Wan vae implementation

README.md

@@ -171,6 +171,7 @@ To generate images, run the following command:
   ```bash
   python -m src.maxdiffusion.generate src/maxdiffusion/configs/base21.yml run_name="my_run"
   ```
+
   ## Flux
 
   First make sure you have permissions to access the Flux repos in Huggingface.

end_to_end/tpu/eval_assert.py

@@ -22,7 +22,6 @@
 """
 
 
-
 # pylint: skip-file
 """Reads and asserts over target values"""
 from absl import app
@@ -47,7 +46,7 @@ def test_final_loss(metrics_file, target_loss, num_samples_str="10"):
   target_loss = float(target_loss)
   num_samples = int(num_samples_str)
   with open(metrics_file, "r", encoding="utf8") as _:
-    last_n_data = get_last_n_data(metrics_file, "learning/loss",num_samples)
+    last_n_data = get_last_n_data(metrics_file, "learning/loss", num_samples)
     avg_last_n_data = sum(last_n_data) / len(last_n_data)
     print(f"Mean of last {len(last_n_data)} losses is {avg_last_n_data}")
     print(f"Target loss is {target_loss}")

requirements.txt

@@ -30,4 +30,6 @@ huggingface_hub==0.30.2
 transformers==4.48.1
 einops==0.8.0
 sentencepiece
-aqtp
+aqtp
+imageio==2.37.0
+imageio-ffmpeg==0.6.0

requirements_with_jax_stable_stack.txt

@@ -31,4 +31,6 @@ tensorflow-datasets>=4.9.6
 tokenizers==0.21.0
 torch==2.5.1
 torchvision==0.20.1
-transformers==4.48.1
+transformers==4.48.1
+imageio==2.37.0
+imageio-ffmpeg==0.6.0

src/maxdiffusion/configs/base_wan_t2v.yml

@@ -0,0 +1,269 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This sentinel is a reminder to choose a real run name.
+run_name: ''
+
+metrics_file: "" # for testing, local file that stores scalar metrics. If empty, no metrics are written.
+# If true save metrics such as loss and TFLOPS to GCS in {base_output_directory}/{run_name}/metrics/
+write_metrics: True
+gcs_metrics: False
+# If true save config to GCS in {base_output_directory}/{run_name}/
+save_config_to_gcs: False
+log_period: 100
+
+pretrained_model_name_or_path: 'Wan-AI/Wan2.1-T2V-14B-Diffusers'
+
+# Flux params
+flux_name: "flux-dev"
+max_sequence_length: 512
+time_shift: True
+base_shift: 0.5
+max_shift: 1.15
+# offloads t5 encoder after text encoding to save memory.
+offload_encoders: True
+
+
+unet_checkpoint: ''
+revision: 'refs/pr/95'
+# This will convert the weights to this dtype.
+# When running inference on TPUv5e, use weights_dtype: 'bfloat16'
+weights_dtype: 'bfloat16'
+# This sets the layer's dtype in the model. Ex: nn.Dense(dtype=activations_dtype)
+activations_dtype: 'bfloat16'
+
+# matmul and conv precision from https://jax.readthedocs.io/en/latest/jax.lax.html#jax.lax.Precision
+# Options are "DEFAULT", "HIGH", "HIGHEST"
+# fp32 activations and fp32 weights with HIGHEST will provide the best precision
+# at the cost of time.
+precision: "DEFAULT"
+
+# if False state is not jitted and instead replicate is called. This is good for debugging on single host
+# It must be True for multi-host.
+jit_initializers: True
+
+# Set true to load weights from pytorch
+from_pt: True
+split_head_dim: True
+attention: 'flash' # Supported attention: dot_product, flash, cudnn_flash_te
+
+flash_block_sizes: {}
+# Use the following flash_block_sizes on v6e (Trillium) due to larger vmem.
+# flash_block_sizes: {
+#   "block_q" : 1536,
+#   "block_kv_compute" : 1536,
+#   "block_kv" : 1536,
+#   "block_q_dkv" : 1536,
+#   "block_kv_dkv" : 1536,
+#   "block_kv_dkv_compute" : 1536,
+#   "block_q_dq" : 1536,
+#   "block_kv_dq" : 1536
+# }
+# GroupNorm groups
+norm_num_groups: 32
+
+# If train_new_unet, unet weights will be randomly initialized to train the unet from scratch
+# else they will be loaded from pretrained_model_name_or_path
+train_new_unet: False
+
+# train text_encoder - Currently not supported for SDXL
+train_text_encoder: False
+text_encoder_learning_rate: 4.25e-6
+
+# https://arxiv.org/pdf/2305.08891.pdf
+snr_gamma: -1.0
+
+timestep_bias: {
+  # a value of later will increase the frequence of the model's final training steps.
+  # none, earlier, later, range
+  strategy: "none",
+  # multiplier for bias, a value of 2.0 will double the weight of the bias, 0.5 will halve it.
+  multiplier: 1.0,
+  # when using strategy=range, the beginning (inclusive) timestep to bias.
+  begin: 0,
+  # when using strategy=range, the final step (inclusive) to bias.
+  end: 1000,
+  # portion of timesteps to bias.
+  # 0.5 will bias one half of the timesteps. Value of strategy determines
+  # whether the biased portions are in the earlier or later timesteps.
+  portion: 0.25
+}
+
+# Override parameters from checkpoints's scheduler.
+diffusion_scheduler_config: {
+  _class_name: 'FlaxEulerDiscreteScheduler',
+  prediction_type: 'epsilon',
+  rescale_zero_terminal_snr: False,
+  timestep_spacing: 'trailing'
+}
+
+# Output directory
+# Create a GCS bucket, e.g. my-maxtext-outputs and set this to "gs://my-maxtext-outputs/"
+base_output_directory: ""
+
+# Hardware
+hardware: 'tpu' # Supported hardware types are 'tpu', 'gpu'
+
+# Parallelism
+mesh_axes: ['data', 'fsdp', 'tensor']
+
+# batch : batch dimension of data and activations
+# hidden :
+# embed : attention qkv dense layer hidden dim named as embed
+# heads : attention head dim = num_heads * head_dim
+# length : attention sequence length
+# temb_in : dense.shape[0] of resnet dense before conv
+# out_c : dense.shape[1] of resnet dense before conv
+# out_channels : conv.shape[-1] activation
+# keep_1 : conv.shape[0] weight
+# keep_2 : conv.shape[1] weight
+# conv_in : conv.shape[2] weight
+# conv_out : conv.shape[-1] weight
+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']]
+
+# One axis for each parallelism type may hold a placeholder (-1)
+# value to auto-shard based on available slices and devices.
+# By default, product of the DCN axes should equal number of slices
+# and product of the ICI axes should equal number of devices per slice.
+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
+
+# Dataset
+# Replace with dataset path or train_data_dir. One has to be set.
+dataset_name: 'diffusers/pokemon-gpt4-captions'
+train_split: 'train'
+dataset_type: 'tf'
+cache_latents_text_encoder_outputs: True
+# cache_latents_text_encoder_outputs only apply to dataset_type="tf",
+# only apply to small dataset that fits in memory
+# prepare image latents and text encoder outputs
+# Reduce memory consumption and reduce step time during training
+# transformed dataset is saved at dataset_save_location
+dataset_save_location: '/tmp/pokemon-gpt4-captions_xl'
+train_data_dir: ''
+dataset_config_name: ''
+jax_cache_dir: ''
+hf_data_dir: ''
+hf_train_files: ''
+hf_access_token: ''
+image_column: 'image'
+caption_column: 'text'
+resolution: 1024
+center_crop: False
+random_flip: False
+# If cache_latents_text_encoder_outputs is True
+# the num_proc is set to 1
+tokenize_captions_num_proc: 4
+transform_images_num_proc: 4
+reuse_example_batch: False
+enable_data_shuffling: True
+
+# checkpoint every number of samples, -1 means don't checkpoint.
+checkpoint_every: -1
+# enables one replica to read the ckpt then broadcast to the rest
+enable_single_replica_ckpt_restoring: False
+
+# Training loop
+learning_rate: 4.e-7
+scale_lr: False
+max_train_samples: -1
+# max_train_steps takes priority over num_train_epochs.
+max_train_steps: 200
+num_train_epochs: 1
+seed: 0
+output_dir: 'sdxl-model-finetuned'
+per_device_batch_size: 1
+
+warmup_steps_fraction: 0.0
+learning_rate_schedule_steps: -1 # By default the length of the schedule is set to the number of steps.
+
+# However you may choose a longer schedule (learning_rate_schedule_steps > steps), in which case the training will end before
+# dropping fully down. Or you may choose a shorter schedule, where the unspecified steps will have a learning rate of 0.
+
+# AdamW optimizer parameters
+adam_b1: 0.9 # Exponential decay rate to track the first moment of past gradients.
+adam_b2: 0.999 # Exponential decay rate to track the second moment of past gradients.
+adam_eps: 1.e-8 # A small constant applied to denominator outside of the square root.
+adam_weight_decay: 1.e-2 # AdamW Weight decay
+max_grad_norm: 1.0
+
+enable_profiler: False
+# Skip first n steps for profiling, to omit things like compilation and to give
+# the iteration time a chance to stabilize.
+skip_first_n_steps_for_profiler: 5
+profiler_steps: 10
+
+# Generation parameters
+prompt: "A magical castle in the middle of a forest, artistic drawing"
+prompt_2: "A magical castle in the middle of a forest, artistic drawing"
+negative_prompt: "purple, red"
+do_classifier_free_guidance: True
+guidance_scale: 3.5
+# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+guidance_rescale: 0.0
+num_inference_steps: 50
+
+# SDXL Lightning parameters
+lightning_from_pt: True
+# Empty or "ByteDance/SDXL-Lightning" to enable lightning.
+lightning_repo: ""
+# Empty or "sdxl_lightning_4step_unet.safetensors" to enable lightning.
+lightning_ckpt: ""
+
+# LoRA parameters
+# Values are lists to support multiple LoRA loading during inference in the future.
+lora_config: {
+  lora_model_name_or_path: [],
+  weight_name: [],
+  adapter_name: [],
+  scale: [],
+  from_pt: []
+}
+# Ex with values:
+# lora_config : {
+#   lora_model_name_or_path: ["ByteDance/Hyper-SD"],
+#   weight_name: ["Hyper-SDXL-2steps-lora.safetensors"],
+#   adapter_name: ["hyper-sdxl"],
+#   scale: [0.7],
+#   from_pt: [True]
+# }
+
+enable_mllog: False
+
+#controlnet
+controlnet_model_name_or_path: 'diffusers/controlnet-canny-sdxl-1.0'
+controlnet_from_pt: True
+controlnet_conditioning_scale: 0.5
+controlnet_image: 'https://upload.wikimedia.org/wikipedia/commons/thumb/c/c1/Google_%22G%22_logo.svg/1024px-Google_%22G%22_logo.svg.png'
+quantization: ''
+# Shard the range finding operation for quantization. By default this is set to number of slices.
+quantization_local_shard_count: -1
+compile_topology_num_slices: -1 # Number of target slices, set to a positive integer.
+

src/maxdiffusion/configuration_utils.py

@@ -464,7 +464,8 @@ def extract_init_dict(cls, config_dict, **kwargs):
     # remove flax internal keys
     if hasattr(cls, "_flax_internal_args"):
       for arg in cls._flax_internal_args:
-        expected_keys.remove(arg)
+        if arg in expected_keys:
+          expected_keys.remove(arg)
 
     # 2. Remove attributes that cannot be expected from expected config attributes
     # remove keys to be ignored

src/maxdiffusion/image_processor.py

@@ -36,6 +36,53 @@
 ]
 
 
+def is_valid_image(image) -> bool:
+  r"""
+  Checks if the input is a valid image.
+
+  A valid image can be:
+  - A `PIL.Image.Image`.
+  - A 2D or 3D `np.ndarray` or `torch.Tensor` (grayscale or color image).
+
+  Args:
+      image (`Union[PIL.Image.Image, np.ndarray, torch.Tensor]`):
+          The image to validate. It can be a PIL image, a NumPy array, or a torch tensor.
+
+  Returns:
+      `bool`:
+          `True` if the input is a valid image, `False` otherwise.
+  """
+  return isinstance(image, PIL.Image.Image) or isinstance(image, (np.ndarray, torch.Tensor)) and image.ndim in (2, 3)
+
+
+def is_valid_image_imagelist(images):
+  r"""
+  Checks if the input is a valid image or list of images.
+
+  The input can be one of the following formats:
+  - A 4D tensor or numpy array (batch of images).
+  - A valid single image: `PIL.Image.Image`, 2D `np.ndarray` or `torch.Tensor` (grayscale image), 3D `np.ndarray` or
+    `torch.Tensor`.
+  - A list of valid images.
+
+  Args:
+      images (`Union[np.ndarray, torch.Tensor, PIL.Image.Image, List]`):
+          The image(s) to check. Can be a batch of images (4D tensor/array), a single image, or a list of valid
+          images.
+
+  Returns:
+      `bool`:
+          `True` if the input is valid, `False` otherwise.
+  """
+  if isinstance(images, (np.ndarray, torch.Tensor)) and images.ndim == 4:
+    return True
+  elif is_valid_image(images):
+    return True
+  elif isinstance(images, list):
+    return all(is_valid_image(image) for image in images)
+  return False
+
+
 class VaeImageProcessor(ConfigMixin):
   """
   Image processor for VAE.