update

src/diffusers/loaders/single_file_model.py

@@ -32,6 +32,7 @@ from .single_file_utils import (
     convert_ldm_vae_checkpoint,
     convert_ltx_transformer_checkpoint_to_diffusers,
     convert_ltx_vae_checkpoint_to_diffusers,
+    convert_mochi_transformer_checkpoint_to_diffusers,
     convert_sd3_transformer_checkpoint_to_diffusers,
     convert_stable_cascade_unet_single_file_to_diffusers,
     create_controlnet_diffusers_config_from_ldm,
@@ -96,6 +97,10 @@ SINGLE_FILE_LOADABLE_CLASSES = {
         "default_subfolder": "vae",
     },
     "AutoencoderDC": {"checkpoint_mapping_fn": convert_autoencoder_dc_checkpoint_to_diffusers},
+    "MochiTransformer3DModel": {
+        "checkpoint_mapping_fn": convert_mochi_transformer_checkpoint_to_diffusers,
+        "default_subfolder": "transformer",
+    },
 }
 
 

src/diffusers/loaders/single_file_utils.py

@@ -106,6 +106,7 @@ CHECKPOINT_KEY_NAMES = {
     ],
     "autoencoder-dc": "decoder.stages.1.op_list.0.main.conv.conv.bias",
     "autoencoder-dc-sana": "encoder.project_in.conv.bias",
+    "mochi-1-preview": ["model.diffusion_model.blocks.0.attn.qkv_x.weight", "blocks.0.attn.qkv_x.weight"],
 }
 
 DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
@@ -157,6 +158,7 @@ DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
     "autoencoder-dc-f64c128": {"pretrained_model_name_or_path": "mit-han-lab/dc-ae-f64c128-mix-1.0-diffusers"},
     "autoencoder-dc-f32c32": {"pretrained_model_name_or_path": "mit-han-lab/dc-ae-f32c32-mix-1.0-diffusers"},
     "autoencoder-dc-f32c32-sana": {"pretrained_model_name_or_path": "mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers"},
+    "mochi-1-preview": {"pretrained_model_name_or_path": "genmo/mochi-1-preview"},
 }
 
 # Use to configure model sample size when original config is provided
@@ -610,6 +612,9 @@ def infer_diffusers_model_type(checkpoint):
         else:
             model_type = "autoencoder-dc-f128c512"
 
+    elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["mochi-1-preview"]):
+        model_type = "mochi-1-preview"
+
     else:
         model_type = "v1"
 
@@ -1750,6 +1755,12 @@ def swap_scale_shift(weight, dim):
     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 get_attn2_layers(state_dict):
     attn2_layers = []
     for key in state_dict.keys():
@@ -2406,3 +2417,101 @@ def convert_autoencoder_dc_checkpoint_to_diffusers(checkpoint, **kwargs):
             handler_fn_inplace(key, converted_state_dict)
 
     return converted_state_dict
+
+
+def convert_mochi_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):
+    new_state_dict = {}
+
+    # Comfy checkpoints add this prefix
+    keys = list(checkpoint.keys())
+    for k in keys:
+        if "model.diffusion_model." in k:
+            checkpoint[k.replace("model.diffusion_model.", "")] = checkpoint.pop(k)
+
+    # Convert patch_embed
+    new_state_dict["patch_embed.proj.weight"] = checkpoint.pop("x_embedder.proj.weight")
+    new_state_dict["patch_embed.proj.bias"] = checkpoint.pop("x_embedder.proj.bias")
+
+    # Convert time_embed
+    new_state_dict["time_embed.timestep_embedder.linear_1.weight"] = checkpoint.pop("t_embedder.mlp.0.weight")
+    new_state_dict["time_embed.timestep_embedder.linear_1.bias"] = checkpoint.pop("t_embedder.mlp.0.bias")
+    new_state_dict["time_embed.timestep_embedder.linear_2.weight"] = checkpoint.pop("t_embedder.mlp.2.weight")
+    new_state_dict["time_embed.timestep_embedder.linear_2.bias"] = checkpoint.pop("t_embedder.mlp.2.bias")
+    new_state_dict["time_embed.pooler.to_kv.weight"] = checkpoint.pop("t5_y_embedder.to_kv.weight")
+    new_state_dict["time_embed.pooler.to_kv.bias"] = checkpoint.pop("t5_y_embedder.to_kv.bias")
+    new_state_dict["time_embed.pooler.to_q.weight"] = checkpoint.pop("t5_y_embedder.to_q.weight")
+    new_state_dict["time_embed.pooler.to_q.bias"] = checkpoint.pop("t5_y_embedder.to_q.bias")
+    new_state_dict["time_embed.pooler.to_out.weight"] = checkpoint.pop("t5_y_embedder.to_out.weight")
+    new_state_dict["time_embed.pooler.to_out.bias"] = checkpoint.pop("t5_y_embedder.to_out.bias")
+    new_state_dict["time_embed.caption_proj.weight"] = checkpoint.pop("t5_yproj.weight")
+    new_state_dict["time_embed.caption_proj.bias"] = checkpoint.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"] = checkpoint.pop(old_prefix + "mod_x.weight")
+        new_state_dict[block_prefix + "norm1.linear.bias"] = checkpoint.pop(old_prefix + "mod_x.bias")
+        if i < num_layers - 1:
+            new_state_dict[block_prefix + "norm1_context.linear.weight"] = checkpoint.pop(old_prefix + "mod_y.weight")
+            new_state_dict[block_prefix + "norm1_context.linear.bias"] = checkpoint.pop(old_prefix + "mod_y.bias")
+        else:
+            new_state_dict[block_prefix + "norm1_context.linear_1.weight"] = checkpoint.pop(
+                old_prefix + "mod_y.weight"
+            )
+            new_state_dict[block_prefix + "norm1_context.linear_1.bias"] = checkpoint.pop(old_prefix + "mod_y.bias")
+
+        # Visual attention
+        qkv_weight = checkpoint.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"] = checkpoint.pop(old_prefix + "attn.q_norm_x.weight")
+        new_state_dict[block_prefix + "attn1.norm_k.weight"] = checkpoint.pop(old_prefix + "attn.k_norm_x.weight")
+        new_state_dict[block_prefix + "attn1.to_out.0.weight"] = checkpoint.pop(old_prefix + "attn.proj_x.weight")
+        new_state_dict[block_prefix + "attn1.to_out.0.bias"] = checkpoint.pop(old_prefix + "attn.proj_x.bias")
+
+        # Context attention
+        qkv_weight = checkpoint.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"] = checkpoint.pop(
+            old_prefix + "attn.q_norm_y.weight"
+        )
+        new_state_dict[block_prefix + "attn1.norm_added_k.weight"] = checkpoint.pop(
+            old_prefix + "attn.k_norm_y.weight"
+        )
+        if i < num_layers - 1:
+            new_state_dict[block_prefix + "attn1.to_add_out.weight"] = checkpoint.pop(
+                old_prefix + "attn.proj_y.weight"
+            )
+            new_state_dict[block_prefix + "attn1.to_add_out.bias"] = checkpoint.pop(old_prefix + "attn.proj_y.bias")
+
+        # MLP
+        new_state_dict[block_prefix + "ff.net.0.proj.weight"] = swap_proj_gate(
+            checkpoint.pop(old_prefix + "mlp_x.w1.weight")
+        )
+        new_state_dict[block_prefix + "ff.net.2.weight"] = checkpoint.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(
+                checkpoint.pop(old_prefix + "mlp_y.w1.weight")
+            )
+            new_state_dict[block_prefix + "ff_context.net.2.weight"] = checkpoint.pop(old_prefix + "mlp_y.w2.weight")
+
+    # Output layers
+    new_state_dict["norm_out.linear.weight"] = swap_scale_shift(checkpoint.pop("final_layer.mod.weight"), dim=0)
+    new_state_dict["norm_out.linear.bias"] = swap_scale_shift(checkpoint.pop("final_layer.mod.bias"), dim=0)
+    new_state_dict["proj_out.weight"] = checkpoint.pop("final_layer.linear.weight")
+    new_state_dict["proj_out.bias"] = checkpoint.pop("final_layer.linear.bias")
+
+    new_state_dict["pos_frequencies"] = checkpoint.pop("pos_frequencies")
+
+    return new_state_dict

src/diffusers/models/transformers/transformer_mochi.py

@@ -20,6 +20,7 @@ import torch.nn as nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
+from ...loaders.single_file_model import FromOriginalModelMixin
 from ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention import FeedForward
@@ -304,7 +305,7 @@ class MochiRoPE(nn.Module):
 
 
 @maybe_allow_in_graph
-class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
     r"""
     A Transformer model for video-like data introduced in [Mochi](https://huggingface.co/genmo/mochi-1-preview).