[Single File] Add single file loading for SANA Transformer

src/diffusers/loaders/single_file_model.py

@@ -37,6 +37,7 @@
     convert_ltx_vae_checkpoint_to_diffusers,
     convert_lumina2_to_diffusers,
     convert_mochi_transformer_checkpoint_to_diffusers,
+    convert_sana_transformer_to_diffusers,
     convert_sd3_transformer_checkpoint_to_diffusers,
     convert_stable_cascade_unet_single_file_to_diffusers,
     convert_wan_transformer_to_diffusers,
@@ -119,6 +120,10 @@
         "checkpoint_mapping_fn": convert_lumina2_to_diffusers,
         "default_subfolder": "transformer",
     },
+    "SanaTransformer2DModel": {
+        "checkpoint_mapping_fn": convert_sana_transformer_to_diffusers,
+        "default_subfolder": "transformer",
+    },
     "WanTransformer3DModel": {
         "checkpoint_mapping_fn": convert_wan_transformer_to_diffusers,
         "default_subfolder": "transformer",

src/diffusers/loaders/single_file_utils.py

@@ -117,6 +117,12 @@
     "hunyuan-video": "txt_in.individual_token_refiner.blocks.0.adaLN_modulation.1.bias",
     "instruct-pix2pix": "model.diffusion_model.input_blocks.0.0.weight",
     "lumina2": ["model.diffusion_model.cap_embedder.0.weight", "cap_embedder.0.weight"],
+    "sana": [
+        "blocks.0.cross_attn.q_linear.weight",
+        "blocks.0.cross_attn.q_linear.bias",
+        "blocks.0.cross_attn.kv_linear.weight",
+        "blocks.0.cross_attn.kv_linear.bias",
+    ],
     "wan": ["model.diffusion_model.head.modulation", "head.modulation"],
     "wan_vae": "decoder.middle.0.residual.0.gamma",
 }
@@ -178,6 +184,7 @@
     "hunyuan-video": {"pretrained_model_name_or_path": "hunyuanvideo-community/HunyuanVideo"},
     "instruct-pix2pix": {"pretrained_model_name_or_path": "timbrooks/instruct-pix2pix"},
     "lumina2": {"pretrained_model_name_or_path": "Alpha-VLLM/Lumina-Image-2.0"},
+    "sana": {"pretrained_model_name_or_path": "Efficient-Large-Model/Sana_1600M_1024px_diffusers"},
     "wan-t2v-1.3B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"},
     "wan-t2v-14B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-T2V-14B-Diffusers"},
     "wan-i2v-14B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"},
@@ -669,6 +676,9 @@ def infer_diffusers_model_type(checkpoint):
     elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["lumina2"]):
         model_type = "lumina2"
 
+    elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["sana"]):
+        model_type = "sana"
+
     elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["wan"]):
         if "model.diffusion_model.patch_embedding.weight" in checkpoint:
             target_key = "model.diffusion_model.patch_embedding.weight"
@@ -2897,6 +2907,111 @@ def convert_lumina_attn_to_diffusers(tensor, diffusers_key):
     return converted_state_dict
 
 
+def convert_sana_transformer_to_diffusers(checkpoint, **kwargs):
+    converted_state_dict = {}
+    keys = list(checkpoint.keys())
+    for k in keys:
+        if "model.diffusion_model." in k:
+            checkpoint[k.replace("model.diffusion_model.", "")] = checkpoint.pop(k)
+
+    num_layers = list(set(int(k.split(".", 2)[1]) for k in checkpoint if "blocks" in k))[-1] + 1  # noqa: C401
+
+    # Positional and patch embeddings.
+    checkpoint.pop("pos_embed")
+    converted_state_dict["patch_embed.proj.weight"] = checkpoint.pop("x_embedder.proj.weight")
+    converted_state_dict["patch_embed.proj.bias"] = checkpoint.pop("x_embedder.proj.bias")
+
+    # Timestep embeddings.
+    converted_state_dict["time_embed.emb.timestep_embedder.linear_1.weight"] = checkpoint.pop(
+        "t_embedder.mlp.0.weight"
+    )
+    converted_state_dict["time_embed.emb.timestep_embedder.linear_1.bias"] = checkpoint.pop("t_embedder.mlp.0.bias")
+    converted_state_dict["time_embed.emb.timestep_embedder.linear_2.weight"] = checkpoint.pop(
+        "t_embedder.mlp.2.weight"
+    )
+    converted_state_dict["time_embed.emb.timestep_embedder.linear_2.bias"] = checkpoint.pop("t_embedder.mlp.2.bias")
+    converted_state_dict["time_embed.linear.weight"] = checkpoint.pop("t_block.1.weight")
+    converted_state_dict["time_embed.linear.bias"] = checkpoint.pop("t_block.1.bias")
+
+    # Caption Projection.
+    checkpoint.pop("y_embedder.y_embedding")
+    converted_state_dict["caption_projection.linear_1.weight"] = checkpoint.pop("y_embedder.y_proj.fc1.weight")
+    converted_state_dict["caption_projection.linear_1.bias"] = checkpoint.pop("y_embedder.y_proj.fc1.bias")
+    converted_state_dict["caption_projection.linear_2.weight"] = checkpoint.pop("y_embedder.y_proj.fc2.weight")
+    converted_state_dict["caption_projection.linear_2.bias"] = checkpoint.pop("y_embedder.y_proj.fc2.bias")
+    converted_state_dict["caption_norm.weight"] = checkpoint.pop("attention_y_norm.weight")
+
+    for i in range(num_layers):
+        converted_state_dict[f"transformer_blocks.{i}.scale_shift_table"] = checkpoint.pop(
+            f"blocks.{i}.scale_shift_table"
+        )
+
+        # Self-Attention
+        sample_q, sample_k, sample_v = torch.chunk(checkpoint.pop(f"blocks.{i}.attn.qkv.weight"), 3, dim=0)
+        converted_state_dict[f"transformer_blocks.{i}.attn1.to_q.weight"] = torch.cat([sample_q])
+        converted_state_dict[f"transformer_blocks.{i}.attn1.to_k.weight"] = torch.cat([sample_k])
+        converted_state_dict[f"transformer_blocks.{i}.attn1.to_v.weight"] = torch.cat([sample_v])
+
+        # Output Projections
+        converted_state_dict[f"transformer_blocks.{i}.attn1.to_out.0.weight"] = checkpoint.pop(
+            f"blocks.{i}.attn.proj.weight"
+        )
+        converted_state_dict[f"transformer_blocks.{i}.attn1.to_out.0.bias"] = checkpoint.pop(
+            f"blocks.{i}.attn.proj.bias"
+        )
+
+        # Cross-Attention
+        converted_state_dict[f"transformer_blocks.{i}.attn2.to_q.weight"] = checkpoint.pop(
+            f"blocks.{i}.cross_attn.q_linear.weight"
+        )
+        converted_state_dict[f"transformer_blocks.{i}.attn2.to_q.bias"] = checkpoint.pop(
+            f"blocks.{i}.cross_attn.q_linear.bias"
+        )
+
+        linear_sample_k, linear_sample_v = torch.chunk(
+            checkpoint.pop(f"blocks.{i}.cross_attn.kv_linear.weight"), 2, dim=0
+        )
+        linear_sample_k_bias, linear_sample_v_bias = torch.chunk(
+            checkpoint.pop(f"blocks.{i}.cross_attn.kv_linear.bias"), 2, dim=0
+        )
+        converted_state_dict[f"transformer_blocks.{i}.attn2.to_k.weight"] = linear_sample_k
+        converted_state_dict[f"transformer_blocks.{i}.attn2.to_v.weight"] = linear_sample_v
+        converted_state_dict[f"transformer_blocks.{i}.attn2.to_k.bias"] = linear_sample_k_bias
+        converted_state_dict[f"transformer_blocks.{i}.attn2.to_v.bias"] = linear_sample_v_bias
+
+        # Output Projections
+        converted_state_dict[f"transformer_blocks.{i}.attn2.to_out.0.weight"] = checkpoint.pop(
+            f"blocks.{i}.cross_attn.proj.weight"
+        )
+        converted_state_dict[f"transformer_blocks.{i}.attn2.to_out.0.bias"] = checkpoint.pop(
+            f"blocks.{i}.cross_attn.proj.bias"
+        )
+
+        # MLP
+        converted_state_dict[f"transformer_blocks.{i}.ff.conv_inverted.weight"] = checkpoint.pop(
+            f"blocks.{i}.mlp.inverted_conv.conv.weight"
+        )
+        converted_state_dict[f"transformer_blocks.{i}.ff.conv_inverted.bias"] = checkpoint.pop(
+            f"blocks.{i}.mlp.inverted_conv.conv.bias"
+        )
+        converted_state_dict[f"transformer_blocks.{i}.ff.conv_depth.weight"] = checkpoint.pop(
+            f"blocks.{i}.mlp.depth_conv.conv.weight"
+        )
+        converted_state_dict[f"transformer_blocks.{i}.ff.conv_depth.bias"] = checkpoint.pop(
+            f"blocks.{i}.mlp.depth_conv.conv.bias"
+        )
+        converted_state_dict[f"transformer_blocks.{i}.ff.conv_point.weight"] = checkpoint.pop(
+            f"blocks.{i}.mlp.point_conv.conv.weight"
+        )
+
+    # Final layer
+    converted_state_dict["proj_out.weight"] = checkpoint.pop("final_layer.linear.weight")
+    converted_state_dict["proj_out.bias"] = checkpoint.pop("final_layer.linear.bias")
+    converted_state_dict["scale_shift_table"] = checkpoint.pop("final_layer.scale_shift_table")
+
+    return converted_state_dict
+
+
 def convert_wan_transformer_to_diffusers(checkpoint, **kwargs):
     converted_state_dict = {}
 

src/diffusers/models/transformers/sana_transformer.py

@@ -18,7 +18,7 @@
 from torch import nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
-from ...loaders import PeftAdapterMixin
+from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
 from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
 from ..attention_processor import (
     Attention,
@@ -195,7 +195,7 @@ def forward(
         return hidden_states
 
 
-class SanaTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+class SanaTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
     r"""
     A 2D Transformer model introduced in [Sana](https://huggingface.co/papers/2410.10629) family of models.
 

tests/single_file/test_sana_transformer.py

@@ -0,0 +1,61 @@
+import gc
+import unittest
+
+import torch
+
+from diffusers import (
+    SanaTransformer2DModel,
+)
+from diffusers.utils.testing_utils import (
+    backend_empty_cache,
+    enable_full_determinism,
+    require_torch_accelerator,
+    torch_device,
+)
+
+
+enable_full_determinism()
+
+
+@require_torch_accelerator
+class SanaTransformer2DModelSingleFileTests(unittest.TestCase):
+    model_class = SanaTransformer2DModel
+    ckpt_path = (
+        "https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px/blob/main/checkpoints/Sana_1600M_1024px.pth"
+    )
+    alternate_keys_ckpt_paths = [
+        "https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px/blob/main/checkpoints/Sana_1600M_1024px.pth"
+    ]
+
+    repo_id = "Efficient-Large-Model/Sana_1600M_1024px_diffusers"
+
+    def setUp(self):
+        super().setUp()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def test_single_file_components(self):
+        model = self.model_class.from_pretrained(self.repo_id, subfolder="transformer")
+        model_single_file = self.model_class.from_single_file(self.ckpt_path)
+
+        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
+        for param_name, param_value in model_single_file.config.items():
+            if param_name in PARAMS_TO_IGNORE:
+                continue
+            assert (
+                model.config[param_name] == param_value
+            ), f"{param_name} differs between single file loading and pretrained loading"
+
+    def test_checkpoint_loading(self):
+        for ckpt_path in self.alternate_keys_ckpt_paths:
+            torch.cuda.empty_cache()
+            model = self.model_class.from_single_file(ckpt_path)
+
+            del model
+            gc.collect()
+            torch.cuda.empty_cache()