# AnyFlow [AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation](https://huggingface.co/papers/2605.13724) 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](https://github.com/NVlabs/AnyFlow), the project page is at [nvlabs.github.io/AnyFlow](https://nvlabs.github.io/AnyFlow), and pretrained models can be found in the [nvidia/anyflow](https://huggingface.co/collections/nvidia/anyflow) collection on Hugging Face. Available Models: | Checkpoint | Backbone | Description | |---|---|---| | [`nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers`](https://huggingface.co/nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers) | Wan2.1 1.3B | Bidirectional T2V | | [`nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers`](https://huggingface.co/nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers) | Wan2.1 14B | Bidirectional T2V | | [`nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers`](https://huggingface.co/nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers) | FAR + Wan2.1 1.3B | Causal T2V / I2V / V2V | | [`nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers`](https://huggingface.co/nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers) | FAR + Wan2.1 14B | Causal T2V / I2V / V2V | > [!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) ```py 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=`` 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=`` 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`. ```py 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) ``` ```py 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) ``` ```py 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`](../models/anyflow_transformer3d) (bidirectional). `AnyFlowFARPipeline` uses [`AnyFlowFARTransformer3DModel`](../models/anyflow_far_transformer3d), 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`](https://github.com/NVlabs/AnyFlow); training is out of scope for diffusers. ## AnyFlowPipeline[[diffusers.AnyFlowPipeline]] - **tokenizer** ([*AutoTokenizer*]) -- Tokenizer from [google/umt5-xxl](https://huggingface.co/google/umt5-xxl). - **text_encoder** ([*UMT5EncoderModel*]) -- [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) text encoder. - **transformer** ([*AnyFlowTransformer3DModel*]) -- Bidirectional flow-map 3D Transformer. - **vae** ([*AutoencoderKLWan*]) -- VAE that encodes/decodes videos to and from latent representations. - **scheduler** ([*FlowMapEulerDiscreteScheduler*]) -- Flow-map sampler. The pipeline drives `scheduler.step(..., timestep, sample, r_timestep)` per inference step. Bidirectional text-to-video generation pipeline for AnyFlow flow-map-distilled checkpoints, introduced in [AnyFlow](https://huggingface.co/papers/2605.13724). AnyFlow learns arbitrary-interval transitions \\(z_t \to z_r\\) rather than the fixed \\(z_t \to z_0\\) mapping of consistency models, so a single distilled checkpoint can be evaluated at 1, 2, 4, 8, 16... NFE without retraining. This pipeline operates over the full video tensor in one bidirectional pass; for chunk-wise autoregressive (causal) generation use `AnyFlowFARPipeline`. Sampling is plain Euler in mean-velocity form (`z_r = z_t - (t - r) * u`) with no re-noising. The released NVIDIA checkpoints fold classifier-free guidance into the model weights, so the default `guidance_scale=1.0` is the recommended setting. This model inherits from [*DiffusionPipeline*]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). - **prompt** (`str` or `List[str]`, *optional*) -- The prompt or prompts to guide the video generation. If not defined, pass `prompt_embeds` instead. - **video** (`torch.Tensor`, *optional*) -- Pre-VAE conditioning frames of shape `(B, T, C, H, W)` in `[0, 1]`. When provided, the pipeline VAE-encodes them and keeps the corresponding latent prefix fixed during sampling. Mutually exclusive with `video_latents`. - **video_latents** (`torch.Tensor`, *optional*) -- Pre-encoded VAE latents in the AnyFlow layout `(B, T_latent, C, H_latent, W_latent)`. Skips VAE encoding on the pipeline side. Mutually exclusive with `video`. - **negative_prompt** (`str` or `List[str]`, *optional*) -- The prompt or prompts to avoid during video generation. Ignored when not using guidance (`guidance_scale < 1`). - **height** (`int`, defaults to `480`) -- The height in pixels of the generated video. - **width** (`int`, defaults to `832`) -- The width in pixels of the generated video. - **num_frames** (`int`, defaults to `81`) -- The number of frames in the generated video. Must satisfy `(num_frames - 1) % vae_scale_factor_temporal == 0`. - **num_inference_steps** (`int`, defaults to `50`) -- The number of denoising steps. Distilled AnyFlow checkpoints support any-step sampling, so values as low as `1`, `2`, `4`, or `8` are typical. Ignored when `sigmas` or `timesteps` is provided. - **sigmas** (`List[float]`, *optional*) -- Custom sigma schedule for any-step sampling, in `[0, 1]` and ordered from noisy to clean. Length determines the effective `num_inference_steps`; the scheduler appends the terminal `0` sigma. - **timesteps** (`List[float]`, *optional*) -- Custom timestep schedule for any-step sampling, in the same units as `self.scheduler.timesteps` (i.e. scaled by `num_train_timesteps`). Mutually exclusive with `sigmas`. - **guidance_scale** (`float`, defaults to `1.0`) -- Classifier-free guidance scale. The released AnyFlow checkpoints fuse CFG into the weights during training; keep at `1.0` unless you know your checkpoint expects otherwise. - **num_videos_per_prompt** (`int`, *optional*, defaults to `1`) -- The number of videos to generate per prompt. - **generator** (`torch.Generator` or `List[torch.Generator]`, *optional*) -- A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. - **latents** (`torch.Tensor`, *optional*) -- Pre-generated noisy latents to use as inputs. If not provided, latents are sampled from the supplied `generator`. - **prompt_embeds** (`torch.Tensor`, *optional*) -- Pre-generated text embeddings. Can be used to tweak text inputs (e.g., prompt weighting). If not provided, embeddings are generated from `prompt`. - **negative_prompt_embeds** (`torch.Tensor`, *optional*) -- Pre-generated negative text embeddings. - **output_type** (`str`, *optional*, defaults to `"np"`) -- The output format. One of `"pil"`, `"np"`, `"pt"`, or `"latent"`. - **return_dict** (`bool`, *optional*, defaults to `True`) -- Whether to return an `AnyFlowPipelineOutput` instead of a plain tuple. - **attention_kwargs** (`dict`, *optional*) -- A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - **callback_on_step_end** (`Callable`, *optional*) -- A function or `PipelineCallback` called at the end of each inference step. See [`callbacks`](../callbacks) for details. - **callback_on_step_end_tensor_inputs** (`List[str]`, *optional*, defaults to `["latents"]`) -- The tensor inputs forwarded to the callback. Must be a subset of `self._callback_tensor_inputs`. - **max_sequence_length** (`int`, defaults to `512`) -- The maximum text-encoder sequence length. Longer prompts are truncated. - **use_mean_velocity** (`bool`, defaults to `True`) -- When `True`, the flow-map model is conditioned on both the source timestep `t` and the target timestep `r` to predict a mean velocity, matching the training-time behavior. Disable to mirror raw Euler stepping (`r = t`).`~AnyFlowPipelineOutput` or `tuple`If `return_dict` is `True`, `AnyFlowPipelineOutput` is returned, otherwise a `tuple` whose first element is the generated video. The call function to the pipeline for generation. Examples: ```python >>> 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) ``` - **prompt** (`str` or `list[str]`, *optional*) -- prompt to be encoded - **negative_prompt** (`str` or `list[str]`, *optional*) -- The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). - **do_classifier_free_guidance** (`bool`, *optional*, defaults to `True`) -- Whether to use classifier free guidance or not. - **num_videos_per_prompt** (`int`, *optional*, defaults to 1) -- Number of videos that should be generated per prompt. torch device to place the resulting embeddings on - **prompt_embeds** (`torch.Tensor`, *optional*) -- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. - **negative_prompt_embeds** (`torch.Tensor`, *optional*) -- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. - **device** -- (`torch.device`, *optional*): torch device - **dtype** -- (`torch.dtype`, *optional*): torch dtype Encodes the prompt into text encoder hidden states. Encode a pixel-space video into AnyFlow's latent layout. Mirrors the single-helper convention of other diffusers pipelines (cf. `WanImageToVideoPipeline.encode_image`): wraps preprocessing, VAE encoding, and latent normalization into one call. Output layout is `(B, T_latent, C, H, W)`, which is what the AnyFlow transformer expects for conditioning frames. ## AnyFlowFARPipeline[[diffusers.AnyFlowFARPipeline]] - **tokenizer** ([*AutoTokenizer*]) -- Tokenizer from [google/umt5-xxl](https://huggingface.co/google/umt5-xxl). - **text_encoder** ([*UMT5EncoderModel*]) -- [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) text encoder. - **transformer** ([*AnyFlowFARTransformer3DModel*]) -- FAR causal flow-map 3D Transformer. - **vae** ([*AutoencoderKLWan*]) -- VAE that encodes/decodes videos to and from latent representations. - **scheduler** ([*FlowMapEulerDiscreteScheduler*]) -- Flow-map sampler. Causal (FAR-based) text-to-video / image-to-video / video-to-video pipeline for AnyFlow checkpoints, introduced in [AnyFlow](https://huggingface.co/papers/2605.13724). The pipeline drives a chunk-wise autoregressive sampling loop: each chunk is denoised with flow-map steps while attending only to past chunks via block-sparse causal attention, and intermediate KV cache is reused across chunks. The task mode (T2V / I2V / V2V) is selected by which conditioning argument is passed to `__call__`: - both `video=None` and `video_latents=None` — pure text-to-video. - `video=&lt;tensor of shape (B, T, C, H, W) in [0, 1] with T = 4n + 1>` — pre-VAE conditioning frames; the pipeline VAE-encodes them. Pass a single-frame video for I2V or a multi-frame clip for V2V. - `video_latents=&lt;latent tensor of shape (B, T_latent, C, H_latent, W_latent)>` — already-encoded latents in the FAR layout (skips the VAE encode step). The FAR backbone is the causal Wan2.1 variant introduced by [FAR](https://huggingface.co/papers/2503.19325). Inference is plain Euler in mean-velocity form per chunk with no re-noising. Joint T2V / I2V / V2V is supported by a single distilled model. This model inherits from [*DiffusionPipeline*]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). - **prompt** (`str` or `List[str]`, *optional*) -- The prompt or prompts to guide the video generation. If not defined, pass `prompt_embeds` instead. - **video** (`torch.Tensor`, *optional*) -- Pre-VAE conditioning frames of shape `(B, T, C, H, W)` in `[0, 1]` (`T = 4n + 1`). When provided, the pipeline VAE-encodes them and keeps the corresponding latent prefix fixed during sampling. Mutually exclusive with `video_latents`. - **video_latents** (`torch.Tensor`, *optional*) -- Pre-encoded VAE latents in the FAR layout `(B, T_latent, C, H_latent, W_latent)`. Skips VAE encoding on the pipeline side. Mutually exclusive with `video`. - **negative_prompt** (`str` or `List[str]`, *optional*) -- The prompt or prompts to avoid during video generation. Ignored when not using guidance (`guidance_scale < 1`). - **height** (`int`, defaults to `480`) -- The height in pixels of the generated video. - **width** (`int`, defaults to `832`) -- The width in pixels of the generated video. - **num_frames** (`int`, defaults to `81`) -- The number of frames in the generated video. Must satisfy `(num_frames - 1) % vae_scale_factor_temporal == 0`. - **num_inference_steps** (`int`, defaults to `50`) -- The number of denoising steps per chunk. Distilled AnyFlow-FAR checkpoints support any-step sampling (1, 2, 4, 8, ...). Ignored when `sigmas` or `timesteps` is provided. - **sigmas** (`List[float]`, *optional*) -- Custom sigma schedule for any-step sampling, in `[0, 1]` and ordered from noisy to clean. Length determines the effective `num_inference_steps`; the scheduler appends the terminal `0` sigma. - **timesteps** (`List[float]`, *optional*) -- Custom timestep schedule for any-step sampling, in the same units as `self.scheduler.timesteps` (i.e. scaled by `num_train_timesteps`). Mutually exclusive with `sigmas`. - **guidance_scale** (`float`, defaults to `1.0`) -- Classifier-free guidance scale. The released AnyFlow checkpoints fuse CFG into the weights during training; keep at `1.0` unless the checkpoint requires otherwise. - **num_videos_per_prompt** (`int`, *optional*, defaults to `1`) -- The number of videos to generate per prompt. - **generator** (`torch.Generator` or `List[torch.Generator]`, *optional*) -- Generator used to seed sampling. - **latents** (`torch.Tensor`, *optional*) -- Pre-generated noisy latents. If not provided, latents are sampled from the supplied `generator`. - **prompt_embeds** (`torch.Tensor`, *optional*) -- Pre-generated text embeddings. If not provided, embeddings are generated from `prompt`. - **negative_prompt_embeds** (`torch.Tensor`, *optional*) -- Pre-generated negative text embeddings. - **output_type** (`str`, *optional*, defaults to `"np"`) -- Output format. One of `"pil"`, `"np"`, `"pt"`, or `"latent"`. - **return_dict** (`bool`, *optional*, defaults to `True`) -- Whether to return an `AnyFlowPipelineOutput` instead of a plain tuple. - **attention_kwargs** (`dict`, *optional*) -- A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - **callback_on_step_end** (`Callable`, *optional*) -- A function or `PipelineCallback` called at the end of each inference step. - **callback_on_step_end_tensor_inputs** (`List[str]`, *optional*, defaults to `["latents"]`) -- Tensor inputs forwarded to the callback. Must be a subset of `self._callback_tensor_inputs`. - **max_sequence_length** (`int`, defaults to `512`) -- The maximum text-encoder sequence length. - **use_mean_velocity** (`bool`, defaults to `True`) -- When `True`, condition the flow-map model on both the source timestep `t` and the target timestep `r` to predict a mean velocity. Disable to mirror raw Euler stepping. - **use_kv_cache** (`bool`, defaults to `True`) -- Reuse the FAR attention KV cache across causal chunks. Disable only for debugging. - **chunk_partition** (`List[int]`, *optional*) -- Per-chunk frame counts. Defaults to `self.transformer.config.chunk_partition` (matched to the released 81-frame checkpoints). When you change `num_frames`, supply a `chunk_partition` that sums to `(num_frames - 1) // vae_scale_factor_temporal + 1`.`~AnyFlowPipelineOutput` or `tuple`If `return_dict` is `True`, an `AnyFlowPipelineOutput` is returned, otherwise a `tuple` whose first element is the generated video. The call function to the pipeline for generation. Examples: ```python >>> 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") >>> # Single-frame I2V: wrap the conditioning image as a (1, 1, 3, H, W) tensor in [0, 1]. >>> 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(1).to("cuda") >>> video = pipe( ... prompt="a cat walks across a sunlit lawn", ... video=context, ... num_inference_steps=4, ... num_frames=81, ... ).frames[0] >>> export_to_video(video, "anyflow_far.mp4", fps=16) ``` - **prompt** (`str` or `list[str]`, *optional*) -- prompt to be encoded - **negative_prompt** (`str` or `list[str]`, *optional*) -- The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). - **do_classifier_free_guidance** (`bool`, *optional*, defaults to `True`) -- Whether to use classifier free guidance or not. - **num_videos_per_prompt** (`int`, *optional*, defaults to 1) -- Number of videos that should be generated per prompt. torch device to place the resulting embeddings on - **prompt_embeds** (`torch.Tensor`, *optional*) -- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. - **negative_prompt_embeds** (`torch.Tensor`, *optional*) -- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. - **device** -- (`torch.device`, *optional*): torch device - **dtype** -- (`torch.dtype`, *optional*): torch dtype Encodes the prompt into text encoder hidden states. Encode a pixel-space video into AnyFlow's latent layout. Mirrors the single-helper convention of other diffusers pipelines (cf. `WanImageToVideoPipeline.encode_image`): wraps preprocessing, VAE encoding, and latent normalization into one call. Output layout is `(B, T_latent, C, H, W)`, which is what the AnyFlow transformer expects for conditioning frames. ## AnyFlowPipelineOutput[[diffusers.pipelines.anyflow.pipeline_output.AnyFlowPipelineOutput]] - **frames** (`torch.Tensor`, `np.ndarray`, or list[list[PIL.Image.Image]]) -- list of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape `(batch_size, num_frames, channels, height, width)`. Output class for AnyFlow pipelines.