Z-Image-Turbo from_single_file

src/diffusers/loaders/single_file_model.py

@@ -49,6 +49,7 @@
     convert_stable_cascade_unet_single_file_to_diffusers,
     convert_wan_transformer_to_diffusers,
     convert_wan_vae_to_diffusers,
+    convert_z_image_transformer_checkpoint_to_diffusers,
     create_controlnet_diffusers_config_from_ldm,
     create_unet_diffusers_config_from_ldm,
     create_vae_diffusers_config_from_ldm,
@@ -167,6 +168,10 @@
         "checkpoint_mapping_fn": convert_flux2_transformer_checkpoint_to_diffusers,
         "default_subfolder": "transformer",
     },
+    "ZImageTransformer2DModel": {
+        "checkpoint_mapping_fn": convert_z_image_transformer_checkpoint_to_diffusers,
+        "default_subfolder": "transformer",
+    },
 }
 
 

src/diffusers/loaders/single_file_utils.py

@@ -120,6 +120,7 @@
     "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"],
+    "z-image-turbo": "cap_embedder.0.weight",
     "sana": [
         "blocks.0.cross_attn.q_linear.weight",
         "blocks.0.cross_attn.q_linear.bias",
@@ -218,6 +219,7 @@
     "cosmos-2.0-t2i-14B": {"pretrained_model_name_or_path": "nvidia/Cosmos-Predict2-14B-Text2Image"},
     "cosmos-2.0-v2w-2B": {"pretrained_model_name_or_path": "nvidia/Cosmos-Predict2-2B-Video2World"},
     "cosmos-2.0-v2w-14B": {"pretrained_model_name_or_path": "nvidia/Cosmos-Predict2-14B-Video2World"},
+    "z-image-turbo": {"pretrained_model_name_or_path": "Tongyi-MAI/Z-Image-Turbo"},
 }
 
 # Use to configure model sample size when original config is provided
@@ -721,6 +723,12 @@ def infer_diffusers_model_type(checkpoint):
     ):
         model_type = "instruct-pix2pix"
 
+    elif (
+        CHECKPOINT_KEY_NAMES["z-image-turbo"] in checkpoint
+        and checkpoint[CHECKPOINT_KEY_NAMES["z-image-turbo"]].shape[0] == 2560
+    ):
+        model_type = "z-image-turbo"
+
     elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["lumina2"]):
         model_type = "lumina2"
 
@@ -3824,3 +3832,56 @@ def update_state_dict(state_dict: dict[str, object], old_key: str, new_key: str)
             handler_fn_inplace(key, converted_state_dict)
 
     return converted_state_dict
+
+
+def convert_z_image_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):
+    Z_IMAGE_KEYS_RENAME_DICT = {
+        "final_layer.": "all_final_layer.2-1.",
+        "x_embedder.": "all_x_embedder.2-1.",
+        ".attention.out.bias": ".attention.to_out.0.bias",
+        ".attention.k_norm.weight": ".attention.norm_k.weight",
+        ".attention.q_norm.weight": ".attention.norm_q.weight",
+        ".attention.out.weight": ".attention.to_out.0.weight",
+    }
+
+    def convert_z_image_fused_attention(key: str, state_dict: dict[str, object]) -> None:
+        if ".attention.qkv.weight" not in key:
+            return
+
+        fused_qkv_weight = state_dict.pop(key)
+        to_q_weight, to_k_weight, to_v_weight = torch.chunk(fused_qkv_weight, 3, dim=0)
+        new_q_name = key.replace(".attention.qkv.weight", ".attention.to_q.weight")
+        new_k_name = key.replace(".attention.qkv.weight", ".attention.to_k.weight")
+        new_v_name = key.replace(".attention.qkv.weight", ".attention.to_v.weight")
+
+        state_dict[new_q_name] = to_q_weight
+        state_dict[new_k_name] = to_k_weight
+        state_dict[new_v_name] = to_v_weight
+        return
+
+    TRANSFORMER_SPECIAL_KEYS_REMAP = {
+        ".attention.qkv.weight": convert_z_image_fused_attention,
+    }
+
+    def update_state_dict(state_dict: dict[str, object], old_key: str, new_key: str) -> None:
+        state_dict[new_key] = state_dict.pop(old_key)
+
+    converted_state_dict = {key: checkpoint.pop(key) for key in list(checkpoint.keys())}
+
+    # Handle single file --> diffusers key remapping via the remap dict
+    for key in list(converted_state_dict.keys()):
+        new_key = key[:]
+        for replace_key, rename_key in Z_IMAGE_KEYS_RENAME_DICT.items():
+            new_key = new_key.replace(replace_key, rename_key)
+
+        update_state_dict(converted_state_dict, key, new_key)
+
+    # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in
+    # special_keys_remap
+    for key in list(converted_state_dict.keys()):
+        for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items():
+            if special_key not in key:
+                continue
+            handler_fn_inplace(key, converted_state_dict)
+
+    return converted_state_dict

src/diffusers/models/transformers/transformer_z_image.py

@@ -70,8 +70,11 @@ def timestep_embedding(t, dim, max_period=10000):
     def forward(self, t):
         t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
         weight_dtype = self.mlp[0].weight.dtype
+        compute_dtype = getattr(self.mlp[0], "compute_dtype", None)
         if weight_dtype.is_floating_point:
             t_freq = t_freq.to(weight_dtype)
+        elif compute_dtype is not None:
+            t_freq = t_freq.to(compute_dtype)
         t_emb = self.mlp(t_freq)
         return t_emb