Add AnyFlow Any-Step Video Diffusion Pipelines (Bidirectional + FAR Causal)

[Enderfga]

What does this PR do?

This PR adds pipelines for AnyFlow (paper, project page, official code, model weights), an any-step video diffusion framework built on flow maps. A single distilled checkpoint can be evaluated at 1, 2, 4, 8, 16, 32 NFE without retraining, and quality scales monotonically with steps — unlike consistency-based distillation, which often degrades as NFE grows.

Two new pipelines are added, both on top of a new FlowMapEulerDiscreteScheduler and reusing WanLoraLoaderMixin:

• AnyFlowPipeline → AnyFlowTransformer3DModel: bidirectional text-to-video built on the Wan2.1 backbone with an AnyFlowDualTimestepTextImageEmbedding conditioning on the source/target timestep pair (t, r).
• AnyFlowFARPipeline → AnyFlowFARTransformer3DModel: frame-level autoregressive variant (block-sparse causal flex_attention + KV cache + compressed-frame patch embedding) jointly handling T2V / I2V / V2V through one context_sequence argument.

Four checkpoints are released under the nvidia/anyflow collection (Wan2.1-T2V-{1.3B,14B} bidi + FAR-Wan2.1-{1.3B,14B} causal). All four have been validated bit-exact against the official NVlabs/AnyFlow reference on H200: forward L2 = 0.00e+00 for scheduler / transformer / bidi pipeline / FAR pipeline; backward grad delta is 4.88e-04, attributable to bf16 kernel non-determinism only (PR-vs-PR = PR-vs-reference, ratio 1.000); inference latency matches the reference at ±0.0% on both pipelines.

T2V inference example:

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

pipe = AnyFlowPipeline.from_pretrained(
    "nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers", torch_dtype=torch.bfloat16
).to("cuda")

prompt = "A red panda eating bamboo in a forest, cinematic lighting"
video = pipe(prompt, num_inference_steps=4, num_frames=33).frames[0]
export_to_video(video, "anyflow_t2v.mp4", fps=16)

I2V inference example with the FAR pipeline (single conditioning frame → autoregressive rollout):

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")

first_frame = load_image("path/to/first_frame.png").resize((832, 480))
arr = np.asarray(first_frame).astype("float32") / 255.0
context = torch.from_numpy(arr).permute(2, 0, 1).unsqueeze(0).unsqueeze(2).to("cuda")

video = pipe(
    prompt="a cat walks across a sunlit lawn",
    context_sequence={"raw": context},
    num_inference_steps=4,
    num_frames=81,
).frames[0]
export_to_video(video, "anyflow_i2v.mp4", fps=16)

Documentation: EN tutorial at docs/source/en/using-diffusers/anyflow.md, ZH tutorial at docs/source/zh/using-diffusers/anyflow.md, and three API pages (pipelines + two transformer model pages). Tests: 22 fast tests (transformer + scheduler, CPU) plus four pipeline test files, with slow integration tests gated on RUN_SLOW=1 @require_torch_accelerator for the released checkpoints.

anyflow-pr-presentation.mp4

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@yiyixuxu @asomoza