PAN 2 Text/Image to Video

PAN 2 is a 29B mixture-of-transformers video DiT with separate video and text streams, a Qwen3.5 text encoder and the Wan2.2 VAE. One pipeline serves text-to-video and image-to-video: pass image_path to condition on a first frame and leave it out for text only.

Model

Hub repo	Pipeline	Workloads	Example
`IFM/PAN2`	`PAN2ModularPipeline`	T2V, I2V	`basic_pan2.py`

PAN2 checkpoints use the Modular Diffusers layout (modular_model_index.json). Any repo id or local directory whose name contains pan2 (or whose manifest declares PAN2ModularPipeline) resolves to this pipeline. The defaults come from the pan2_t2v and pan2_i2v presets:

Setting	Default
Resolution	704x1248 (height and width must be multiples of 32)
Frames	313, 13 s at 24 fps (`num_frames - 1` must be a multiple of 4)
Steps	50, uniform sigmas from 1 to 0 with the checkpoint's flow shift (9 for `IFM/PAN2-TEST`, the reference sampler's value)
Guidance	3.0, classifier-free guidance. Image-to-video keeps the first latent frame out of guidance (`i2v_skip_first_latent_cfg`, on by default, as in the reference sampler)
Negative prompt	None unless the request carries one. The reference sampler always sends the release's structured negative caption (`negative_prompt.json` of the PAN2 repository) with its three `time_range_sec` values set to the clip length, `0 to 20` for 481 frames at 24 fps

PAN 2 is trained on the structured captions its prompt rewriter produces, serialized as one-line JSON. Short raw prompts work, with weaker results.

Usage

python examples/inference/basic/basic_pan2.py

from fastvideo import VideoGenerator

generator = VideoGenerator.from_pretrained(
    "IFM/PAN2",
    num_gpus=1,
    dit_cpu_offload=False,
    dit_layerwise_offload=False,
    text_encoder_cpu_offload=True,
)
generator.generate_video(
    "A red fox trots through fresh snow in a pine forest at dawn.",
    image_path=None,  # or the path of a first frame for image-to-video
    output_path="video_samples_pan2",
)

For image-to-video, the first frame is scaled to cover the requested resolution and center cropped, then encoded by the VAE into the first latent frame of the conditioning input.

Parallelism

With num_gpus > 1, FastVideo runs Ulysses sequence parallelism: the video tokens are sharded across ranks and the text tokens are replicated. The number of GPUs must divide the 32 attention heads. Sequence-parallel padding of the video tokens is trimmed before attention, so no attention mask is needed.

Padding is always excluded, without an opt-in setting. At 704x1248 with 313 frames, the 67,782 video tokens need two padding tokens at SP=4. They are removed before joining the text tokens in every attention block. This provides the same exclusion as vLLM-Omni's unconditional padding mask in revision e0c4c731. The trimmed sequence can use either FlashAttention or Torch SDPA.

PAN2_NUM_GPUS=4 python examples/inference/basic/basic_pan2.py

Memory and offloading

The example keeps the DiT on the GPUs and offloads the text encoder between requests. Sequence parallelism shards video tokens; each rank still holds the DiT weights. Increasing num_gpus alone does not shard those weights. For devices that cannot hold the DiT, see CPU offloading for dit_cpu_offload and dit_layerwise_offload. Hardware recipes and timings reported by vLLM-Omni use its own offload implementation; they have not been validated for FastVideo.

The full IFM/PAN2 checkpoint is not public yet. Local parity tests use the public random-weight wuqing157/tiny-pan2-modular-pipe checkpoint; it verifies the integration and produces noise rather than useful video.

Differences from the vLLM-Omni integration

The optional prompt and video guardrails (pan2-guardrail) are not included.
The Cache-DiT quality policy and CFG-parallel execution are vLLM-Omni serving features and are not ported.
The text encoder is a FastVideo-native Qwen3.5 port. Its Gated DeltaNet layers use a PyTorch chunked delta rule instead of the fla kernels.

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