Ltx-vid conversion script

setup.sh

@@ -110,4 +110,4 @@ else
 fi
 
 # Install maxdiffusion
-pip3 install -U . || echo "Failed to install maxdiffusion" >&2
+pip3 install -e . || echo "Failed to install maxdiffusion" >&2

src/maxdiffusion/__init__.py

@@ -373,6 +373,7 @@
   _import_structure["models.modeling_flax_utils"] = ["FlaxModelMixin"]
   _import_structure["models.unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
   _import_structure["models.flux.transformers.transformer_flux_flax"] = ["FluxTransformer2DModel"]
+  _import_structure["models.ltx_video.transformers.transformer3d"] = ["Transformer3DModel"]
   _import_structure["models.vae_flax"] = ["FlaxAutoencoderKL"]
   _import_structure["pipelines"].extend(["FlaxDiffusionPipeline"])
   _import_structure["schedulers"].extend(
@@ -453,6 +454,7 @@
     from .models.modeling_flax_utils import FlaxModelMixin
     from .models.unet_2d_condition_flax import FlaxUNet2DConditionModel
     from .models.flux.transformers.transformer_flux_flax import FluxTransformer2DModel
+    from .models.ltx_video.transformers.transformer3d import Transformer3DModel
     from .models.vae_flax import FlaxAutoencoderKL
     from .pipelines import FlaxDiffusionPipeline
     from .schedulers import (

src/maxdiffusion/checkpointing/checkpointing_utils.py

@@ -213,8 +213,11 @@ def load_state_if_possible(
     max_logging.log(f"restoring from this run's directory latest step {latest_step}")
     try:
       if not enable_single_replica_ckpt_restoring:
-        item = {checkpoint_item: orbax.checkpoint.args.PyTreeRestore(item=abstract_unboxed_pre_state)}
-        return checkpoint_manager.restore(latest_step, args=orbax.checkpoint.args.Composite(**item))
+        if checkpoint_item == " ":
+          return checkpoint_manager.restore(latest_step, args=ocp.args.StandardRestore(abstract_unboxed_pre_state))
+        else:
+          item = {checkpoint_item: orbax.checkpoint.args.PyTreeRestore(item=abstract_unboxed_pre_state)}
+          return checkpoint_manager.restore(latest_step, args=orbax.checkpoint.args.Composite(**item)) 
 
       def map_to_pspec(data):
         pspec = data.sharding.spec

src/maxdiffusion/configs/ltx_video.yml

@@ -0,0 +1,51 @@
+#hardware
+hardware: 'tpu'
+skip_jax_distributed_system: False
+
+jax_cache_dir: ''
+weights_dtype: 'bfloat16'
+activations_dtype: 'bfloat16'
+
+
+run_name: ''
+output_dir: 'ltx-video-output'
+save_config_to_gcs: False
+
+#parallelism
+mesh_axes: ['data', 'fsdp', 'tensor']
+logical_axis_rules: [
+                      ['batch', 'data'],
+                      ['activation_batch', ['data','fsdp']],
+                      ['activation_heads', 'tensor'],
+                      ['activation_kv', 'tensor'],
+                      ['mlp','tensor'],
+                      ['embed','fsdp'],
+                      ['heads', 'tensor'],
+                      ['conv_batch', ['data','fsdp']],
+                      ['out_channels', 'tensor'],
+                      ['conv_out', 'fsdp'],
+                    ]
+data_sharding: [['data', 'fsdp', 'tensor']]
+dcn_data_parallelism: 1  # recommended DCN axis to be auto-sharded
+dcn_fsdp_parallelism: -1
+dcn_tensor_parallelism: 1
+ici_data_parallelism: -1
+ici_fsdp_parallelism: 1  # recommended ICI axis to be auto-sharded
+ici_tensor_parallelism: 1
+
+
+
+
+learning_rate_schedule_steps: -1
+max_train_steps: 500 #TODO: change this
+pretrained_model_name_or_path: ''
+unet_checkpoint: ''
+dataset_name: 'diffusers/pokemon-gpt4-captions'
+train_split: 'train'
+dataset_type: 'tf'
+cache_latents_text_encoder_outputs: True
+per_device_batch_size: 1
+compile_topology_num_slices: -1 
+quantization_local_shard_count: -1
+jit_initializers: True 
+enable_single_replica_ckpt_restoring: False

src/maxdiffusion/generate_ltx_video.py

@@ -0,0 +1,92 @@
+from json import encoder
+from absl import app
+from typing import Sequence
+import jax
+from flax import linen as nn
+import json
+from flax.linen import partitioning as nn_partitioning
+from maxdiffusion.models.ltx_video.transformers.transformer3d import Transformer3DModel
+import os
+import functools
+import jax.numpy as jnp
+from maxdiffusion import pyconfig
+from maxdiffusion.max_utils import (
+    create_device_mesh,
+    setup_initial_state,
+    get_memory_allocations,
+)
+from jax.sharding import Mesh, PartitionSpec as P
+import orbax.checkpoint as ocp
+
+
+def validate_transformer_inputs(prompt_embeds, fractional_coords, latents, noise_cond, segment_ids, encoder_attention_segment_ids):
+  print("prompts_embeds.shape: ", prompt_embeds.shape, prompt_embeds.dtype)
+  print("fractional_coords.shape: ", fractional_coords.shape, fractional_coords.dtype)
+  print("latents.shape: ", latents.shape, latents.dtype)
+  print("noise_cond.shape: ", noise_cond.shape, noise_cond.dtype)
+  print("noise_cond.shape: ", noise_cond.shape, noise_cond.dtype)
+  print("segment_ids.shape: ", segment_ids.shape, segment_ids.dtype)
+  print("encoder_attention_segment_ids.shape: ", encoder_attention_segment_ids.shape, encoder_attention_segment_ids.dtype)
+
+def run(config):
+  key = jax.random.PRNGKey(0)
+
+  devices_array = create_device_mesh(config) 
+  mesh = Mesh(devices_array, config.mesh_axes)
+
+  base_dir = os.path.dirname(__file__)
+
+  ##load in model config
+  config_path = os.path.join(base_dir, "models/ltx_video/xora_v1.2-13B-balanced-128.json")
+  with open(config_path, "r") as f:
+    model_config = json.load(f)
+  relative_ckpt_path = model_config["ckpt_path"]
+
+  ignored_keys = ["_class_name", "_diffusers_version", "_name_or_path", "causal_temporal_positioning", "in_channels", "ckpt_path"]
+  in_channels = model_config["in_channels"]
+  for name in ignored_keys:
+    if name in model_config:
+      del model_config[name]
+
+
+  transformer = Transformer3DModel(**model_config, dtype=jnp.float32, gradient_checkpointing="matmul_without_batch", sharding_mesh=mesh)
+  transformer_param_shapes = transformer.init_weights(in_channels, model_config['caption_channels'], eval_only = True)
+
+  weights_init_fn = functools.partial(
+      transformer.init_weights, 
+      in_channels, 
+      model_config['caption_channels'],
+      eval_only = True
+  )
+
+  absolute_ckpt_path = os.path.abspath(relative_ckpt_path)
+
+  checkpoint_manager = ocp.CheckpointManager(absolute_ckpt_path)
+  transformer_state, transformer_state_shardings = setup_initial_state(
+    model=transformer,
+    tx=None,
+    config=config,
+    mesh=mesh,
+    weights_init_fn=weights_init_fn,
+    checkpoint_manager=checkpoint_manager,
+    checkpoint_item=" ",
+    model_params=None,
+    training=False,
+  )
+
+
+
+
+
+def main(argv: Sequence[str]) -> None:
+  pyconfig.initialize(argv)
+  run(pyconfig.config)
+
+
+if __name__ == "__main__":
+  app.run(main)
+
+
+
+
+

src/maxdiffusion/max_utils.py

@@ -402,7 +402,10 @@ def setup_initial_state(
           config.enable_single_replica_ckpt_restoring,
       )
       if state:
-        state = state[checkpoint_item]
+        if checkpoint_item == " ":
+          state = state
+        else:
+          state = state[checkpoint_item]
     if not state:
       max_logging.log(f"Could not find the item in orbax, creating state...")
       init_train_state_partial = functools.partial(

src/maxdiffusion/models/__init__.py

@@ -14,7 +14,7 @@
 
 from typing import TYPE_CHECKING
 
-from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, is_flax_available, is_torch_available
+from maxdiffusion.utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, is_flax_available, is_torch_available
 
 
 _import_structure = {}
@@ -32,7 +32,7 @@
   from .vae_flax import FlaxAutoencoderKL
   from .lora import *
   from .flux.transformers.transformer_flux_flax import FluxTransformer2DModel
-
+  from .ltx_video.transformers.transformer3d import Transformer3DModel
 else:
   import sys
 

src/maxdiffusion/models/attention_flax.py

@@ -1188,4 +1188,4 @@ def setup(self):
   def __call__(self, hidden_states, deterministic=True):
     hidden_states = self.proj(hidden_states)
     hidden_linear, hidden_gelu = jnp.split(hidden_states, 2, axis=2)
-    return self.dropout_layer(hidden_linear * nn.gelu(hidden_gelu), deterministic=deterministic)
+    return self.dropout_layer(hidden_linear * nn.gelu(hidden_gelu), deterministic=deterministic)

src/maxdiffusion/models/ltx_video/gradient_checkpoint.py

@@ -0,0 +1,70 @@
+from enum import Enum, auto
+from typing import Optional
+
+import jax
+from flax import linen as nn
+
+SKIP_GRADIENT_CHECKPOINT_KEY = "skip"
+
+
+class GradientCheckpointType(Enum):
+    """
+    Defines the type of the gradient checkpoint we will have
+
+    NONE - means no gradient checkpoint
+    FULL - means full gradient checkpoint, wherever possible (minimum memory usage)
+    MATMUL_WITHOUT_BATCH - means gradient checkpoint for every linear/matmul operation,
+                            except for ones that involve batch dimension - that means that all attention and projection
+                            layers will have gradient checkpoint, but not the backward with respect to the parameters
+    """
+
+    NONE = auto()
+    FULL = auto()
+    MATMUL_WITHOUT_BATCH = auto()
+
+    @classmethod
+    def from_str(cls, s: Optional[str] = None) -> "GradientCheckpointType":
+        """
+        Constructs the gradient checkpoint type from a string
+
+        Args:
+            s (Optional[str], optional): The name of the gradient checkpointing policy. Defaults to None.
+
+        Returns:
+            GradientCheckpointType: The policy that corresponds to the string
+        """
+        if s is None:
+            s = "none"
+        return GradientCheckpointType[s.upper()]
+
+    def to_jax_policy(self):
+        """
+        Converts the gradient checkpoint type to a jax policy
+        """
+        match self:
+            case GradientCheckpointType.NONE:
+                return SKIP_GRADIENT_CHECKPOINT_KEY
+            case GradientCheckpointType.FULL:
+                return None
+            case GradientCheckpointType.MATMUL_WITHOUT_BATCH:
+                return jax.checkpoint_policies.checkpoint_dots_with_no_batch_dims
+
+    def apply(self, module: nn.Module) -> nn.Module:
+        """
+        Applies a gradient checkpoint policy to a module
+        if no policy is needed, it will return the module as is
+
+        Args:
+            module (nn.Module): the module to apply the policy to
+
+        Returns:
+            nn.Module: the module with the policy applied
+        """
+        policy = self.to_jax_policy()
+        if policy == SKIP_GRADIENT_CHECKPOINT_KEY:
+            return module
+        return nn.remat(  # pylint: disable=invalid-name
+            module,
+            prevent_cse=False,
+            policy=policy,
+        )

src/maxdiffusion/models/ltx_video/linear.py

@@ -0,0 +1,109 @@
+from typing import Union, Iterable, Tuple, Optional, Callable
+
+import numpy as np
+import jax
+import jax.numpy as jnp
+from flax import linen as nn
+from flax.linen.initializers import lecun_normal
+
+
+Shape = Tuple[int, ...]
+Initializer = Callable[[jax.random.PRNGKey, Shape, jax.numpy.dtype], jax.Array]
+InitializerAxis = Union[int, Shape]
+
+
+def _normalize_axes(axes: Iterable[int], ndim: int) -> Tuple[int]:
+    # A tuple by convention. len(axes_tuple) then also gives the rank efficiently.
+    return tuple(ax if ax >= 0 else ndim + ax for ax in axes)
+
+
+def _canonicalize_tuple(x):
+    if isinstance(x, Iterable):
+        return tuple(x)
+    else:
+        return (x,)
+
+
+NdInitializer = Callable[[jax.random.PRNGKey, Shape, jnp.dtype, InitializerAxis, InitializerAxis], jax.Array]
+KernelInitializer = Callable[[jax.random.PRNGKey, Shape, jnp.dtype, InitializerAxis, InitializerAxis], jax.Array]
+
+
+class DenseGeneral(nn.Module):
+    """A linear transformation with flexible axes.
+
+    Adapted from https://github.com/AI-Hypercomputer/maxtext/blob/4bf3beaa5e721745427bfed09938427e369c2aaf/MaxText/layers/linears.py#L86
+
+    Attributes:
+      features: tuple with numbers of output features.
+      axis: tuple with axes to apply the transformation on.
+      weight_dtype: the dtype of the weights (default: float32).
+      dtype: the dtype of the computation (default: float32).
+      kernel_init: initializer function for the weight matrix.
+      use_bias: whether to add bias in linear transformation.
+      bias_norm: whether to add normalization before adding bias.
+      quant: quantization config, defaults to None implying no quantization.
+    """
+
+    features: Union[Iterable[int], int]
+    axis: Union[Iterable[int], int] = -1
+    weight_dtype: jnp.dtype = jnp.float32
+    dtype: np.dtype = jnp.float32
+    kernel_init: KernelInitializer = lecun_normal()
+    kernel_axes: Tuple[Optional[str], ...] = ()
+    use_bias: bool = False
+    matmul_precision: str = "default"
+
+    bias_init: Initializer = jax.nn.initializers.constant(0.0)
+
+    @nn.compact
+    def __call__(self, inputs: jax.Array) -> jax.Array:
+        """Applies a linear transformation to the inputs along multiple dimensions.
+
+        Args:
+          inputs: The nd-array to be transformed.
+
+        Returns:
+          The transformed input.
+        """
+
+        def compute_dot_general(inputs, kernel, axis, contract_ind):
+            """Computes a dot_general operation that may be quantized."""
+            dot_general = jax.lax.dot_general
+            matmul_precision = jax.lax.Precision(self.matmul_precision)
+            return dot_general(inputs, kernel, ((axis, contract_ind), ((), ())), precision=matmul_precision)
+
+        features = _canonicalize_tuple(self.features)
+        axis = _canonicalize_tuple(self.axis)
+
+        inputs = jnp.asarray(inputs, self.dtype)
+        axis = _normalize_axes(axis, inputs.ndim)
+
+        kernel_shape = tuple(inputs.shape[ax] for ax in axis) + features
+        kernel_in_axis = np.arange(len(axis))
+        kernel_out_axis = np.arange(len(axis), len(axis) + len(features))
+        kernel = self.param(
+            "kernel",
+            nn.with_logical_partitioning(self.kernel_init, self.kernel_axes),
+            kernel_shape,
+            self.weight_dtype,
+        )
+        kernel = jnp.asarray(kernel, self.dtype)
+
+        contract_ind = tuple(range(0, len(axis)))
+        output = compute_dot_general(inputs, kernel, axis, contract_ind)
+
+        if self.use_bias:
+            bias_axes, bias_shape = (
+                self.kernel_axes[-len(features) :],
+                kernel_shape[-len(features) :],
+            )
+            bias = self.param(
+                "bias",
+                nn.with_logical_partitioning(self.bias_init, bias_axes),
+                bias_shape,
+                self.weight_dtype,
+            )
+            bias = jnp.asarray(bias, self.dtype)
+
+            output += bias
+        return output