Wan caus vid (#196)

Adds caus_vid model for faster inference.


---------

Co-authored-by: Juan Acevedo <juancevedo@google.com>

Author: entrpn

src/maxdiffusion/configs/base_wan_14b.yml

@@ -29,6 +29,9 @@ log_period: 100
 
 pretrained_model_name_or_path: 'Wan-AI/Wan2.1-T2V-14B-Diffusers'
 
+# Overrides the transformer from pretrained_model_name_or_path
+wan_transformer_pretrained_model_name_or_path: ''
+
 unet_checkpoint: ''
 revision: ''
 # This will convert the weights to this dtype.

src/maxdiffusion/models/wan/wan_utils.py

@@ -1,4 +1,5 @@
 import json
+import torch
 import jax
 import jax.numpy as jnp
 from maxdiffusion import max_logging
@@ -7,6 +8,8 @@
 from flax.traverse_util import unflatten_dict, flatten_dict
 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"
+
 
 def _tuple_str_to_int(in_tuple):
   out_list = []
@@ -25,7 +28,68 @@ def rename_for_nnx(key):
   return new_key
 
 
+def load_causvid_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="causal_model.pt")
+      loaded_state_dict = torch.load(ckpt_shard_path)
+
+      tensors = {}
+      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 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")
+
+        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_wan_transformer(pretrained_model_name_or_path: str, eval_shapes: dict, device: str, hf_download: bool = True):
+
+  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)
+  else:
+    return load_base_wan_transformer(pretrained_model_name_or_path, eval_shapes, device, hf_download)
+
+
+def load_base_wan_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:

src/maxdiffusion/pipelines/wan/wan_pipeline.py

@@ -95,7 +95,7 @@ def create_model(rngs: nnx.Rngs, wan_config: dict):
   # 4. Load pretrained weights and move them to device using the state shardings from (3) above.
   # This helps with loading sharded weights directly into the accelerators without fist copying them
   # all to one device and then distributing them, thus using low HBM memory.
-  params = load_wan_transformer(config.pretrained_model_name_or_path, params, "cpu")
+  params = load_wan_transformer(config.wan_transformer_pretrained_model_name_or_path, params, "cpu")
   params = jax.tree_util.tree_map(lambda x: x.astype(config.weights_dtype), params)
   for path, val in flax.traverse_util.flatten_dict(params).items():
     sharding = logical_state_sharding[path].value

src/maxdiffusion/pyconfig.py

@@ -25,6 +25,7 @@
 import yaml
 from . import max_logging
 from . import max_utils
+from .models.wan.wan_utils import CAUSVID_TRANSFORMER_MODEL_NAME_OR_PATH
 
 
 def string_to_bool(s: str) -> bool:
@@ -102,6 +103,7 @@ def __init__(self, argv: list[str], **kwargs):
       jax.config.update("jax_compilation_cache_dir", raw_keys["jax_cache_dir"])
 
     _HyperParameters.user_init(raw_keys)
+    _HyperParameters.wan_init(raw_keys)
     self.keys = raw_keys
     for k in sorted(raw_keys.keys()):
       max_logging.log(f"Config param {k}: {raw_keys[k]}")
@@ -110,6 +112,23 @@ def _load_kwargs(self, argv: list[str]):
     args_dict = dict(a.split("=", 1) for a in argv[2:])
     return args_dict
 
+  @staticmethod
+  def wan_init(raw_keys):
+    if "wan_transformer_pretrained_model_name_or_path" in 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:
+        # Set correct parameters for CausVid in case of user error.
+        raw_keys["guidance_scale"] = 1.0
+        num_inference_steps = raw_keys["num_inference_steps"]
+        if num_inference_steps > 10:
+          max_logging.log(
+              f"Warning: Try setting num_inference_steps to less than 8 steps when using CausVid, currently you are setting {num_inference_steps} steps."
+          )
+      else:
+        raise ValueError(f"{transformer_pretrained_model_name_or_path} transformer model is not supported for Wan 2.1")
+
   @staticmethod
   def user_init(raw_keys):
     """Transformations between the config data and configs used at runtime"""