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AnyFlow

AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation from NVIDIA, National University of Singapore, and Massachusetts Institute of Technology, by Yuchao Gu, Guian Fang, Yuxin Jiang, Weijia Mao, Song Han, Han Cai, Mike Zheng Shou.

TL;DR: AnyFlow is the first any-step video diffusion framework built on flow maps, which enables a single model (bidirectional or causal) to adapt to arbitrary inference budgets.

Few-step video generation has been significantly advanced by consistency models. However, their performance often degrades in any-step video diffusion models due to the fixed-point formulation. To address this limitation, we present AnyFlow, the first any-step video diffusion distillation framework built on flow maps. Instead of learning only the mapping z_t → z_0, AnyFlow learns transitions z_t → z_r over arbitrary time intervals, enabling a single model to adapt to different inference budgets. We design an improved forward flow map training recipe that fine-tunes pretrained video diffusion models into flow map models, and introduce Flow Map Backward Simulation to enable on-policy distillation for flow map models. Extensive experiments across both bidirectional and causal architectures, at scales ranging from 1.3B to 14B, on text-to-video and image-to-video tasks demonstrate that AnyFlow outperforms consistency-based baselines while preserving high fidelity and flexible sampling under varying step budgets.

The AnyFlow pipelines were contributed by the AnyFlow Team. The original code is available on GitHub, the project page is at nvlabs.github.io/AnyFlow, and pretrained models can be found in the nvidia/anyflow collection on Hugging Face.

Available Models:

[!TIP] AnyFlowPipeline is designed for bidirectional diffusion models in text-to-video (T2V) generation. AnyFlowFARPipeline is a chunk-wise causal diffusion model that supports text-to-video (T2V) generation, image-to-video (I2V) generation, and video continuation (V2V).

Generation with AnyFlow (Bidirectional T2V)

import torch
from diffusers import AnyFlowPipeline
from diffusers.utils import export_to_video

pipe = AnyFlowPipeline.from_pretrained(
    "nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers", torch_dtype=torch.bfloat16
).to("cuda")

prompt = (
    "An astronaut runs smoothly and appears almost weightless on the lunar surface, "
    "as seen from a low-angle shot that highlights the vast, desolate background of the moon. "
    "The moon's craters and rocky terrain are clearly visible, creating a stark contrast against "
    "the running astronaut who moves with graceful, fluid motions."
)
video = pipe(prompt, num_inference_steps=4, num_frames=81).frames[0]
export_to_video(video, "anyflow_t2v.mp4", fps=16)

Generation with AnyFlow (FAR Causal)

The causal pipeline selects between T2V / I2V / V2V via the video (or video_latents) argument: omit both for plain text-to-video, or pass video=<tensor> of shape (B, T, C, H, W) in [0, 1] with T = 4n + 1 to condition on existing frames. Use a single conditioning frame for I2V and a longer clip for V2V continuation. If you already have pre-encoded latents in the model layout, pass them via video_latents=<tensor> to skip VAE encoding. video and video_latents are mutually exclusive.

[!IMPORTANT] The released checkpoints bake chunk_partition=[1, 3, 3, 3, 3, 3, 3, 2] (sum 21) into the transformer config, matched to the canonical 81 raw frames (21 latent frames at the VAE temporal stride of 4). When you change num_frames, pass a matching chunk_partition summing to (num_frames - 1) // 4 + 1, otherwise the pipeline raises a ValueError.

import torch
from diffusers import AnyFlowFARPipeline
from diffusers.utils import export_to_video

pipe = AnyFlowFARPipeline.from_pretrained(
    "nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers", torch_dtype=torch.bfloat16
).to("cuda")

prompt = (
    "An astronaut runs smoothly and appears almost weightless on the lunar surface, "
    "as seen from a low-angle shot that highlights the vast, desolate background of the moon."
)
video = pipe(prompt, num_inference_steps=4, num_frames=81).frames[0]
export_to_video(video, "anyflow_far_t2v.mp4", fps=16)

import numpy as np
import torch
from diffusers import AnyFlowFARPipeline
from diffusers.utils import export_to_video, load_image

pipe = AnyFlowFARPipeline.from_pretrained(
    "nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers", torch_dtype=torch.bfloat16
).to("cuda")

# Example conditioning image from the AnyFlow repo.
first_frame = load_image(
    "https://raw.githubusercontent.com/NVlabs/AnyFlow/main/assets/evaluation/example/images/1.jpg"
).resize((832, 480))
arr = np.asarray(first_frame).astype("float32") / 255.0  # (480, 832, 3)
context_tensor = torch.from_numpy(arr).permute(2, 0, 1).unsqueeze(0).unsqueeze(1).to("cuda")  # (1, 1, 3, 480, 832)

prompt = (
    "A towering, battle-scarred humanoid robot, reminiscent of a Transformer with powerful, segmented armor "
    "and glowing red optics, walking through the skeletal remains of a city ruin. Twisted metal and shattered "
    "concrete crunch under its heavy steps, as the robot scans the desolate, dust-choked skyline under an dark sky."
)
video = pipe(
    prompt=prompt,
    video=context_tensor,
    num_inference_steps=4,
    num_frames=81,
).frames[0]
export_to_video(video, "anyflow_far_i2v.mp4", fps=16)

import numpy as np
import torch
from diffusers import AnyFlowFARPipeline
from diffusers.utils import export_to_video, load_video

pipe = AnyFlowFARPipeline.from_pretrained(
    "nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers", torch_dtype=torch.bfloat16
).to("cuda")

# Example conditioning clip from the AnyFlow repo — take the first 9 frames (3 latent frames at VAE temporal stride 4).
context_frames = load_video(
    "https://raw.githubusercontent.com/NVlabs/AnyFlow/main/assets/evaluation/example/videos/2.mp4"
)[:9]
arr = np.stack([np.asarray(f.resize((832, 480))) for f in context_frames]).astype("float32") / 255.0
context_tensor = torch.from_numpy(arr).permute(0, 3, 1, 2).unsqueeze(0).to("cuda")  # (1, 9, 3, 480, 832)

prompt = (
    "A focused trail runner's powerful strides through a dense, sun-dappled forest. "
    "The camera tracks alongside, highlighting muscular exertion, sweat, and determined facial expression."
)
video = pipe(
    prompt=prompt,
    video=context_tensor,
    num_inference_steps=4,
    num_frames=81,
    # Override chunk_partition so the first chunk covers exactly the 3 latent context frames.
    chunk_partition=[3, 3, 3, 3, 3, 3, 3],
).frames[0]
export_to_video(video, "anyflow_far_v2v.mp4", fps=16)

Notes

• Classifier-free guidance is fused into the released checkpoints, so inference does not run a second guided forward pass. Keep the default guidance_scale=1.0 unless your own checkpoint requires otherwise.
• FlowMapEulerDiscreteScheduler is general-purpose. You can attach it to any flow-map-distilled checkpoint via from_pretrained(..., scheduler=FlowMapEulerDiscreteScheduler.from_config(...)).
• AnyFlowPipeline uses AnyFlowTransformer3DModel (bidirectional). AnyFlowFARPipeline uses AnyFlowFARTransformer3DModel, which adds a compressed-frame patch embedding and the FAR causal block-mask.
• LoRA loading is supported via WanLoraLoaderMixin, the same mixin used by the upstream Wan pipelines.
• For training recipes (forward flow-map training and on-policy distillation), refer to the original AnyFlow training framework at NVlabs/AnyFlow; training is out of scope for diffusers.

AnyFlowPipeline

[[autodoc]] AnyFlowPipeline

• all
• call

AnyFlowFARPipeline

[[autodoc]] AnyFlowFARPipeline

• all
• call

AnyFlowPipelineOutput

[[autodoc]] pipelines.anyflow.pipeline_output.AnyFlowPipelineOutput