update

Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

docs/source/en/_toctree.yml

@@ -270,6 +270,8 @@
         title: LatteTransformer3DModel
       - local: api/models/lumina_nextdit2d
         title: LuminaNextDiT2DModel
+      - local: api/models/mochi_transformer3d
+        title: MochiTransformer3DModel
       - local: api/models/pixart_transformer2d
         title: PixArtTransformer2DModel
       - local: api/models/prior_transformer
@@ -306,6 +308,8 @@
         title: AutoencoderKLAllegro
       - local: api/models/autoencoderkl_cogvideox
         title: AutoencoderKLCogVideoX
+      - local: api/models/autoencoderkl_mochi
+        title: AutoencoderKLMochi
       - local: api/models/asymmetricautoencoderkl
         title: AsymmetricAutoencoderKL
       - local: api/models/consistency_decoder_vae
@@ -400,6 +404,8 @@
       title: Lumina-T2X
     - local: api/pipelines/marigold
       title: Marigold
+    - local: api/pipelines/mochi
+      title: Mochi
     - local: api/pipelines/panorama
       title: MultiDiffusion
     - local: api/pipelines/musicldm

docs/source/en/api/models/autoencoderkl_mochi.md

@@ -0,0 +1,32 @@
+<!-- Copyright 2024 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. -->
+
+# AutoencoderKLMochi
+
+The 3D variational autoencoder (VAE) model with KL loss used in [Mochi](https://github.com/genmoai/models) was introduced in [Mochi 1 Preview](https://huggingface.co/genmo/mochi-1-preview) by Tsinghua University & ZhipuAI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AutoencoderKLMochi
+
+vae = AutoencoderKLMochi.from_pretrained("genmo/mochi-1-preview", subfolder="vae", torch_dtype=torch.float32).to("cuda")
+```
+
+## AutoencoderKLMochi
+
+[[autodoc]] AutoencoderKLMochi
+    - decode
+    - all
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/models/mochi_transformer3d.md

@@ -0,0 +1,30 @@
+<!-- Copyright 2024 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. -->
+
+# MochiTransformer3DModel
+
+A Diffusion Transformer model for 3D video-like data was introduced in [Mochi-1 Preview](https://huggingface.co/genmo/mochi-1-preview) by Genmo.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import MochiTransformer3DModel
+
+vae = MochiTransformer3DModel.from_pretrained("genmo/mochi-1-preview", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
+```
+
+## MochiTransformer3DModel
+
+[[autodoc]] MochiTransformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -0,0 +1,36 @@
+<!-- Copyright 2024 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.
+-->
+
+# Mochi
+
+[Mochi 1 Preview](https://huggingface.co/genmo/mochi-1-preview) from Genmo.
+
+*Mochi 1 preview is an open state-of-the-art video generation model with high-fidelity motion and strong prompt adherence in preliminary evaluation. This model dramatically closes the gap between closed and open video generation systems. The model is released under a permissive Apache 2.0 license.*
+
+<Tip>
+
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+## MochiPipeline
+
+[[autodoc]] MochiPipeline
+  - all
+  - __call__
+
+## MochiPipelineOutput
+
+[[autodoc]] pipelines.mochi.pipeline_output.MochiPipelineOutput

scripts/convert_mochi_to_diffusers.py

@@ -0,0 +1,461 @@
+import argparse
+from contextlib import nullcontext
+
+import torch
+from accelerate import init_empty_weights
+from safetensors.torch import load_file
+from transformers import T5EncoderModel, T5Tokenizer
+
+from diffusers import AutoencoderKLMochi, FlowMatchEulerDiscreteScheduler, MochiPipeline, MochiTransformer3DModel
+from diffusers.utils.import_utils import is_accelerate_available
+
+
+CTX = init_empty_weights if is_accelerate_available else nullcontext
+
+TOKENIZER_MAX_LENGTH = 256
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--transformer_checkpoint_path", default=None, type=str)
+parser.add_argument("--vae_encoder_checkpoint_path", default=None, type=str)
+parser.add_argument("--vae_decoder_checkpoint_path", default=None, type=str)
+parser.add_argument("--output_path", required=True, type=str)
+parser.add_argument("--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving")
+parser.add_argument("--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory")
+parser.add_argument("--dtype", type=str, default=None)
+
+args = parser.parse_args()
+
+
+# This is specific to `AdaLayerNormContinuous`:
+# Diffusers implementation split the linear projection into the scale, shift while Mochi split it into shift, scale
+def swap_scale_shift(weight, dim):
+    shift, scale = weight.chunk(2, dim=0)
+    new_weight = torch.cat([scale, shift], dim=0)
+    return new_weight
+
+
+def swap_proj_gate(weight):
+    proj, gate = weight.chunk(2, dim=0)
+    new_weight = torch.cat([gate, proj], dim=0)
+    return new_weight
+
+
+def convert_mochi_transformer_checkpoint_to_diffusers(ckpt_path):
+    original_state_dict = load_file(ckpt_path, device="cpu")
+    new_state_dict = {}
+
+    # Convert patch_embed
+    new_state_dict["patch_embed.proj.weight"] = original_state_dict.pop("x_embedder.proj.weight")
+    new_state_dict["patch_embed.proj.bias"] = original_state_dict.pop("x_embedder.proj.bias")
+
+    # Convert time_embed
+    new_state_dict["time_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop("t_embedder.mlp.0.weight")
+    new_state_dict["time_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop("t_embedder.mlp.0.bias")
+    new_state_dict["time_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop("t_embedder.mlp.2.weight")
+    new_state_dict["time_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop("t_embedder.mlp.2.bias")
+    new_state_dict["time_embed.pooler.to_kv.weight"] = original_state_dict.pop("t5_y_embedder.to_kv.weight")
+    new_state_dict["time_embed.pooler.to_kv.bias"] = original_state_dict.pop("t5_y_embedder.to_kv.bias")
+    new_state_dict["time_embed.pooler.to_q.weight"] = original_state_dict.pop("t5_y_embedder.to_q.weight")
+    new_state_dict["time_embed.pooler.to_q.bias"] = original_state_dict.pop("t5_y_embedder.to_q.bias")
+    new_state_dict["time_embed.pooler.to_out.weight"] = original_state_dict.pop("t5_y_embedder.to_out.weight")
+    new_state_dict["time_embed.pooler.to_out.bias"] = original_state_dict.pop("t5_y_embedder.to_out.bias")
+    new_state_dict["time_embed.caption_proj.weight"] = original_state_dict.pop("t5_yproj.weight")
+    new_state_dict["time_embed.caption_proj.bias"] = original_state_dict.pop("t5_yproj.bias")
+
+    # Convert transformer blocks
+    num_layers = 48
+    for i in range(num_layers):
+        block_prefix = f"transformer_blocks.{i}."
+        old_prefix = f"blocks.{i}."
+
+        # norm1
+        new_state_dict[block_prefix + "norm1.linear.weight"] = original_state_dict.pop(old_prefix + "mod_x.weight")
+        new_state_dict[block_prefix + "norm1.linear.bias"] = original_state_dict.pop(old_prefix + "mod_x.bias")
+        if i < num_layers - 1:
+            new_state_dict[block_prefix + "norm1_context.linear.weight"] = original_state_dict.pop(
+                old_prefix + "mod_y.weight"
+            )
+            new_state_dict[block_prefix + "norm1_context.linear.bias"] = original_state_dict.pop(
+                old_prefix + "mod_y.bias"
+            )
+        else:
+            new_state_dict[block_prefix + "norm1_context.linear_1.weight"] = original_state_dict.pop(
+                old_prefix + "mod_y.weight"
+            )
+            new_state_dict[block_prefix + "norm1_context.linear_1.bias"] = original_state_dict.pop(
+                old_prefix + "mod_y.bias"
+            )
+
+        # Visual attention
+        qkv_weight = original_state_dict.pop(old_prefix + "attn.qkv_x.weight")
+        q, k, v = qkv_weight.chunk(3, dim=0)
+
+        new_state_dict[block_prefix + "attn1.to_q.weight"] = q
+        new_state_dict[block_prefix + "attn1.to_k.weight"] = k
+        new_state_dict[block_prefix + "attn1.to_v.weight"] = v
+        new_state_dict[block_prefix + "attn1.norm_q.weight"] = original_state_dict.pop(
+            old_prefix + "attn.q_norm_x.weight"
+        )
+        new_state_dict[block_prefix + "attn1.norm_k.weight"] = original_state_dict.pop(
+            old_prefix + "attn.k_norm_x.weight"
+        )
+        new_state_dict[block_prefix + "attn1.to_out.0.weight"] = original_state_dict.pop(
+            old_prefix + "attn.proj_x.weight"
+        )
+        new_state_dict[block_prefix + "attn1.to_out.0.bias"] = original_state_dict.pop(old_prefix + "attn.proj_x.bias")
+
+        # Context attention
+        qkv_weight = original_state_dict.pop(old_prefix + "attn.qkv_y.weight")
+        q, k, v = qkv_weight.chunk(3, dim=0)
+
+        new_state_dict[block_prefix + "attn1.add_q_proj.weight"] = q
+        new_state_dict[block_prefix + "attn1.add_k_proj.weight"] = k
+        new_state_dict[block_prefix + "attn1.add_v_proj.weight"] = v
+        new_state_dict[block_prefix + "attn1.norm_added_q.weight"] = original_state_dict.pop(
+            old_prefix + "attn.q_norm_y.weight"
+        )
+        new_state_dict[block_prefix + "attn1.norm_added_k.weight"] = original_state_dict.pop(
+            old_prefix + "attn.k_norm_y.weight"
+        )
+        if i < num_layers - 1:
+            new_state_dict[block_prefix + "attn1.to_add_out.weight"] = original_state_dict.pop(
+                old_prefix + "attn.proj_y.weight"
+            )
+            new_state_dict[block_prefix + "attn1.to_add_out.bias"] = original_state_dict.pop(
+                old_prefix + "attn.proj_y.bias"
+            )
+
+        # MLP
+        new_state_dict[block_prefix + "ff.net.0.proj.weight"] = swap_proj_gate(
+            original_state_dict.pop(old_prefix + "mlp_x.w1.weight")
+        )
+        new_state_dict[block_prefix + "ff.net.2.weight"] = original_state_dict.pop(old_prefix + "mlp_x.w2.weight")
+        if i < num_layers - 1:
+            new_state_dict[block_prefix + "ff_context.net.0.proj.weight"] = swap_proj_gate(
+                original_state_dict.pop(old_prefix + "mlp_y.w1.weight")
+            )
+            new_state_dict[block_prefix + "ff_context.net.2.weight"] = original_state_dict.pop(
+                old_prefix + "mlp_y.w2.weight"
+            )
+
+    # Output layers
+    new_state_dict["norm_out.linear.weight"] = swap_scale_shift(
+        original_state_dict.pop("final_layer.mod.weight"), dim=0
+    )
+    new_state_dict["norm_out.linear.bias"] = swap_scale_shift(original_state_dict.pop("final_layer.mod.bias"), dim=0)
+    new_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight")
+    new_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias")
+
+    new_state_dict["pos_frequencies"] = original_state_dict.pop("pos_frequencies")
+
+    print("Remaining Keys:", original_state_dict.keys())
+
+    return new_state_dict
+
+
+def convert_mochi_vae_state_dict_to_diffusers(encoder_ckpt_path, decoder_ckpt_path):
+    encoder_state_dict = load_file(encoder_ckpt_path, device="cpu")
+    decoder_state_dict = load_file(decoder_ckpt_path, device="cpu")
+    new_state_dict = {}
+
+    # ==== Decoder =====
+    prefix = "decoder."
+
+    # Convert conv_in
+    new_state_dict[f"{prefix}conv_in.weight"] = decoder_state_dict.pop("blocks.0.0.weight")
+    new_state_dict[f"{prefix}conv_in.bias"] = decoder_state_dict.pop("blocks.0.0.bias")
+
+    # Convert block_in (MochiMidBlock3D)
+    for i in range(3):  # layers_per_block[-1] = 3
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop(
+            f"blocks.0.{i+1}.stack.0.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop(
+            f"blocks.0.{i+1}.stack.0.bias"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop(
+            f"blocks.0.{i+1}.stack.2.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop(
+            f"blocks.0.{i+1}.stack.2.bias"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop(
+            f"blocks.0.{i+1}.stack.3.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop(
+            f"blocks.0.{i+1}.stack.3.bias"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop(
+            f"blocks.0.{i+1}.stack.5.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop(
+            f"blocks.0.{i+1}.stack.5.bias"
+        )
+
+    # Convert up_blocks (MochiUpBlock3D)
+    down_block_layers = [6, 4, 3]  # layers_per_block[-2], layers_per_block[-3], layers_per_block[-4]
+    for block in range(3):
+        for i in range(down_block_layers[block]):
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop(
+                f"blocks.{block+1}.blocks.{i}.stack.0.weight"
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop(
+                f"blocks.{block+1}.blocks.{i}.stack.0.bias"
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop(
+                f"blocks.{block+1}.blocks.{i}.stack.2.weight"
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop(
+                f"blocks.{block+1}.blocks.{i}.stack.2.bias"
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop(
+                f"blocks.{block+1}.blocks.{i}.stack.3.weight"
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop(
+                f"blocks.{block+1}.blocks.{i}.stack.3.bias"
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop(
+                f"blocks.{block+1}.blocks.{i}.stack.5.weight"
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop(
+                f"blocks.{block+1}.blocks.{i}.stack.5.bias"
+            )
+        new_state_dict[f"{prefix}up_blocks.{block}.proj.weight"] = decoder_state_dict.pop(
+            f"blocks.{block+1}.proj.weight"
+        )
+        new_state_dict[f"{prefix}up_blocks.{block}.proj.bias"] = decoder_state_dict.pop(f"blocks.{block+1}.proj.bias")
+
+    # Convert block_out (MochiMidBlock3D)
+    for i in range(3):  # layers_per_block[0] = 3
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop(
+            f"blocks.4.{i}.stack.0.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop(
+            f"blocks.4.{i}.stack.0.bias"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop(
+            f"blocks.4.{i}.stack.2.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop(
+            f"blocks.4.{i}.stack.2.bias"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop(
+            f"blocks.4.{i}.stack.3.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop(
+            f"blocks.4.{i}.stack.3.bias"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop(
+            f"blocks.4.{i}.stack.5.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop(
+            f"blocks.4.{i}.stack.5.bias"
+        )
+
+    # Convert proj_out (Conv1x1 ~= nn.Linear)
+    new_state_dict[f"{prefix}proj_out.weight"] = decoder_state_dict.pop("output_proj.weight")
+    new_state_dict[f"{prefix}proj_out.bias"] = decoder_state_dict.pop("output_proj.bias")
+
+    print("Remaining Decoder Keys:", decoder_state_dict.keys())
+
+    # ==== Encoder =====
+    prefix = "encoder."
+
+    new_state_dict[f"{prefix}proj_in.weight"] = encoder_state_dict.pop("layers.0.weight")
+    new_state_dict[f"{prefix}proj_in.bias"] = encoder_state_dict.pop("layers.0.bias")
+
+    # Convert block_in (MochiMidBlock3D)
+    for i in range(3):  # layers_per_block[0] = 3
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.0.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.0.bias"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.2.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.2.bias"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.3.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.3.bias"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.5.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.5.bias"
+        )
+
+    # Convert down_blocks (MochiDownBlock3D)
+    down_block_layers = [3, 4, 6]  # layers_per_block[1], layers_per_block[2], layers_per_block[3]
+    for block in range(3):
+        new_state_dict[f"{prefix}down_blocks.{block}.conv_in.conv.weight"] = encoder_state_dict.pop(
+            f"layers.{block+4}.layers.0.weight"
+        )
+        new_state_dict[f"{prefix}down_blocks.{block}.conv_in.conv.bias"] = encoder_state_dict.pop(
+            f"layers.{block+4}.layers.0.bias"
+        )
+
+        for i in range(down_block_layers[block]):
+            # Convert resnets
+            new_state_dict[
+                f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"
+            ] = encoder_state_dict.pop(f"layers.{block+4}.layers.{i+1}.stack.0.weight")
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.0.bias"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.2.weight"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.2.bias"
+            )
+            new_state_dict[
+                f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"
+            ] = encoder_state_dict.pop(f"layers.{block+4}.layers.{i+1}.stack.3.weight")
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.3.bias"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.5.weight"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.5.bias"
+            )
+
+            # Convert attentions
+            qkv_weight = encoder_state_dict.pop(f"layers.{block+4}.layers.{i+1}.attn_block.attn.qkv.weight")
+            q, k, v = qkv_weight.chunk(3, dim=0)
+
+            new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_q.weight"] = q
+            new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_k.weight"] = k
+            new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_v.weight"] = v
+            new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.weight"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.weight"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.bias"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.weight"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.attn_block.norm.weight"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.attn_block.norm.bias"
+            )
+
+    # Convert block_out (MochiMidBlock3D)
+    for i in range(3):  # layers_per_block[-1] = 3
+        # Convert resnets
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.0.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.0.bias"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.2.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.2.bias"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.3.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.3.bias"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.5.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.5.bias"
+        )
+
+        # Convert attentions
+        qkv_weight = encoder_state_dict.pop(f"layers.{i+7}.attn_block.attn.qkv.weight")
+        q, k, v = qkv_weight.chunk(3, dim=0)
+
+        new_state_dict[f"{prefix}block_out.attentions.{i}.to_q.weight"] = q
+        new_state_dict[f"{prefix}block_out.attentions.{i}.to_k.weight"] = k
+        new_state_dict[f"{prefix}block_out.attentions.{i}.to_v.weight"] = v
+        new_state_dict[f"{prefix}block_out.attentions.{i}.to_out.0.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.attn_block.attn.out.weight"
+        )
+        new_state_dict[f"{prefix}block_out.attentions.{i}.to_out.0.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.attn_block.attn.out.bias"
+        )
+        new_state_dict[f"{prefix}block_out.norms.{i}.norm_layer.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.attn_block.norm.weight"
+        )
+        new_state_dict[f"{prefix}block_out.norms.{i}.norm_layer.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.attn_block.norm.bias"
+        )
+
+    # Convert output layers
+    new_state_dict[f"{prefix}norm_out.norm_layer.weight"] = encoder_state_dict.pop("output_norm.weight")
+    new_state_dict[f"{prefix}norm_out.norm_layer.bias"] = encoder_state_dict.pop("output_norm.bias")
+    new_state_dict[f"{prefix}proj_out.weight"] = encoder_state_dict.pop("output_proj.weight")
+
+    print("Remaining Encoder Keys:", encoder_state_dict.keys())
+
+    return new_state_dict
+
+
+def main(args):
+    if args.dtype is None:
+        dtype = None
+    if args.dtype == "fp16":
+        dtype = torch.float16
+    elif args.dtype == "bf16":
+        dtype = torch.bfloat16
+    elif args.dtype == "fp32":
+        dtype = torch.float32
+    else:
+        raise ValueError(f"Unsupported dtype: {args.dtype}")
+
+    transformer = None
+    vae = None
+
+    if args.transformer_checkpoint_path is not None:
+        converted_transformer_state_dict = convert_mochi_transformer_checkpoint_to_diffusers(
+            args.transformer_checkpoint_path
+        )
+        transformer = MochiTransformer3DModel()
+        transformer.load_state_dict(converted_transformer_state_dict, strict=True)
+        if dtype is not None:
+            transformer = transformer.to(dtype=dtype)
+
+    if args.vae_encoder_checkpoint_path is not None and args.vae_decoder_checkpoint_path is not None:
+        vae = AutoencoderKLMochi(latent_channels=12, out_channels=3)
+        converted_vae_state_dict = convert_mochi_vae_state_dict_to_diffusers(
+            args.vae_encoder_checkpoint_path, args.vae_decoder_checkpoint_path
+        )
+        vae.load_state_dict(converted_vae_state_dict, strict=True)
+        if dtype is not None:
+            vae = vae.to(dtype=dtype)
+
+    text_encoder_id = "google/t5-v1_1-xxl"
+    tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH)
+    text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir)
+
+    # Apparently, the conversion does not work anymore without this :shrug:
+    for param in text_encoder.parameters():
+        param.data = param.data.contiguous()
+
+    pipe = MochiPipeline(
+        scheduler=FlowMatchEulerDiscreteScheduler(invert_sigmas=True),
+        vae=vae,
+        text_encoder=text_encoder,
+        tokenizer=tokenizer,
+        transformer=transformer,
+    )
+    pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", push_to_hub=args.push_to_hub)
+
+
+if __name__ == "__main__":
+    main(args)

src/diffusers/__init__.py

@@ -83,6 +83,7 @@ else:
             "AutoencoderKL",
             "AutoencoderKLAllegro",
             "AutoencoderKLCogVideoX",
+            "AutoencoderKLMochi",
             "AutoencoderKLTemporalDecoder",
             "AutoencoderOobleck",
             "AutoencoderTiny",
@@ -102,6 +103,7 @@ else:
             "Kandinsky3UNet",
             "LatteTransformer3DModel",
             "LuminaNextDiT2DModel",
+            "MochiTransformer3DModel",
             "ModelMixin",
             "MotionAdapter",
             "MultiAdapter",
@@ -311,6 +313,7 @@ else:
             "LuminaText2ImgPipeline",
             "MarigoldDepthPipeline",
             "MarigoldNormalsPipeline",
+            "MochiPipeline",
             "MusicLDMPipeline",
             "PaintByExamplePipeline",
             "PIAPipeline",
@@ -565,6 +568,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AutoencoderKL,
             AutoencoderKLAllegro,
             AutoencoderKLCogVideoX,
+            AutoencoderKLMochi,
             AutoencoderKLTemporalDecoder,
             AutoencoderOobleck,
             AutoencoderTiny,
@@ -584,6 +588,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             Kandinsky3UNet,
             LatteTransformer3DModel,
             LuminaNextDiT2DModel,
+            MochiTransformer3DModel,
             ModelMixin,
             MotionAdapter,
             MultiAdapter,
@@ -772,6 +777,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LuminaText2ImgPipeline,
             MarigoldDepthPipeline,
             MarigoldNormalsPipeline,
+            MochiPipeline,
             MusicLDMPipeline,
             PaintByExamplePipeline,
             PIAPipeline,

src/diffusers/models/__init__.py

@@ -30,6 +30,7 @@ if is_torch_available():
     _import_structure["autoencoders.autoencoder_kl"] = ["AutoencoderKL"]
     _import_structure["autoencoders.autoencoder_kl_allegro"] = ["AutoencoderKLAllegro"]
     _import_structure["autoencoders.autoencoder_kl_cogvideox"] = ["AutoencoderKLCogVideoX"]
+    _import_structure["autoencoders.autoencoder_kl_mochi"] = ["AutoencoderKLMochi"]
     _import_structure["autoencoders.autoencoder_kl_temporal_decoder"] = ["AutoencoderKLTemporalDecoder"]
     _import_structure["autoencoders.autoencoder_oobleck"] = ["AutoencoderOobleck"]
     _import_structure["autoencoders.autoencoder_tiny"] = ["AutoencoderTiny"]
@@ -58,6 +59,7 @@ if is_torch_available():
     _import_structure["transformers.transformer_allegro"] = ["AllegroTransformer3DModel"]
     _import_structure["transformers.transformer_cogview3plus"] = ["CogView3PlusTransformer2DModel"]
     _import_structure["transformers.transformer_flux"] = ["FluxTransformer2DModel"]
+    _import_structure["transformers.transformer_mochi"] = ["MochiTransformer3DModel"]
     _import_structure["transformers.transformer_sd3"] = ["SD3Transformer2DModel"]
     _import_structure["transformers.transformer_temporal"] = ["TransformerTemporalModel"]
     _import_structure["unets.unet_1d"] = ["UNet1DModel"]
@@ -85,6 +87,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AutoencoderKL,
             AutoencoderKLAllegro,
             AutoencoderKLCogVideoX,
+            AutoencoderKLMochi,
             AutoencoderKLTemporalDecoder,
             AutoencoderOobleck,
             AutoencoderTiny,
@@ -110,6 +113,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             HunyuanDiT2DModel,
             LatteTransformer3DModel,
             LuminaNextDiT2DModel,
+            MochiTransformer3DModel,
             PixArtTransformer2DModel,
             PriorTransformer,
             SD3Transformer2DModel,

src/diffusers/models/activations.py

@@ -136,6 +136,7 @@ class SwiGLU(nn.Module):
 
     def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
         super().__init__()
+
         self.proj = nn.Linear(dim_in, dim_out * 2, bias=bias)
         self.activation = nn.SiLU()
 

src/diffusers/models/attention_processor.py

@@ -120,14 +120,16 @@ class Attention(nn.Module):
         _from_deprecated_attn_block: bool = False,
         processor: Optional["AttnProcessor"] = None,
         out_dim: int = None,
+        out_context_dim: int = None,
         context_pre_only=None,
         pre_only=False,
         elementwise_affine: bool = True,
+        is_causal: bool = False,
     ):
         super().__init__()
 
         # To prevent circular import.
-        from .normalization import FP32LayerNorm, RMSNorm
+        from .normalization import FP32LayerNorm, LpNorm, RMSNorm
 
         self.inner_dim = out_dim if out_dim is not None else dim_head * heads
         self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads
@@ -142,8 +144,10 @@ class Attention(nn.Module):
         self.dropout = dropout
         self.fused_projections = False
         self.out_dim = out_dim if out_dim is not None else query_dim
+        self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim
         self.context_pre_only = context_pre_only
         self.pre_only = pre_only
+        self.is_causal = is_causal
 
         # we make use of this private variable to know whether this class is loaded
         # with an deprecated state dict so that we can convert it on the fly
@@ -192,6 +196,9 @@ class Attention(nn.Module):
         elif qk_norm == "rms_norm":
             self.norm_q = RMSNorm(dim_head, eps=eps)
             self.norm_k = RMSNorm(dim_head, eps=eps)
+        elif qk_norm == "l2":
+            self.norm_q = LpNorm(p=2, dim=-1)
+            self.norm_k = LpNorm(p=2, dim=-1)
         else:
             raise ValueError(f"unknown qk_norm: {qk_norm}. Should be None,'layer_norm','fp32_layer_norm','rms_norm'")
 
@@ -241,7 +248,7 @@ class Attention(nn.Module):
             self.to_out.append(nn.Dropout(dropout))
 
         if self.context_pre_only is not None and not self.context_pre_only:
-            self.to_add_out = nn.Linear(self.inner_dim, self.out_dim, bias=out_bias)
+            self.to_add_out = nn.Linear(self.inner_dim, self.out_context_dim, bias=out_bias)
 
         if qk_norm is not None and added_kv_proj_dim is not None:
             if qk_norm == "fp32_layer_norm":
@@ -1886,6 +1893,7 @@ class FluxAttnProcessor2_0:
             hidden_states = attn.to_out[0](hidden_states)
             # dropout
             hidden_states = attn.to_out[1](hidden_states)
+
             encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
 
             return hidden_states, encoder_hidden_states
@@ -2714,6 +2722,91 @@ class AttnProcessor2_0:
         return hidden_states
 
 
+class MochiVaeAttnProcessor2_0:
+    r"""
+    Attention processor used in Mochi VAE.
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        is_single_frame = hidden_states.shape[1] == 1
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if is_single_frame:
+            hidden_states = attn.to_v(hidden_states)
+
+            # linear proj
+            hidden_states = attn.to_out[0](hidden_states)
+            # dropout
+            hidden_states = attn.to_out[1](hidden_states)
+
+            if attn.residual_connection:
+                hidden_states = hidden_states + residual
+
+            hidden_states = hidden_states / attn.rescale_output_factor
+            return hidden_states
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=attn.is_causal
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
 class StableAudioAttnProcessor2_0:
     r"""
     Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
@@ -3389,6 +3482,94 @@ class LuminaAttnProcessor2_0:
         return hidden_states
 
 
+class MochiAttnProcessor2_0:
+    """Attention processor used in Mochi."""
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("MochiAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        query = query.unflatten(2, (attn.heads, -1))
+        key = key.unflatten(2, (attn.heads, -1))
+        value = value.unflatten(2, (attn.heads, -1))
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        encoder_query = attn.add_q_proj(encoder_hidden_states)
+        encoder_key = attn.add_k_proj(encoder_hidden_states)
+        encoder_value = attn.add_v_proj(encoder_hidden_states)
+
+        encoder_query = encoder_query.unflatten(2, (attn.heads, -1))
+        encoder_key = encoder_key.unflatten(2, (attn.heads, -1))
+        encoder_value = encoder_value.unflatten(2, (attn.heads, -1))
+
+        if attn.norm_added_q is not None:
+            encoder_query = attn.norm_added_q(encoder_query)
+        if attn.norm_added_k is not None:
+            encoder_key = attn.norm_added_k(encoder_key)
+
+        if image_rotary_emb is not None:
+
+            def apply_rotary_emb(x, freqs_cos, freqs_sin):
+                x_even = x[..., 0::2].float()
+                x_odd = x[..., 1::2].float()
+
+                cos = (x_even * freqs_cos - x_odd * freqs_sin).to(x.dtype)
+                sin = (x_even * freqs_sin + x_odd * freqs_cos).to(x.dtype)
+
+                return torch.stack([cos, sin], dim=-1).flatten(-2)
+
+            query = apply_rotary_emb(query, *image_rotary_emb)
+            key = apply_rotary_emb(key, *image_rotary_emb)
+
+        query, key, value = query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2)
+        encoder_query, encoder_key, encoder_value = (
+            encoder_query.transpose(1, 2),
+            encoder_key.transpose(1, 2),
+            encoder_value.transpose(1, 2),
+        )
+
+        sequence_length = query.size(2)
+        encoder_sequence_length = encoder_query.size(2)
+
+        query = torch.cat([query, encoder_query], dim=2)
+        key = torch.cat([key, encoder_key], dim=2)
+        value = torch.cat([value, encoder_value], dim=2)
+
+        hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
+        hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
+        hidden_states = hidden_states.to(query.dtype)
+
+        hidden_states, encoder_hidden_states = hidden_states.split_with_sizes(
+            (sequence_length, encoder_sequence_length), dim=1
+        )
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if hasattr(attn, "to_add_out"):
+            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+        return hidden_states, encoder_hidden_states
+
+
 class FusedAttnProcessor2_0:
     r"""
     Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). It uses

src/diffusers/models/autoencoders/__init__.py

@@ -2,6 +2,7 @@ from .autoencoder_asym_kl import AsymmetricAutoencoderKL
 from .autoencoder_kl import AutoencoderKL
 from .autoencoder_kl_allegro import AutoencoderKLAllegro
 from .autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
+from .autoencoder_kl_mochi import AutoencoderKLMochi
 from .autoencoder_kl_temporal_decoder import AutoencoderKLTemporalDecoder
 from .autoencoder_oobleck import AutoencoderOobleck
 from .autoencoder_tiny import AutoencoderTiny

src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py

@@ -94,11 +94,13 @@ class CogVideoXCausalConv3d(nn.Module):
 
         time_kernel_size, height_kernel_size, width_kernel_size = kernel_size
 
-        self.pad_mode = pad_mode
-        time_pad = dilation * (time_kernel_size - 1) + (1 - stride)
-        height_pad = height_kernel_size // 2
-        width_pad = width_kernel_size // 2
+        # TODO(aryan): configure calculation based on stride and dilation in the future.
+        # Since CogVideoX does not use it, it is currently tailored to "just work" with Mochi
+        time_pad = time_kernel_size - 1
+        height_pad = (height_kernel_size - 1) // 2
+        width_pad = (width_kernel_size - 1) // 2
 
+        self.pad_mode = pad_mode
         self.height_pad = height_pad
         self.width_pad = width_pad
         self.time_pad = time_pad
@@ -107,7 +109,7 @@ class CogVideoXCausalConv3d(nn.Module):
         self.temporal_dim = 2
         self.time_kernel_size = time_kernel_size
 
-        stride = (stride, 1, 1)
+        stride = stride if isinstance(stride, tuple) else (stride, 1, 1)
         dilation = (dilation, 1, 1)
         self.conv = CogVideoXSafeConv3d(
             in_channels=in_channels,
@@ -120,18 +122,24 @@ class CogVideoXCausalConv3d(nn.Module):
     def fake_context_parallel_forward(
         self, inputs: torch.Tensor, conv_cache: Optional[torch.Tensor] = None
     ) -> torch.Tensor:
-        kernel_size = self.time_kernel_size
-        if kernel_size > 1:
-            cached_inputs = [conv_cache] if conv_cache is not None else [inputs[:, :, :1]] * (kernel_size - 1)
-            inputs = torch.cat(cached_inputs + [inputs], dim=2)
+        if self.pad_mode == "replicate":
+            inputs = F.pad(inputs, self.time_causal_padding, mode="replicate")
+        else:
+            kernel_size = self.time_kernel_size
+            if kernel_size > 1:
+                cached_inputs = [conv_cache] if conv_cache is not None else [inputs[:, :, :1]] * (kernel_size - 1)
+                inputs = torch.cat(cached_inputs + [inputs], dim=2)
         return inputs
 
     def forward(self, inputs: torch.Tensor, conv_cache: Optional[torch.Tensor] = None) -> torch.Tensor:
         inputs = self.fake_context_parallel_forward(inputs, conv_cache)
-        conv_cache = inputs[:, :, -self.time_kernel_size + 1 :].clone()
 
-        padding_2d = (self.width_pad, self.width_pad, self.height_pad, self.height_pad)
-        inputs = F.pad(inputs, padding_2d, mode="constant", value=0)
+        if self.pad_mode == "replicate":
+            conv_cache = None
+        else:
+            padding_2d = (self.width_pad, self.width_pad, self.height_pad, self.height_pad)
+            conv_cache = inputs[:, :, -self.time_kernel_size + 1 :].clone()
+            inputs = F.pad(inputs, padding_2d, mode="constant", value=0)
 
         output = self.conv(inputs)
         return output, conv_cache

src/diffusers/models/embeddings.py

@@ -1356,6 +1356,41 @@ class LuminaCombinedTimestepCaptionEmbedding(nn.Module):
         return conditioning
 
 
+class MochiCombinedTimestepCaptionEmbedding(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        pooled_projection_dim: int,
+        text_embed_dim: int,
+        time_embed_dim: int = 256,
+        num_attention_heads: int = 8,
+    ) -> None:
+        super().__init__()
+
+        self.time_proj = Timesteps(num_channels=time_embed_dim, flip_sin_to_cos=True, downscale_freq_shift=0.0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=time_embed_dim, time_embed_dim=embedding_dim)
+        self.pooler = MochiAttentionPool(
+            num_attention_heads=num_attention_heads, embed_dim=text_embed_dim, output_dim=embedding_dim
+        )
+        self.caption_proj = nn.Linear(text_embed_dim, pooled_projection_dim)
+
+    def forward(
+        self,
+        timestep: torch.LongTensor,
+        encoder_hidden_states: torch.Tensor,
+        encoder_attention_mask: torch.Tensor,
+        hidden_dtype: Optional[torch.dtype] = None,
+    ):
+        time_proj = self.time_proj(timestep)
+        time_emb = self.timestep_embedder(time_proj.to(dtype=hidden_dtype))
+
+        pooled_projections = self.pooler(encoder_hidden_states, encoder_attention_mask)
+        caption_proj = self.caption_proj(encoder_hidden_states)
+
+        conditioning = time_emb + pooled_projections
+        return conditioning, caption_proj
+
+
 class TextTimeEmbedding(nn.Module):
     def __init__(self, encoder_dim: int, time_embed_dim: int, num_heads: int = 64):
         super().__init__()
@@ -1484,6 +1519,88 @@ class AttentionPooling(nn.Module):
         return a[:, 0, :]  # cls_token
 
 
+class MochiAttentionPool(nn.Module):
+    def __init__(
+        self,
+        num_attention_heads: int,
+        embed_dim: int,
+        output_dim: Optional[int] = None,
+    ) -> None:
+        super().__init__()
+
+        self.output_dim = output_dim or embed_dim
+        self.num_attention_heads = num_attention_heads
+
+        self.to_kv = nn.Linear(embed_dim, 2 * embed_dim)
+        self.to_q = nn.Linear(embed_dim, embed_dim)
+        self.to_out = nn.Linear(embed_dim, self.output_dim)
+
+    @staticmethod
+    def pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -> torch.Tensor:
+        """
+        Pool tokens in x using mask.
+
+        NOTE: We assume x does not require gradients.
+
+        Args:
+            x: (B, L, D) tensor of tokens.
+            mask: (B, L) boolean tensor indicating which tokens are not padding.
+
+        Returns:
+            pooled: (B, D) tensor of pooled tokens.
+        """
+        assert x.size(1) == mask.size(1)  # Expected mask to have same length as tokens.
+        assert x.size(0) == mask.size(0)  # Expected mask to have same batch size as tokens.
+        mask = mask[:, :, None].to(dtype=x.dtype)
+        mask = mask / mask.sum(dim=1, keepdim=True).clamp(min=1)
+        pooled = (x * mask).sum(dim=1, keepdim=keepdim)
+        return pooled
+
+    def forward(self, x: torch.Tensor, mask: torch.BoolTensor) -> torch.Tensor:
+        r"""
+        Args:
+            x (`torch.Tensor`):
+                Tensor of shape `(B, S, D)` of input tokens.
+            mask (`torch.Tensor`):
+                Boolean ensor of shape `(B, S)` indicating which tokens are not padding.
+
+        Returns:
+            `torch.Tensor`:
+                `(B, D)` tensor of pooled tokens.
+        """
+        D = x.size(2)
+
+        # Construct attention mask, shape: (B, 1, num_queries=1, num_keys=1+L).
+        attn_mask = mask[:, None, None, :].bool()  # (B, 1, 1, L).
+        attn_mask = F.pad(attn_mask, (1, 0), value=True)  # (B, 1, 1, 1+L).
+
+        # Average non-padding token features. These will be used as the query.
+        x_pool = self.pool_tokens(x, mask, keepdim=True)  # (B, 1, D)
+
+        # Concat pooled features to input sequence.
+        x = torch.cat([x_pool, x], dim=1)  # (B, L+1, D)
+
+        # Compute queries, keys, values. Only the mean token is used to create a query.
+        kv = self.to_kv(x)  # (B, L+1, 2 * D)
+        q = self.to_q(x[:, 0])  # (B, D)
+
+        # Extract heads.
+        head_dim = D // self.num_attention_heads
+        kv = kv.unflatten(2, (2, self.num_attention_heads, head_dim))  # (B, 1+L, 2, H, head_dim)
+        kv = kv.transpose(1, 3)  # (B, H, 2, 1+L, head_dim)
+        k, v = kv.unbind(2)  # (B, H, 1+L, head_dim)
+        q = q.unflatten(1, (self.num_attention_heads, head_dim))  # (B, H, head_dim)
+        q = q.unsqueeze(2)  # (B, H, 1, head_dim)
+
+        # Compute attention.
+        x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=0.0)  # (B, H, 1, head_dim)
+
+        # Concatenate heads and run output.
+        x = x.squeeze(2).flatten(1, 2)  # (B, D = H * head_dim)
+        x = self.to_out(x)
+        return x
+
+
 def get_fourier_embeds_from_boundingbox(embed_dim, box):
     """
     Args:

src/diffusers/models/normalization.py

@@ -234,6 +234,33 @@ class LuminaRMSNormZero(nn.Module):
         return x, gate_msa, scale_mlp, gate_mlp
 
 
+class MochiRMSNormZero(nn.Module):
+    r"""
+    Adaptive RMS Norm used in Mochi.
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+    """
+
+    def __init__(
+        self, embedding_dim: int, hidden_dim: int, eps: float = 1e-5, elementwise_affine: bool = False
+    ) -> None:
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, hidden_dim)
+        self.norm = RMSNorm(embedding_dim, eps=eps, elementwise_affine=elementwise_affine)
+
+    def forward(
+        self, hidden_states: torch.Tensor, emb: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        emb = self.linear(self.silu(emb))
+        scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1)
+        hidden_states = self.norm(hidden_states) * (1 + scale_msa[:, None])
+
+        return hidden_states, gate_msa, scale_mlp, gate_mlp
+
+
 class AdaLayerNormSingle(nn.Module):
     r"""
     Norm layer adaptive layer norm single (adaLN-single).
@@ -356,20 +383,21 @@ class LuminaLayerNormContinuous(nn.Module):
         out_dim: Optional[int] = None,
     ):
         super().__init__()
+
         # AdaLN
         self.silu = nn.SiLU()
         self.linear_1 = nn.Linear(conditioning_embedding_dim, embedding_dim, bias=bias)
+
         if norm_type == "layer_norm":
             self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias)
+        elif norm_type == "rms_norm":
+            self.norm = RMSNorm(embedding_dim, eps=eps, elementwise_affine=elementwise_affine)
         else:
             raise ValueError(f"unknown norm_type {norm_type}")
-        # linear_2
+
+        self.linear_2 = None
         if out_dim is not None:
-            self.linear_2 = nn.Linear(
-                embedding_dim,
-                out_dim,
-                bias=bias,
-            )
+            self.linear_2 = nn.Linear(embedding_dim, out_dim, bias=bias)
 
     def forward(
         self,
@@ -526,3 +554,14 @@ class GlobalResponseNorm(nn.Module):
         gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
         nx = gx / (gx.mean(dim=-1, keepdim=True) + 1e-6)
         return self.gamma * (x * nx) + self.beta + x
+
+
+class LpNorm(nn.Module):
+    def __init__(self, p: int = 2, dim: int = -1):
+        super().__init__()
+
+        self.p = p
+        self.dim = dim
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return F.normalize(hidden_states, p=self.p, dim=self.dim)

src/diffusers/models/transformers/__init__.py

@@ -17,5 +17,6 @@ if is_torch_available():
     from .transformer_allegro import AllegroTransformer3DModel
     from .transformer_cogview3plus import CogView3PlusTransformer2DModel
     from .transformer_flux import FluxTransformer2DModel
+    from .transformer_mochi import MochiTransformer3DModel
     from .transformer_sd3 import SD3Transformer2DModel
     from .transformer_temporal import TransformerTemporalModel

src/diffusers/models/transformers/transformer_mochi.py

@@ -0,0 +1,387 @@
+# Copyright 2024 The Genmo 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, Optional, Tuple
+
+import torch
+import torch.nn as nn
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...utils import is_torch_version, logging
+from ...utils.torch_utils import maybe_allow_in_graph
+from ..attention import FeedForward
+from ..attention_processor import Attention, MochiAttnProcessor2_0
+from ..embeddings import MochiCombinedTimestepCaptionEmbedding, PatchEmbed
+from ..modeling_outputs import Transformer2DModelOutput
+from ..modeling_utils import ModelMixin
+from ..normalization import AdaLayerNormContinuous, LuminaLayerNormContinuous, MochiRMSNormZero, RMSNorm
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@maybe_allow_in_graph
+class MochiTransformerBlock(nn.Module):
+    r"""
+    Transformer block used in [Mochi](https://huggingface.co/genmo/mochi-1-preview).
+
+    Args:
+        dim (`int`):
+            The number of channels in the input and output.
+        num_attention_heads (`int`):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`):
+            The number of channels in each head.
+        qk_norm (`str`, defaults to `"rms_norm"`):
+            The normalization layer to use.
+        activation_fn (`str`, defaults to `"swiglu"`):
+            Activation function to use in feed-forward.
+        context_pre_only (`bool`, defaults to `False`):
+            Whether or not to process context-related conditions with additional layers.
+        eps (`float`, defaults to `1e-6`):
+            Epsilon value for normalization layers.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        pooled_projection_dim: int,
+        qk_norm: str = "rms_norm",
+        activation_fn: str = "swiglu",
+        context_pre_only: bool = False,
+        eps: float = 1e-6,
+    ) -> None:
+        super().__init__()
+
+        self.context_pre_only = context_pre_only
+        self.ff_inner_dim = (4 * dim * 2) // 3
+        self.ff_context_inner_dim = (4 * pooled_projection_dim * 2) // 3
+
+        self.norm1 = MochiRMSNormZero(dim, 4 * dim, eps=eps, elementwise_affine=False)
+
+        if not context_pre_only:
+            self.norm1_context = MochiRMSNormZero(dim, 4 * pooled_projection_dim, eps=eps, elementwise_affine=False)
+        else:
+            self.norm1_context = LuminaLayerNormContinuous(
+                embedding_dim=pooled_projection_dim,
+                conditioning_embedding_dim=dim,
+                eps=eps,
+                elementwise_affine=False,
+                norm_type="rms_norm",
+                out_dim=None,
+            )
+
+        self.attn1 = Attention(
+            query_dim=dim,
+            cross_attention_dim=None,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            bias=False,
+            qk_norm=qk_norm,
+            added_kv_proj_dim=pooled_projection_dim,
+            added_proj_bias=False,
+            out_dim=dim,
+            out_context_dim=pooled_projection_dim,
+            context_pre_only=context_pre_only,
+            processor=MochiAttnProcessor2_0(),
+            eps=eps,
+            elementwise_affine=True,
+        )
+
+        # TODO(aryan): norm_context layers are not needed when `context_pre_only` is True
+        self.norm2 = RMSNorm(dim, eps=eps, elementwise_affine=False)
+        self.norm2_context = RMSNorm(pooled_projection_dim, eps=eps, elementwise_affine=False)
+
+        self.norm3 = RMSNorm(dim, eps=eps, elementwise_affine=False)
+        self.norm3_context = RMSNorm(pooled_projection_dim, eps=eps, elementwise_affine=False)
+
+        self.ff = FeedForward(dim, inner_dim=self.ff_inner_dim, activation_fn=activation_fn, bias=False)
+        self.ff_context = None
+        if not context_pre_only:
+            self.ff_context = FeedForward(
+                pooled_projection_dim,
+                inner_dim=self.ff_context_inner_dim,
+                activation_fn=activation_fn,
+                bias=False,
+            )
+
+        self.norm4 = RMSNorm(dim, eps=eps, elementwise_affine=False)
+        self.norm4_context = RMSNorm(pooled_projection_dim, eps=eps, elementwise_affine=False)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb)
+
+        if not self.context_pre_only:
+            norm_encoder_hidden_states, enc_gate_msa, enc_scale_mlp, enc_gate_mlp = self.norm1_context(
+                encoder_hidden_states, temb
+            )
+        else:
+            norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb)
+
+        attn_hidden_states, context_attn_hidden_states = self.attn1(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+        )
+
+        hidden_states = hidden_states + self.norm2(attn_hidden_states) * torch.tanh(gate_msa).unsqueeze(1)
+        norm_hidden_states = self.norm3(hidden_states) * (1 + scale_mlp.unsqueeze(1))
+        ff_output = self.ff(norm_hidden_states)
+        hidden_states = hidden_states + self.norm4(ff_output) * torch.tanh(gate_mlp).unsqueeze(1)
+
+        if not self.context_pre_only:
+            encoder_hidden_states = encoder_hidden_states + self.norm2_context(
+                context_attn_hidden_states
+            ) * torch.tanh(enc_gate_msa).unsqueeze(1)
+            norm_encoder_hidden_states = self.norm3_context(encoder_hidden_states) * (1 + enc_scale_mlp.unsqueeze(1))
+            context_ff_output = self.ff_context(norm_encoder_hidden_states)
+            encoder_hidden_states = encoder_hidden_states + self.norm4_context(context_ff_output) * torch.tanh(
+                enc_gate_mlp
+            ).unsqueeze(1)
+
+        return hidden_states, encoder_hidden_states
+
+
+class MochiRoPE(nn.Module):
+    r"""
+    RoPE implementation used in [Mochi](https://huggingface.co/genmo/mochi-1-preview).
+
+    Args:
+        base_height (`int`, defaults to `192`):
+            Base height used to compute interpolation scale for rotary positional embeddings.
+        base_width (`int`, defaults to `192`):
+            Base width used to compute interpolation scale for rotary positional embeddings.
+    """
+
+    def __init__(self, base_height: int = 192, base_width: int = 192) -> None:
+        super().__init__()
+
+        self.target_area = base_height * base_width
+
+    def _centers(self, start, stop, num, device, dtype) -> torch.Tensor:
+        edges = torch.linspace(start, stop, num + 1, device=device, dtype=dtype)
+        return (edges[:-1] + edges[1:]) / 2
+
+    def _get_positions(
+        self,
+        num_frames: int,
+        height: int,
+        width: int,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ) -> torch.Tensor:
+        scale = (self.target_area / (height * width)) ** 0.5
+
+        t = torch.arange(num_frames, device=device, dtype=dtype)
+        h = self._centers(-height * scale / 2, height * scale / 2, height, device, dtype)
+        w = self._centers(-width * scale / 2, width * scale / 2, width, device, dtype)
+
+        grid_t, grid_h, grid_w = torch.meshgrid(t, h, w, indexing="ij")
+
+        positions = torch.stack([grid_t, grid_h, grid_w], dim=-1).view(-1, 3)
+        return positions
+
+    def _create_rope(self, freqs: torch.Tensor, pos: torch.Tensor) -> torch.Tensor:
+        freqs = torch.einsum("nd,dhf->nhf", pos, freqs.float())
+        freqs_cos = torch.cos(freqs)
+        freqs_sin = torch.sin(freqs)
+        return freqs_cos, freqs_sin
+
+    def forward(
+        self,
+        pos_frequencies: torch.Tensor,
+        num_frames: int,
+        height: int,
+        width: int,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        pos = self._get_positions(num_frames, height, width, device, dtype)
+        rope_cos, rope_sin = self._create_rope(pos_frequencies, pos)
+        return rope_cos, rope_sin
+
+
+@maybe_allow_in_graph
+class MochiTransformer3DModel(ModelMixin, ConfigMixin):
+    r"""
+    A Transformer model for video-like data introduced in [Mochi](https://huggingface.co/genmo/mochi-1-preview).
+
+    Args:
+        patch_size (`int`, defaults to `2`):
+            The size of the patches to use in the patch embedding layer.
+        num_attention_heads (`int`, defaults to `24`):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, defaults to `128`):
+            The number of channels in each head.
+        num_layers (`int`, defaults to `48`):
+            The number of layers of Transformer blocks to use.
+        in_channels (`int`, defaults to `12`):
+            The number of channels in the input.
+        out_channels (`int`, *optional*, defaults to `None`):
+            The number of channels in the output.
+        qk_norm (`str`, defaults to `"rms_norm"`):
+            The normalization layer to use.
+        text_embed_dim (`int`, defaults to `4096`):
+            Input dimension of text embeddings from the text encoder.
+        time_embed_dim (`int`, defaults to `256`):
+            Output dimension of timestep embeddings.
+        activation_fn (`str`, defaults to `"swiglu"`):
+            Activation function to use in feed-forward.
+        max_sequence_length (`int`, defaults to `256`):
+            The maximum sequence length of text embeddings supported.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: int = 2,
+        num_attention_heads: int = 24,
+        attention_head_dim: int = 128,
+        num_layers: int = 48,
+        pooled_projection_dim: int = 1536,
+        in_channels: int = 12,
+        out_channels: Optional[int] = None,
+        qk_norm: str = "rms_norm",
+        text_embed_dim: int = 4096,
+        time_embed_dim: int = 256,
+        activation_fn: str = "swiglu",
+        max_sequence_length: int = 256,
+    ) -> None:
+        super().__init__()
+
+        inner_dim = num_attention_heads * attention_head_dim
+        out_channels = out_channels or in_channels
+
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_channels=in_channels,
+            embed_dim=inner_dim,
+            pos_embed_type=None,
+        )
+
+        self.time_embed = MochiCombinedTimestepCaptionEmbedding(
+            embedding_dim=inner_dim,
+            pooled_projection_dim=pooled_projection_dim,
+            text_embed_dim=text_embed_dim,
+            time_embed_dim=time_embed_dim,
+            num_attention_heads=8,
+        )
+
+        self.pos_frequencies = nn.Parameter(torch.full((3, num_attention_heads, attention_head_dim // 2), 0.0))
+        self.rope = MochiRoPE()
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                MochiTransformerBlock(
+                    dim=inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    pooled_projection_dim=pooled_projection_dim,
+                    qk_norm=qk_norm,
+                    activation_fn=activation_fn,
+                    context_pre_only=i == num_layers - 1,
+                )
+                for i in range(num_layers)
+            ]
+        )
+
+        self.norm_out = AdaLayerNormContinuous(
+            inner_dim, inner_dim, elementwise_affine=False, eps=1e-6, norm_type="layer_norm"
+        )
+        self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)
+
+        self.gradient_checkpointing = False
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: torch.LongTensor,
+        encoder_attention_mask: torch.Tensor,
+        return_dict: bool = True,
+    ) -> torch.Tensor:
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p = self.config.patch_size
+
+        post_patch_height = height // p
+        post_patch_width = width // p
+
+        temb, encoder_hidden_states = self.time_embed(
+            timestep, encoder_hidden_states, encoder_attention_mask, hidden_dtype=hidden_states.dtype
+        )
+
+        hidden_states = hidden_states.permute(0, 2, 1, 3, 4).flatten(0, 1)
+        hidden_states = self.patch_embed(hidden_states)
+        hidden_states = hidden_states.unflatten(0, (batch_size, -1)).flatten(1, 2)
+
+        image_rotary_emb = self.rope(
+            self.pos_frequencies,
+            num_frames,
+            post_patch_height,
+            post_patch_width,
+            device=hidden_states.device,
+            dtype=torch.float32,
+        )
+
+        for i, block in enumerate(self.transformer_blocks):
+            if self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+            else:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+        hidden_states = self.norm_out(hidden_states, temb)
+        hidden_states = self.proj_out(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch_size, num_frames, post_patch_height, post_patch_width, p, p, -1)
+        hidden_states = hidden_states.permute(0, 6, 1, 2, 4, 3, 5)
+        output = hidden_states.reshape(batch_size, -1, num_frames, height, width)
+
+        if not return_dict:
+            return (output,)
+        return Transformer2DModelOutput(sample=output)

src/diffusers/pipelines/__init__.py

@@ -247,6 +247,7 @@ else:
             "MarigoldNormalsPipeline",
         ]
     )
+    _import_structure["mochi"] = ["MochiPipeline"]
     _import_structure["musicldm"] = ["MusicLDMPipeline"]
     _import_structure["paint_by_example"] = ["PaintByExamplePipeline"]
     _import_structure["pia"] = ["PIAPipeline"]
@@ -571,6 +572,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             MarigoldDepthPipeline,
             MarigoldNormalsPipeline,
         )
+        from .mochi import MochiPipeline
         from .musicldm import MusicLDMPipeline
         from .pag import (
             AnimateDiffPAGPipeline,

src/diffusers/pipelines/mochi/__init__.py

@@ -0,0 +1,48 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_mochi"] = ["MochiPipeline"]
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+    else:
+        from .pipeline_mochi import MochiPipeline
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/mochi/pipeline_mochi.py

@@ -0,0 +1,724 @@
+# Copyright 2024 Black Forest Labs 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.
+
+import inspect
+from typing import Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from transformers import T5EncoderModel, T5TokenizerFast
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...models.autoencoders import AutoencoderKL
+from ...models.transformers import MochiTransformer3DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import (
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+)
+from ...utils.torch_utils import randn_tensor
+from ...video_processor import VideoProcessor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import MochiPipelineOutput
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import MochiPipeline
+        >>> from diffusers.utils import export_to_video
+
+        >>> pipe = MochiPipeline.from_pretrained("genmo/mochi-1-preview", torch_dtype=torch.bfloat16)
+        >>> pipe.enable_model_cpu_offload()
+        >>> pipe.enable_vae_tiling()
+        >>> prompt = "Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k."
+        >>> frames = pipe(prompt, num_inference_steps=28, guidance_scale=3.5).frames[0]
+        >>> export_to_video(frames, "mochi.mp4")
+        ```
+"""
+
+
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.16,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+# from: https://github.com/genmoai/models/blob/075b6e36db58f1242921deff83a1066887b9c9e1/src/mochi_preview/infer.py#L77
+def linear_quadratic_schedule(num_steps, threshold_noise, linear_steps=None):
+    if linear_steps is None:
+        linear_steps = num_steps // 2
+    linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)]
+    threshold_noise_step_diff = linear_steps - threshold_noise * num_steps
+    quadratic_steps = num_steps - linear_steps
+    quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps**2)
+    linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps**2)
+    const = quadratic_coef * (linear_steps**2)
+    quadratic_sigma_schedule = [
+        quadratic_coef * (i**2) + linear_coef * i + const for i in range(linear_steps, num_steps)
+    ]
+    sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule
+    sigma_schedule = [1.0 - x for x in sigma_schedule]
+    return sigma_schedule
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class MochiPipeline(DiffusionPipeline):
+    r"""
+    The mochi pipeline for text-to-video generation.
+
+    Reference: https://github.com/genmoai/models
+
+    Args:
+        transformer ([`MochiTransformer3DModel`]):
+            Conditional Transformer architecture to denoise the encoded video latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`T5EncoderModel`]):
+            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
+            the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
+        tokenizer (`T5TokenizerFast`):
+            Second Tokenizer of class
+            [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
+    """
+
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    _optional_components = []
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: T5EncoderModel,
+        tokenizer: T5TokenizerFast,
+        transformer: MochiTransformer3DModel,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+        # TODO: determine these scaling factors from model parameters
+        self.vae_spatial_scale_factor = 8
+        self.vae_temporal_scale_factor = 6
+        self.patch_size = 2
+
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_scale_factor)
+        self.tokenizer_max_length = (
+            self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
+        )
+        self.default_height = 480
+        self.default_width = 848
+
+    # Adapted from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_videos_per_prompt: int = 1,
+        max_sequence_length: int = 256,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        prompt_attention_mask = text_inputs.attention_mask
+        prompt_attention_mask = prompt_attention_mask.bool().to(device)
+
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because `max_sequence_length` is set to "
+                f" {max_sequence_length} tokens: {removed_text}"
+            )
+
+        prompt_embeds = self.text_encoder(text_input_ids.to(device))[0]
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+        prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
+        prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1)
+
+        return prompt_embeds, prompt_attention_mask
+
+    # Adapted from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        do_classifier_free_guidance: bool = True,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 256,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            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
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds(
+                prompt=prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds, negative_prompt_attention_mask = self._get_t5_prompt_embeds(
+                prompt=negative_prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        callback_on_step_end_tensor_inputs=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_attention_mask=None,
+        negative_prompt_attention_mask=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if prompt_embeds is not None and prompt_attention_mask is None:
+            raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
+
+        if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
+            raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.")
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+            if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
+                raise ValueError(
+                    "`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but"
+                    f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
+                    f" {negative_prompt_attention_mask.shape}."
+                )
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.vae.enable_tiling()
+
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        num_frames,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        height = height // self.vae_spatial_scale_factor
+        width = width // self.vae_spatial_scale_factor
+        num_frames = (num_frames - 1) // self.vae_temporal_scale_factor + 1
+
+        shape = (batch_size, num_channels_latents, num_frames, height, width)
+
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        return latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1.0
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_frames: int = 19,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 4.5,
+        num_videos_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 256,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            height (`int`, *optional*, defaults to `self.default_height`):
+                The height in pixels of the generated image. This is set to 480 by default for the best results.
+            width (`int`, *optional*, defaults to `self.default_width`):
+                The width in pixels of the generated image. This is set to 848 by default for the best results.
+            num_frames (`int`, defaults to `19`):
+                The number of video frames to generate
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, defaults to `4.5`):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            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*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            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.
+            prompt_attention_mask (`torch.Tensor`, *optional*):
+                Pre-generated attention mask for text embeddings.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not
+                provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
+            negative_prompt_attention_mask (`torch.FloatTensor`, *optional*):
+                Pre-generated attention mask for negative text embeddings.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.mochi.MochiPipelineOutput`] instead of a plain tuple.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to `256`):
+                Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.mochi.MochiPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.mochi.MochiPipelineOutput`] is returned, otherwise a `tuple`
+                is returned where the first element is a list with the generated images.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        height = height or self.default_height
+        width = width or self.default_width
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt=prompt,
+            height=height,
+            width=width,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            negative_prompt_attention_mask=negative_prompt_attention_mask,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # 3. Prepare text embeddings
+        (
+            prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_embeds,
+            negative_prompt_attention_mask,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            negative_prompt_attention_mask=negative_prompt_attention_mask,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            num_frames,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timestep
+        # from https://github.com/genmoai/models/blob/075b6e36db58f1242921deff83a1066887b9c9e1/src/mochi_preview/infer.py#L77
+        threshold_noise = 0.025
+        sigmas = linear_quadratic_schedule(num_inference_steps, threshold_noise)
+        sigmas = np.array(sigmas)
+
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0]).to(latents.dtype)
+
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep=timestep,
+                    encoder_attention_mask=prompt_attention_mask,
+                    return_dict=False,
+                )[0]
+
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if output_type == "latent":
+            video = latents
+        else:
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
+            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean).view(1, 12, 1, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std).view(1, 12, 1, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
+            else:
+                latents = latents / self.vae.config.scaling_factor
+
+            video = self.vae.decode(latents, return_dict=False)[0]
+            video = self.video_processor.postprocess_video(video, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return MochiPipelineOutput(frames=video)

src/diffusers/pipelines/mochi/pipeline_output.py

@@ -0,0 +1,20 @@
+from dataclasses import dataclass
+
+import torch
+
+from diffusers.utils import BaseOutput
+
+
+@dataclass
+class MochiPipelineOutput(BaseOutput):
+    r"""
+    Output class for CogVideo 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)`.
+    """
+
+    frames: torch.Tensor

src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py

@@ -71,6 +71,7 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
         max_shift: Optional[float] = 1.15,
         base_image_seq_len: Optional[int] = 256,
         max_image_seq_len: Optional[int] = 4096,
+        invert_sigmas: bool = False,
     ):
         timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
         timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
@@ -204,9 +205,15 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
         sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
         timesteps = sigmas * self.config.num_train_timesteps
 
-        self.timesteps = timesteps.to(device=device)
-        self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
+        if self.config.invert_sigmas:
+            sigmas = 1.0 - sigmas
+            timesteps = sigmas * self.config.num_train_timesteps
+            sigmas = torch.cat([sigmas, torch.ones(1, device=sigmas.device)])
+        else:
+            sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
 
+        self.timesteps = timesteps.to(device=device)
+        self.sigmas = sigmas
         self._step_index = None
         self._begin_index = None
 

src/diffusers/utils/dummy_pt_objects.py

@@ -92,6 +92,21 @@ class AutoencoderKLCogVideoX(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class AutoencoderKLMochi(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 AutoencoderKLTemporalDecoder(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -377,6 +392,21 @@ class LuminaNextDiT2DModel(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class MochiTransformer3DModel(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 ModelMixin(metaclass=DummyObject):
     _backends = ["torch"]
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -1052,6 +1052,21 @@ class MarigoldNormalsPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class MochiPipeline(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 MusicLDMPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/models/transformers/test_models_transformer_mochi.py

@@ -0,0 +1,80 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc.
+#
+# 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 unittest
+
+import torch
+
+from diffusers import MochiTransformer3DModel
+from diffusers.utils.testing_utils import enable_full_determinism, torch_device
+
+from ..test_modeling_common import ModelTesterMixin
+
+
+enable_full_determinism()
+
+
+class MochiTransformerTests(ModelTesterMixin, unittest.TestCase):
+    model_class = MochiTransformer3DModel
+    main_input_name = "hidden_states"
+    uses_custom_attn_processor = True
+
+    @property
+    def dummy_input(self):
+        batch_size = 2
+        num_channels = 4
+        num_frames = 2
+        height = 16
+        width = 16
+        embedding_dim = 16
+        sequence_length = 16
+
+        hidden_states = torch.randn((batch_size, num_channels, num_frames, height, width)).to(torch_device)
+        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
+        encoder_attention_mask = torch.ones((batch_size, sequence_length)).bool().to(torch_device)
+        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
+
+        return {
+            "hidden_states": hidden_states,
+            "encoder_hidden_states": encoder_hidden_states,
+            "timestep": timestep,
+            "encoder_attention_mask": encoder_attention_mask,
+        }
+
+    @property
+    def input_shape(self):
+        return (4, 2, 16, 16)
+
+    @property
+    def output_shape(self):
+        return (4, 2, 16, 16)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "patch_size": 2,
+            "num_attention_heads": 2,
+            "attention_head_dim": 8,
+            "num_layers": 2,
+            "pooled_projection_dim": 16,
+            "in_channels": 4,
+            "out_channels": None,
+            "qk_norm": "rms_norm",
+            "text_embed_dim": 16,
+            "time_embed_dim": 4,
+            "activation_fn": "swiglu",
+            "max_sequence_length": 16,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict

tests/pipelines/mochi/test_mochi.py

@@ -0,0 +1,299 @@
+# Copyright 2024 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 gc
+import inspect
+import unittest
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer, T5EncoderModel
+
+from diffusers import AutoencoderKLMochi, FlowMatchEulerDiscreteScheduler, MochiPipeline, MochiTransformer3DModel
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    numpy_cosine_similarity_distance,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
+
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineTesterMixin, to_np
+
+
+enable_full_determinism()
+
+
+class MochiPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = MochiPipeline
+    params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
+    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "generator",
+            "latents",
+            "return_dict",
+            "callback_on_step_end",
+            "callback_on_step_end_tensor_inputs",
+        ]
+    )
+    test_xformers_attention = False
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        transformer = MochiTransformer3DModel(
+            patch_size=2,
+            num_attention_heads=2,
+            attention_head_dim=8,
+            num_layers=2,
+            pooled_projection_dim=16,
+            in_channels=12,
+            out_channels=None,
+            qk_norm="rms_norm",
+            text_embed_dim=32,
+            time_embed_dim=4,
+            activation_fn="swiglu",
+            max_sequence_length=16,
+        )
+        transformer.pos_frequencies.data = transformer.pos_frequencies.new_full(transformer.pos_frequencies.shape, 0)
+
+        torch.manual_seed(0)
+        vae = AutoencoderKLMochi(
+            latent_channels=12,
+            out_channels=3,
+            encoder_block_out_channels=(32, 32, 32, 32),
+            decoder_block_out_channels=(32, 32, 32, 32),
+            layers_per_block=(1, 1, 1, 1, 1),
+        )
+
+        torch.manual_seed(0)
+        scheduler = FlowMatchEulerDiscreteScheduler()
+        text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+        components = {
+            "transformer": transformer,
+            "vae": vae,
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+        }
+        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)
+        inputs = {
+            "prompt": "dance monkey",
+            "negative_prompt": "",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 4.5,
+            "height": 16,
+            "width": 16,
+            # 6 * k + 1 is the recommendation
+            "num_frames": 7,
+            "max_sequence_length": 16,
+            "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, (7, 3, 16, 16))
+        expected_video = torch.randn(7, 3, 16, 16)
+        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_inference_batch_single_identical(self):
+        self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3)
+
+    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):
+        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",
+        )
+
+
+@slow
+@require_torch_gpu
+class MochiPipelineIntegrationTests(unittest.TestCase):
+    prompt = "A painting of a squirrel eating a burger."
+
+    def setUp(self):
+        super().setUp()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_cogvideox(self):
+        generator = torch.Generator("cpu").manual_seed(0)
+
+        pipe = MochiPipeline.from_pretrained("genmo/mochi-1-preview", torch_dtype=torch.float16)
+        pipe.enable_model_cpu_offload()
+        prompt = self.prompt
+
+        videos = pipe(
+            prompt=prompt,
+            height=480,
+            width=848,
+            num_frames=19,
+            generator=generator,
+            num_inference_steps=2,
+            output_type="pt",
+        ).frames
+
+        video = videos[0]
+        expected_video = torch.randn(1, 16, 480, 848, 3).numpy()
+
+        max_diff = numpy_cosine_similarity_distance(video, expected_video)
+        assert max_diff < 1e-3, f"Max diff is too high. got {video}"