docs

2026-01-27 17:22:53 +03:00 · 2024-10-25 10:27:28 +02:00
parent 2798ed4c7a
commit 5f43c6a41f
4 changed files with 134 additions and 2 deletions
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -302,6 +302,8 @@
        title: AutoencoderKL
      - 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
@@ -394,6 +396,8 @@
      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
--- a/docs/source/en/api/models/autoencoderkl_mochi.md
+++ b/docs/source/en/api/models/autoencoderkl_mochi.md
@@ -0,0 +1,33 @@
+<!-- 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
+    - encode
+    - all
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput
--- a/docs/source/en/api/pipelines/mochi.md
+++ b/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
--- a/src/diffusers/models/transformers/transformer_mochi.py
+++ b/src/diffusers/models/transformers/transformer_mochi.py
@@ -34,6 +34,26 @@ 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,
@@ -42,7 +62,7 @@ class MochiTransformerBlock(nn.Module):
        pooled_projection_dim: int,
        qk_norm: str = "rms_norm",
        activation_fn: str = "swiglu",
-        context_pre_only: bool = True,
+        context_pre_only: bool = False,
        eps: float = 1e-6,
    ) -> None:
        super().__init__()
@@ -82,6 +102,7 @@ class MochiTransformerBlock(nn.Module):
            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)

@@ -145,7 +166,17 @@ class MochiTransformerBlock(nn.Module):


 class MochiRoPE(nn.Module):
-    def __init__(self, base_height: int = 192, base_width: int = 192, theta: float = 10000.0) -> None:
+    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
@@ -195,6 +226,34 @@ class MochiRoPE(nn.Module):

@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