameter is modulated by `strength`.
+            min_guidance_scale (`float`, *optional*, defaults to 1.0):
+                The minimum guidance scale. Used for the classifier free guidance with first frame.
+            max_guidance_scale (`float`, *optional*, defaults to 3.0):
+                The maximum guidance scale. Used for the classifier free guidance with last frame.
+            fps (`int`, *optional*, defaults to 7):
+                Frames per second. The rate at which the generated images shall be exported to a video after generation.
+                Note that Stable Diffusion Video's UNet was micro-conditioned on fps-1 during training.
+            motion_bucket_id (`int`, *optional*, defaults to 127):
+                The motion bucket ID. Used as conditioning for the generation. The higher the number the more motion will be in the video.
+            noise_aug_strength (`int`, *optional*, defaults to 0.02):
+                The amount of noise added to the init image, the higher it is the less the video will look like the init image. Increase it for more motion.
+            decode_chunk_size (`int`, *optional*):
+                The number of frames to decode at a time. The higher the chunk size, the higher the temporal consistency
+                between frames, but also the higher the memory consumption. By default, the decoder will decode all frames at once
+                for maximal quality. Reduce `decode_chunk_size` to reduce memory usage.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned where the first element is a list of list with the generated frames.
+
+        Examples:
+
+        ```py
+        from diffusers import StableVideoDiffusionPipeline
+        from diffusers.utils import load_image, export_to_video
+
+        pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16")
+        pipe.to("cuda")
+
+        image = load_image("https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200")
+        image = image.resize((1024, 576))
+
+        frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0]
+        export_to_video(frames, "generated.mp4", fps=7)
+        ```
+        """
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        num_frames = num_frames if num_frames is not None else self.unet.config.num_frames
+        decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(image, height, width)
+
+        # 2. Define call parameters
+        if isinstance(image, PIL.Image.Image):
+            batch_size = 1
+        elif isinstance(image, list):
+            batch_size = len(image)
+        else:
+            batch_size = image.shape[0]
+        device = pose_embedding.device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = max_guidance_scale > 1.0
+
+        # 3. Encode input image
+        image_embeddings = self._encode_image(image, device, num_videos_per_prompt, do_classifier_free_guidance, do_resize_normalize=do_resize_normalize)
+
+        # NOTE: Stable Diffusion Video was conditioned on fps - 1, which
+        # is why it is reduced here.
+        # See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188
+        fps = fps - 1
+
+        # 4. Encode input image using VAE
+        if do_image_process:
+            image = self.image_processor.preprocess(image, height=height, width=width).to(image_embeddings.device)
+        noise = randn_tensor(image.shape, generator=generator, device=image.device, dtype=image.dtype)
+        image = image + noise_aug_strength * noise
+
+        needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+        if needs_upcasting:
+            self.vae.to(dtype=torch.float32)
+
+        image_latents = self._encode_vae_image(image, device, num_videos_per_prompt, do_classifier_free_guidance)
+        image_latents = image_latents.to(image_embeddings.dtype)
+
+        # cast back to fp16 if needed
+        if needs_upcasting:
+            self.vae.to(dtype=torch.float16)
+
+        # Repeat the image latents for each frame so we can concatenate them with the noise
+        # image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width]
+        image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1)
+
+        # 5. Get Added Time IDs
+        added_time_ids = self._get_add_time_ids(
+            fps,
+            motion_bucket_id,
+            noise_aug_strength,
+            image_embeddings.dtype,
+            batch_size,
+            num_videos_per_prompt,
+            do_classifier_free_guidance,
+        )
+        added_time_ids = added_time_ids.to(device)
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_videos_per_prompt,
+            num_frames,
+            num_channels_latents,
+            height,
+            width,
+            image_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )           # [bs, frame, c, h, w]
+
+        # 7. Prepare guidance scale
+        guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0)
+        guidance_scale = guidance_scale.to(device, latents.dtype)
+        guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1)
+        guidance_scale = _append_dims(guidance_scale, latents.ndim)     # [bs, frame, 1, 1, 1]
+
+        self._guidance_scale = guidance_scale
+
+        # 8. Prepare pose features
+        assert pose_embedding.ndim == 5                         # [b, f, c, h, w]
+        pose_features = self.pose_encoder(pose_embedding)       # list of [b, c, f, h, w]
+        pose_features = [torch.cat([x, x], dim=0) for x in pose_features] if do_classifier_free_guidance else pose_features
+
+        # 9. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # Concatenate image_latents over channels dimention
+                latent_model_input = torch.cat([latent_model_input, image_latents], dim=2)
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=image_embeddings,
+                    added_time_ids=added_time_ids,
+                    pose_features=pose_features,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents).prev_sample
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if not output_type == "latent":
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+            frames = self.decode_latents(latents, num_frames, decode_chunk_size)        # [b, c, f, h, w]
+            frames = tensor2vid(frames, self.image_processor, output_type=output_type)
+        else:
+            frames = latents
+
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return frames
+
+        return StableVideoDiffusionPipelineOutput(frames=frames)

cameractrl/utils/convert_from_ckpt.py

@@ -0,0 +1,556 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Conversion script for the Stable Diffusion checkpoints."""
+
+import re
+from transformers import CLIPTextModel
+
+def shave_segments(path, n_shave_prefix_segments=1):
+    """
+    Removes segments. Positive values shave the first segments, negative shave the last segments.
+    """
+    if n_shave_prefix_segments >= 0:
+        return ".".join(path.split(".")[n_shave_prefix_segments:])
+    else:
+        return ".".join(path.split(".")[:n_shave_prefix_segments])
+
+
+def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside resnets to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item.replace("in_layers.0", "norm1")
+        new_item = new_item.replace("in_layers.2", "conv1")
+
+        new_item = new_item.replace("out_layers.0", "norm2")
+        new_item = new_item.replace("out_layers.3", "conv2")
+
+        new_item = new_item.replace("emb_layers.1", "time_emb_proj")
+        new_item = new_item.replace("skip_connection", "conv_shortcut")
+
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside resnets to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        new_item = new_item.replace("nin_shortcut", "conv_shortcut")
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def renew_attention_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside attentions to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
+        #         new_item = new_item.replace('norm.bias', 'group_norm.bias')
+
+        #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
+        #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
+
+        #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside attentions to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        new_item = new_item.replace("norm.weight", "group_norm.weight")
+        new_item = new_item.replace("norm.bias", "group_norm.bias")
+
+        new_item = new_item.replace("q.weight", "query.weight")
+        new_item = new_item.replace("q.bias", "query.bias")
+
+        new_item = new_item.replace("k.weight", "key.weight")
+        new_item = new_item.replace("k.bias", "key.bias")
+
+        new_item = new_item.replace("v.weight", "value.weight")
+        new_item = new_item.replace("v.bias", "value.bias")
+
+        new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
+        new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
+
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def assign_to_checkpoint(
+    paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
+):
+    """
+    This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits
+    attention layers, and takes into account additional replacements that may arise.
+
+    Assigns the weights to the new checkpoint.
+    """
+    assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
+
+    # Splits the attention layers into three variables.
+    if attention_paths_to_split is not None:
+        for path, path_map in attention_paths_to_split.items():
+            old_tensor = old_checkpoint[path]
+            channels = old_tensor.shape[0] // 3
+
+            target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
+
+            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
+
+            old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
+            query, key, value = old_tensor.split(channels // num_heads, dim=1)
+
+            checkpoint[path_map["query"]] = query.reshape(target_shape)
+            checkpoint[path_map["key"]] = key.reshape(target_shape)
+            checkpoint[path_map["value"]] = value.reshape(target_shape)
+
+    for path in paths:
+        new_path = path["new"]
+
+        # These have already been assigned
+        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
+            continue
+
+        # Global renaming happens here
+        new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
+        new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
+        new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
+
+        if additional_replacements is not None:
+            for replacement in additional_replacements:
+                new_path = new_path.replace(replacement["old"], replacement["new"])
+
+        # proj_attn.weight has to be converted from conv 1D to linear
+        if "proj_attn.weight" in new_path:
+            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
+        else:
+            checkpoint[new_path] = old_checkpoint[path["old"]]
+
+
+def conv_attn_to_linear(checkpoint):
+    keys = list(checkpoint.keys())
+    attn_keys = ["query.weight", "key.weight", "value.weight"]
+    for key in keys:
+        if ".".join(key.split(".")[-2:]) in attn_keys:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0, 0]
+        elif "proj_attn.weight" in key:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0]
+
+
+def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False, controlnet=False):
+    """
+    Takes a state dict and a config, and returns a converted checkpoint.
+    """
+
+    # extract state_dict for UNet
+    unet_state_dict = {}
+    keys = list(checkpoint.keys())
+
+    if controlnet:
+        unet_key = "control_model."
+    else:
+        unet_key = "model.diffusion_model."
+
+    # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
+    if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
+        print(f"Checkpoint {path} has both EMA and non-EMA weights.")
+        print(
+            "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
+            " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
+        )
+        for key in keys:
+            if key.startswith("model.diffusion_model"):
+                flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
+                unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
+    else:
+        if sum(k.startswith("model_ema") for k in keys) > 100:
+            print(
+                "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
+                " weights (usually better for inference), please make sure to add the `--extract_ema` flag."
+            )
+
+        for key in keys:
+            if key.startswith(unet_key):
+                unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
+
+    new_checkpoint = {}
+
+    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
+    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
+    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
+    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
+
+    if config["class_embed_type"] is None:
+        # No parameters to port
+        ...
+    elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
+        new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
+        new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
+        new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
+        new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
+    else:
+        raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}")
+
+    new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
+    new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
+
+    if not controlnet:
+        new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
+        new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
+        new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
+        new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
+
+    # Retrieves the keys for the input blocks only
+    num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
+    input_blocks = {
+        layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
+        for layer_id in range(num_input_blocks)
+    }
+
+    # Retrieves the keys for the middle blocks only
+    num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
+    middle_blocks = {
+        layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
+        for layer_id in range(num_middle_blocks)
+    }
+
+    # Retrieves the keys for the output blocks only
+    num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
+    output_blocks = {
+        layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
+        for layer_id in range(num_output_blocks)
+    }
+
+    for i in range(1, num_input_blocks):
+        block_id = (i - 1) // (config["layers_per_block"] + 1)
+        layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
+
+        resnets = [
+            key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
+        ]
+        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
+
+        if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.weight"
+            )
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.bias"
+            )
+
+        paths = renew_resnet_paths(resnets)
+        meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
+        assign_to_checkpoint(
+            paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+        )
+
+        if len(attentions):
+            paths = renew_attention_paths(attentions)
+            meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
+            assign_to_checkpoint(
+                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+            )
+
+    resnet_0 = middle_blocks[0]
+    attentions = middle_blocks[1]
+    resnet_1 = middle_blocks[2]
+
+    resnet_0_paths = renew_resnet_paths(resnet_0)
+    assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
+
+    resnet_1_paths = renew_resnet_paths(resnet_1)
+    assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
+
+    attentions_paths = renew_attention_paths(attentions)
+    meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(
+        attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+    )
+
+    for i in range(num_output_blocks):
+        block_id = i // (config["layers_per_block"] + 1)
+        layer_in_block_id = i % (config["layers_per_block"] + 1)
+        output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
+        output_block_list = {}
+
+        for layer in output_block_layers:
+            layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
+            if layer_id in output_block_list:
+                output_block_list[layer_id].append(layer_name)
+            else:
+                output_block_list[layer_id] = [layer_name]
+
+        if len(output_block_list) > 1:
+            resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
+            attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
+
+            resnet_0_paths = renew_resnet_paths(resnets)
+            paths = renew_resnet_paths(resnets)
+
+            meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
+            assign_to_checkpoint(
+                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+            )
+
+            output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
+            if ["conv.bias", "conv.weight"] in output_block_list.values():
+                index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
+                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
+                    f"output_blocks.{i}.{index}.conv.weight"
+                ]
+                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
+                    f"output_blocks.{i}.{index}.conv.bias"
+                ]
+
+                # Clear attentions as they have been attributed above.
+                if len(attentions) == 2:
+                    attentions = []
+
+            if len(attentions):
+                paths = renew_attention_paths(attentions)
+                meta_path = {
+                    "old": f"output_blocks.{i}.1",
+                    "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
+                }
+                assign_to_checkpoint(
+                    paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+                )
+        else:
+            resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
+            for path in resnet_0_paths:
+                old_path = ".".join(["output_blocks", str(i), path["old"]])
+                new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
+
+                new_checkpoint[new_path] = unet_state_dict[old_path]
+
+    if controlnet:
+        # conditioning embedding
+
+        orig_index = 0
+
+        new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.weight"
+        )
+        new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.bias"
+        )
+
+        orig_index += 2
+
+        diffusers_index = 0
+
+        while diffusers_index < 6:
+            new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop(
+                f"input_hint_block.{orig_index}.weight"
+            )
+            new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop(
+                f"input_hint_block.{orig_index}.bias"
+            )
+            diffusers_index += 1
+            orig_index += 2
+
+        new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.weight"
+        )
+        new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.bias"
+        )
+
+        # down blocks
+        for i in range(num_input_blocks):
+            new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight")
+            new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias")
+
+        # mid block
+        new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight")
+        new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias")
+
+    return new_checkpoint
+
+
+def convert_ldm_vae_checkpoint(checkpoint, config):
+    # extract state dict for VAE
+    vae_state_dict = {}
+    keys = list(checkpoint.keys())
+    vae_key = "first_stage_model." if any(k.startswith("first_stage_model.") for k in keys) else ""
+    for key in keys:
+        if key.startswith(vae_key):
+            vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
+
+    new_checkpoint = {}
+
+    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
+    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
+    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
+    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
+    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
+    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
+
+    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
+    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
+    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
+    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
+    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
+    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
+
+    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
+    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
+    new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
+    new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
+
+    # Retrieves the keys for the encoder down blocks only
+    num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
+    down_blocks = {
+        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
+    }
+
+    # Retrieves the keys for the decoder up blocks only
+    num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
+    up_blocks = {
+        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
+    }
+
+    for i in range(num_down_blocks):
+        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
+
+        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.weight"
+            )
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.bias"
+            )
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
+    paths = renew_vae_attention_paths(mid_attentions)
+    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+    conv_attn_to_linear(new_checkpoint)
+
+    for i in range(num_up_blocks):
+        block_id = num_up_blocks - 1 - i
+        resnets = [
+            key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
+        ]
+
+        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.weight"
+            ]
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.bias"
+            ]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
+    paths = renew_vae_attention_paths(mid_attentions)
+    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+    conv_attn_to_linear(new_checkpoint)
+    return new_checkpoint
+
+
+def convert_ldm_clip_checkpoint(checkpoint):
+    text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
+    keys = list(checkpoint.keys())
+
+    text_model_dict = {}
+
+    for key in keys:
+        if key.startswith("cond_stage_model.transformer"):
+            text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
+
+    text_model.load_state_dict(text_model_dict)
+
+    return text_model
+
+
+textenc_conversion_lst = [
+    ("cond_stage_model.model.positional_embedding", "text_model.embeddings.position_embedding.weight"),
+    ("cond_stage_model.model.token_embedding.weight", "text_model.embeddings.token_embedding.weight"),
+    ("cond_stage_model.model.ln_final.weight", "text_model.final_layer_norm.weight"),
+    ("cond_stage_model.model.ln_final.bias", "text_model.final_layer_norm.bias"),
+]
+textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}
+
+textenc_transformer_conversion_lst = [
+    # (stable-diffusion, HF Diffusers)
+    ("resblocks.", "text_model.encoder.layers."),
+    ("ln_1", "layer_norm1"),
+    ("ln_2", "layer_norm2"),
+    (".c_fc.", ".fc1."),
+    (".c_proj.", ".fc2."),
+    (".attn", ".self_attn"),
+    ("ln_final.", "transformer.text_model.final_layer_norm."),
+    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
+    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
+]
+protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}
+textenc_pattern = re.compile("|".join(protected.keys()))

cameractrl/utils/convert_lora_safetensor_to_diffusers.py

@@ -0,0 +1,154 @@
+# coding=utf-8
+# Copyright 2023, Haofan Wang, Qixun Wang, All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" Conversion script for the LoRA's safetensors checkpoints. """
+
+import argparse
+
+import torch
+from safetensors.torch import load_file
+
+from diffusers import StableDiffusionPipeline
+import pdb
+
+
+
+def convert_motion_lora_ckpt_to_diffusers(pipeline, state_dict, alpha=1.0):
+    # directly update weight in diffusers model
+    for key in state_dict:
+        # only process lora down key
+        if "up." in key: continue
+
+        up_key    = key.replace(".down.", ".up.")
+        model_key = key.replace("processor.", "").replace("_lora", "").replace("down.", "").replace("up.", "")
+        model_key = model_key.replace("to_out.", "to_out.0.")
+        layer_infos = model_key.split(".")[:-1]
+
+        curr_layer = pipeline.unet
+        while len(layer_infos) > 0:
+            temp_name = layer_infos.pop(0)
+            curr_layer = curr_layer.__getattr__(temp_name)
+
+        weight_down = state_dict[key]
+        weight_up   = state_dict[up_key]
+        curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).to(curr_layer.weight.data.device)
+
+    return pipeline
+
+
+
+def convert_lora(pipeline, state_dict, LORA_PREFIX_UNET="lora_unet", LORA_PREFIX_TEXT_ENCODER="lora_te", alpha=0.6):
+    # load base model
+    # pipeline = StableDiffusionPipeline.from_pretrained(base_model_path, torch_dtype=torch.float32)
+
+    # load LoRA weight from .safetensors
+    # state_dict = load_file(checkpoint_path)
+
+    visited = []
+
+    # directly update weight in diffusers model
+    for key in state_dict:
+        # it is suggested to print out the key, it usually will be something like below
+        # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
+
+        # as we have set the alpha beforehand, so just skip
+        if ".alpha" in key or key in visited:
+            continue
+
+        if "text" in key:
+            layer_infos = key.split(".")[0].split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_")
+            curr_layer = pipeline.text_encoder
+        else:
+            layer_infos = key.split(".")[0].split(LORA_PREFIX_UNET + "_")[-1].split("_")
+            curr_layer = pipeline.unet
+
+        # find the target layer
+        temp_name = layer_infos.pop(0)
+        while len(layer_infos) > -1:
+            try:
+                curr_layer = curr_layer.__getattr__(temp_name)
+                if len(layer_infos) > 0:
+                    temp_name = layer_infos.pop(0)
+                elif len(layer_infos) == 0:
+                    break
+            except Exception:
+                if len(temp_name) > 0:
+                    temp_name += "_" + layer_infos.pop(0)
+                else:
+                    temp_name = layer_infos.pop(0)
+
+        pair_keys = []
+        if "lora_down" in key:
+            pair_keys.append(key.replace("lora_down", "lora_up"))
+            pair_keys.append(key)
+        else:
+            pair_keys.append(key)
+            pair_keys.append(key.replace("lora_up", "lora_down"))
+
+        # update weight
+        if len(state_dict[pair_keys[0]].shape) == 4:
+            weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32)
+            weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32)
+            curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3).to(curr_layer.weight.data.device)
+        else:
+            weight_up = state_dict[pair_keys[0]].to(torch.float32)
+            weight_down = state_dict[pair_keys[1]].to(torch.float32)
+            curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).to(curr_layer.weight.data.device)
+
+        # update visited list
+        for item in pair_keys:
+            visited.append(item)
+
+    return pipeline
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
+    )
+    parser.add_argument(
+        "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
+    )
+    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
+    parser.add_argument(
+        "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
+    )
+    parser.add_argument(
+        "--lora_prefix_text_encoder",
+        default="lora_te",
+        type=str,
+        help="The prefix of text encoder weight in safetensors",
+    )
+    parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
+    parser.add_argument(
+        "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
+    )
+    parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
+
+    args = parser.parse_args()
+
+    base_model_path = args.base_model_path
+    checkpoint_path = args.checkpoint_path
+    dump_path = args.dump_path
+    lora_prefix_unet = args.lora_prefix_unet
+    lora_prefix_text_encoder = args.lora_prefix_text_encoder
+    alpha = args.alpha
+
+    pipe = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
+
+    pipe = pipe.to(args.device)
+    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

cameractrl/utils/util.py

@@ -0,0 +1,148 @@
+import os
+import functools
+import logging
+import sys
+import imageio
+import atexit
+import importlib
+import torch
+import torchvision
+import numpy as np
+from termcolor import colored
+
+from einops import rearrange
+
+
+def instantiate_from_config(config, **additional_kwargs):
+    if not "target" in config:
+        if config == '__is_first_stage__':
+            return None
+        elif config == "__is_unconditional__":
+            return None
+        raise KeyError("Expected key `target` to instantiate.")
+
+    additional_kwargs.update(config.get("kwargs", dict()))
+    return get_obj_from_str(config["target"])(**additional_kwargs)
+
+
+def get_obj_from_str(string, reload=False):
+    module, cls = string.rsplit(".", 1)
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8):
+    videos = rearrange(videos, "b c t h w -> t b c h w")
+    outputs = []
+    for x in videos:
+        x = torchvision.utils.make_grid(x, nrow=n_rows)
+        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+        if rescale:
+            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
+        x = (x * 255).numpy().astype(np.uint8)
+        outputs.append(x)
+
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    imageio.mimsave(path, outputs, fps=fps)
+
+
+# Logger utils are copied from detectron2
+class _ColorfulFormatter(logging.Formatter):
+    def __init__(self, *args, **kwargs):
+        self._root_name = kwargs.pop("root_name") + "."
+        self._abbrev_name = kwargs.pop("abbrev_name", "")
+        if len(self._abbrev_name):
+            self._abbrev_name = self._abbrev_name + "."
+        super(_ColorfulFormatter, self).__init__(*args, **kwargs)
+
+    def formatMessage(self, record):
+        record.name = record.name.replace(self._root_name, self._abbrev_name)
+        log = super(_ColorfulFormatter, self).formatMessage(record)
+        if record.levelno == logging.WARNING:
+            prefix = colored("WARNING", "red", attrs=["blink"])
+        elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
+            prefix = colored("ERROR", "red", attrs=["blink", "underline"])
+        else:
+            return log
+        return prefix + " " + log
+
+
+# cache the opened file object, so that different calls to `setup_logger`
+# with the same file name can safely write to the same file.
+@functools.lru_cache(maxsize=None)
+def _cached_log_stream(filename):
+    # use 1K buffer if writing to cloud storage
+    io = open(filename, "a", buffering=1024 if "://" in filename else -1)
+    atexit.register(io.close)
+    return io
+
+@functools.lru_cache()
+def setup_logger(output, distributed_rank, color=True, name='AnimateDiff', abbrev_name=None):
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.DEBUG)
+    logger.propagate = False
+
+    if abbrev_name is None:
+        abbrev_name = 'AD'
+    plain_formatter = logging.Formatter(
+        "[%(asctime)s] %(name)s:%(lineno)d %(levelname)s: %(message)s", datefmt="%m/%d %H:%M:%S"
+    )
+
+    # stdout logging: master only
+    if distributed_rank == 0:
+        ch = logging.StreamHandler(stream=sys.stdout)
+        ch.setLevel(logging.DEBUG)
+        if color:
+            formatter = _ColorfulFormatter(
+                colored("[%(asctime)s %(name)s:%(lineno)d]: ", "green") + "%(message)s",
+                datefmt="%m/%d %H:%M:%S",
+                root_name=name,
+                abbrev_name=str(abbrev_name),
+            )
+        else:
+            formatter = plain_formatter
+        ch.setFormatter(formatter)
+        logger.addHandler(ch)
+
+    # file logging: all workers
+    if output is not None:
+        if output.endswith(".txt") or output.endswith(".log"):
+            filename = output
+        else:
+            filename = os.path.join(output, "log.txt")
+        if distributed_rank > 0:
+            filename = filename + ".rank{}".format(distributed_rank)
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
+
+        fh = logging.StreamHandler(_cached_log_stream(filename))
+        fh.setLevel(logging.DEBUG)
+        fh.setFormatter(plain_formatter)
+        logger.addHandler(fh)
+
+    return logger
+
+
+def format_time(elapsed_time):
+    # Time thresholds
+    minute = 60
+    hour = 60 * minute
+    day = 24 * hour
+
+    days, remainder = divmod(elapsed_time, day)
+    hours, remainder = divmod(remainder, hour)
+    minutes, seconds = divmod(remainder, minute)
+
+    formatted_time = ""
+
+    if days > 0:
+        formatted_time += f"{int(days)} days "
+    if hours > 0:
+        formatted_time += f"{int(hours)} hours "
+    if minutes > 0:
+        formatted_time += f"{int(minutes)} minutes "
+    if seconds > 0:
+        formatted_time += f"{seconds:.2f} seconds"
+
+    return formatted_time.strip()

configs/train_cameractrl/svd_320_576_cameractrl.yaml

@@ -0,0 +1,87 @@
+output_dir: "output/cameractrl_model"
+pretrained_model_path: "/mnt/petrelfs/liangzhengyang.d/.cache/huggingface/hub/models--stabilityai--stable-video-diffusion-img2vid/snapshots/2586584918a955489b599d4dc76b6bb3fdb3fbb2"
+unet_subfolder: "unet"
+down_block_types: ['CrossAttnDownBlockSpatioTemporalPoseCond', 'CrossAttnDownBlockSpatioTemporalPoseCond', 'CrossAttnDownBlockSpatioTemporalPoseCond', 'DownBlockSpatioTemporal']
+up_block_types: ['UpBlockSpatioTemporal', 'CrossAttnUpBlockSpatioTemporalPoseCond', 'CrossAttnUpBlockSpatioTemporalPoseCond', 'CrossAttnUpBlockSpatioTemporalPoseCond']
+
+train_data:
+  root_path:       "/mnt/petrelfs/share_data/hehao/datasets/RealEstate10k"
+  annotation_json:       "annotations/train.json"
+  sample_stride: 8
+  sample_n_frames: 14
+  relative_pose: true
+  zero_t_first_frame: true
+  sample_size: [320, 576]
+  rescale_fxy: true
+  shuffle_frames: false
+  use_flip: false
+
+validation_data:
+  root_path:       "/mnt/petrelfs/share_data/hehao/datasets/RealEstate10k"
+  annotation_json:       "annotations/validation.json"
+  sample_stride: 8
+  sample_n_frames: 14
+  relative_pose: true
+  zero_t_first_frame: true
+  sample_size: [320, 576]
+  rescale_fxy: true
+  shuffle_frames: false
+  use_flip: false
+  return_clip_name: true
+
+random_null_image_ratio: 0.15
+
+pose_encoder_kwargs:
+  downscale_factor: 8
+  channels: [320, 640, 1280, 1280]
+  nums_rb: 2
+  cin: 384
+  ksize: 1
+  sk: true
+  use_conv: false
+  compression_factor: 1
+  temporal_attention_nhead: 8
+  attention_block_types: ["Temporal_Self", ]
+  temporal_position_encoding: true
+  temporal_position_encoding_max_len: 14
+
+attention_processor_kwargs:
+  add_spatial: false
+  add_temporal: true
+  attn_processor_name: 'attn1'
+  pose_feature_dimensions: [320, 640, 1280, 1280]
+  query_condition: true
+  key_value_condition: true
+  scale: 1.0
+
+do_sanity_check: true
+sample_before_training: false
+
+max_train_epoch:      -1
+max_train_steps:      50000
+validation_steps:       2500
+validation_steps_tuple: [500, ]
+
+learning_rate:    3.e-5
+
+P_mean: 0.7
+P_std: 1.6
+condition_image_noise_mean: -3.0
+condition_image_noise_std: 0.5
+sample_latent: true
+first_image_cond: true
+
+num_inference_steps: 25
+min_guidance_scale: 1.0
+max_guidance_scale: 3.0
+
+num_workers: 8
+train_batch_size: 1
+checkpointing_epochs: -1
+checkpointing_steps:  10000
+
+mixed_precision_training: false
+enable_xformers_memory_efficient_attention: true
+
+global_seed: 42
+logger_interval: 10

configs/train_cameractrl/svdxt_320_576_cameractrl.yaml

@@ -0,0 +1,88 @@
+output_dir: "output/cameractrl_model"
+pretrained_model_path: "/mnt/petrelfs/liangzhengyang.d/.cache/huggingface/hub/models--stabilityai--stable-video-diffusion-img2vid-xt/snapshots/4420c0886aad9930787308c62d9dd8befd4900f6"
+unet_subfolder: "unet"
+down_block_types: ['CrossAttnDownBlockSpatioTemporalPoseCond', 'CrossAttnDownBlockSpatioTemporalPoseCond', 'CrossAttnDownBlockSpatioTemporalPoseCond', 'DownBlockSpatioTemporal']
+up_block_types: ['UpBlockSpatioTemporal', 'CrossAttnUpBlockSpatioTemporalPoseCond', 'CrossAttnUpBlockSpatioTemporalPoseCond', 'CrossAttnUpBlockSpatioTemporalPoseCond']
+
+train_data:
+  root_path:       "/mnt/petrelfs/share_data/hehao/datasets/RealEstate10k"
+  annotation_json:       "annotations/train.json"
+  sample_stride: 5
+  sample_n_frames: 25
+  relative_pose: true
+  zero_t_first_frame: true
+  sample_size: [320, 576]
+  rescale_fxy: true
+  shuffle_frames: false
+  use_flip: false
+
+validation_data:
+  root_path:       "/mnt/petrelfs/share_data/hehao/datasets/RealEstate10k"
+  annotation_json:       "annotations/validation.json"
+  sample_stride: 5
+  sample_n_frames: 25
+  relative_pose: true
+  zero_t_first_frame: true
+  sample_size: [320, 576]
+  rescale_fxy: true
+  shuffle_frames: false
+  use_flip: false
+  return_clip_name: true
+
+random_null_image_ratio: 0.15
+
+pose_encoder_kwargs:
+  downscale_factor: 8
+  channels: [320, 640, 1280, 1280]
+  nums_rb: 2
+  cin: 384
+  ksize: 1
+  sk: true
+  use_conv: false
+  compression_factor: 1
+  temporal_attention_nhead: 8
+  attention_block_types: ["Temporal_Self", ]
+  temporal_position_encoding: true
+  temporal_position_encoding_max_len: 25
+
+attention_processor_kwargs:
+  add_spatial: false
+  add_temporal: true
+  attn_processor_name: 'attn1'
+  pose_feature_dimensions: [320, 640, 1280, 1280]
+  query_condition: true
+  key_value_condition: true
+  scale: 1.0
+
+do_sanity_check: false
+sample_before_training: false
+video_length: 25
+
+max_train_epoch:      -1
+max_train_steps:      50000
+validation_steps:       2500
+validation_steps_tuple: [1000, ]
+
+learning_rate:    3.e-5
+
+P_mean: 0.7
+P_std: 1.6
+condition_image_noise_mean: -3.0
+condition_image_noise_std: 0.5
+sample_latent: true
+first_image_cond: true
+
+num_inference_steps: 25
+min_guidance_scale: 1.0
+max_guidance_scale: 3.0
+
+num_workers: 8
+train_batch_size: 1
+checkpointing_epochs: -1
+checkpointing_steps:  10000
+
+mixed_precision_training: false
+enable_xformers_memory_efficient_attention: true
+
+global_seed: 42
+logger_interval: 10

inference_cameractrl.py

@@ -0,0 +1,255 @@
+import argparse
+import json
+import os
+import torch
+import numpy as np
+from tqdm import tqdm
+from omegaconf import OmegaConf
+from PIL import Image
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+from diffusers import AutoencoderKLTemporalDecoder, EulerDiscreteScheduler
+from diffusers.utils.import_utils import is_xformers_available
+from diffusers.models.attention_processor import AttnProcessor2_0
+from packaging import version as pver
+
+from cameractrl.pipelines.pipeline_animation import StableVideoDiffusionPipelinePoseCond
+from cameractrl.models.unet import UNetSpatioTemporalConditionModelPoseCond
+from cameractrl.models.pose_adaptor import CameraPoseEncoder
+from cameractrl.utils.util import save_videos_grid
+
+
+class Camera(object):
+    def __init__(self, entry):
+        fx, fy, cx, cy = entry[1:5]
+        self.fx = fx
+        self.fy = fy
+        self.cx = cx
+        self.cy = cy
+        w2c_mat = np.array(entry[7:]).reshape(3, 4)
+        w2c_mat_4x4 = np.eye(4)
+        w2c_mat_4x4[:3, :] = w2c_mat
+        self.w2c_mat = w2c_mat_4x4
+        self.c2w_mat = np.linalg.inv(w2c_mat_4x4)
+
+
+def setup_for_distributed(is_master):
+    """
+    This function disables printing when not in master process
+    """
+    import builtins as __builtin__
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop('force', False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def custom_meshgrid(*args):
+    # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid
+    if pver.parse(torch.__version__) < pver.parse('1.10'):
+        return torch.meshgrid(*args)
+    else:
+        return torch.meshgrid(*args, indexing='ij')
+
+
+def get_relative_pose(cam_params, zero_first_frame_scale):
+    abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
+    abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
+    source_cam_c2w = abs_c2ws[0]
+    if zero_first_frame_scale:
+        cam_to_origin = 0
+    else:
+        cam_to_origin = np.linalg.norm(source_cam_c2w[:3, 3])
+    target_cam_c2w = np.array([
+        [1, 0, 0, 0],
+        [0, 1, 0, -cam_to_origin],
+        [0, 0, 1, 0],
+        [0, 0, 0, 1]
+    ])
+    abs2rel = target_cam_c2w @ abs_w2cs[0]
+    ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
+    ret_poses = np.array(ret_poses, dtype=np.float32)
+    return ret_poses
+
+
+def ray_condition(K, c2w, H, W, device):
+    # c2w: B, V, 4, 4
+    # K: B, V, 4
+
+    B = K.shape[0]
+
+    j, i = custom_meshgrid(
+        torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+        torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
+    )
+    i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]
+    j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]
+
+    fx, fy, cx, cy = K.chunk(4, dim=-1)  # B,V, 1
+
+    zs = torch.ones_like(i)  # [B, HxW]
+    xs = (i - cx) / fx * zs
+    ys = (j - cy) / fy * zs
+    zs = zs.expand_as(ys)
+
+    directions = torch.stack((xs, ys, zs), dim=-1)  # B, V, HW, 3
+    directions = directions / directions.norm(dim=-1, keepdim=True)  # B, V, HW, 3
+
+    rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2)  # B, V, 3, HW
+    rays_o = c2w[..., :3, 3]  # B, V, 3
+    rays_o = rays_o[:, :, None].expand_as(rays_d)  # B, V, 3, HW
+    # c2w @ dirctions
+    rays_dxo = torch.linalg.cross(rays_o, rays_d)
+    plucker = torch.cat([rays_dxo, rays_d], dim=-1)
+    plucker = plucker.reshape(B, c2w.shape[1], H, W, 6)  # B, V, H, W, 6
+    return plucker
+
+
+def get_pipeline(ori_model_path, unet_subfolder, down_block_types, up_block_types, pose_encoder_kwargs,
+                 attention_processor_kwargs, pose_adaptor_ckpt, enable_xformers, device):
+    noise_scheduler = EulerDiscreteScheduler.from_pretrained(ori_model_path, subfolder="scheduler")
+    feature_extractor = CLIPImageProcessor.from_pretrained(ori_model_path, subfolder="feature_extractor")
+    image_encoder = CLIPVisionModelWithProjection.from_pretrained(ori_model_path, subfolder="image_encoder")
+    vae = AutoencoderKLTemporalDecoder.from_pretrained(ori_model_path, subfolder="vae")
+    unet = UNetSpatioTemporalConditionModelPoseCond.from_pretrained(ori_model_path,
+                                                                    subfolder=unet_subfolder,
+                                                                    down_block_types=down_block_types,
+                                                                    up_block_types=up_block_types)
+    pose_encoder = CameraPoseEncoder(**pose_encoder_kwargs)
+    print("Setting the attention processors")
+    unet.set_pose_cond_attn_processor(enable_xformers=(enable_xformers and is_xformers_available()), **attention_processor_kwargs)
+    print(f"Loading weights of camera encoder and attention processor from {pose_adaptor_ckpt}")
+    ckpt_dict = torch.load(pose_adaptor_ckpt, map_location=unet.device)
+    pose_encoder_state_dict = ckpt_dict['pose_encoder_state_dict']
+    pose_encoder_m, pose_encoder_u = pose_encoder.load_state_dict(pose_encoder_state_dict)
+    assert len(pose_encoder_m) == 0 and len(pose_encoder_u) == 0
+    attention_processor_state_dict = ckpt_dict['attention_processor_state_dict']
+    _, attention_processor_u = unet.load_state_dict(attention_processor_state_dict, strict=False)
+    assert len(attention_processor_u) == 0
+    print("Loading done")
+    vae.set_attn_processor(AttnProcessor2_0())
+    vae.to(device)
+    image_encoder.to(device)
+    unet.to(device)
+    pipeline = StableVideoDiffusionPipelinePoseCond(
+        vae=vae,
+        image_encoder=image_encoder,
+        unet=unet,
+        scheduler=noise_scheduler,
+        feature_extractor=feature_extractor,
+        pose_encoder=pose_encoder
+    )
+    pipeline = pipeline.to(device)
+    return pipeline
+
+
+def main(args):
+    os.makedirs(os.path.join(args.out_root, 'generated_videos'), exist_ok=True)
+    os.makedirs(os.path.join(args.out_root, 'reference_images'), exist_ok=True)
+    rank = args.local_rank
+    setup_for_distributed(rank == 0)
+    gpu_id = rank % torch.cuda.device_count()
+    model_configs = OmegaConf.load(args.model_config)
+    device = f"cuda:{gpu_id}"
+    print(f'Constructing pipeline')
+    pipeline = get_pipeline(args.ori_model_path, model_configs['unet_subfolder'], model_configs['down_block_types'],
+                            model_configs['up_block_types'], model_configs['pose_encoder_kwargs'],
+                            model_configs['attention_processor_kwargs'], args.pose_adaptor_ckpt, args.enable_xformers, device)
+    print('Done')
+
+    print('Loading K, R, t matrix')
+    with open(args.trajectory_file, 'r') as f:
+        poses = f.readlines()
+    poses = [pose.strip().split(' ') for pose in poses[1:]]
+    cam_params = [[float(x) for x in pose] for pose in poses]
+    cam_params = [Camera(cam_param) for cam_param in cam_params]
+
+    sample_wh_ratio = args.image_width / args.image_height
+    pose_wh_ratio = args.original_pose_width / args.original_pose_height
+    if pose_wh_ratio > sample_wh_ratio:
+        resized_ori_w = args.image_height * pose_wh_ratio
+        for cam_param in cam_params:
+            cam_param.fx = resized_ori_w * cam_param.fx / args.image_width
+    else:
+        resized_ori_h = args.image_width / pose_wh_ratio
+        for cam_param in cam_params:
+            cam_param.fy = resized_ori_h * cam_param.fy / args.image_height
+    intrinsic = np.asarray([[cam_param.fx * args.image_width,
+                             cam_param.fy * args.image_height,
+                             cam_param.cx * args.image_width,
+                             cam_param.cy * args.image_height]
+                            for cam_param in cam_params], dtype=np.float32)
+    K = torch.as_tensor(intrinsic)[None]  # [1, 1, 4]
+    c2ws = get_relative_pose(cam_params, zero_first_frame_scale=True)
+    c2ws = torch.as_tensor(c2ws)[None]  # [1, n_frame, 4, 4]
+    plucker_embedding = ray_condition(K, c2ws, args.image_height, args.image_width, device='cpu')       # b f h w 6
+    plucker_embedding = plucker_embedding.permute(0, 1, 4, 2, 3).contiguous().to(device=device)
+
+    prompt_dict = json.load(open(args.prompt_file, 'r'))
+    prompt_images = prompt_dict['image_paths']
+    prompt_captions = prompt_dict['captions']
+    N = int(len(prompt_images) // args.n_procs)
+    remainder = int(len(prompt_images) % args.n_procs)
+    prompts_per_gpu = [N + 1 if gpu_id < remainder else N for gpu_id in range(args.n_procs)]
+    low_idx = sum(prompts_per_gpu[:gpu_id])
+    high_idx = low_idx + prompts_per_gpu[gpu_id]
+    prompt_images = prompt_images[low_idx: high_idx]
+    prompt_captions = prompt_captions[low_idx: high_idx]
+    print(f"rank {rank} / {torch.cuda.device_count()}, number of prompts: {len(prompt_images)}")
+
+    generator = torch.Generator(device=device)
+    generator.manual_seed(42)
+
+    for prompt_image, prompt_caption in tqdm(zip(prompt_images, prompt_captions)):
+        save_name = "_".join(prompt_caption.split(" "))
+        condition_image = Image.open(prompt_image)
+        with torch.no_grad():
+            sample = pipeline(
+                image=condition_image,
+                pose_embedding=plucker_embedding,
+                height=args.image_height,
+                width=args.image_width,
+                num_frames=args.num_frames,
+                num_inference_steps=args.num_inference_steps,
+                min_guidance_scale=args.min_guidance_scale,
+                max_guidance_scale=args.max_guidance_scale,
+                do_image_process=True,
+                generator=generator,
+                output_type='pt'
+            ).frames[0].transpose(0, 1).cpu()      # [3, f, h, w] 0-1
+        resized_condition_image = condition_image.resize((args.image_width, args.image_height))
+        save_videos_grid(sample[None], f"{os.path.join(args.out_root, 'generated_videos')}/{save_name}.mp4", rescale=False)
+        resized_condition_image.save(os.path.join(args.out_root, 'reference_images', f'{save_name}.png'))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--out_root", type=str)
+    parser.add_argument("--image_height", type=int, default=320)
+    parser.add_argument("--image_width", type=int, default=576)
+    parser.add_argument("--num_frames", type=int, default=14)
+    parser.add_argument("--ori_model_path", type=str)
+    parser.add_argument("--unet_subfolder", type=str, default='unet')
+    parser.add_argument("--enable_xformers", action='store_true')
+    parser.add_argument("--pose_adaptor_ckpt", default=None)
+    parser.add_argument("--num_inference_steps", type=int, default=25)
+    parser.add_argument("--min_guidance_scale", type=float, default=1.0)
+    parser.add_argument("--max_guidance_scale", type=float, default=3.0)
+    parser.add_argument("--prompt_file", required=True, help='prompts path, json or txt')
+    parser.add_argument("--trajectory_file", required=True)
+    parser.add_argument("--original_pose_width", type=int, default=1280)
+    parser.add_argument("--original_pose_height", type=int, default=720)
+    parser.add_argument("--model_config", required=True)
+    parser.add_argument("--n_procs", type=int, default=8)
+
+    # DDP args
+    parser.add_argument("--world_size", default=1, type=int,
+                        help="number of the distributed processes.")
+    parser.add_argument('--local-rank', type=int, default=-1,
+                        help='Replica rank on the current node. This field is required '
+                             'by `torch.distributed.launch`.')
+    args = parser.parse_args()
+    main(args)

requirements.txt

@@ -0,0 +1,20 @@
+--extra-index-url https://download.pytorch.org/whl/cu121
+torch
+torchvision
+diffusers==0.24.0
+imageio==2.27.0
+transformers==4.39.3
+gradio==4.26.0
+imageio==2.27.0
+imageio-ffmpeg==0.4.9
+accelerate==0.30.0
+opencv-python
+gdown
+einops
+decord
+omegaconf
+safetensors
+gradio
+wandb
+triton
+termcolor