Hunyuan Video Framepack (#11428)

* add transformer

* add pipeline

* fixes

* make fix-copies

* update

* add flux mu shift

* update example snippet

* debug

* cleanup

* batch_size=1 optimization

* add pipeline test

* fix for model cpu offloading'

* add last_image support; credits: https://github.com/lllyasviel/FramePack/pull/167

* update example with flf2v

* update penguin url

* fix test

* address review comment: https://github.com/huggingface/diffusers/pull/11428#discussion_r2071032371

* address review comment: https://github.com/huggingface/diffusers/pull/11428#discussion_r2071087689



* Update src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video_framepack.py

---------
Co-authored-by: Linoy Tsaban <57615435+linoytsaban@users.noreply.github.com>

docs/source/en/api/pipelines/hunyuan_video.md

@@ -52,6 +52,7 @@ The following models are available for the image-to-video pipeline:
 | [`Skywork/SkyReels-V1-Hunyuan-I2V`](https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-I2V) | Skywork's custom finetune of HunyuanVideo (de-distilled). Performs best with `97x544x960` resolution. Performs best at `97x544x960` resolution, `guidance_scale=1.0`, `true_cfg_scale=6.0` and a negative prompt. |
 | [`hunyuanvideo-community/HunyuanVideo-I2V-33ch`](https://huggingface.co/hunyuanvideo-community/HunyuanVideo-I2V) | Tecent's official HunyuanVideo 33-channel I2V model. Performs best at resolutions of 480, 720, 960, 1280. A higher `shift` value when initializing the scheduler is recommended (good values are between 7 and 20). |
 | [`hunyuanvideo-community/HunyuanVideo-I2V`](https://huggingface.co/hunyuanvideo-community/HunyuanVideo-I2V) | Tecent's official HunyuanVideo 16-channel I2V model. Performs best at resolutions of 480, 720, 960, 1280. A higher `shift` value when initializing the scheduler is recommended (good values are between 7 and 20) |
+- [`lllyasviel/FramePackI2V_HY`](https://huggingface.co/lllyasviel/FramePackI2V_HY) | lllyasviel's paper introducing a new technique for long-context video generation called [Framepack](https://arxiv.org/abs/2504.12626). |
 
 ## Quantization
 

src/diffusers/__init__.py

@@ -175,6 +175,7 @@ else:
             "HunyuanDiT2DControlNetModel",
             "HunyuanDiT2DModel",
             "HunyuanDiT2DMultiControlNetModel",
+            "HunyuanVideoFramepackTransformer3DModel",
             "HunyuanVideoTransformer3DModel",
             "I2VGenXLUNet",
             "Kandinsky3UNet",
@@ -376,6 +377,7 @@ else:
             "HunyuanDiTPAGPipeline",
             "HunyuanDiTPipeline",
             "HunyuanSkyreelsImageToVideoPipeline",
+            "HunyuanVideoFramepackPipeline",
             "HunyuanVideoImageToVideoPipeline",
             "HunyuanVideoPipeline",
             "I2VGenXLPipeline",
@@ -770,6 +772,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             HunyuanDiT2DControlNetModel,
             HunyuanDiT2DModel,
             HunyuanDiT2DMultiControlNetModel,
+            HunyuanVideoFramepackTransformer3DModel,
             HunyuanVideoTransformer3DModel,
             I2VGenXLUNet,
             Kandinsky3UNet,
@@ -950,6 +953,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             HunyuanDiTPAGPipeline,
             HunyuanDiTPipeline,
             HunyuanSkyreelsImageToVideoPipeline,
+            HunyuanVideoFramepackPipeline,
             HunyuanVideoImageToVideoPipeline,
             HunyuanVideoPipeline,
             I2VGenXLPipeline,

src/diffusers/models/__init__.py

@@ -79,6 +79,7 @@ if is_torch_available():
     _import_structure["transformers.transformer_flux"] = ["FluxTransformer2DModel"]
     _import_structure["transformers.transformer_hidream_image"] = ["HiDreamImageTransformer2DModel"]
     _import_structure["transformers.transformer_hunyuan_video"] = ["HunyuanVideoTransformer3DModel"]
+    _import_structure["transformers.transformer_hunyuan_video_framepack"] = ["HunyuanVideoFramepackTransformer3DModel"]
     _import_structure["transformers.transformer_ltx"] = ["LTXVideoTransformer3DModel"]
     _import_structure["transformers.transformer_lumina2"] = ["Lumina2Transformer2DModel"]
     _import_structure["transformers.transformer_mochi"] = ["MochiTransformer3DModel"]
@@ -156,6 +157,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             FluxTransformer2DModel,
             HiDreamImageTransformer2DModel,
             HunyuanDiT2DModel,
+            HunyuanVideoFramepackTransformer3DModel,
             HunyuanVideoTransformer3DModel,
             LatteTransformer3DModel,
             LTXVideoTransformer3DModel,

src/diffusers/models/transformers/__init__.py

@@ -23,6 +23,7 @@ if is_torch_available():
     from .transformer_flux import FluxTransformer2DModel
     from .transformer_hidream_image import HiDreamImageTransformer2DModel
     from .transformer_hunyuan_video import HunyuanVideoTransformer3DModel
+    from .transformer_hunyuan_video_framepack import HunyuanVideoFramepackTransformer3DModel
     from .transformer_ltx import LTXVideoTransformer3DModel
     from .transformer_lumina2 import Lumina2Transformer2DModel
     from .transformer_mochi import MochiTransformer3DModel

src/diffusers/models/transformers/transformer_hunyuan_video_framepack.py

+# Copyright 2025 The Framepack Team, The Hunyuan Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
+from ...utils import USE_PEFT_BACKEND, get_logger, scale_lora_layers, unscale_lora_layers
+from ..cache_utils import CacheMixin
+from ..embeddings import get_1d_rotary_pos_embed
+from ..modeling_outputs import Transformer2DModelOutput
+from ..modeling_utils import ModelMixin
+from ..normalization import AdaLayerNormContinuous
+from .transformer_hunyuan_video import (
+    HunyuanVideoConditionEmbedding,
+    HunyuanVideoPatchEmbed,
+    HunyuanVideoSingleTransformerBlock,
+    HunyuanVideoTokenRefiner,
+    HunyuanVideoTransformerBlock,
+)
+
+
+logger = get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class HunyuanVideoFramepackRotaryPosEmbed(nn.Module):
+    def __init__(self, patch_size: int, patch_size_t: int, rope_dim: List[int], theta: float = 256.0) -> None:
+        super().__init__()
+
+        self.patch_size = patch_size
+        self.patch_size_t = patch_size_t
+        self.rope_dim = rope_dim
+        self.theta = theta
+
+    def forward(self, frame_indices: torch.Tensor, height: int, width: int, device: torch.device):
+        height = height // self.patch_size
+        width = width // self.patch_size
+        grid = torch.meshgrid(
+            frame_indices.to(device=device, dtype=torch.float32),
+            torch.arange(0, height, device=device, dtype=torch.float32),
+            torch.arange(0, width, device=device, dtype=torch.float32),
+            indexing="ij",
+        )  # 3 * [W, H, T]
+        grid = torch.stack(grid, dim=0)  # [3, W, H, T]
+
+        freqs = []
+        for i in range(3):
+            freq = get_1d_rotary_pos_embed(self.rope_dim[i], grid[i].reshape(-1), self.theta, use_real=True)
+            freqs.append(freq)
+
+        freqs_cos = torch.cat([f[0] for f in freqs], dim=1)  # (W * H * T, D / 2)
+        freqs_sin = torch.cat([f[1] for f in freqs], dim=1)  # (W * H * T, D / 2)
+
+        return freqs_cos, freqs_sin
+
+
+class FramepackClipVisionProjection(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int):
+        super().__init__()
+        self.up = nn.Linear(in_channels, out_channels * 3)
+        self.down = nn.Linear(out_channels * 3, out_channels)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.up(hidden_states)
+        hidden_states = F.silu(hidden_states)
+        hidden_states = self.down(hidden_states)
+        return hidden_states
+
+
+class HunyuanVideoHistoryPatchEmbed(nn.Module):
+    def __init__(self, in_channels: int, inner_dim: int):
+        super().__init__()
+        self.proj = nn.Conv3d(in_channels, inner_dim, kernel_size=(1, 2, 2), stride=(1, 2, 2))
+        self.proj_2x = nn.Conv3d(in_channels, inner_dim, kernel_size=(2, 4, 4), stride=(2, 4, 4))
+        self.proj_4x = nn.Conv3d(in_channels, inner_dim, kernel_size=(4, 8, 8), stride=(4, 8, 8))
+
+    def forward(
+        self,
+        latents_clean: Optional[torch.Tensor] = None,
+        latents_clean_2x: Optional[torch.Tensor] = None,
+        latents_clean_4x: Optional[torch.Tensor] = None,
+    ):
+        if latents_clean is not None:
+            latents_clean = self.proj(latents_clean)
+            latents_clean = latents_clean.flatten(2).transpose(1, 2)
+        if latents_clean_2x is not None:
+            latents_clean_2x = _pad_for_3d_conv(latents_clean_2x, (2, 4, 4))
+            latents_clean_2x = self.proj_2x(latents_clean_2x)
+            latents_clean_2x = latents_clean_2x.flatten(2).transpose(1, 2)
+        if latents_clean_4x is not None:
+            latents_clean_4x = _pad_for_3d_conv(latents_clean_4x, (4, 8, 8))
+            latents_clean_4x = self.proj_4x(latents_clean_4x)
+            latents_clean_4x = latents_clean_4x.flatten(2).transpose(1, 2)
+        return latents_clean, latents_clean_2x, latents_clean_4x
+
+
+class HunyuanVideoFramepackTransformer3DModel(
+    ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin
+):
+    _supports_gradient_checkpointing = True
+    _skip_layerwise_casting_patterns = ["x_embedder", "context_embedder", "norm"]
+    _no_split_modules = [
+        "HunyuanVideoTransformerBlock",
+        "HunyuanVideoSingleTransformerBlock",
+        "HunyuanVideoHistoryPatchEmbed",
+        "HunyuanVideoTokenRefiner",
+    ]
+
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 16,
+        out_channels: int = 16,
+        num_attention_heads: int = 24,
+        attention_head_dim: int = 128,
+        num_layers: int = 20,
+        num_single_layers: int = 40,
+        num_refiner_layers: int = 2,
+        mlp_ratio: float = 4.0,
+        patch_size: int = 2,
+        patch_size_t: int = 1,
+        qk_norm: str = "rms_norm",
+        guidance_embeds: bool = True,
+        text_embed_dim: int = 4096,
+        pooled_projection_dim: int = 768,
+        rope_theta: float = 256.0,
+        rope_axes_dim: Tuple[int] = (16, 56, 56),
+        image_condition_type: Optional[str] = None,
+        has_image_proj: int = False,
+        image_proj_dim: int = 1152,
+        has_clean_x_embedder: int = False,
+    ) -> None:
+        super().__init__()
+
+        inner_dim = num_attention_heads * attention_head_dim
+        out_channels = out_channels or in_channels
+
+        # 1. Latent and condition embedders
+        self.x_embedder = HunyuanVideoPatchEmbed((patch_size_t, patch_size, patch_size), in_channels, inner_dim)
+        self.context_embedder = HunyuanVideoTokenRefiner(
+            text_embed_dim, num_attention_heads, attention_head_dim, num_layers=num_refiner_layers
+        )
+        self.time_text_embed = HunyuanVideoConditionEmbedding(
+            inner_dim, pooled_projection_dim, guidance_embeds, image_condition_type
+        )
+
+        # 2. RoPE
+        self.rope = HunyuanVideoFramepackRotaryPosEmbed(patch_size, patch_size_t, rope_axes_dim, rope_theta)
+
+        # 3. Dual stream transformer blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                HunyuanVideoTransformerBlock(
+                    num_attention_heads, attention_head_dim, mlp_ratio=mlp_ratio, qk_norm=qk_norm
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        # 4. Single stream transformer blocks
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                HunyuanVideoSingleTransformerBlock(
+                    num_attention_heads, attention_head_dim, mlp_ratio=mlp_ratio, qk_norm=qk_norm
+                )
+                for _ in range(num_single_layers)
+            ]
+        )
+
+        # 5. Output projection
+        self.norm_out = AdaLayerNormContinuous(inner_dim, inner_dim, elementwise_affine=False, eps=1e-6)
+        self.proj_out = nn.Linear(inner_dim, patch_size_t * patch_size * patch_size * out_channels)
+
+        # Framepack specific modules
+        self.image_projection = FramepackClipVisionProjection(image_proj_dim, inner_dim) if has_image_proj else None
+
+        self.clean_x_embedder = None
+        if has_clean_x_embedder:
+            self.clean_x_embedder = HunyuanVideoHistoryPatchEmbed(in_channels, inner_dim)
+
+        self.use_gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.LongTensor,
+        encoder_hidden_states: torch.Tensor,
+        encoder_attention_mask: torch.Tensor,
+        pooled_projections: torch.Tensor,
+        image_embeds: torch.Tensor,
+        indices_latents: torch.Tensor,
+        guidance: Optional[torch.Tensor] = None,
+        latents_clean: Optional[torch.Tensor] = None,
+        indices_latents_clean: Optional[torch.Tensor] = None,
+        latents_history_2x: Optional[torch.Tensor] = None,
+        indices_latents_history_2x: Optional[torch.Tensor] = None,
+        latents_history_4x: Optional[torch.Tensor] = None,
+        indices_latents_history_4x: Optional[torch.Tensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ):
+        if attention_kwargs is not None:
+            attention_kwargs = attention_kwargs.copy()
+            lora_scale = attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p, p_t = self.config.patch_size, self.config.patch_size_t
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p
+        post_patch_width = width // p
+        original_context_length = post_patch_num_frames * post_patch_height * post_patch_width
+
+        if indices_latents is None:
+            indices_latents = torch.arange(0, num_frames).unsqueeze(0).expand(batch_size, -1)
+
+        hidden_states = self.x_embedder(hidden_states)
+        image_rotary_emb = self.rope(
+            frame_indices=indices_latents, height=height, width=width, device=hidden_states.device
+        )
+
+        latents_clean, latents_history_2x, latents_history_4x = self.clean_x_embedder(
+            latents_clean, latents_history_2x, latents_history_4x
+        )
+
+        if latents_clean is not None and indices_latents_clean is not None:
+            image_rotary_emb_clean = self.rope(
+                frame_indices=indices_latents_clean, height=height, width=width, device=hidden_states.device
+            )
+        if latents_history_2x is not None and indices_latents_history_2x is not None:
+            image_rotary_emb_history_2x = self.rope(
+                frame_indices=indices_latents_history_2x, height=height, width=width, device=hidden_states.device
+            )
+        if latents_history_4x is not None and indices_latents_history_4x is not None:
+            image_rotary_emb_history_4x = self.rope(
+                frame_indices=indices_latents_history_4x, height=height, width=width, device=hidden_states.device
+            )
+
+        hidden_states, image_rotary_emb = self._pack_history_states(
+            hidden_states,
+            latents_clean,
+            latents_history_2x,
+            latents_history_4x,
+            image_rotary_emb,
+            image_rotary_emb_clean,
+            image_rotary_emb_history_2x,
+            image_rotary_emb_history_4x,
+            post_patch_height,
+            post_patch_width,
+        )
+
+        temb, _ = self.time_text_embed(timestep, pooled_projections, guidance)
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states, timestep, encoder_attention_mask)
+
+        encoder_hidden_states_image = self.image_projection(image_embeds)
+        attention_mask_image = encoder_attention_mask.new_ones((batch_size, encoder_hidden_states_image.shape[1]))
+
+        # must cat before (not after) encoder_hidden_states, due to attn masking
+        encoder_hidden_states = torch.cat([encoder_hidden_states_image, encoder_hidden_states], dim=1)
+        encoder_attention_mask = torch.cat([attention_mask_image, encoder_attention_mask], dim=1)
+
+        latent_sequence_length = hidden_states.shape[1]
+        condition_sequence_length = encoder_hidden_states.shape[1]
+        sequence_length = latent_sequence_length + condition_sequence_length
+        attention_mask = torch.zeros(
+            batch_size, sequence_length, device=hidden_states.device, dtype=torch.bool
+        )  # [B, N]
+        effective_condition_sequence_length = encoder_attention_mask.sum(dim=1, dtype=torch.int)  # [B,]
+        effective_sequence_length = latent_sequence_length + effective_condition_sequence_length
+
+        if batch_size == 1:
+            encoder_hidden_states = encoder_hidden_states[:, : effective_condition_sequence_length[0]]
+            attention_mask = None
+        else:
+            for i in range(batch_size):
+                attention_mask[i, : effective_sequence_length[i]] = True
+            # [B, 1, 1, N], for broadcasting across attention heads
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(1)
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for block in self.transformer_blocks:
+                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
+                    block, hidden_states, encoder_hidden_states, temb, attention_mask, image_rotary_emb
+                )
+
+            for block in self.single_transformer_blocks:
+                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
+                    block, hidden_states, encoder_hidden_states, temb, attention_mask, image_rotary_emb
+                )
+
+        else:
+            for block in self.transformer_blocks:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states, encoder_hidden_states, temb, attention_mask, image_rotary_emb
+                )
+
+            for block in self.single_transformer_blocks:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states, encoder_hidden_states, temb, attention_mask, image_rotary_emb
+                )
+
+        hidden_states = hidden_states[:, -original_context_length:]
+        hidden_states = self.norm_out(hidden_states, temb)
+        hidden_states = self.proj_out(hidden_states)
+
+        hidden_states = hidden_states.reshape(
+            batch_size, post_patch_num_frames, post_patch_height, post_patch_width, -1, p_t, p, p
+        )
+        hidden_states = hidden_states.permute(0, 4, 1, 5, 2, 6, 3, 7)
+        hidden_states = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (hidden_states,)
+        return Transformer2DModelOutput(sample=hidden_states)
+
+    def _pack_history_states(
+        self,
+        hidden_states: torch.Tensor,
+        latents_clean: Optional[torch.Tensor] = None,
+        latents_history_2x: Optional[torch.Tensor] = None,
+        latents_history_4x: Optional[torch.Tensor] = None,
+        image_rotary_emb: Tuple[torch.Tensor, torch.Tensor] = None,
+        image_rotary_emb_clean: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        image_rotary_emb_history_2x: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        image_rotary_emb_history_4x: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        height: int = None,
+        width: int = None,
+    ):
+        image_rotary_emb = list(image_rotary_emb)  # convert tuple to list for in-place modification
+
+        if latents_clean is not None and image_rotary_emb_clean is not None:
+            hidden_states = torch.cat([latents_clean, hidden_states], dim=1)
+            image_rotary_emb[0] = torch.cat([image_rotary_emb_clean[0], image_rotary_emb[0]], dim=0)
+            image_rotary_emb[1] = torch.cat([image_rotary_emb_clean[1], image_rotary_emb[1]], dim=0)
+
+        if latents_history_2x is not None and image_rotary_emb_history_2x is not None:
+            hidden_states = torch.cat([latents_history_2x, hidden_states], dim=1)
+            image_rotary_emb_history_2x = self._pad_rotary_emb(image_rotary_emb_history_2x, height, width, (2, 2, 2))
+            image_rotary_emb[0] = torch.cat([image_rotary_emb_history_2x[0], image_rotary_emb[0]], dim=0)
+            image_rotary_emb[1] = torch.cat([image_rotary_emb_history_2x[1], image_rotary_emb[1]], dim=0)
+
+        if latents_history_4x is not None and image_rotary_emb_history_4x is not None:
+            hidden_states = torch.cat([latents_history_4x, hidden_states], dim=1)
+            image_rotary_emb_history_4x = self._pad_rotary_emb(image_rotary_emb_history_4x, height, width, (4, 4, 4))
+            image_rotary_emb[0] = torch.cat([image_rotary_emb_history_4x[0], image_rotary_emb[0]], dim=0)
+            image_rotary_emb[1] = torch.cat([image_rotary_emb_history_4x[1], image_rotary_emb[1]], dim=0)
+
+        return hidden_states, tuple(image_rotary_emb)
+
+    def _pad_rotary_emb(
+        self,
+        image_rotary_emb: Tuple[torch.Tensor],
+        height: int,
+        width: int,
+        kernel_size: Tuple[int, int, int],
+    ):
+        # freqs_cos, freqs_sin have shape [W * H * T, D / 2], where D is attention head dim
+        freqs_cos, freqs_sin = image_rotary_emb
+        freqs_cos = freqs_cos.unsqueeze(0).permute(0, 2, 1).unflatten(2, (-1, height, width))
+        freqs_sin = freqs_sin.unsqueeze(0).permute(0, 2, 1).unflatten(2, (-1, height, width))
+        freqs_cos = _pad_for_3d_conv(freqs_cos, kernel_size)
+        freqs_sin = _pad_for_3d_conv(freqs_sin, kernel_size)
+        freqs_cos = _center_down_sample_3d(freqs_cos, kernel_size)
+        freqs_sin = _center_down_sample_3d(freqs_sin, kernel_size)
+        freqs_cos = freqs_cos.flatten(2).permute(0, 2, 1).squeeze(0)
+        freqs_sin = freqs_sin.flatten(2).permute(0, 2, 1).squeeze(0)
+        return freqs_cos, freqs_sin
+
+
+def _pad_for_3d_conv(x, kernel_size):
+    if isinstance(x, (tuple, list)):
+        return tuple(_pad_for_3d_conv(i, kernel_size) for i in x)
+    b, c, t, h, w = x.shape
+    pt, ph, pw = kernel_size
+    pad_t = (pt - (t % pt)) % pt
+    pad_h = (ph - (h % ph)) % ph
+    pad_w = (pw - (w % pw)) % pw
+    return torch.nn.functional.pad(x, (0, pad_w, 0, pad_h, 0, pad_t), mode="replicate")
+
+
+def _center_down_sample_3d(x, kernel_size):
+    if isinstance(x, (tuple, list)):
+        return tuple(_center_down_sample_3d(i, kernel_size) for i in x)
+    return torch.nn.functional.avg_pool3d(x, kernel_size, stride=kernel_size)

src/diffusers/pipelines/__init__.py

@@ -227,6 +227,7 @@ else:
         "HunyuanVideoPipeline",
         "HunyuanSkyreelsImageToVideoPipeline",
         "HunyuanVideoImageToVideoPipeline",
+        "HunyuanVideoFramepackPipeline",
     ]
     _import_structure["kandinsky"] = [
         "KandinskyCombinedPipeline",
@@ -589,6 +590,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .hidream_image import HiDreamImagePipeline
         from .hunyuan_video import (
             HunyuanSkyreelsImageToVideoPipeline,
+            HunyuanVideoFramepackPipeline,
             HunyuanVideoImageToVideoPipeline,
             HunyuanVideoPipeline,
         )

src/diffusers/pipelines/hunyuan_video/__init__.py

@@ -24,6 +24,7 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["pipeline_hunyuan_skyreels_image2video"] = ["HunyuanSkyreelsImageToVideoPipeline"]
     _import_structure["pipeline_hunyuan_video"] = ["HunyuanVideoPipeline"]
+    _import_structure["pipeline_hunyuan_video_framepack"] = ["HunyuanVideoFramepackPipeline"]
     _import_structure["pipeline_hunyuan_video_image2video"] = ["HunyuanVideoImageToVideoPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
@@ -36,6 +37,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     else:
         from .pipeline_hunyuan_skyreels_image2video import HunyuanSkyreelsImageToVideoPipeline
         from .pipeline_hunyuan_video import HunyuanVideoPipeline
+        from .pipeline_hunyuan_video_framepack import HunyuanVideoFramepackPipeline
         from .pipeline_hunyuan_video_image2video import HunyuanVideoImageToVideoPipeline
 
 else:

src/diffusers/pipelines/hunyuan_video/pipeline_output.py

 from dataclasses import dataclass
+from typing import List, Union
 
+import numpy as np
+import PIL.Image
 import torch
 
 from diffusers.utils import BaseOutput
@@ -18,3 +21,19 @@ class HunyuanVideoPipelineOutput(BaseOutput):
     """
 
     frames: torch.Tensor
+
+
+@dataclass
+class HunyuanVideoFramepackPipelineOutput(BaseOutput):
+    r"""
+    Output class for HunyuanVideo pipelines.
+
+    Args:
+        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)`. Or, a list of torch tensors where each tensor
+            corresponds to a latent that decodes to multiple frames.
+    """
+
+    frames: Union[torch.Tensor, np.ndarray, List[List[PIL.Image.Image]], List[torch.Tensor]]

src/diffusers/utils/dummy_pt_objects.py

@@ -565,6 +565,21 @@ class HunyuanDiT2DMultiControlNetModel(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class HunyuanVideoFramepackTransformer3DModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class HunyuanVideoTransformer3DModel(metaclass=DummyObject):
     _backends = ["torch"]
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -692,6 +692,21 @@ class HunyuanSkyreelsImageToVideoPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class HunyuanVideoFramepackPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class HunyuanVideoImageToVideoPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/hunyuan_video/test_hunyuan_video_framepack.py

+# Copyright 2025 The HuggingFace Team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import unittest
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import (
+    CLIPTextConfig,
+    CLIPTextModel,
+    CLIPTokenizer,
+    LlamaConfig,
+    LlamaModel,
+    LlamaTokenizer,
+    SiglipImageProcessor,
+    SiglipVisionModel,
+)
+
+from diffusers import (
+    AutoencoderKLHunyuanVideo,
+    FasterCacheConfig,
+    FlowMatchEulerDiscreteScheduler,
+    HunyuanVideoFramepackPipeline,
+    HunyuanVideoFramepackTransformer3DModel,
+)
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    torch_device,
+)
+
+from ..test_pipelines_common import (
+    FasterCacheTesterMixin,
+    PipelineTesterMixin,
+    PyramidAttentionBroadcastTesterMixin,
+    to_np,
+)
+
+
+enable_full_determinism()
+
+
+class HunyuanVideoFramepackPipelineFastTests(
+    PipelineTesterMixin, PyramidAttentionBroadcastTesterMixin, FasterCacheTesterMixin, unittest.TestCase
+):
+    pipeline_class = HunyuanVideoFramepackPipeline
+    params = frozenset(
+        ["image", "prompt", "height", "width", "guidance_scale", "prompt_embeds", "pooled_prompt_embeds"]
+    )
+    batch_params = frozenset(["image", "prompt"])
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "generator",
+            "return_dict",
+            "callback_on_step_end",
+            "callback_on_step_end_tensor_inputs",
+        ]
+    )
+
+    supports_dduf = False
+    # there is no xformers processor for Flux
+    test_xformers_attention = False
+    test_layerwise_casting = True
+    test_group_offloading = True
+
+    faster_cache_config = FasterCacheConfig(
+        spatial_attention_block_skip_range=2,
+        spatial_attention_timestep_skip_range=(-1, 901),
+        unconditional_batch_skip_range=2,
+        attention_weight_callback=lambda _: 0.5,
+        is_guidance_distilled=True,
+    )
+
+    def get_dummy_components(self, num_layers: int = 1, num_single_layers: int = 1):
+        torch.manual_seed(0)
+        transformer = HunyuanVideoFramepackTransformer3DModel(
+            in_channels=4,
+            out_channels=4,
+            num_attention_heads=2,
+            attention_head_dim=10,
+            num_layers=num_layers,
+            num_single_layers=num_single_layers,
+            num_refiner_layers=1,
+            patch_size=2,
+            patch_size_t=1,
+            guidance_embeds=True,
+            text_embed_dim=16,
+            pooled_projection_dim=8,
+            rope_axes_dim=(2, 4, 4),
+            image_condition_type=None,
+            has_image_proj=True,
+            image_proj_dim=32,
+            has_clean_x_embedder=True,
+        )
+
+        torch.manual_seed(0)
+        vae = AutoencoderKLHunyuanVideo(
+            in_channels=3,
+            out_channels=3,
+            latent_channels=4,
+            down_block_types=(
+                "HunyuanVideoDownBlock3D",
+                "HunyuanVideoDownBlock3D",
+                "HunyuanVideoDownBlock3D",
+                "HunyuanVideoDownBlock3D",
+            ),
+            up_block_types=(
+                "HunyuanVideoUpBlock3D",
+                "HunyuanVideoUpBlock3D",
+                "HunyuanVideoUpBlock3D",
+                "HunyuanVideoUpBlock3D",
+            ),
+            block_out_channels=(8, 8, 8, 8),
+            layers_per_block=1,
+            act_fn="silu",
+            norm_num_groups=4,
+            scaling_factor=0.476986,
+            spatial_compression_ratio=8,
+            temporal_compression_ratio=4,
+            mid_block_add_attention=True,
+        )
+
+        torch.manual_seed(0)
+        scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)
+
+        llama_text_encoder_config = LlamaConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=16,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=2,
+            pad_token_id=1,
+            vocab_size=1000,
+            hidden_act="gelu",
+            projection_dim=32,
+        )
+        clip_text_encoder_config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=8,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=2,
+            pad_token_id=1,
+            vocab_size=1000,
+            hidden_act="gelu",
+            projection_dim=32,
+        )
+
+        torch.manual_seed(0)
+        text_encoder = LlamaModel(llama_text_encoder_config)
+        tokenizer = LlamaTokenizer.from_pretrained("finetrainers/dummy-hunyaunvideo", subfolder="tokenizer")
+
+        torch.manual_seed(0)
+        text_encoder_2 = CLIPTextModel(clip_text_encoder_config)
+        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        feature_extractor = SiglipImageProcessor.from_pretrained(
+            "hf-internal-testing/tiny-random-SiglipVisionModel", size={"height": 30, "width": 30}
+        )
+        image_encoder = SiglipVisionModel.from_pretrained("hf-internal-testing/tiny-random-SiglipVisionModel")
+
+        components = {
+            "transformer": transformer,
+            "vae": vae,
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "text_encoder_2": text_encoder_2,
+            "tokenizer": tokenizer,
+            "tokenizer_2": tokenizer_2,
+            "feature_extractor": feature_extractor,
+            "image_encoder": image_encoder,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+
+        image_height = 32
+        image_width = 32
+        image = Image.new("RGB", (image_width, image_height))
+        inputs = {
+            "image": image,
+            "prompt": "dance monkey",
+            "prompt_template": {
+                "template": "{}",
+                "crop_start": 0,
+            },
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 4.5,
+            "height": image_height,
+            "width": image_width,
+            "num_frames": 9,
+            "latent_window_size": 3,
+            "max_sequence_length": 256,
+            "output_type": "pt",
+        }
+        return inputs
+
+    def test_inference(self):
+        device = "cpu"
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        video = pipe(**inputs).frames
+        generated_video = video[0]
+
+        self.assertEqual(generated_video.shape, (13, 3, 32, 32))
+        expected_video = torch.randn(13, 3, 32, 32)
+        max_diff = np.abs(generated_video - expected_video).max()
+        self.assertLessEqual(max_diff, 1e10)
+
+    def test_callback_inputs(self):
+        sig = inspect.signature(self.pipeline_class.__call__)
+        has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
+        has_callback_step_end = "callback_on_step_end" in sig.parameters
+
+        if not (has_callback_tensor_inputs and has_callback_step_end):
+            return
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        self.assertTrue(
+            hasattr(pipe, "_callback_tensor_inputs"),
+            f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
+        )
+
+        def callback_inputs_subset(pipe, i, t, callback_kwargs):
+            # iterate over callback args
+            for tensor_name, tensor_value in callback_kwargs.items():
+                # check that we're only passing in allowed tensor inputs
+                assert tensor_name in pipe._callback_tensor_inputs
+
+            return callback_kwargs
+
+        def callback_inputs_all(pipe, i, t, callback_kwargs):
+            for tensor_name in pipe._callback_tensor_inputs:
+                assert tensor_name in callback_kwargs
+
+            # iterate over callback args
+            for tensor_name, tensor_value in callback_kwargs.items():
+                # check that we're only passing in allowed tensor inputs
+                assert tensor_name in pipe._callback_tensor_inputs
+
+            return callback_kwargs
+
+        inputs = self.get_dummy_inputs(torch_device)
+
+        # Test passing in a subset
+        inputs["callback_on_step_end"] = callback_inputs_subset
+        inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
+        output = pipe(**inputs)[0]
+
+        # Test passing in a everything
+        inputs["callback_on_step_end"] = callback_inputs_all
+        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
+        output = pipe(**inputs)[0]
+
+        def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
+            is_last = i == (pipe.num_timesteps - 1)
+            if is_last:
+                callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
+            return callback_kwargs
+
+        inputs["callback_on_step_end"] = callback_inputs_change_tensor
+        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
+        output = pipe(**inputs)[0]
+        assert output.abs().sum() < 1e10
+
+    def test_attention_slicing_forward_pass(
+        self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
+    ):
+        if not self.test_attention_slicing:
+            return
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        for component in pipe.components.values():
+            if hasattr(component, "set_default_attn_processor"):
+                component.set_default_attn_processor()
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        generator_device = "cpu"
+        inputs = self.get_dummy_inputs(generator_device)
+        output_without_slicing = pipe(**inputs)[0]
+
+        pipe.enable_attention_slicing(slice_size=1)
+        inputs = self.get_dummy_inputs(generator_device)
+        output_with_slicing1 = pipe(**inputs)[0]
+
+        pipe.enable_attention_slicing(slice_size=2)
+        inputs = self.get_dummy_inputs(generator_device)
+        output_with_slicing2 = pipe(**inputs)[0]
+
+        if test_max_difference:
+            max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max()
+            max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max()
+            self.assertLess(
+                max(max_diff1, max_diff2),
+                expected_max_diff,
+                "Attention slicing should not affect the inference results",
+            )
+
+    def test_vae_tiling(self, expected_diff_max: float = 0.2):
+        # Seems to require higher tolerance than the other tests
+        expected_diff_max = 0.6
+        generator_device = "cpu"
+        components = self.get_dummy_components()
+
+        pipe = self.pipeline_class(**components)
+        pipe.to("cpu")
+        pipe.set_progress_bar_config(disable=None)
+
+        # Without tiling
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        output_without_tiling = pipe(**inputs)[0]
+
+        # With tiling
+        pipe.vae.enable_tiling(
+            tile_sample_min_height=96,
+            tile_sample_min_width=96,
+            tile_sample_stride_height=64,
+            tile_sample_stride_width=64,
+        )
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        output_with_tiling = pipe(**inputs)[0]
+
+        self.assertLess(
+            (to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
+            expected_diff_max,
+            "VAE tiling should not affect the inference results",
+        )
+
+    # TODO(aryan): Create a dummy gemma model with smol vocab size
+    @unittest.skip(
+        "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error."
+    )
+    def test_inference_batch_consistent(self):
+        pass
+
+    @unittest.skip(
+        "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error."
+    )
+    def test_inference_batch_single_identical(self):
+        pass