Fixes to the FUN-wrapper
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malkuthro
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control_camera_video.shape torch.Size([1, 6, 161, 704, 1248])
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control_camera_latents.shape torch.Size([1, 24, 41, 704, 1248])
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CUDA Compute Capability: 8.9
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Detected model in_channels: 36
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Model cross attention type: t2v, num_heads: 40, num_layers: 40
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Model variant detected: 14B_2.2
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model_type FLOW
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Loading LoRA: Wan22_Lightning\4steps_1022_i2v\wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_1022 with strength: 1.0
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Using accelerate to load and assign model weights to device...
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Loading transformer parameters to cuda:0: 0%| | 0/1101 [00:00<?, ?it/Loading transformer parameters to cuda:0: 1%| | 11/1101 [00:00<00:12,Loading transformer parameters to cuda:0: 3%|▎ | 38/1101 [00:00<00:05,Loading transformer parameters to cuda:0: 6%|▌ | 68/1101 [00:00<00:04,Loading transformer parameters to cuda:0: 9%|▉ | 97/1101 [00:00<00:04,Loading transformer parameters to cuda:0: 11%|█▏ | 126/1101 [00:00<00:03Loading transformer parameters to cuda:0: 14%|█▍ | 155/1101 [00:00<00:03Loading transformer parameters to cuda:0: 17%|█▋ | 182/1101 [00:00<00:03Loading transformer parameters to cuda:0: 19%|█▉ | 209/1101 [00:00<00:03Loading transformer parameters to cuda:0: 22%|██▏ | 242/1101 [00:00<00:03Loading transformer parameters to cuda:0: 25%|██▍ | 271/1101 [00:01<00:03Loading transformer parameters to cuda:0: 100%|█████████▉| 1098/1101 [00:01<00:0Loading transformer parameters to cuda:0: 100%|██████████| 1101/1101 [00:01<00:00, 783.50it/s]
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Using 1095 LoRA weight patches for WanVideo model
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------- Scheduler info -------
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Total timesteps: tensor([999, 961, 909, 833, 714, 499], device='cuda:0')
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Using timesteps: tensor([999, 961, 909], device='cuda:0')
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Using sigmas: tensor([1.0000, 0.9615, 0.9090, 0.8333])
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------------------------------
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sigmas: tensor([1.0000, 0.9615, 0.9090, 0.8333])
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image_cond shape: torch.Size([20, 21, 88, 156])
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Input sequence length: 72072
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Sampling 81 frames at 1248x704 with 3 steps
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0%| | 0/3 [00:00<?, ?it/s]Error during model prediction: The size of tensor a (21) must match the size of tensor b (41) at non-singleton dimension 2
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0%| | 0/3 [00:02<?, ?it/s]
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Error during sampling: The size of tensor a (21) must match the size of tensor b (41) at non-singleton dimension 2
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!!! Exception during processing !!! The size of tensor a (21) must match the size of tensor b (41) at non-singleton dimension 2
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Traceback (most recent call last):
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File "C:\Users\ai.machine\AppData\Local\Programs\@comfyorgcomfyui-electron\resources\ComfyUI\execution.py", line 510, in execute
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output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, hidden_inputs=hidden_inputs)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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File "C:\Users\ai.machine\AppData\Local\Programs\@comfyorgcomfyui-electron\resources\ComfyUI\execution.py", line 324, in get_output_data
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return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, hidden_inputs=hidden_inputs)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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File "C:\Users\ai.machine\AppData\Local\Programs\@comfyorgcomfyui-electron\resources\ComfyUI\execution.py", line 298, in _async_map_node_over_list
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await process_inputs(input_dict, i)
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File "C:\Users\ai.machine\AppData\Local\Programs\@comfyorgcomfyui-electron\resources\ComfyUI\execution.py", line 286, in process_inputs
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result = f(**inputs)
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^^^^^^^^^^^
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File "C:\Users\ai.machine\Documents\ComfyUI\custom_nodes\ComfyUI-WanVideoWrapper\nodes_sampler.py", line 3036, in process
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raise e
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File "C:\Users\ai.machine\Documents\ComfyUI\custom_nodes\ComfyUI-WanVideoWrapper\nodes_sampler.py", line 2921, in process
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noise_pred, noise_pred_ovi, self.cache_state = predict_with_cfg(
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^^^^^^^^^^^^^^^^^
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File "C:\Users\ai.machine\Documents\ComfyUI\custom_nodes\ComfyUI-WanVideoWrapper\nodes_sampler.py", line 1543, in predict_with_cfg
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raise e
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File "C:\Users\ai.machine\Documents\ComfyUI\custom_nodes\ComfyUI-WanVideoWrapper\nodes_sampler.py", line 1414, in predict_with_cfg
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noise_pred_cond, noise_pred_ovi, cache_state_cond = transformer(
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^^^^^^^^^^^^
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File "C:\Users\ai.machine\Documents\ComfyUI\.venv\Lib\site-packages\torch\nn\modules\module.py", line 1751, in _wrapped_call_impl
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return self._call_impl(*args, **kwargs)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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File "C:\Users\ai.machine\Documents\ComfyUI\.venv\Lib\site-packages\torch\nn\modules\module.py", line 1762, in _call_impl
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return forward_call(*args, **kwargs)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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File "C:\Users\ai.machine\Documents\ComfyUI\custom_nodes\ComfyUI-WanVideoWrapper\wanvideo\modules\model.py", line 2302, in forward
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x = [u + v for u, v in zip(x, fun_camera)]
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~~^~~
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RuntimeError: The size of tensor a (21) must match the size of tensor b (41) at non-singleton dimension 2
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Prompt executed in 19.18 seconds
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@@ -0,0 +1,80 @@
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# Nuke Camera Export → ComfyUI Pose Pipeline
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This document captures the end-to-end workflow we established for converting Nuke camera animations into RealEstate10k-compatible pose files that ComfyUI can ingest via the Koolook loader.
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## 1. Source Data
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- **Nuke camera**: standard `Camera` node with Translation / Rotation channels keyed over time.
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- **Export step**: use Nuke's *Export Ascii* tool with the column mapping shown in `ASCI_source_data/AnimationChannels_and_values_firstFrame_plus-ASCI-window.png`.
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- Column order: `[translate.x, rotate.x, translate.y, rotate.y, rotate.z, translate.z]`.
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- Rotations are exported in **degrees** using Nuke's **ZXY rotation order** (see `DataValidation-nuke.png`).
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- **Raw file**: `nuke_CAM_exporter/inputs/camTrack_v01.asci` (162 lines covering frames 1001–1161 in this example).
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```
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Line 1 (frame 1001):
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-1.0521379710 -0.7493053079 0.2045794427 -102.4243546000 4.2196946140 -0.2049491256
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```
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This line corresponds to the highlighted translation / rotation values keyed in Nuke (blue fields in the screenshot).
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## 2. Conversion Script
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File: `nuke_CAM_exporter/nuke_ASCI-2-Pose_converter.py`
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Key behavior:
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- Parses command-line arguments for input path, output path, fps, intrinsics, and placeholder pose width/height.
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- Reads each ASCI row using the known column order and converts rotation degrees → radians.
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- Builds a rotation matrix using Nuke's ZXY order (`euler_zxy_to_matrix`).
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- Writes RealEstate10k-style rows:
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- Columns 1–7: timestamp (µs), fx, fy, cx, cy, pose_width, pose_height.
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- Columns 8–19: `[R|t]` flattened row-major (each row’s three rotation entries followed by that row’s translation component).
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- Header line records all parameters for traceability.
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Usage example:
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```
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cd nuke_CAM_exporter
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python nuke_ASCI-2-Pose_converter.py \
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inputs/camTrack_v01.asci \
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outputs/camTrack_v01_converted.txt \
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--fps 25 --fx 0.5 --fy 0.5 --cx 0.5 --cy 0.5 --width 1280 --height 720
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```
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```
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python nuke_ASCI-2-Pose_converter.py `
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inputs/camTrack_v01.asci `
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outputs/camTrack_v01_converted.txt `
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--fps 25 --fx 0.5 --fy 0.5 --cx 0.5 --cy 0.5 `
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--width 1248 --height 704
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```
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Snippet (core formatting function):
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```python
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def format_pose_rows(rotation, translation):
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r = rotation
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t = translation
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return [
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r[0][0], r[0][1], r[0][2], t[0],
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r[1][0], r[1][1], r[1][2], t[1],
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r[2][0], r[2][1], r[2][2], t[2],
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]
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```
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## 3. Output
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- Default target: `nuke_CAM_exporter/outputs/camTrack_v01_converted.txt`
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- First rows look like:
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```
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source:inputs/camTrack_v01.asci fps:25.0 fx:0.5 fy:0.5 cx:0.5 cy:0.5 width:1280.0 height:720.0
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0.000000000000 0.500000000000 ... 0.716894083480 0.478508303891 -0.507043071325 0.148742702111 ...
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```
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- Each row’s translation components (`... 0.1487427 ... -102.4243 ... -0.2049491`) now match the camera values from Nuke frame 1.
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## 4. Documentation
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- `docs/camTrack_v01_converted_README.txt` explains:
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- Source file, fps, intrinsics, and placeholder dimensions.
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- Column mapping `[Tx, Rx, Ty, Ry, Rz, Tz]` and the ZXY rotation order.
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- Steps for regenerating the TXT and importing it into ComfyUI (`Koolook Load Camera Poses (Absolute Path)` + `CameraPoseVisualizer`).
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## 5. Validation Checklist
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1. In Nuke, verify the first keyframe values match the ASCI line (screenshot `DataValidation-nuke.png`).
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2. After running the converter, open the TXT and confirm the translation entries in row 1 match the screenshot.
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3. Load the TXT through the Koolook loader + CameraPoseVisualizer to visually confirm the path.
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Keep this document handy as a “resume” of the pipeline so future adjustments reference the same assumptions and file locations.
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-81
@@ -1,4 +1,3 @@
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source:inputs/camTrack_v01.asci fps:25.0 fx:0.5 fy:0.5 cx:0.5 cy:0.5 width:1920.0 height:1080.0
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@@ -80,83 +79,3 @@ source:inputs/camTrack_v01.asci fps:25.0 fx:0.5 fy:0.5 cx:0.5 cy:0.5 width:1920.
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1600000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.215238403712 -0.008059355507 -0.976528277295 -0.911290109200 0.069427178491 0.997309402472 -0.023533436391 0.204272344700 0.974090497056 -0.072862902292 -0.214099745481 -0.255267441300
|
||||
1640000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.209417531008 -0.008849522613 -0.977786266858 -0.907849013800 0.069294207079 0.997310715636 -0.023867327943 0.204223468900 0.975367935998 -0.072753160944 -0.208241127063 -0.255409210900
|
||||
1680000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.203213204988 -0.009582714206 -0.979087618606 -0.904316902200 0.069067434984 0.997320938532 -0.024096368803 0.204199448200 0.976695491309 -0.072519770775 -0.202006930830 -0.255549937500
|
||||
1720000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.196861869278 -0.010110988459 -0.980379096236 -0.900898337400 0.069070490960 0.997319304303 -0.024155176328 0.204177424300 0.977995230920 -0.072470498669 -0.195635771575 -0.255636185400
|
||||
1760000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.190210926574 -0.010064365419 -0.981691658292 -0.897367358200 0.069142856830 0.997327068879 -0.023621664431 0.204169228700 0.979305401170 -0.072370064459 -0.189006626892 -0.255732566100
|
||||
1800000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.183294023552 -0.010279764166 -0.983004388281 -0.893841385800 0.069109413586 0.997336579716 -0.023315996946 0.204177871300 0.980625917404 -0.072208539720 -0.182095406058 -0.255804985800
|
||||
1840000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.177140509916 -0.010859391283 -0.984125659338 -0.890301227600 0.068934810000 0.997346381626 -0.023413394193 0.204201638700 0.981768420615 -0.071987975928 -0.175921856529 -0.255855619900
|
||||
1880000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.170966757116 -0.011396355743 -0.985210886581 -0.886787593400 0.068701911467 0.997361380996 -0.023458965414 0.204235032200 0.982878637128 -0.071696574348 -0.169732689558 -0.255866140100
|
||||
1920000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.164400600664 -0.011617840126 -0.986325234541 -0.883300840900 0.068451319346 0.997385654293 -0.023157579561 0.204277336600 0.984015680455 -0.071322383598 -0.163175544175 -0.255825609000
|
||||
1960000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.158187385248 -0.011856792632 -0.987337919669 -0.879817426200 0.068671852500 0.997374609729 -0.022979654948 0.204333409700 0.985018237305 -0.071437415517 -0.156957853711 -0.255727499700
|
||||
2000000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.152133943210 -0.011911987907 -0.988288099629 -0.876335561300 0.068933795920 0.997364470221 -0.022632837237 0.204397022700 0.985953038995 -0.071569672945 -0.150911851129 -0.255564957900
|
||||
2040000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.145957348152 -0.011364050934 -0.989225611712 -0.872860431700 0.069102622777 0.997374529006 -0.021653554321 0.204475462400 0.986874500656 -0.071518579655 -0.144788855643 -0.255332171900
|
||||
2080000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.140091941886 -0.010965143496 -0.990077781514 -0.869397163400 0.069146738191 0.997389009230 -0.020830095191 0.204577848300 0.987721102547 -0.071378777632 -0.138967959212 -0.255035966600
|
||||
2120000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.135028851087 -0.011199382759 -0.990778372392 -0.866033732900 0.069171422760 0.997389981434 -0.020701188558 0.204697236400 0.988424262980 -0.071328807365 -0.133901746039 -0.254666477400
|
||||
2160000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.130916649400 -0.011767643141 -0.991323536231 -0.862811863400 0.069352207674 0.997371216979 -0.020998257841 0.204837158300 0.988964661755 -0.071499497317 -0.129756385904 -0.254227757500
|
||||
2200000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.127344422244 -0.012244713978 -0.991782972783 -0.859717845900 0.069427757786 0.997361095824 -0.021228070688 0.204983070500 0.989425684209 -0.071560544408 -0.126158249481 -0.253735244300
|
||||
2240000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.124406755885 -0.012485668375 -0.992152743873 -0.856754362600 0.069882652439 0.997327505256 -0.021313426491 0.205146789600 0.989767333255 -0.071985799613 -0.123201747860 -0.253185808700
|
||||
2280000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.122473860270 -0.012572879087 -0.992392098045 -0.853856444400 0.070369944261 0.997293110855 -0.021319521245 0.205319643000 0.989973850410 -0.072445660691 -0.121257584306 -0.252583682500
|
||||
2320000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.120689062540 -0.012626983585 -0.992610049047 -0.851020216900 0.070237907185 0.997304397430 -0.021226758130 0.205517783800 0.990202396774 -0.072280690035 -0.119476839892 -0.251913785900
|
||||
2360000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.118712375660 -0.012682457062 -0.992847685775 -0.848259806600 0.069781597948 0.997339469156 -0.021083449721 0.205750241900 0.990473573829 -0.071785364436 -0.117511535603 -0.251163721100
|
||||
2400000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.116578692950 -0.012746863828 -0.993099655529 -0.845597446000 0.069172615537 0.997385292153 -0.020921956316 0.206016346800 0.990769679396 -0.071134354983 -0.115392139816 -0.250327438100
|
||||
2440000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.114968025666 -0.012931122982 -0.993285024116 -0.843070566700 0.068860162420 0.997406218906 -0.020955011799 0.206310018900 0.990979632034 -0.070806924424 -0.113779384545 -0.249404400600
|
||||
2480000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.113665951537 -0.013244992270 -0.993430733187 -0.840680003200 0.068617426046 0.997418848820 -0.021149204734 0.206604346600 0.991146659332 -0.070570604346 -0.112463725255 -0.248405739700
|
||||
2520000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.112217401111 -0.013584098125 -0.993590824820 -0.838398456600 0.068535873621 0.997419600314 -0.021376972102 0.206890627700 0.991317350254 -0.070495483454 -0.110996837332 -0.247346371400
|
||||
2560000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.111248297694 -0.013780260037 -0.993697097054 -0.836193978800 0.068923728580 0.997389199057 -0.021547743331 0.207159325500 0.991399685242 -0.070886458773 -0.110008063631 -0.246234864000
|
||||
2600000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.110099752346 -0.013924560045 -0.993822997903 -0.834011972000 0.069236261836 0.997365458111 -0.021644468417 0.207406088700 0.991506119285 -0.071191639914 -0.108845605451 -0.245090335600
|
||||
2640000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.108460223714 -0.014080022612 -0.994001072854 -0.831835329500 0.069268339053 0.997362332226 -0.021685835487 0.207645803700 0.991684565311 -0.071204853902 -0.107198841897 -0.243917003300
|
||||
2680000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.106512244624 -0.014253140510 -0.994209228347 -0.829649746400 0.069331946883 0.997357038813 -0.021725981497 0.207886531900 0.991891235412 -0.071244544466 -0.105242538915 -0.242729738400
|
||||
2720000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.104473183094 -0.014509817841 -0.994421851731 -0.827462375200 0.069542570021 0.997339479771 -0.021858477657 0.208142980900 0.992093334807 -0.071438275993 -0.103186180054 -0.241532340600
|
||||
2760000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.102037704439 -0.014807357100 -0.994670321789 -0.825273156200 0.069253950838 0.997357514365 -0.021951738742 0.208423808200 0.992366966987 -0.071124754595 -0.100742603291 -0.240333512400
|
||||
2800000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.099090713984 -0.015251561592 -0.994961516980 -0.823149442700 0.068688940911 0.997392653051 -0.022129731956 0.208733826900 0.992704820074 -0.070535703789 -0.097784736500 -0.239115402100
|
||||
2840000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.096068948633 -0.015777927130 -0.995249624026 -0.821090519400 0.068451728322 0.997402489238 -0.022419530566 0.209066957200 0.993018186137 -0.070280377607 -0.094739382123 -0.237888604400
|
||||
2880000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.092729900776 -0.016223902215 -0.995559114518 -0.819127142400 0.068636797969 0.997384637828 -0.022646725816 0.209405005000 0.993322785134 -0.070432018447 -0.091373821803 -0.236652284900
|
||||
2920000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.088943098396 -0.016724573440 -0.995896286714 -0.817254245300 0.068620217221 0.997380501878 -0.022877942701 0.209733247800 0.993670162094 -0.070373454632 -0.087562468255 -0.235412806300
|
||||
2960000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.084718829015 -0.017311351157 -0.996254504196 -0.815495967900 0.068363357266 0.997391989634 -0.023144554380 0.210044413800 0.994056925630 -0.070068082143 -0.083314419347 -0.234154686300
|
||||
3000000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.080210481446 -0.017914646653 -0.996616949536 -0.813854932800 0.067981377974 0.997412131292 -0.023400269248 0.210339024700 0.994457043273 -0.069628340404 -0.078785044885 -0.232870519200
|
||||
3040000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.075547554441 -0.018293433166 -0.996974381477 -0.812318086600 0.067700528353 0.997430490288 -0.023431933455 0.210623934900 0.994841296629 -0.069265917649 -0.074114959176 -0.231552347500
|
||||
3080000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.070943289636 -0.018610286844 -0.997306726579 -0.810864448500 0.067507796502 0.997443950754 -0.023415006208 0.210903957500 0.995193321454 -0.068987117115 -0.069505615637 -0.230204060700
|
||||
3120000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.066344947441 -0.018946067591 -0.997616857552 -0.809481561200 0.067383393580 0.997452147567 -0.023424166638 0.211180225000 0.995518872859 -0.068776884459 -0.064899259976 -0.228826955000
|
||||
3160000.000000000000 0.500000000000 0.500000000000 0.500000000000 0.500000000000 1248.000000000000 704.000000000000 -0.061542697747 -0.019367955298 -0.997916518884 -0.808167278800 0.067169842562 0.997464729000 -0.023501630805 0.211454033900 0.995841708608 -0.068476249224 -0.060085727822 -0.227421745700
|
||||
@@ -0,0 +1,27 @@
|
||||
# Wan FunCamera Wrapper Fix – Status & TODOs
|
||||
|
||||
## Summary of Current State
|
||||
- **ASCI Export/Converter**
|
||||
- Confirmed Nuke export columns `[Tx, Rx, Ty, Ry, Rz, Tz]` (degrees, ZXY order).
|
||||
- `nuke_ASCI-2-Pose_converter.py` converts to RealEstate-style `[R|t]` rows and works for arbitrary frame counts.
|
||||
- Latest file `outputs/camTrack_v01_converted_v5.txt` trimmed to 81 frames to match Wan’s multiple-of-4 plus one requirement.
|
||||
- **ComfyUI/Wan Errors**
|
||||
- Resolved the earlier reshape error (frame count mismatch).
|
||||
- Still hitting lightning/transformer error: `tensor a (21)` vs `tensor b (41)` when adding video tokens to camera tokens.
|
||||
- Lightning LoRA disabled but Wan 14B still emits 21 tokens while FunCamera still outputs 41 tokens—needs alignment fix in the wrapper.
|
||||
- **Next Step Identified**
|
||||
- Modify `wanvideo/fun_camera` node (or wrapper) so `control_camera_latents` downsample to the same token count as `image_cond` (21) before the transformer addition.
|
||||
|
||||
## TODOs
|
||||
1. **Patch FunCamera Node**
|
||||
- In `ComfyUI-WanVideoWrapper/fun_camera/nodes.py`, after producing `control_camera_latents`, reshape/average tokens in groups of 2 to reduce from 41 → 21.
|
||||
- Ensure it works for both lightning (21 tokens) and full (41 tokens) by detecting the target token count based on `image_cond.shape[1]`.
|
||||
2. **Verify Pipeline After Patch**
|
||||
- Re-run the 81-frame test prompt with `camTrack_v01_converted_v5.txt` and confirm sampling completes without the tensor-size error.
|
||||
- Document the wrapper change and any node parameters that need to be exposed for future adjustments.
|
||||
3. **Optional**: expose camera stride/chunk parameters in ComfyUI UI so we can adjust without code edits.
|
||||
|
||||
## Notes for Later
|
||||
- All pose TXT files live under `nuke_CAM_exporter/outputs/`. `v5` is the latest 81-frame version with width/height = 1248×704.
|
||||
- Debug logs are under `nuke_CAM_exporter/debugging_issues/` (`bugs_reports_e.txt` contains the 21 vs 41 stack trace).
|
||||
- Keep `wrapper_FIX/todo_and_resume.md` updated as we iterate on the FunCamera wrapper.
|
||||
@@ -0,0 +1,198 @@
|
||||
import numpy as np
|
||||
import os
|
||||
import torch
|
||||
from einops import rearrange
|
||||
|
||||
script_directory = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
class Camera(object):
|
||||
"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
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):
|
||||
"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
# ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid
|
||||
return torch.meshgrid(*args)
|
||||
|
||||
|
||||
def get_relative_pose(cam_params):
|
||||
"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
|
||||
abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
|
||||
cam_to_origin = 0
|
||||
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):
|
||||
"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
# 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.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
|
||||
# plucker = plucker.permute(0, 1, 4, 2, 3)
|
||||
return plucker
|
||||
|
||||
def process_poses(poses, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu', return_poses=False):
|
||||
"""Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
|
||||
cam_params = [[float(x) for x in pose] for pose in poses]
|
||||
if return_poses:
|
||||
return cam_params
|
||||
else:
|
||||
cam_params = [Camera(cam_param) for cam_param in cam_params]
|
||||
|
||||
sample_wh_ratio = width / height
|
||||
pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed
|
||||
|
||||
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) # Assuming this function is defined elsewhere
|
||||
c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4]
|
||||
plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W
|
||||
plucker_embedding = plucker_embedding[None]
|
||||
plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
|
||||
return plucker_embedding
|
||||
|
||||
class WanVideoFunCameraEmbeds:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"poses": ("CAMERACTRL_POSES", ),
|
||||
"width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}),
|
||||
"height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Strength of the camera motion"}),
|
||||
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the steps to apply camera motion"}),
|
||||
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the steps to apply camera motion"}),
|
||||
},
|
||||
# "optional": {
|
||||
# "fun_ref_image": ("LATENT", {"tooltip": "Reference latent for the Fun 1.1 -model"}),
|
||||
# }
|
||||
}
|
||||
|
||||
|
||||
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
|
||||
RETURN_NAMES = ("image_embeds",)
|
||||
FUNCTION = "process"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def process(self, poses, width, height, strength, start_percent, end_percent, fun_ref_image=None):
|
||||
num_frames = len(poses)
|
||||
|
||||
control_camera_video = process_poses(poses, width, height)
|
||||
control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0)
|
||||
print("control_camera_video.shape", control_camera_video.shape)
|
||||
|
||||
# Rearrange dimensions
|
||||
# Concatenate and transpose dimensions
|
||||
control_camera_latents = torch.concat(
|
||||
[
|
||||
torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2),
|
||||
control_camera_video[:, :, 1:]
|
||||
], dim=2
|
||||
).transpose(1, 2)
|
||||
|
||||
# Reshape, transpose, and view into desired shape
|
||||
b, f, c, h, w = control_camera_latents.shape
|
||||
control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
|
||||
control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
|
||||
print("control_camera_latents.shape", control_camera_latents.shape)
|
||||
|
||||
target_tokens = 21 # Wan 14B emits 21 tokens per chunk in Lightning/3-step configs
|
||||
actual_tokens = control_camera_latents.shape[2] # e.g. 41
|
||||
if actual_tokens != target_tokens:
|
||||
if actual_tokens % target_tokens != 0:
|
||||
raise ValueError(f"Cannot align {actual_tokens} camera tokens to {target_tokens}")
|
||||
group = actual_tokens // target_tokens
|
||||
control_camera_latents = (
|
||||
control_camera_latents
|
||||
.reshape(b, target_tokens, group, h, w)
|
||||
.mean(dim=2, keepdim=False)
|
||||
)
|
||||
|
||||
vae_stride = (4, 8, 8)
|
||||
|
||||
target_shape = (16, (num_frames - 1) // vae_stride[0] + 1,
|
||||
height // vae_stride[1],
|
||||
width // vae_stride[2])
|
||||
|
||||
embeds = {
|
||||
"target_shape": target_shape,
|
||||
"num_frames": num_frames,
|
||||
"control_embeds": {
|
||||
"control_camera_latents": control_camera_latents * strength,
|
||||
"control_camera_start_percent": start_percent,
|
||||
"control_camera_end_percent": end_percent,
|
||||
"fun_ref_image": fun_ref_image["samples"][:,:, 0] if fun_ref_image is not None else None,
|
||||
}
|
||||
}
|
||||
|
||||
return (embeds,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WanVideoFunCameraEmbeds": WanVideoFunCameraEmbeds,
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"WanVideoFunCameraEmbeds": "WanVideo FunCamera Embeds",
|
||||
}
|
||||
@@ -0,0 +1,186 @@
|
||||
import numpy as np
|
||||
import os
|
||||
import torch
|
||||
from einops import rearrange
|
||||
|
||||
script_directory = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
class Camera(object):
|
||||
"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
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):
|
||||
"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
# ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid
|
||||
return torch.meshgrid(*args)
|
||||
|
||||
|
||||
def get_relative_pose(cam_params):
|
||||
"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
|
||||
abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
|
||||
cam_to_origin = 0
|
||||
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):
|
||||
"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
# 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.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
|
||||
# plucker = plucker.permute(0, 1, 4, 2, 3)
|
||||
return plucker
|
||||
|
||||
def process_poses(poses, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu', return_poses=False):
|
||||
"""Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
|
||||
cam_params = [[float(x) for x in pose] for pose in poses]
|
||||
if return_poses:
|
||||
return cam_params
|
||||
else:
|
||||
cam_params = [Camera(cam_param) for cam_param in cam_params]
|
||||
|
||||
sample_wh_ratio = width / height
|
||||
pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed
|
||||
|
||||
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) # Assuming this function is defined elsewhere
|
||||
c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4]
|
||||
plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W
|
||||
plucker_embedding = plucker_embedding[None]
|
||||
plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
|
||||
return plucker_embedding
|
||||
|
||||
class WanVideoFunCameraEmbeds:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"poses": ("CAMERACTRL_POSES", ),
|
||||
"width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}),
|
||||
"height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Strength of the camera motion"}),
|
||||
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the steps to apply camera motion"}),
|
||||
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the steps to apply camera motion"}),
|
||||
},
|
||||
# "optional": {
|
||||
# "fun_ref_image": ("LATENT", {"tooltip": "Reference latent for the Fun 1.1 -model"}),
|
||||
# }
|
||||
}
|
||||
|
||||
|
||||
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
|
||||
RETURN_NAMES = ("image_embeds",)
|
||||
FUNCTION = "process"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def process(self, poses, width, height, strength, start_percent, end_percent, fun_ref_image=None):
|
||||
num_frames = len(poses)
|
||||
|
||||
control_camera_video = process_poses(poses, width, height)
|
||||
control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0)
|
||||
print("control_camera_video.shape", control_camera_video.shape)
|
||||
|
||||
# Rearrange dimensions
|
||||
# Concatenate and transpose dimensions
|
||||
control_camera_latents = torch.concat(
|
||||
[
|
||||
torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2),
|
||||
control_camera_video[:, :, 1:]
|
||||
], dim=2
|
||||
).transpose(1, 2)
|
||||
|
||||
# Reshape, transpose, and view into desired shape
|
||||
b, f, c, h, w = control_camera_latents.shape
|
||||
control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
|
||||
control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
|
||||
print("control_camera_latents.shape", control_camera_latents.shape)
|
||||
|
||||
vae_stride = (4, 8, 8)
|
||||
|
||||
target_shape = (16, (num_frames - 1) // vae_stride[0] + 1,
|
||||
height // vae_stride[1],
|
||||
width // vae_stride[2])
|
||||
|
||||
embeds = {
|
||||
"target_shape": target_shape,
|
||||
"num_frames": num_frames,
|
||||
"control_embeds": {
|
||||
"control_camera_latents": control_camera_latents * strength,
|
||||
"control_camera_start_percent": start_percent,
|
||||
"control_camera_end_percent": end_percent,
|
||||
"fun_ref_image": fun_ref_image["samples"][:,:, 0] if fun_ref_image is not None else None,
|
||||
}
|
||||
}
|
||||
|
||||
return (embeds,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WanVideoFunCameraEmbeds": WanVideoFunCameraEmbeds,
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"WanVideoFunCameraEmbeds": "WanVideo FunCamera Embeds",
|
||||
}
|
||||
Reference in New Issue
Block a user