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README.md

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+---
+license: mit
+tags:
+  - video-diffusion
+  - diffusion-forcing
+  - autoregressive-video
+  - world-models
+---
+
+# Clean-Context Autoregressive Video Diffusion — Checkpoints
+
+Trained denoisers from *What Matters in Clean-Context Autoregressive Video Diffusion*. Each checkpoint is a Diffusion-Forcing video denoiser trained on
+32-frame windows of 64×64 RGB; see the paper for the full setup and the
+[code repository](https://github.com/jwei302/cct) for training and evaluation.
+
+## Main checkpoints (16)
+
+Four training configurations — **DF** (Diffusion Forcing baseline),
+**Mask-only** (masked prefix loss), **Clean-only** (clean prefix), and
+**Full** (clean prefix + masked loss) — crossed with two denoiser backbones
+and two datasets. Naming: `{backbone}_{dataset}_{config}.ckpt`.
+
+| File | Backbone | Dataset | Config |
+|---|---|---|---|
+| `unet_dmlab_df.ckpt` | U-Net | DMLab | DF |
+| `unet_dmlab_mask_only.ckpt` | U-Net | DMLab | Mask-only |
+| `unet_dmlab_clean_only.ckpt` | U-Net | DMLab | Clean-only |
+| `unet_dmlab_full.ckpt` | U-Net | DMLab | Full |
+| `unet_minecraft_df.ckpt` | U-Net | Minecraft | DF |
+| `unet_minecraft_mask_only.ckpt` | U-Net | Minecraft | Mask-only |
+| `unet_minecraft_clean_only.ckpt` | U-Net | Minecraft | Clean-only |
+| `unet_minecraft_full.ckpt` | U-Net | Minecraft | Full |
+| `dit_dmlab_df.ckpt` | DiT | DMLab | DF |
+| `dit_dmlab_mask_only.ckpt` | DiT | DMLab | Mask-only |
+| `dit_dmlab_clean_only.ckpt` | DiT | DMLab | Clean-only |
+| `dit_dmlab_full.ckpt` | DiT | DMLab | Full |
+| `dit_minecraft_df.ckpt` | DiT | Minecraft | DF |
+| `dit_minecraft_mask_only.ckpt` | DiT | Minecraft | Mask-only |
+| `dit_minecraft_clean_only.ckpt` | DiT | Minecraft | Clean-only |
+| `dit_minecraft_full.ckpt` | DiT | Minecraft | Full |
+
+All sixteen share the same optimiser, schedule, and diffusion settings
+(AdamW, lr 8×10⁻⁵, 100k steps, batch 8, fp16, cosine schedule with K=1000,
+v-prediction; DDIM with 100 steps, η=0 at inference); only the architecture
+and the training configuration differ. The U-Net is the 3D-convolutional
+Diffusion-Forcing backbone (≈18.65 M params); the DiT is a strictly
+frame-causal diffusion transformer (≈18.84 M params).
+
+## Ablation checkpoints (`ablation/`)
+
+The causal-GroupNorm ablation (paper §4 / Table 4): the U-Net trained on
+DMLab to a matched 30k-step budget, with the standard (leaky) temporal
+GroupNorm versus a frame-causal variant, under DF and Full.
+
+| File | GroupNorm | Config |
+|---|---|---|
+| `ablation/unet_dmlab_leaky_df.ckpt` | leaky (standard) | DF |
+| `ablation/unet_dmlab_leaky_full.ckpt` | leaky (standard) | Full |
+| `ablation/unet_dmlab_framecausal_df.ckpt` | frame-causal | DF |
+| `ablation/unet_dmlab_framecausal_full.ckpt` | frame-causal | Full |
+
+## Loading
+
+These are PyTorch-Lightning checkpoints. Load them with the matching config
+from the [code repository](https://github.com/jwei302/cct); the U-Net and
+DiT backbones and all training settings are specified there.

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