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-0,0 +1,565 @@ +Run dir : output/test_1ep +Log file: output/test_1ep/train.log +GPU: NVIDIA RTX PRO 6000 Blackwell Workstation Edition | VRAM: 95.0 GiB | PyTorch: 2.10.0+cu128 +wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from /home/projectx/.netrc. +wandb: Currently logged in as: jasonjiangxv (hku-xgboost) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin +wandb: Waiting for wandb.init()... + m wandb: Waiting for wandb.init()... + m +WARNING: WandB init failed — training will continue without logging. + Error: Error uploading run: returned error 403: {"data":{"upsertBucket":null},"errors":[{"message":"permission denied","path":["upsertBucket"],"extensions":{"code":"PERMISSION_ERROR"}}]} + Fix: check WANDB_API_KEY permissions, or run with --no-wandb to silence this. + +wandb: WARNING Changes to your `wandb` environment variables will be ignored because your `wandb` session has already started. For more information on how to modify your settings with `wandb.init()` arguments, please refer to https://wandb.me/wandb-init. + +Final Configuration: + Paths: + transformer_path weights/flux2_dev_fp8mixed.safetensors + vae_path weights/flux2-vae.safetensors + controlnet_path weights/FLUX.2-dev-Fun-Controlnet-Union-2602.safetensors + dataset_dir dataset + color_map_path configs/color_map.json + output_dir output/test_1ep + text_encoder_path weights/mistral_3_small_flux2_fp8.safetensors + precomputed_embeddings output/text_embeddings_global.pt + Model: + image_size 1024 + num_classes 6 + control_in_dim 3072 + fusion_dim 768 + num_fusion_blocks 3 + num_heads 12 + num_fourier_bands 32 + boundary_threshold 0.1 + Training: + num_epochs 1 + batch_size 4 + learning_rate 0.0003 + weight_decay 0.01 + max_grad_norm 1.0 + grad_accum_steps 4 + guidance_scale 3.5 + num_workers 0 + Text Encoder: + text_seq_len 512 + text_dim 15360 + Logging: + log_interval 10 + save_every_n_epochs 5 + val_every_n_epochs 1 + WandB: + wandb_entity hku-xg-boost + wandb_project test_1ep + Resume: + resume_from (not set) +[MEM @ pre-flight] RAM: 27.0/188.2 GiB (14.4%) | VRAM: 0.0/95.0 GiB (0.0%) + +Pre-flight checks... + ✓ torch + ✓ diffusers + ✓ safetensors + ✓ Pillow + ✓ tifffile + ✓ wandb + ✓ transformers + ✓ psutil + ✓ GPU: NVIDIA RTX PRO 6000 Blackwell Workstation Edition (95.0 GiB VRAM) + ✓ transformer_path: weights/flux2_dev_fp8mixed.safetensors (35.5 GB) + ✓ vae_path: weights/flux2-vae.safetensors (0.3 GB) + ✓ controlnet_path: weights/FLUX.2-dev-Fun-Controlnet-Union-2602.safetensors (8.2 GB) + ✓ text_encoder_path: weights/mistral_3_small_flux2_fp8.safetensors (18.0 GB) + ✓ train/rgb: 400 files + ✓ train/seg: 400 files + ✓ train/depth: 400 files + ✓ val/rgb: 80 files + ✓ val/seg: 80 files + ✓ val/depth: 80 files + ✓ test/rgb: 30 files + ✓ test/seg: 30 files + ✓ test/depth: 30 files + ✓ prompt.json found + +All pre-flight checks passed. + +============================================================ + [1/8] Text Embeddings +============================================================ + Loading cached embedding from output/text_embeddings_global.pt + Loaded global text embedding from output/text_embeddings_global.pt (shape: torch.Size([512, 15360])) + +============================================================ + [2/8] Loading VAE +============================================================ + Done (2.1s), VRAM: 0.16 GiB +[MEM @ after VAE] RAM: 27.4/188.2 GiB (14.6%) | VRAM: 0.2/95.0 GiB (0.2%) + +============================================================ + [3/8] Loading Transformer +============================================================ + Dequantizing FP8 transformer weights... + Dequantized 128 FP8 tensors + Converting ComfyUI → diffusers keys... + Converted: 331 diffusers keys + Loading ControlNet weights... + ControlNet: 76 keys + Creating Flux2ControlTransformer2DModel (control_in_dim=3072)... + Skipped 2 control_img_in keys (dim mismatch): + control_img_in.bias [6144] + control_img_in.weight [6144, 260] + Missing: 2, Unexpected: 0 + Initialized control_img_in.weight [6144, 3072] on cuda + Initialized control_img_in.bias [6144] on cuda + FP8 compression: 203 frozen Linears, 67.9 → 37.9 GiB (saved 30.0 GiB) + Done (41.2s), VRAM: 37.87 GiB + Gradient checkpointing: enabled + Backbone FROZEN: all transformer params set requires_grad=False + Gradients will still propagate to HDC²A via control_context autograd +[MEM @ after Transformer] RAM: 29.2/188.2 GiB (15.5%) | VRAM: 37.9/95.0 GiB (39.9%) + +============================================================ + [4/8] Creating HDC²A Adapter +============================================================ +HDC²A: 52.4M params +Control: 0.0M params +Total trainable: 52.4M params + +============================================================ + [4.5/8] Applying LoRA to ControlNet Control Blocks +============================================================ + LoRA rank=32, alpha=32.0, dropout=0 + LoRA control_transformer_blocks.0.attn.to_q [6144→6144] + LoRA control_transformer_blocks.0.attn.to_k [6144→6144] + LoRA control_transformer_blocks.0.attn.to_v [6144→6144] + LoRA control_transformer_blocks.0.attn.add_q_proj [6144→6144] + LoRA control_transformer_blocks.0.attn.add_k_proj [6144→6144] + LoRA control_transformer_blocks.0.attn.add_v_proj [6144→6144] + LoRA control_transformer_blocks.0.attn.to_out.0 [6144→6144] + LoRA control_transformer_blocks.1.attn.to_q [6144→6144] + LoRA control_transformer_blocks.1.attn.to_k [6144→6144] + LoRA control_transformer_blocks.1.attn.to_v [6144→6144] + LoRA control_transformer_blocks.1.attn.add_q_proj [6144→6144] + LoRA control_transformer_blocks.1.attn.add_k_proj [6144→6144] + LoRA control_transformer_blocks.1.attn.add_v_proj [6144→6144] + LoRA control_transformer_blocks.1.attn.to_out.0 [6144→6144] + LoRA control_transformer_blocks.2.attn.to_q [6144→6144] + LoRA control_transformer_blocks.2.attn.to_k [6144→6144] + LoRA control_transformer_blocks.2.attn.to_v [6144→6144] + LoRA control_transformer_blocks.2.attn.add_q_proj [6144→6144] + LoRA control_transformer_blocks.2.attn.add_k_proj [6144→6144] + LoRA control_transformer_blocks.2.attn.add_v_proj [6144→6144] + LoRA control_transformer_blocks.2.attn.to_out.0 [6144→6144] + LoRA control_transformer_blocks.3.attn.to_q [6144→6144] + LoRA control_transformer_blocks.3.attn.to_k [6144→6144] + LoRA control_transformer_blocks.3.attn.to_v [6144→6144] + LoRA control_transformer_blocks.3.attn.to_out.0 [6144→6144] + + LoRA modules injected: 25 + LoRA trainable params: 9.83M + +Parameter Statistics: + HDC²A Adapter: total=52.4M trainable=52.4M + ControlNet (frozen): total=4143.4M LoRA trainable=9.83M + Flux2 backbone: total=0.0M trainable=0.0M ✓ + ────────────────────────────────────────────────── + Total trainable: HDC²A 52.4M + LoRA 9.83M = 62.19M + +============================================================ + [5/8] Building Optimizer +============================================================ + AdamW: adapter_lr=3.00e-04, backbone_lr=0.00e+00 + param_group 'adapter': 112 tensors, lr=3.00e-04 + Scheduler: 400 warmup steps → cosine over ~25 steps + [6/8] Resume: skipped (no checkpoint specified) + +============================================================ + [7/8] Forward Sanity Check +============================================================ + [test 1/4] Forward pass (eval mode)... + Output shape: torch.Size([1, 4096, 128]) + Output stats: mean=0.0347, std=0.4922 + VRAM peak (forward): 68.44 GiB + [test 2/4] Loss computation (train mode)... + Loss value: 1.428600 + [test 3/4] Backward pass... + Backward completed. VRAM peak (backward): 49.17 GiB + [test 4/4] Gradient flow check... + HDC²A: 112/112 params have non-zero grad + Control: 25/50 params have non-zero grad + Top grad norms (HDC²A): + semantic_encoder.conv_stem.6.weight: 0.005615 + depth_encoder.conv_stem.6.weight: 0.005402 + W_s.weight: 0.004395 + W_d.weight: 0.004181 + fusion_blocks.0.ffn_sem.2.weight: 0.003937 + Test result: PASSED +[MEM @ after test] RAM: 29.4/188.2 GiB (15.6%) | VRAM: 38.0/95.0 GiB (40.0%) + +============================================================ + [8/8] Loading Data +============================================================ +[Data] Data augmentation: disabled +[Data] Train: 400 samples, batch_size=4 +[Data] Train: using global text embeddings +[Data] Val: 80 samples, batch_size=4 +[Data] Test: 30 samples, batch_size=4 + +====================================================================== +Starting training: 1 epochs × 100 steps = 100 total steps + batch_size=4, grad_accum=4, world_size=1, effective_bs=16 + adapter_lr=3.00e-04, weight_decay=0.01 +====================================================================== + [Milestone Vis] --skip-stepvis: only final step 99 + [Milestone Vis] 10 grids: train_0(5), train_1(5), val_0(5), val_1(5), test_0(5), test_1(5), test_2(5), test_3(5), test_4(5), test_5(5) + +--- Epoch 0/0 (0% done) --- + [Epoch 0][10/100] loss=0.831323 avg=0.930761 VRAM=38.6GiB | 10.0% done | ETA(epoch): 2143s +Traceback (most recent call last): + File "/home/projectx/ControlNet_training/HDC2A_training/train_script.py", line 1316, in + File "/home/projectx/ControlNet_training/HDC2A_training/train_script.py", line 1177, in main + # Train + + File "/home/projectx/ControlNet_training/HDC2A_training/scripts/train.py", line 171, in train_one_epoch + output = transformer( + ^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl + return forward_call(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/projectx/ControlNet_training/HDC2A_training/models/videox_fun/models/flux2_transformer2d_control.py", line 315, in forward + encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/_compile.py", line 54, in inner + return disable_fn(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 1181, in _fn + return fn(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/utils/checkpoint.py", line 512, in checkpoint + ret = function(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/projectx/ControlNet_training/HDC2A_training/models/videox_fun/models/flux2_transformer2d_control.py", line 311, in custom_forward + return module(*inputs) + ^^^^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl + return forward_call(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/projectx/ControlNet_training/HDC2A_training/models/videox_fun/models/flux2_transformer2d.py", line 553, in forward + norm_hidden_states = self.norm(hidden_states) + ^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl + return forward_call(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/nn/modules/normalization.py", line 229, in forward + return F.layer_norm( + ^^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/nn/functional.py", line 2935, in layer_norm + return torch.layer_norm( + ^^^^^^^^^^^^^^^^^ + File "/home/projectx/miniconda/envs/flux_train/lib/python3.12/site-packages/torch/utils/checkpoint.py", line 1144, in pack_hook + def pack_hook(x): + +KeyboardInterrupt +Run dir : output/test_1ep +Log file: output/test_1ep/train.log +GPU: NVIDIA RTX PRO 6000 Blackwell Workstation Edition | VRAM: 95.0 GiB | PyTorch: 2.10.0+cu128 +wandb: [wandb.login()] Loaded credentials for https://api.wandb.ai from WANDB_API_KEY. +wandb: Currently logged in as: hkujasonjiang (hku-xg-boost) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin +wandb: Waiting for wandb.init()... + m wandb: Waiting for wandb.init()... + m wandb: setting up run dc9dk3jk (0.3s) + m wandb: setting up run dc9dk3jk (0.3s) + m wandb: setting up run dc9dk3jk (0.3s) + m wandb: setting up run dc9dk3jk (0.3s) + m wandb: Tracking run with wandb version 0.25.1 +wandb: Run data is saved locally in /home/projectx/ControlNet_training/HDC2A_training/wandb/run-20260416_030951-dc9dk3jk +wandb: Run `wandb offline` to turn off syncing. +wandb: Syncing run test_1ep-train-20260416-030951 +wandb: View project at https://wandb.ai/hku-xg-boost/test_1ep +wandb: View run at https://wandb.ai/hku-xg-boost/test_1ep/runs/dc9dk3jk + +Final Configuration: + Paths: + transformer_path weights/flux2_dev_fp8mixed.safetensors + vae_path weights/flux2-vae.safetensors + controlnet_path weights/FLUX.2-dev-Fun-Controlnet-Union-2602.safetensors + dataset_dir dataset + color_map_path configs/color_map.json + output_dir output/test_1ep + text_encoder_path weights/mistral_3_small_flux2_fp8.safetensors + precomputed_embeddings output/text_embeddings_global.pt + Model: + image_size 1024 + num_classes 6 + control_in_dim 3072 + fusion_dim 768 + num_fusion_blocks 3 + num_heads 12 + num_fourier_bands 32 + boundary_threshold 0.1 + Training: + num_epochs 1 + batch_size 4 + learning_rate 0.0003 + weight_decay 0.01 + max_grad_norm 1.0 + grad_accum_steps 4 + guidance_scale 3.5 + num_workers 0 + Text Encoder: + text_seq_len 512 + text_dim 15360 + Logging: + log_interval 10 + save_every_n_epochs 5 + val_every_n_epochs 1 + WandB: + wandb_entity hku-xg-boost + wandb_project test_1ep + Resume: + resume_from (not set) +[MEM @ pre-flight] RAM: 25.8/188.2 GiB (13.7%) | VRAM: 0.0/95.0 GiB (0.0%) + +Pre-flight checks... + ✓ torch + ✓ diffusers + ✓ safetensors + ✓ Pillow + ✓ tifffile + ✓ wandb + ✓ transformers + ✓ psutil + ✓ GPU: NVIDIA RTX PRO 6000 Blackwell Workstation Edition (95.0 GiB VRAM) + ✓ transformer_path: weights/flux2_dev_fp8mixed.safetensors (35.5 GB) + ✓ vae_path: weights/flux2-vae.safetensors (0.3 GB) + ✓ controlnet_path: weights/FLUX.2-dev-Fun-Controlnet-Union-2602.safetensors (8.2 GB) + ✓ text_encoder_path: weights/mistral_3_small_flux2_fp8.safetensors (18.0 GB) + ✓ train/rgb: 400 files + ✓ train/seg: 400 files + ✓ train/depth: 400 files + ✓ val/rgb: 80 files + ✓ val/seg: 80 files + ✓ val/depth: 80 files + ✓ test/rgb: 30 files + ✓ test/seg: 30 files + ✓ test/depth: 30 files + ✓ prompt.json found + +All pre-flight checks passed. + +============================================================ + [1/8] Text Embeddings +============================================================ + Loading cached embedding from output/text_embeddings_global.pt + Loaded global text embedding from output/text_embeddings_global.pt (shape: torch.Size([512, 15360])) + +============================================================ + [2/8] Loading VAE +============================================================ + Done (2.1s), VRAM: 0.16 GiB +[MEM @ after VAE] RAM: 26.3/188.2 GiB (14.0%) | VRAM: 0.2/95.0 GiB (0.2%) + +============================================================ + [3/8] Loading Transformer +============================================================ + Dequantizing FP8 transformer weights... + Dequantized 128 FP8 tensors + Converting ComfyUI → diffusers keys... + Converted: 331 diffusers keys + Loading ControlNet weights... + ControlNet: 76 keys + Creating Flux2ControlTransformer2DModel (control_in_dim=3072)... + Skipped 2 control_img_in keys (dim mismatch): + control_img_in.bias [6144] + control_img_in.weight [6144, 260] + Missing: 2, Unexpected: 0 + Initialized control_img_in.weight [6144, 3072] on cuda + Initialized control_img_in.bias [6144] on cuda + FP8 compression: 203 frozen Linears, 67.9 → 37.9 GiB (saved 30.0 GiB) + Done (34.8s), VRAM: 37.87 GiB + Gradient checkpointing: enabled + Backbone FROZEN: all transformer params set requires_grad=False + Gradients will still propagate to HDC²A via control_context autograd +[MEM @ after Transformer] RAM: 28.1/188.2 GiB (14.9%) | VRAM: 37.9/95.0 GiB (39.9%) + +============================================================ + [4/8] Creating HDC²A Adapter +============================================================ +HDC²A: 52.4M params +Control: 0.0M params +Total trainable: 52.4M params + +============================================================ + [4.5/8] Applying LoRA to ControlNet Control Blocks +============================================================ + LoRA rank=32, alpha=32.0, dropout=0 + LoRA control_transformer_blocks.0.attn.to_q [6144→6144] + LoRA control_transformer_blocks.0.attn.to_k [6144→6144] + LoRA control_transformer_blocks.0.attn.to_v [6144→6144] + LoRA control_transformer_blocks.0.attn.add_q_proj [6144→6144] + LoRA control_transformer_blocks.0.attn.add_k_proj [6144→6144] + LoRA control_transformer_blocks.0.attn.add_v_proj [6144→6144] + LoRA control_transformer_blocks.0.attn.to_out.0 [6144→6144] + LoRA control_transformer_blocks.1.attn.to_q [6144→6144] + LoRA control_transformer_blocks.1.attn.to_k [6144→6144] + LoRA control_transformer_blocks.1.attn.to_v [6144→6144] + LoRA control_transformer_blocks.1.attn.add_q_proj [6144→6144] + LoRA control_transformer_blocks.1.attn.add_k_proj [6144→6144] + LoRA control_transformer_blocks.1.attn.add_v_proj [6144→6144] + LoRA control_transformer_blocks.1.attn.to_out.0 [6144→6144] + LoRA control_transformer_blocks.2.attn.to_q [6144→6144] + LoRA control_transformer_blocks.2.attn.to_k [6144→6144] + LoRA control_transformer_blocks.2.attn.to_v [6144→6144] + LoRA control_transformer_blocks.2.attn.add_q_proj [6144→6144] + LoRA control_transformer_blocks.2.attn.add_k_proj [6144→6144] + LoRA control_transformer_blocks.2.attn.add_v_proj [6144→6144] + LoRA control_transformer_blocks.2.attn.to_out.0 [6144→6144] + LoRA control_transformer_blocks.3.attn.to_q [6144→6144] + LoRA control_transformer_blocks.3.attn.to_k [6144→6144] + LoRA control_transformer_blocks.3.attn.to_v [6144→6144] + LoRA control_transformer_blocks.3.attn.to_out.0 [6144→6144] + + LoRA modules injected: 25 + LoRA trainable params: 9.83M + +Parameter Statistics: + HDC²A Adapter: total=52.4M trainable=52.4M + ControlNet (frozen): total=4143.4M LoRA trainable=9.83M + Flux2 backbone: total=0.0M trainable=0.0M ✓ + ────────────────────────────────────────────────── + Total trainable: HDC²A 52.4M + LoRA 9.83M = 62.19M + +============================================================ + [5/8] Building Optimizer +============================================================ + AdamW: adapter_lr=3.00e-04, backbone_lr=0.00e+00 + param_group 'adapter': 112 tensors, lr=3.00e-04 + Scheduler: 400 warmup steps → cosine over ~25 steps + [6/8] Resume: skipped (no checkpoint specified) + +============================================================ + [7/8] Forward Sanity Check +============================================================ + [test 1/4] Forward pass (eval mode)... + Output shape: torch.Size([1, 4096, 128]) + Output stats: mean=0.0400, std=0.5195 + VRAM peak (forward): 68.44 GiB + [test 2/4] Loss computation (train mode)... + Loss value: 1.361904 + [test 3/4] Backward pass... + Backward completed. VRAM peak (backward): 49.17 GiB + [test 4/4] Gradient flow check... + HDC²A: 112/112 params have non-zero grad + Control: 25/50 params have non-zero grad + Top grad norms (HDC²A): + semantic_encoder.conv_stem.6.weight: 0.003357 + depth_encoder.conv_stem.6.weight: 0.002869 + W_s.weight: 0.002670 + W_d.weight: 0.002441 + fusion_blocks.0.ffn_sem.2.weight: 0.002213 + Test result: PASSED +[MEM @ after test] RAM: 28.3/188.2 GiB (15.1%) | VRAM: 38.0/95.0 GiB (40.0%) + +============================================================ + [8/8] Loading Data +============================================================ +[Data] Data augmentation: disabled +[Data] Train: 400 samples, batch_size=4 +[Data] Train: using global text embeddings +[Data] Val: 80 samples, batch_size=4 +[Data] Test: 30 samples, batch_size=4 + +====================================================================== +Starting training: 1 epochs × 100 steps = 100 total steps + batch_size=4, grad_accum=4, world_size=1, effective_bs=16 + adapter_lr=3.00e-04, weight_decay=0.01 +====================================================================== + [Milestone Vis] --skip-stepvis: only final step 99 + [Milestone Vis] 10 grids: train_0(5), train_1(5), val_0(5), val_1(5), test_0(5), test_1(5), test_2(5), test_3(5), test_4(5), test_5(5) + +--- Epoch 0/0 (0% done) --- + [Epoch 0][10/100] loss=0.972434 avg=0.900439 VRAM=38.6GiB | 10.0% done | ETA(epoch): 2130s + [Epoch 0][20/100] loss=0.943036 avg=0.909896 VRAM=38.5GiB | 20.0% done | ETA(epoch): 1907s + [Epoch 0][30/100] loss=1.013289 avg=0.904051 VRAM=38.6GiB | 30.0% done | ETA(epoch): 1676s + [Epoch 0][40/100] loss=0.901198 avg=0.909269 VRAM=38.5GiB | 40.0% done | ETA(epoch): 1436s + [Epoch 0][50/100] loss=0.903481 avg=0.915978 VRAM=38.6GiB | 50.0% done | ETA(epoch): 1198s + [Epoch 0][60/100] loss=0.916328 avg=0.912784 VRAM=38.5GiB | 60.0% done | ETA(epoch): 960s + [Epoch 0][70/100] loss=0.942200 avg=0.916784 VRAM=38.6GiB | 70.0% done | ETA(epoch): 720s + [Epoch 0][80/100] loss=0.968736 avg=0.923469 VRAM=38.5GiB | 80.0% done | ETA(epoch): 480s + [Epoch 0][90/100] loss=0.965420 avg=0.923386 VRAM=38.6GiB | 90.0% done | ETA(epoch): 241s + [Epoch 0][100/100] loss=0.994884 avg=0.923228 VRAM=38.5GiB | 100.0% done | ETA(epoch): 0s + [MilestoneVis] train_0 step 99 ✓ + [MilestoneGrid] train_0 → output/test_1ep/milestone_vis/milestone_grid_train_0.png + [MilestoneVis] train_1 step 99 ✓ + [MilestoneGrid] train_1 → output/test_1ep/milestone_vis/milestone_grid_train_1.png + [MilestoneVis] val_0 step 99 ✓ + [MilestoneGrid] val_0 → output/test_1ep/milestone_vis/milestone_grid_val_0.png + [MilestoneVis] val_1 step 99 ✓ + [MilestoneGrid] val_1 → output/test_1ep/milestone_vis/milestone_grid_val_1.png + [MilestoneVis] test_0 step 99 ✓ + [MilestoneGrid] test_0 → output/test_1ep/milestone_vis/milestone_grid_test_0.png + [MilestoneVis] test_1 step 99 ✓ + [MilestoneGrid] test_1 → output/test_1ep/milestone_vis/milestone_grid_test_1.png + [MilestoneVis] test_2 step 99 ✓ + [MilestoneGrid] test_2 → output/test_1ep/milestone_vis/milestone_grid_test_2.png + [MilestoneVis] test_3 step 99 ✓ + [MilestoneGrid] test_3 → output/test_1ep/milestone_vis/milestone_grid_test_3.png + [MilestoneVis] test_4 step 99 ✓ + [MilestoneGrid] test_4 → output/test_1ep/milestone_vis/milestone_grid_test_4.png + [MilestoneVis] test_5 step 99 ✓ + [MilestoneGrid] test_5 → output/test_1ep/milestone_vis/milestone_grid_test_5.png + [HF Upload WARN] upload failed: No module named 'ControlNet_training' + Train loss: 0.923228 (4244.0s) ETA: 0min + Val loss: 0.970268 [t_0.0-0.2=1.1177 t_0.2-0.4=1.0436 t_0.4-0.6=0.8649 t_0.6-0.8=0.7663 t_0.8-1.0=1.1423] + Checkpoint saved: output/test_1ep/checkpoint_epoch_0000 (BEST) +[MEM @ epoch 0 end] RAM: 29.7/188.2 GiB (15.8%) | VRAM: 38.2/95.0 GiB (40.2%) + [LossCurve] saved → output/test_1ep/loss_curve.png + [HF Upload WARN] final upload failed: No module named 'ControlNet_training' + +====================================================================== +Training complete. +====================================================================== +wandb: updating run metadata (0.0s) + m wandb: updating run metadata (0.0s) + m wandb: updating run metadata (0.0s) + m wandb: updating run metadata (0.0s) + m wandb: Finishing up... + m wandb: Finishing up... + m wandb: Finishing up... + m wandb: Finishing up... + m wandb: Finishing up... + m wandb: uploading config.yaml 3.3KB/3.3KB (0.4s) + m wandb: uploading config.yaml 3.3KB/3.3KB (0.4s) + m wandb: uploading config.yaml 3.3KB/3.3KB (0.4s) + m wandb: uploading config.yaml 3.3KB/3.3KB (0.4s) + m wandb: uploading config.yaml 3.3KB/3.3KB (0.4s) + m wandb: uploading config.yaml 3.3KB/3.3KB (0.9s) + m wandb: uploading config.yaml 3.3KB/3.3KB (0.9s) + m wandb: uploading config.yaml 3.3KB/3.3KB (0.9s) + m wandb: uploading config.yaml 3.3KB/3.3KB (0.9s) + m wandb: uploading config.yaml 3.3KB/3.3KB (0.9s) + m wandb: +wandb: Run summary: +wandb: adapter_lr 2e-05 +wandb: best_val_loss 0.97027 +wandb: cpu_pct 7.5 +wandb: epoch 0 +wandb: final_train_loss 0.92323 +wandb: global_step 99 +wandb: gpu_mem_gib 38.21917 +wandb: step_loss 0.99488 +wandb: total_epochs 1 +wandb: train_loss 0.92323 +wandb: +6 ... +wandb: +wandb: View run test_1ep-train-20260416-030951 at: https://wandb.ai/hku-xg-boost/test_1ep/runs/dc9dk3jk +wandb: View project at: https://wandb.ai/hku-xg-boost/test_1ep +wandb: Synced 4 W&B file(s), 11 media file(s), 0 artifact file(s) and 0 other file(s) +wandb: Find logs at: ./wandb/run-20260416_030951-dc9dk3jk/logs