add fusion x support.

Author: jfacevedo-google

src/maxdiffusion/models/wan/wan_utils.py

@@ -9,6 +9,7 @@
 from ..modeling_flax_pytorch_utils import (rename_key, rename_key_and_reshape_tensor, torch2jax, validate_flax_state_dict)
 
 CAUSVID_TRANSFORMER_MODEL_NAME_OR_PATH = "lightx2v/Wan2.1-T2V-14B-CausVid"
+WAN_21_FUSION_X_MODEL_NAME_OR_PATH = "vrgamedevgirl84/Wan14BT2VFusioniX"
 
 
 def _tuple_str_to_int(in_tuple):
@@ -27,6 +28,66 @@ def rename_for_nnx(key):
     new_key = key[:-1] + ("scale",)
   return new_key
 
+def rename_for_custom_trasformer(key):
+  renamed_pt_key = key.replace("model.diffusion_model.", "")
+
+  renamed_pt_key = renamed_pt_key.replace("head.modulation", "scale_shift_table")
+  renamed_pt_key = renamed_pt_key.replace("head.head", "proj_out")
+  renamed_pt_key = renamed_pt_key.replace("text_embedding_0", "condition_embedder.text_embedder.linear_1")
+  renamed_pt_key = renamed_pt_key.replace("text_embedding_2", "condition_embedder.text_embedder.linear_2")
+  renamed_pt_key = renamed_pt_key.replace("time_embedding_0", "condition_embedder.time_embedder.linear_1")
+  renamed_pt_key = renamed_pt_key.replace("time_embedding_2", "condition_embedder.time_embedder.linear_2")
+  renamed_pt_key = renamed_pt_key.replace("time_projection_1", "condition_embedder.time_proj")
+
+  renamed_pt_key = renamed_pt_key.replace("blocks_", "blocks.")
+  renamed_pt_key = renamed_pt_key.replace("self_attn", "attn1")
+  renamed_pt_key = renamed_pt_key.replace("cross_attn", "attn2")
+  renamed_pt_key = renamed_pt_key.replace(".q.", ".query.")
+  renamed_pt_key = renamed_pt_key.replace(".k.", ".key.")
+  renamed_pt_key = renamed_pt_key.replace(".v.", ".value.")
+  renamed_pt_key = renamed_pt_key.replace(".o.", ".proj_attn.")
+  renamed_pt_key = renamed_pt_key.replace("ffn_0", "ffn.act_fn.proj")
+  renamed_pt_key = renamed_pt_key.replace("ffn_2", "ffn.proj_out")
+  renamed_pt_key = renamed_pt_key.replace(".modulation", ".scale_shift_table")
+  renamed_pt_key = renamed_pt_key.replace("norm3", "norm2.layer_norm")
+
+  return renamed_pt_key
+
+def load_fusionx_transformer(pretrained_model_name_or_path: str, eval_shapes: dict, device: str, hf_download: bool = True):
+  device = jax.devices(device)[0]
+  with jax.default_device(device):
+    if hf_download:
+      ckpt_shard_path = hf_hub_download(pretrained_model_name_or_path, filename="Wan14BT2VFusioniX_fp16_.safetensors")
+      tensors = {}
+      with safe_open(ckpt_shard_path, framework="pt") as f:
+        for k in f.keys():
+          tensors[k] = torch2jax(f.get_tensor(k))
+
+      flax_state_dict = {}
+      cpu = jax.local_devices(backend="cpu")[0]
+      flattened_dict = flatten_dict(eval_shapes)
+      # turn all block numbers to strings just for matching weights.
+      # Later they will be turned back to ints.
+      random_flax_state_dict = {}
+      for key in flattened_dict:
+        string_tuple = tuple([str(item) for item in key])
+        random_flax_state_dict[string_tuple] = flattened_dict[key]
+      for pt_key, tensor in tensors.items():
+        renamed_pt_key = rename_key(pt_key)
+        
+        renamed_pt_key = rename_for_custom_trasformer(renamed_pt_key)
+
+        pt_tuple_key = tuple(renamed_pt_key.split("."))
+
+        flax_key, flax_tensor = rename_key_and_reshape_tensor(pt_tuple_key, tensor, random_flax_state_dict)
+        flax_key = rename_for_nnx(flax_key)
+        flax_key = _tuple_str_to_int(flax_key)
+        flax_state_dict[flax_key] = jax.device_put(jnp.asarray(flax_tensor), device=cpu)
+      validate_flax_state_dict(eval_shapes, flax_state_dict)
+      flax_state_dict = unflatten_dict(flax_state_dict)
+      del tensors
+      jax.clear_caches()
+      return flax_state_dict
 
 def load_causvid_transformer(pretrained_model_name_or_path: str, eval_shapes: dict, device: str, hf_download: bool = True):
   device = jax.devices(device)[0]
@@ -48,25 +109,7 @@ def load_causvid_transformer(pretrained_model_name_or_path: str, eval_shapes: di
       for pt_key, tensor in loaded_state_dict.items():
         tensor = torch2jax(tensor)
         renamed_pt_key = rename_key(pt_key)
-        renamed_pt_key = renamed_pt_key.replace("head.modulation", "scale_shift_table")
-        renamed_pt_key = renamed_pt_key.replace("head.head", "proj_out")
-        renamed_pt_key = renamed_pt_key.replace("text_embedding_0", "condition_embedder.text_embedder.linear_1")
-        renamed_pt_key = renamed_pt_key.replace("text_embedding_2", "condition_embedder.text_embedder.linear_2")
-        renamed_pt_key = renamed_pt_key.replace("time_embedding_0", "condition_embedder.time_embedder.linear_1")
-        renamed_pt_key = renamed_pt_key.replace("time_embedding_2", "condition_embedder.time_embedder.linear_2")
-        renamed_pt_key = renamed_pt_key.replace("time_projection_1", "condition_embedder.time_proj")
-
-        renamed_pt_key = renamed_pt_key.replace("blocks_", "blocks.")
-        renamed_pt_key = renamed_pt_key.replace("self_attn", "attn1")
-        renamed_pt_key = renamed_pt_key.replace("cross_attn", "attn2")
-        renamed_pt_key = renamed_pt_key.replace(".q.", ".query.")
-        renamed_pt_key = renamed_pt_key.replace(".k.", ".key.")
-        renamed_pt_key = renamed_pt_key.replace(".v.", ".value.")
-        renamed_pt_key = renamed_pt_key.replace(".o.", ".proj_attn.")
-        renamed_pt_key = renamed_pt_key.replace("ffn_0", "ffn.act_fn.proj")
-        renamed_pt_key = renamed_pt_key.replace("ffn_2", "ffn.proj_out")
-        renamed_pt_key = renamed_pt_key.replace(".modulation", ".scale_shift_table")
-        renamed_pt_key = renamed_pt_key.replace("norm3", "norm2.layer_norm")
+        renamed_pt_key = rename_for_custom_trasformer(renamed_pt_key)
 
         pt_tuple_key = tuple(renamed_pt_key.split("."))
 
@@ -85,6 +128,8 @@ def load_wan_transformer(pretrained_model_name_or_path: str, eval_shapes: dict,
 
   if pretrained_model_name_or_path == CAUSVID_TRANSFORMER_MODEL_NAME_OR_PATH:
     return load_causvid_transformer(pretrained_model_name_or_path, eval_shapes, device, hf_download)
+  elif pretrained_model_name_or_path == WAN_21_FUSION_X_MODEL_NAME_OR_PATH:
+    return load_fusionx_transformer(pretrained_model_name_or_path, eval_shapes, device, hf_download)
   else:
     return load_base_wan_transformer(pretrained_model_name_or_path, eval_shapes, device, hf_download)
 

src/maxdiffusion/pyconfig.py

@@ -25,7 +25,7 @@
 import yaml
 from . import max_logging
 from . import max_utils
-from .models.wan.wan_utils import CAUSVID_TRANSFORMER_MODEL_NAME_OR_PATH
+from .models.wan.wan_utils import CAUSVID_TRANSFORMER_MODEL_NAME_OR_PATH, WAN_21_FUSION_X_MODEL_NAME_OR_PATH
 
 
 def string_to_bool(s: str) -> bool:
@@ -118,7 +118,7 @@ def wan_init(raw_keys):
       transformer_pretrained_model_name_or_path = raw_keys["wan_transformer_pretrained_model_name_or_path"]
       if transformer_pretrained_model_name_or_path == "":
         raw_keys["wan_transformer_pretrained_model_name_or_path"] = raw_keys["pretrained_model_name_or_path"]
-      elif transformer_pretrained_model_name_or_path == CAUSVID_TRANSFORMER_MODEL_NAME_OR_PATH:
+      elif transformer_pretrained_model_name_or_path == CAUSVID_TRANSFORMER_MODEL_NAME_OR_PATH or transformer_pretrained_model_name_or_path == WAN_21_FUSION_X_MODEL_NAME_OR_PATH:
         # Set correct parameters for CausVid in case of user error.
         raw_keys["guidance_scale"] = 1.0
         num_inference_steps = raw_keys["num_inference_steps"]