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ckpt_t1.pth

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ckpt_t1_weight_opt.pth

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ckpt_t2.pth

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ckpt_t2_weight_opt.pth

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config.yaml

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+# Quantization configuration for Wan2.2 I2V model
+# W4A4 NVFP4: 4-bit FP4 (E2M1) weights and activations using NVFP4 format
+#
+# Full Q-VDiT approach:
+# - PTQ: Quantize ALL Linear layers with NVFP4 weights (per-channel) and activations (per-token)
+# - TQE: Token-aware Quantization Estimator (LoRA-like correction, already in QuantLayer)
+# - TMD: Temporal Maintenance Distillation (KL divergence on frame similarity)
+
+# Layers to keep in full precision (input/output projections + first/last blocks)
+part_fp_list: "./t2v/configs/quant/wan/remain_fp.txt"
+
+# Model identification
+model:
+  model_id: "wan_i2v_14b"
+  model_type: 'wan'
+
+# Conditional generation flag
+conditional: True
+
+# Calibration data settings (reduced batch_size for Wan 14B due to memory constraints)
+calib_data:
+  path: null  # Set via command line
+  n_steps: 10  # Number of timesteps to use for calibration
+  batch_size: 1  # Reduced from 4 for Wan 14B model (saves ~60GB GPU memory)
+  n_samples: 3   # Number of samples per timestep
+
+# Quantization settings
+quant:
+  # Weight quantization (NVFP4 E2M1 per-channel)
+  weight:
+    quantizer:
+      quant_type: 'nvfp4'  # Use NVFP4 E2M1 floating-point quantizer
+      n_bits: 4
+      per_group: 'channel'  # Per-channel quantization for weights
+      scale_method: 'absmax'
+    optimization:
+      iters: 1000  # Increased from 200 - sufficient iterations for LoRA/delta convergence
+      use_grad: False
+      loss:
+        # TMD (Temporal Maintenance Distillation) - uses frame-wise similarity preservation
+        # Works with Wan's 5D latent tensors [B,C,T,H,W]
+        reconstruction_loss_type: 'relation'
+        lambda_coeff: 1.0
+        b_range: [10, 2]
+        warmup: 0.0
+        decay_start: 0.0
+        p: 2.0
+      params:
+        delta:
+          lr: 1.e-6  # Q-VDiT: 1e-6 for weight scale params
+
+  # Activation quantization (NVFP4 E2M1 dynamic per-token)
+  activation:
+    quantizer:
+      quant_type: 'nvfp4'  # Use NVFP4 E2M1 floating-point quantizer
+      n_bits: 4
+      per_group: 'token'  # Per-token quantization for activations
+      dynamic: True  # Dynamic quantization (compute scale on-the-fly with STE)
+      scale_method: 'absmax'
+      # Token count configuration for Wan model
+      n_tokens: 5120  # Combined spatial-temporal tokens
+      n_text_tokens: 512  # Text encoder sequence length
+      n_image_tokens: 257  # CLIP image encoder (256 patches + 1 CLS)
+      # Smooth quantization settings
+      # NOTE: Using single timerange is more robust for Wan2.2 since each transformer
+      # only sees specific timestep ranges. Multiple timeranges risk uncovered ranges.
+      smooth_quant:
+        enable: True
+        channel_wise_scale_type: 'momentum_act_max'
+        momentum: 0.95
+        alpha: [0.11]  # Single alpha for single timerange
+        timerange: [[0, 1000]]  # Single timerange - simpler and more robust
+
+  # TQE (Token-aware Quantization Estimator) parameters
+  # LoRA-like low-rank correction already implemented in QuantLayer
+  tqe:
+    lr: 1.e-5  # Q-VDiT: 1e-5 for TQE (LoRA) params
+    # Memory optimization: process layers in batches during M initialization
+    layer_batch_size: 50  # Number of layers to process at once (reduces peak memory)
+    # Optional: filter layers for TQE M initialization
+    # 'attention' = only init M for attention layers (.attn1., .attn2.)
+    # null = init M for all QuantLayers (default)
+    layer_filter: 'attention'  # Reduces from ~1200 layers to ~240 layers
+
+  # Gradient checkpointing (recommended for 14B model)
+  grad_checkpoint: True
+
+# Timestep-wise quantization (optional)
+timestep_wise: False
+
+# CFG (Classifier-Free Guidance) split handling
+cfg_split: False

metadata.pt

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+size 2020