nodes/aigc-apps-VideoX-Fun
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update one-forcing and causal forcing params

Browse Source
This commit is contained in:
bubbliiiing
2026-10-30 16:33:15 +08:00
parent a15b293128
commit f00a7d480e
17 changed files with 4866 additions and 86 deletions
Download Patch File Download Diff File Expand all files Collapse all files
scripts/wan2.1_causal_forcing/README_TRAIN_CAUSAL_DMD_DISTILL.md
+11 -8
View File
@@ -173,7 +173,7 @@ accelerate launch --mixed_precision="bf16" --use_fsdp \
--num_train_epochs=100 \
--checkpointing_steps=200 \
--learning_rate=2.0e-06 \
--learning_rate_critic=2.0e-06 \
--learning_rate_critic=4.0e-07 \
--lr_scheduler="constant_with_warmup" \
--lr_warmup_steps=100 \
--seed=42 \
@@ -192,8 +192,10 @@ accelerate launch --mixed_precision="bf16" --use_fsdp \
--enable_bucket \
--num_frame_per_block=3 \
--use_kv_cache_training \
--denoising_step_indices_list 1000 667 334 1 \
--real_guidance_scale=6.0 \
--use_ema \
--denoising_step_indices_list 1000 750 500 250 \
--real_guidance_scale=4.0 \
--score_timestep_clamp \
--randomize_step_indices \
--fake_guidance_scale=0.0 \
--gen_update_interval=5 \
@@ -222,7 +224,7 @@ bash scripts/wan2.1_causal_forcing/train_causal_dmd.sh
| `--num_train_epochs` | Number of training epochs | 100 |
| `--checkpointing_steps` | Save checkpoint every N steps | 200 |
| `--learning_rate` | Generator learning rate | 2e-06 |
| `--learning_rate_critic` | Critic learning rate | 2e-06 |
| `--learning_rate_critic` | Critic learning rate | 4.0e-07 |
| `--lr_scheduler` | LR scheduler type | `constant_with_warmup` |
| `--lr_warmup_steps` | LR warmup steps | 100 |
| `--output_dir` | Output directory | `output_dir_wan2.1_causal_forcing_dmd` |
@@ -235,6 +237,7 @@ bash scripts/wan2.1_causal_forcing/train_causal_dmd.sh
| `--max_grad_norm` | Gradient clipping threshold | 10.0 |
| `--trainable_modules` | Trainable modules (`"."` = all) | `"."` |
| `--low_vram` | Enable low VRAM mode (offload VAE/text encoder) | - |
| `--use_ema` | Maintain an EMA copy of the generator weights | - |
**Video Sampling Parameters**:
@@ -255,8 +258,8 @@ bash scripts/wan2.1_causal_forcing/train_causal_dmd.sh
| Parameter | Description | Example Value |
|-----------|-------------|---------------|
| `--denoising_step_indices_list` | Denoising step indices (DMD core param). Example runs 4-step; parser default `[1000, 500]` = 2-step | `1000 667 334 1` |
| `--real_guidance_scale` | CFG scale for the real-score (14B teacher) | 6.0 |
| `--denoising_step_indices_list` | Denoising step indices (DMD core param). Example runs 4-step; parser default `[1000, 500]` = 2-step | `1000 750 500 250` |
| `--real_guidance_scale` | CFG scale for the real-score (14B teacher) | 4.0 |
| `--randomize_step_indices` | Randomize the denoising step indices during training | - |
| `--fake_guidance_scale` | CFG scale for the fake-score (generator). 0.0 = no CFG | 0.0 |
| `--gen_update_interval` | Generator update interval (generator updates every N critic steps) | 5 |
@@ -273,7 +276,7 @@ bash scripts/wan2.1_causal_forcing/train_causal_dmd.sh
### 5.4 Optional One-Forcing GAN and Precision Controls
These options reuse the [One-Forcing](https://github.com/Aurora-edu/One-Forcing) mechanisms in `scripts/wan2.1_self_forcing/train_distill.py`. All GAN/R1/R2 weights default to **0**, and both precision flags default to **off** in argparse. The AR diffusion, CCD, and DMD launchers enable `--fsdp_reduce_fp32`; GAN/R1/R2 and `--score_timestep_clamp` remain off. AR diffusion and CCD do not gain GAN losses.
These options reuse the [One-Forcing](https://github.com/Aurora-edu/One-Forcing) mechanisms in `scripts/wan2.1_self_forcing/train_distill.py`. All GAN/R1/R2 weights default to **0**, and both precision flags default to **off** in argparse. The AR diffusion, CCD, and DMD launchers enable `--fsdp_reduce_fp32`; GAN/R1/R2 remain off, and the DMD launcher additionally enables `--score_timestep_clamp`. AR diffusion and CCD do not gain GAN losses.
The discriminator shares the **fake-score backbone**, with one learnable register token per selected feature layer. Its head is optimized by the critic optimizer, not the generator optimizer. Generator loss is `DMD + weighted GAN`; critic loss is `denoising + weighted GAN + R1 + R2`.
@@ -290,7 +293,7 @@ The discriminator shares the **fake-score backbone**, with one learnable registe
| `--r2_weight` | Normalized finite-difference penalty on noisy generated latents | `0.0` |
| `--r2_sigma` | R2 perturbation standard deviation | `0.01` |
| `--fsdp_reduce_fp32` | FSDP FP32 gradient reduction; parameter gathering stays low precision | off (enabled in the launcher) |
| `--score_timestep_clamp` | Clamp DMD, generator GAN, and critic timesteps to `[0.02, 0.98] * num_train_timesteps` | off |
| `--score_timestep_clamp` | Clamp DMD, generator GAN, and critic timesteps to `[0.02, 0.98] * num_train_timesteps` | off (enabled in the DMD launcher) |
For logits `D_fake` and `D_real`, ordinary GAN losses are `softplus(-D_fake)` for the generator and `softplus(-D_real) + softplus(D_fake)` for the discriminator. Relativistic losses instead use `softplus(-(D_fake - D_real))` and `softplus(-(D_real - D_fake))`.
scripts/wan2.1_causal_forcing/README_TRAIN_CAUSAL_DMD_DISTILL_zh-CN.md
+11 -8
View File
@@ -173,7 +173,7 @@ accelerate launch --mixed_precision="bf16" --use_fsdp \
--num_train_epochs=100 \
--checkpointing_steps=200 \
--learning_rate=2.0e-06 \
--learning_rate_critic=2.0e-06 \
--learning_rate_critic=4.0e-07 \
--lr_scheduler="constant_with_warmup" \
--lr_warmup_steps=100 \
--seed=42 \
@@ -192,8 +192,10 @@ accelerate launch --mixed_precision="bf16" --use_fsdp \
--enable_bucket \
--num_frame_per_block=3 \
--use_kv_cache_training \
--denoising_step_indices_list 1000 667 334 1 \
--real_guidance_scale=6.0 \
--use_ema \
--denoising_step_indices_list 1000 750 500 250 \
--real_guidance_scale=4.0 \
--score_timestep_clamp \
--randomize_step_indices \
--fake_guidance_scale=0.0 \
--gen_update_interval=5 \
@@ -222,7 +224,7 @@ bash scripts/wan2.1_causal_forcing/train_causal_dmd.sh
| `--num_train_epochs` | 训练 epoch 数 | 100 |
| `--checkpointing_steps` | 每 N 步保存 checkpoint | 200 |
| `--learning_rate` | 生成器学习率 | 2e-06 |
| `--learning_rate_critic` | 判别器学习率 | 2e-06 |
| `--learning_rate_critic` | 判别器学习率 | 4.0e-07 |
| `--lr_scheduler` | 学习率调度器 | `constant_with_warmup` |
| `--lr_warmup_steps` | 学习率预热步数 | 100 |
| `--output_dir` | 输出目录 | `output_dir_wan2.1_causal_forcing_dmd` |
@@ -235,6 +237,7 @@ bash scripts/wan2.1_causal_forcing/train_causal_dmd.sh
| `--max_grad_norm` | 梯度裁剪阈值 | 10.0 |
| `--trainable_modules` | 可训练模块（`"."` = 全部） | `"."` |
| `--low_vram` | 低显存模式（卸载 VAE/文本编码器） | - |
| `--use_ema` | 维护生成器权重的 EMA 副本 | - |
**视频采样参数**：
@@ -255,8 +258,8 @@ bash scripts/wan2.1_causal_forcing/train_causal_dmd.sh
| 参数 | 说明 | 示例值 |
|------|------|--------|
| `--denoising_step_indices_list` | 去噪步骤索引（DMD 核心参数）。示例为 4 步；解析器默认 `[1000, 500]` = 2 步 | `1000 667 334 1` |
| `--real_guidance_scale` | real-score（14B teacher）的 CFG scale | 6.0 |
| `--denoising_step_indices_list` | 去噪步骤索引（DMD 核心参数）。示例为 4 步；解析器默认 `[1000, 500]` = 2 步 | `1000 750 500 250` |
| `--real_guidance_scale` | real-score（14B teacher）的 CFG scale | 4.0 |
| `--randomize_step_indices` | 训练时随机化去噪步骤索引 | - |
| `--fake_guidance_scale` | fake-score（生成器）的 CFG scale。0.0 = 无 CFG | 0.0 |
| `--gen_update_interval` | 生成器更新间隔（每 N 步判别器更新后更新 1 次生成器） | 5 |
@@ -272,7 +275,7 @@ bash scripts/wan2.1_causal_forcing/train_causal_dmd.sh
### 5.4 可选 One-Forcing GAN 与精度控制
这些选项复用 `scripts/wan2.1_self_forcing/train_distill.py` 中的 [One-Forcing](https://github.com/Aurora-edu/One-Forcing) 机制。GAN/R1/R2 权重默认均为 **0**，两个精度开关在 argparse 中默认均**关闭**。AR diffusion、CCD 和 DMD 的 launcher 均启用 `--fsdp_reduce_fp32`；GAN/R1/R2 和 `--score_timestep_clamp` 仍关闭。AR diffusion 和 CCD 不添加 GAN 损失。
这些选项复用 `scripts/wan2.1_self_forcing/train_distill.py` 中的 [One-Forcing](https://github.com/Aurora-edu/One-Forcing) 机制。GAN/R1/R2 权重默认均为 **0**，两个精度开关在 argparse 中默认均**关闭**。AR diffusion、CCD 和 DMD 的 launcher 均启用 `--fsdp_reduce_fp32`；GAN/R1/R2 仍关闭，DMD 的 launcher 额外启用 `--score_timestep_clamp`。AR diffusion 和 CCD 不添加 GAN 损失。
判别器共享 **fake-score backbone**，每个选定特征层对应一个可学习 register token。判别器头由 critic optimizer 更新，不加入 generator optimizer。生成器损失为 `DMD + 加权 GAN`，critic 损失为 `denoising + 加权 GAN + R1 + R2`。
@@ -289,7 +292,7 @@ bash scripts/wan2.1_causal_forcing/train_causal_dmd.sh
| `--r2_weight` | 噪声生成 latent 上的归一化有限差分正则权重 | `0.0` |
| `--r2_sigma` | R2 扰动标准差 | `0.01` |
| `--fsdp_reduce_fp32` | FSDP 使用 FP32 梯度归约，参数仍以低精度聚合 | 关闭（launcher 已启用） |
| `--score_timestep_clamp` | 将 DMD、生成器 GAN 和 critic 时间步限制到 `[0.02, 0.98] * num_train_timesteps` | 关闭 |
| `--score_timestep_clamp` | 将 DMD、生成器 GAN 和 critic 时间步限制到 `[0.02, 0.98] * num_train_timesteps` | 关闭（DMD launcher 已启用） |
设 logits 为 `D_fake` 和 `D_real`，普通 GAN 的生成器损失为 `softplus(-D_fake)`，判别器损失为 `softplus(-D_real) + softplus(D_fake)`。相对论模式分别使用 `softplus(-(D_fake - D_real))` 和 `softplus(-(D_real - D_fake))`。
scripts/wan2.1_causal_forcing/train_ar_diffusion.sh
+1
View File
@@ -37,6 +37,7 @@ accelerate launch --mixed_precision="bf16" --use_fsdp \
--output_dir="output_dir_wan2.1_causal_forcing_ar_diffusion" \
--gradient_checkpointing \
--mixed_precision="bf16" \
--fsdp_reduce_fp32 \
--adam_weight_decay=3e-2 \
--adam_epsilon=1e-10 \
--vae_mini_batch=1 \
scripts/wan2.1_causal_forcing/train_causal_consistency_distill.sh
+1
View File
@@ -41,6 +41,7 @@ accelerate launch --mixed_precision="bf16" --use_fsdp \
--output_dir="output_dir_wan2.1_causal_forcing_ccd" \
--gradient_checkpointing \
--mixed_precision="bf16" \
--fsdp_reduce_fp32 \
--adam_weight_decay=0.0 \
--adam_beta1=0.0 \
--adam_beta2=0.999 \
scripts/wan2.1_causal_forcing/train_causal_dmd.sh
+6 -3
View File
@@ -38,13 +38,14 @@ accelerate launch --mixed_precision="bf16" --use_fsdp \
--num_train_epochs=100 \
--checkpointing_steps=200 \
--learning_rate=2.0e-06 \
--learning_rate_critic=2.0e-06 \
--learning_rate_critic=4.0e-07 \
--lr_scheduler="constant_with_warmup" \
--lr_warmup_steps=100 \
--seed=42 \
--output_dir="output_dir_wan2.1_causal_forcing_dmd" \
--gradient_checkpointing \
--mixed_precision="bf16" \
--fsdp_reduce_fp32 \
--adam_weight_decay=0.0 \
--adam_beta1=0.0 \
--adam_beta2=0.999 \
@@ -56,8 +57,10 @@ accelerate launch --mixed_precision="bf16" --use_fsdp \
--enable_bucket \
--num_frame_per_block=3 \
--use_kv_cache_training \
--denoising_step_indices_list 1000 667 334 1 \
--real_guidance_scale=6.0 \
--use_ema \
--denoising_step_indices_list 1000 750 500 250 \
--real_guidance_scale=4.0 \
--score_timestep_clamp \
--randomize_step_indices \
--fake_guidance_scale=0.0 \
--gen_update_interval=5 \