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motion-stream

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zirobtc commited on
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60b86d7
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1 Parent(s): 0e267a7

Initial upload of MotionStreamer code, excluding large extracted data and output folders.

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Files changed (13) hide show
  1. TRAIN_motionstreamer.sh +2 -3
  2. demo_mixed.py +2 -3
  3. demo_motionstreamer.py +1 -1
  4. demo_output_streamer/A_man_crawls_forward_lick_a_zombie_cfg4.5.bvh +0 -0
  5. demo_output_streamer/A_man_walks_in_a_circular_path,_with_his_torso_slighlty_leaned_back._cfg4.5.bvh +0 -0
  6. demo_output_streamer/He_moves_back_and_forth_cfg4.0.bvh +0 -0
  7. demo_output_streamer/He_runs_forward_cfg4.5.bvh +0 -0
  8. demo_output_streamer/He_runs_forward_cfg7.0.bvh +0 -0
  9. demo_output_streamer/a_person_is_running_forward_final.bvh +204 -0
  10. demo_output_streamer/he_jumps_up_cfg10.0.bvh +0 -0
  11. demo_output_streamer/he_waves_his_hands_cfg7.0.bvh +0 -0
  12. demo_output_streamer/then_he_raises_both_arms_to_the_sky_cfg7.0.bvh +0 -0
  13. train_motionstreamer.py +43 -17
TRAIN_motionstreamer.sh CHANGED
@@ -7,10 +7,9 @@ echo "Using $NUM_GPUS GPUs, each with a batch size of $BATCH_SIZE"
7
  accelerate launch --num_processes $NUM_GPUS train_motionstreamer.py \
8
  --batch-size $BATCH_SIZE \
9
  --lr 0.0001 \
10
- --total-iter 200000 \
11
  --out-dir Experiments \
12
  --exp-name motionstreamer_model \
13
  --dataname t2m_babel_272 \
14
  --latent_dir babel_272_stream/t2m_babel_latents \
15
- --num_gpus $NUM_GPUS
16
- --resume-trans Experiments/motionstreamer_model/100k.pth \
 
7
  accelerate launch --num_processes $NUM_GPUS train_motionstreamer.py \
8
  --batch-size $BATCH_SIZE \
9
  --lr 0.0001 \
10
+ --total-iter 100000 \
11
  --out-dir Experiments \
12
  --exp-name motionstreamer_model \
13
  --dataname t2m_babel_272 \
14
  --latent_dir babel_272_stream/t2m_babel_latents \
15
+ --num_gpus $NUM_GPUS
 
demo_mixed.py CHANGED
@@ -82,7 +82,7 @@ if __name__ == '__main__':
82
 
83
  # --- Load Models ---
84
  print("Loading models for MotionStreamer...")
85
- t5_model = SentenceTransformer('sentencetõ-xxl/')
86
  t5_model.eval().to(comp_device)
87
 
88
  print("Loading Causal TAE (t2m_babel) checkpoint...")
@@ -100,8 +100,7 @@ if __name__ == '__main__':
100
 
101
  ckpt = torch.load('Experiments/motionstreamer_model/latest.pth', map_location='cpu')
102
  # Handle DataParallel wrapper if present
103
- new_ckpt = {k.replace('module.', ''): v for k, v in ckpt['trans'].items()}
104
- trans_encoder.load_state_dict(new_ckpt, strict=True)
105
  trans_encoder.eval().to(comp_device)
106
 
107
  print("Loading mean/std from BABEL dataset...")
 
82
 
83
  # --- Load Models ---
84
  print("Loading models for MotionStreamer...")
85
+ t5_model = SentenceTransformer('sentencet5-xxl/')
86
  t5_model.eval().to(comp_device)
87
 
88
  print("Loading Causal TAE (t2m_babel) checkpoint...")
 
100
 
101
  ckpt = torch.load('Experiments/motionstreamer_model/latest.pth', map_location='cpu')
102
  # Handle DataParallel wrapper if present
103
+ trans_encoder.load_state_dict(ckpt['trans'], strict=True)
 
104
  trans_encoder.eval().to(comp_device)
105
 
106
  print("Loading mean/std from BABEL dataset...")
demo_motionstreamer.py CHANGED
@@ -127,7 +127,7 @@ if __name__ == '__main__':
127
  # --- KEY PARAMETER TO TUNE ---
128
  # The author often uses a higher CFG scale for more dynamic motions.
129
  # Let's try 7.0, which is a common value in their code.
130
- cfg_scale = 7.0
131
 
132
  print(f"Generating motion for text: '{args.text}' with CFG scale: {cfg_scale}")
133
  with torch.no_grad():
 
127
  # --- KEY PARAMETER TO TUNE ---
128
  # The author often uses a higher CFG scale for more dynamic motions.
129
  # Let's try 7.0, which is a common value in their code.
130
+ cfg_scale = 10.0
131
 
132
  print(f"Generating motion for text: '{args.text}' with CFG scale: {cfg_scale}")
133
  with torch.no_grad():
demo_output_streamer/A_man_crawls_forward_lick_a_zombie_cfg4.5.bvh ADDED
The diff for this file is too large to render. See raw diff
 
demo_output_streamer/A_man_walks_in_a_circular_path,_with_his_torso_slighlty_leaned_back._cfg4.5.bvh ADDED
The diff for this file is too large to render. See raw diff
 
demo_output_streamer/He_moves_back_and_forth_cfg4.0.bvh ADDED
The diff for this file is too large to render. See raw diff
 
demo_output_streamer/He_runs_forward_cfg4.5.bvh ADDED
The diff for this file is too large to render. See raw diff
 
demo_output_streamer/He_runs_forward_cfg7.0.bvh ADDED
The diff for this file is too large to render. See raw diff
 
demo_output_streamer/a_person_is_running_forward_final.bvh ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ HIERARCHY
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+ ROOT Pelvis
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+ {
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+ OFFSET 0.000000 0.000000 0.000000
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+ CHANNELS 6 Xposition Yposition Zposition Zrotation Yrotation Xrotation
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+ JOINT Left_hip
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+ {
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+ OFFSET 0.069520 -0.091406 -0.006815
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Left_knee
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+ {
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+ OFFSET 0.034277 -0.375199 -0.004496
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Left_ankle
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+ {
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+ OFFSET -0.013596 -0.397961 -0.043693
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Left_foot
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+ {
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+ OFFSET 0.026358 -0.055791 0.119288
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ End Site
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+ {
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+ OFFSET 0.000000 0.000000 0.000000
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+ }
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+ }
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+ }
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+ }
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+ }
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+ JOINT Right_hip
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+ {
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+ OFFSET -0.067670 -0.090522 -0.004320
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Right_knee
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+ {
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+ OFFSET -0.038290 -0.382569 -0.008850
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Right_ankle
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+ {
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+ OFFSET 0.015774 -0.398415 -0.042312
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Right_foot
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+ {
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+ OFFSET -0.025372 -0.048144 0.123348
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ End Site
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+ {
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+ OFFSET 0.000000 0.000000 0.000000
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+ }
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+ }
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+ }
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+ }
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+ }
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+ JOINT Spine1
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+ {
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+ OFFSET -0.002533 0.108963 -0.026696
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Spine2
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+ {
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+ OFFSET 0.005487 0.135180 0.001092
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Spine3
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+ {
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+ OFFSET 0.001457 0.052922 0.025425
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Neck
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+ {
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+ OFFSET -0.002778 0.213870 -0.042857
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Head
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+ {
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+ OFFSET 0.005152 0.064970 0.051349
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ End Site
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+ {
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+ OFFSET 0.000000 0.000000 0.000000
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+ }
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+ }
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+ }
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+ JOINT Left_collar
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+ {
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+ OFFSET 0.078845 0.121749 -0.034090
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Left_shoulder
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+ {
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+ OFFSET 0.090977 0.030469 -0.008868
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Left_elbow
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+ {
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+ OFFSET 0.259612 -0.012772 -0.027456
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Left_wrist
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+ {
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+ OFFSET 0.249234 0.008986 -0.001171
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Left_hand
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+ {
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+ OFFSET 0.084042 -0.008162 -0.014945
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ End Site
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+ {
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+ OFFSET 0.000000 0.000000 0.000000
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ JOINT Right_collar
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+ {
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+ OFFSET -0.081759 0.118833 -0.038615
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Right_shoulder
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+ {
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+ OFFSET -0.096012 0.032551 -0.009143
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Right_elbow
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+ {
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+ OFFSET -0.253742 -0.013329 -0.021401
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Right_wrist
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+ {
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+ OFFSET -0.255298 0.007772 -0.005559
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ JOINT Right_hand
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+ {
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+ OFFSET -0.084622 -0.006117 -0.010315
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+ CHANNELS 3 Zrotation Yrotation Xrotation
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+ End Site
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+ {
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+ OFFSET 0.000000 0.000000 0.000000
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ MOTION
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+ Frames: 60
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+ Frame Time: 0.033333
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+ 0.816048 0.882455 0.414683 -179.351450 65.486257 -176.853133 -1.076445 3.766379 -5.377969 0.542109 4.220122 21.291899 -0.065325 11.417233 -22.474507 -0.040200 -0.066306 -0.042392 -1.607600 -1.893011 -13.401810 5.253906 -8.973606 36.196076 -2.697522 -13.614004 -17.331470 -0.009279 -0.075223 0.002314 -0.343046 -3.008795 18.214926 0.142112 1.494350 0.642285 -0.224415 -0.524040 1.737780 1.687004 -4.466527 -13.226091 0.506349 -1.769099 0.380152 -26.276363 -9.068264 4.012490 -44.914611 -6.331141 14.461655 18.145328 -54.171165 -3.802045 4.657444 -7.369923 -9.343031 0.000000 0.000000 0.000000 27.403133 5.763748 2.901325 45.916898 5.636062 9.512252 -6.826594 46.237525 4.036871 -12.295172 5.393704 -9.842427 0.000000 0.000000 0.000000
197
+ 0.819444 0.874161 0.415234 -178.589939 65.233331 -177.515396 -0.222374 1.339023 -16.225205 0.718611 6.073609 40.149052 0.167692 10.672294 -21.282875 0.026620 -0.058824 -0.058656 -0.899663 0.072725 -19.364687 5.829642 -9.542280 44.066326 -4.837696 -13.873882 -17.671996 0.035221 -0.060089 0.004589 -0.078113 -2.913414 22.559515 0.407885 -0.251582 2.533985 -0.296177 -1.398519 2.960630 1.363975 -8.778901 -11.315568 1.318337 -6.641866 -4.179073 -26.696702 -9.269353 4.938610 -48.678036 -9.706050 12.869182 21.610191 -45.542649 -10.502290 5.383787 -4.023927 -10.165557 0.000000 0.000000 0.000000 27.535026 4.570507 2.323086 49.653581 4.210542 10.171236 -10.942499 48.399605 -0.322080 -12.344796 1.041889 -13.524085 0.000000 0.000000 0.000000
198
+ 0.823744 0.867359 0.414184 -178.526768 65.724163 -177.843050 2.351788 1.837022 -23.361342 0.932151 10.668856 57.287110 -1.796906 9.966831 -16.351287 0.018658 -0.104194 -0.066396 1.408724 3.798168 -15.486561 6.929763 -3.121067 33.698457 -7.328786 -14.953860 -21.122943 0.124180 -0.030489 0.006780 0.140702 -1.338219 23.020660 0.493589 -0.202608 4.961725 0.554609 -1.291866 2.573733 0.255807 -8.724044 -11.999114 -0.175511 -5.003978 -7.397807 -28.907030 -11.334769 7.780331 -51.299417 -8.798134 14.057688 16.140333 -38.278004 -8.634074 7.798513 -3.030063 -9.398370 0.000000 0.000000 0.000000 28.364831 6.143303 3.246440 48.788125 3.057390 13.322674 -9.594528 53.088284 2.874439 -13.882409 1.476534 -11.640233 0.000000 0.000000 0.000000
199
+ 0.829788 0.877421 0.411251 -178.936465 65.681786 -178.710333 0.374234 2.777716 -11.085549 3.505783 7.878182 25.317662 -1.409494 14.605693 -17.005368 -0.088726 -0.110467 -0.027673 4.895471 1.316630 -7.583323 3.757321 -1.363094 18.135570 -6.944570 -14.435703 -18.040329 -0.016970 -0.017644 -0.008146 0.050573 -1.899663 18.577620 1.313762 1.588782 2.496289 0.626705 -1.067365 2.031233 -0.210906 -4.825712 -14.059835 -2.825320 -1.316996 -4.428487 -26.691355 -12.009219 7.582957 -50.487203 -6.109832 16.281116 12.917734 -44.365201 -3.434445 6.489999 -6.116643 -8.882204 0.000000 0.000000 0.000000 25.374793 6.798715 3.982755 43.778496 4.356446 13.172908 -2.672237 56.419024 10.861591 -12.759828 5.055120 -8.542375 0.000000 0.000000 0.000000
200
+ 0.835260 0.878528 0.408799 -178.926430 65.688041 -178.789449 -0.311384 2.950106 -3.146070 3.067282 4.155294 12.516863 -1.591607 13.682577 -18.485671 -0.034085 -0.060899 -0.028225 5.358229 0.810288 -4.222355 2.347007 -1.233806 15.312309 -5.881072 -15.443876 -19.828139 -0.055696 0.024078 -0.013529 -0.775317 -2.579330 13.296331 1.194876 1.860429 1.453592 1.099305 -1.141429 1.365260 -0.947438 -1.445860 -15.053886 -2.851882 3.352797 -1.759404 -26.433514 -12.041020 7.721597 -46.781303 -5.809919 14.935407 12.956797 -47.487381 -1.889243 5.028820 -6.822734 -7.391979 0.000000 0.000000 0.000000 25.236836 7.519466 3.371148 41.587002 6.004117 12.138295 -3.622270 52.299424 8.941945 -9.731959 7.655692 -6.002454 0.000000 0.000000 0.000000
201
+ 0.840836 0.871625 0.406428 179.679471 65.683383 -179.763288 -0.499808 2.520156 -2.823067 2.578924 3.150744 14.504630 -2.438713 12.918988 -21.185295 -0.034011 -0.109736 -0.007611 3.665839 1.010060 -3.810272 1.852518 0.689026 16.880288 -4.827147 -15.121375 -21.928438 -0.050088 0.012149 -0.001536 -0.631222 -2.165750 10.881202 -0.171274 2.152089 1.059642 0.489335 -0.423335 0.416625 -1.269162 -2.151641 -16.880266 -1.220028 4.453321 -0.215190 -25.503680 -11.408465 5.858597 -45.822158 -7.427535 14.847367 10.355554 -49.134405 1.241272 4.058862 -8.095349 -8.568936 0.000000 0.000000 0.000000 25.199721 7.218848 3.194402 42.668856 6.745329 11.780326 -3.068387 47.249060 10.483050 -7.950164 10.463971 -3.812808 0.000000 0.000000 0.000000
202
+ 0.846252 0.873175 0.404191 177.488063 65.686249 178.227182 -0.105462 3.083319 -2.371927 1.077947 3.426616 14.425343 -2.301269 12.677675 -21.329482 -0.031876 -0.118185 0.018881 1.104639 0.326848 -4.223697 2.823111 1.489189 18.528303 -4.242301 -13.962431 -22.781763 -0.058288 -0.035216 0.006382 -0.193671 -2.325471 10.035058 -0.723090 1.830313 0.308893 0.059366 -0.193089 0.122891 -1.899381 -1.997402 -17.246934 -0.536619 4.930013 0.532757 -25.707072 -11.146532 4.781972 -44.551019 -7.846528 15.144159 6.693106 -50.265939 4.054263 3.745387 -9.669014 -9.761909 0.000000 0.000000 0.000000 26.249659 6.932351 2.959332 43.257310 6.762915 13.066326 -0.100795 45.254284 14.555549 -8.951488 11.422844 -3.376786 0.000000 0.000000 0.000000
203
+ 0.851675 0.876603 0.401949 177.539005 65.697871 179.465966 -0.147698 3.496710 -1.363904 0.800040 3.309038 12.082730 -2.478882 11.881670 -21.644429 -0.013400 -0.137915 0.039065 -0.069925 -0.804204 -4.683452 3.568286 0.670165 16.911704 -4.218455 -13.649490 -22.740852 -0.039262 -0.030128 -0.019366 0.307031 -2.369320 9.559995 -0.771793 1.861258 -0.751383 -0.237133 0.091465 -0.301456 -1.329725 -1.916458 -19.354778 0.405668 5.416620 0.217110 -26.327158 -9.719279 4.681535 -43.816495 -5.738820 14.341195 8.500570 -48.507306 0.787070 3.616845 -10.331108 -11.218221 0.000000 0.000000 0.000000 26.968075 7.275334 3.208947 43.313054 8.294633 13.001190 -0.802843 45.026552 13.255631 -8.742796 11.016863 -2.724368 0.000000 0.000000 0.000000
204
+ 0.856718 0.878200 0.399796 177.743181 65.713952 -179.795958 -0.205179 3.182202 -1.306067 0.630680 2.735050 13.815692 -2.494093 11.705913 -23.269054 -0.013892 -0.099522 0.026047 0.098002 -0.152065 -4.807350 3.268459 1.388061 17.263891 -3.489694 -14.340579 -22.837787 -0.027334 -0.002937 -0.026748 0.314244 -2.331389 9.421638 -0.617679 1.943092 -0.999889 -0.225731 0.017297 -0.145950 -0.914800 -2.079476 -18.564156 0.325454 4.845646 0.845023 -26.100017 -9.397981 5.047129 -43.761896 -5.203642 14.707951 8.611539 -48.611694 1.791959 3.860235 -10.092359 -10.773820 0.000000 0.000000 0.000000 26.268908 7.310264 3.013157 43.774571 8.420285 12.473307 -0.286932 44.706139 13.232984 -8.960872 9.634745 -3.992932 0.000000 0.000000 0.000000
demo_output_streamer/he_jumps_up_cfg10.0.bvh ADDED
The diff for this file is too large to render. See raw diff
 
demo_output_streamer/he_waves_his_hands_cfg7.0.bvh ADDED
The diff for this file is too large to render. See raw diff
 
demo_output_streamer/then_he_raises_both_arms_to_the_sky_cfg7.0.bvh ADDED
The diff for this file is too large to render. See raw diff
 
train_motionstreamer.py CHANGED
@@ -90,6 +90,15 @@ config = LLaMAHFConfig.from_name('Normal_size')
90
  config.block_size = 78
91
  trans_encoder = LLaMAHF(config, args.num_diffusion_head_layers, args.latent_dim, comp_device)
92
 
 
 
 
 
 
 
 
 
 
93
  if args.resume_trans is not None:
94
  print('loading transformer checkpoint from {}'.format(args.resume_trans))
95
  ckpt = torch.load(args.resume_trans, map_location='cpu')
@@ -100,19 +109,21 @@ if args.resume_trans is not None:
100
  else:
101
  new_key = key
102
  new_ckpt_trans[new_key] = ckpt['trans'][key]
103
- trans_encoder.load_state_dict(new_ckpt_trans, strict=True)
 
 
 
 
 
 
 
 
 
 
104
  trans_encoder.train()
105
- trans_encoder.to(comp_device)
106
-
107
 
108
- ##### ---- Optimizer & Scheduler ---- #####
109
- optimizer = utils_model.initial_optim(args.decay_option, args.lr, args.weight_decay, trans_encoder, args.optimizer)
110
- scheduler = WarmupCosineDecayScheduler(optimizer, args.total_iter//10, args.total_iter)
111
-
112
- t5_model, trans_encoder, optimizer, train_loader = accelerator.prepare(t5_model, trans_encoder, optimizer, train_loader)
113
  train_loader_iter = dataset_TM_train_motionstreamer.cycle(train_loader)
114
 
115
-
116
  diffmlps_batch_mul = 4
117
  def lengths_to_mask(lengths, max_len):
118
  mask = torch.arange(max_len, device=lengths.device).expand(len(lengths), max_len) < lengths.unsqueeze(1)
@@ -202,8 +213,6 @@ def forward_loss_withmask_2_forward_streaming(latents, trans, m_lens, feat_text,
202
 
203
  ##### ---- Training Loop ---- #####
204
  avg_loss_cls = 0.
205
-
206
- start_iter = 100001
207
  end_iter = args.total_iter + 1
208
 
209
  pbar = tqdm(range(start_iter, end_iter), desc="Training MotionStreamer")
@@ -234,12 +243,26 @@ for nb_iter in pbar:
234
  else:
235
  loss_cls = forward_loss_withmask_2_forward_streaming(latents=input_latent, trans=trans_encoder, m_lens = m_tokens_len, feat_text=feat_text, step=nb_iter, total_steps=args.total_iter, A_token_length=A_token_length)
236
 
237
-
238
- # backward & optimizer step
239
- optimizer.zero_grad()
 
 
 
 
 
 
 
240
  accelerator.backward(loss_cls)
241
- optimizer.step()
242
- scheduler.step(nb_iter)
 
 
 
 
 
 
 
243
 
244
  avg_loss_cls = avg_loss_cls + loss_cls.item()
245
 
@@ -262,7 +285,10 @@ for nb_iter in pbar:
262
  # save checkpoint
263
  if accelerator.is_main_process:
264
  torch.save({
265
- 'trans': trans_encoder.state_dict(),
 
 
 
266
  }, os.path.join(args.out_dir, f'latest.pth'))
267
 
268
  accelerator.wait_for_everyone()
 
90
  config.block_size = 78
91
  trans_encoder = LLaMAHF(config, args.num_diffusion_head_layers, args.latent_dim, comp_device)
92
 
93
+
94
+
95
+
96
+ ##### ---- Optimizer & Scheduler ---- #####
97
+ optimizer = utils_model.initial_optim(args.decay_option, args.lr, args.weight_decay, trans_encoder, args.optimizer)
98
+ scheduler = WarmupCosineDecayScheduler(optimizer, args.total_iter//10, args.total_iter)
99
+ t5_model, trans_encoder, optimizer, train_loader = accelerator.prepare(t5_model, trans_encoder, optimizer, train_loader)
100
+
101
+ start_iter = 1
102
  if args.resume_trans is not None:
103
  print('loading transformer checkpoint from {}'.format(args.resume_trans))
104
  ckpt = torch.load(args.resume_trans, map_location='cpu')
 
109
  else:
110
  new_key = key
111
  new_ckpt_trans[new_key] = ckpt['trans'][key]
112
+ accelerator.unwrap_model(trans_encoder).load_state_dict(new_ckpt_trans, strict=True)
113
+
114
+ # Load optimizer, scheduler, and iteration number if they exist
115
+ if 'optimizer' in ckpt:
116
+ optimizer.load_state_dict(ckpt['optimizer'])
117
+ if 'scheduler' in ckpt:
118
+ scheduler.load_state_dict(ckpt['scheduler'])
119
+ if 'iter' in ckpt:
120
+ start_iter = ckpt['iter'] + 1
121
+ print(f"Resuming training from iteration {start_iter}")
122
+
123
  trans_encoder.train()
 
 
124
 
 
 
 
 
 
125
  train_loader_iter = dataset_TM_train_motionstreamer.cycle(train_loader)
126
 
 
127
  diffmlps_batch_mul = 4
128
  def lengths_to_mask(lengths, max_len):
129
  mask = torch.arange(max_len, device=lengths.device).expand(len(lengths), max_len) < lengths.unsqueeze(1)
 
213
 
214
  ##### ---- Training Loop ---- #####
215
  avg_loss_cls = 0.
 
 
216
  end_iter = args.total_iter + 1
217
 
218
  pbar = tqdm(range(start_iter, end_iter), desc="Training MotionStreamer")
 
243
  else:
244
  loss_cls = forward_loss_withmask_2_forward_streaming(latents=input_latent, trans=trans_encoder, m_lens = m_tokens_len, feat_text=feat_text, step=nb_iter, total_steps=args.total_iter, A_token_length=A_token_length)
245
 
246
+ # Set how many batches to accumulate before an update.
247
+ # We want to simulate a batch size of ~256 with our batch size of 30.
248
+ # 256 / 30 is ~8.5, so we'll use 8.
249
+ accumulation_steps = 8
250
+
251
+ # Scale the loss to average it over the accumulation steps.
252
+ # This keeps the gradient magnitude stable.
253
+ loss_cls = loss_cls / accumulation_steps
254
+
255
+ # Accumulate the gradients.
256
  accelerator.backward(loss_cls)
257
+
258
+ # Only update the model weights every N steps.
259
+ if (nb_iter + 1) % accumulation_steps == 0:
260
+ # Now, apply the accumulated gradients.
261
+ optimizer.step()
262
+ # Step the scheduler ONLY when the optimizer steps.
263
+ scheduler.step(nb_iter)
264
+ # Clear the gradients for the next accumulation cycle.
265
+ optimizer.zero_grad()
266
 
267
  avg_loss_cls = avg_loss_cls + loss_cls.item()
268
 
 
285
  # save checkpoint
286
  if accelerator.is_main_process:
287
  torch.save({
288
+ 'trans': accelerator.unwrap_model(trans_encoder).state_dict(),
289
+ 'optimizer': optimizer.state_dict(),
290
+ 'scheduler': scheduler.state_dict(),
291
+ 'iter': nb_iter
292
  }, os.path.join(args.out_dir, f'latest.pth'))
293
 
294
  accelerator.wait_for_everyone()