Flux inference implementation

README.md

@@ -17,6 +17,7 @@
 [![Unit Tests](https://github.com/google/maxtext/actions/workflows/UnitTests.yml/badge.svg)](https://github.com/google/maxdiffusion/actions/workflows/UnitTests.yml)
 
 # What's new?
+- **`2025/02/08`**: Flux schnell & dev inference.
 - **`2024/12/12`**: Load multiple LoRAs for inference.
 - **`2024/10/22`**: LoRA support for Hyper SDXL.
 - **`2024/8/1`**: Orbax is the new default checkpointer. You can still use `pipeline.save_pretrained` after training to save in diffusers format.
@@ -46,6 +47,7 @@ MaxDiffusion supports
     * [Training](#training)
       * [Dreambooth](#dreambooth)
     * [Inference](#inference)
+      * [Flux](#flux) 
       * [Hyper-SD XL LoRA](#hyper-sdxl-lora)
       * [Load Multiple LoRA](#load-multiple-lora)
       * [SDXL Lightning](#sdxl-lightning)
@@ -133,6 +135,39 @@ 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.
+
+  Expected results on 1024 x 1024 images with flash attention and bfloat16:
+
+  | Model | Accelerator | Sharding Strategy | Batch Size | Steps | time (secs) |
+  | --- | --- | --- | --- | --- | --- |
+  | Flux-dev | v4-8 | DDP | 4 | 28 | 23 |
+  | Flux-schnell | v4-8 | DDP | 4 | 4 | 2.2 |
+  | Flux-dev | v6e-4 | DDP | 4 | 28 | 5.5 |
+  | Flux-schnell | v6e-4 | DDP | 4 | 4 | 0.8 |
+  | Flux-schnell | v6e-4 | FSDP | 4 | 4 | 1.2 |
+
+  Schnell:
+
+  ```bash
+  python src/maxdiffusion/generate_flux.py src/maxdiffusion/configs/base_flux_schnell.yml jax_cache_dir=/tmp/cache_dir run_name=flux_test output_dir=/tmp/ prompt="photograph of an electronics chip in the shape of a race car with trillium written on its side" per_device_batch_size=1
+  ```
+
+  Dev:
+
+  ```bash
+  python src/maxdiffusion/generate_flux.py src/maxdiffusion/configs/base_flux_dev.yml jax_cache_dir=/tmp/cache_dir run_name=flux_test output_dir=/tmp/ prompt="photograph of an electronics chip in the shape of a race car with trillium written on its side" per_device_batch_size=1
+  ```
+
+  If you are using a TPU v6e (Trillium), you can use optimized flash block sizes for faster inference. Uncomment Flux-dev [config](src/maxdiffusion/configs/base_flux_dev.yml#60) and Flux-schnell [config](src/maxdiffusion/configs/base_flux_schnell.yml#68)
+
+  To keep text encoders, vae and transformer on HBM memory at all times, the following command shards the model across devices. 
+
+  ```bash
+  python src/maxdiffusion/generate_flux.py src/maxdiffusion/configs/base_flux_schnell.yml jax_cache_dir=/tmp/cache_dir run_name=flux_test output_dir=/tmp/ prompt="photograph of an electronics chip in the shape of a race car with trillium written on its side" per_device_batch_size=1 ici_data_parallelism=1 ici_fsdp_parallelism=-1 offload_encoders=False
+  ```
 
   ## Hyper SDXL LoRA
 

requirements.txt

@@ -6,10 +6,10 @@ absl-py
 datasets
 flax>=0.10.2
 optax>=0.2.3
-torch>=2.3.1
-torchvision>=0.18.1
+torch==2.5.1
+torchvision==0.20.1
 ftfy
-tensorboard==2.17.0
+tensorboard>=2.17.0
 tensorboardx==2.6.2.2
 tensorboard-plugin-profile==2.15.2
 Jinja2
@@ -25,5 +25,8 @@ ruff>=0.1.5,<=0.2
 git+https://github.com/mlperf/logging.git
 opencv-python-headless==4.10.0.84
 orbax-checkpoint==0.10.2
-tokenizers==0.20.0
-huggingface_hub==0.24.7
+tokenizers==0.21.0
+huggingface_hub==0.24.7
+transformers==4.48.1
+einops==0.8.0
+sentencepiece

src/maxdiffusion/__init__.py

@@ -372,6 +372,7 @@
   _import_structure["models.controlnet_flax"] = ["FlaxControlNetModel"]
   _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.vae_flax"] = ["FlaxAutoencoderKL"]
   _import_structure["pipelines"].extend(["FlaxDiffusionPipeline"])
   _import_structure["schedulers"].extend(
@@ -451,6 +452,7 @@
     from .models.controlnet_flax import FlaxControlNetModel
     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.vae_flax import FlaxAutoencoderKL
     from .pipelines import FlaxDiffusionPipeline
     from .schedulers import (

src/maxdiffusion/checkpointing/base_stable_diffusion_checkpointer.py

@@ -32,7 +32,7 @@
     max_logging,
 )
 
-from maxdiffusion.transformers import (CLIPTokenizer, FlaxCLIPTextModel, CLIPTextConfig, FlaxCLIPTextModelWithProjection)
+from transformers import (CLIPTokenizer, FlaxCLIPTextModel, CLIPTextConfig, FlaxCLIPTextModelWithProjection)
 
 from maxdiffusion.checkpointing.checkpointing_utils import (
     create_orbax_checkpoint_manager,
@@ -88,11 +88,14 @@ def create_unet_state(self, pipeline, params, checkpoint_item_name, is_training)
         config=self.config,
         mesh=self.mesh,
         weights_init_fn=weights_init_fn,
-        model_params=None if self.config.train_new_unet else params.get("unet", None),
+        model_params=None,
         checkpoint_manager=self.checkpoint_manager,
         checkpoint_item=checkpoint_item_name,
         training=is_training,
     )
+    if not self.config.train_new_unet:
+      unet_state = unet_state.replace(params=params.get("unet", None))
+      unet_state = jax.device_put(unet_state, state_mesh_shardings)
     return unet_state, state_mesh_shardings, learning_rate_scheduler
 
   def create_vae_state(self, pipeline, params, checkpoint_item_name, is_training=False):
@@ -150,17 +153,20 @@ def create_text_encoder_2_state(self, pipeline, params, checkpoint_item_name, is
         input_shape=(self.total_train_batch_size, pipeline.tokenizer.model_max_length),
     )
 
-    return max_utils.setup_initial_state(
+    state, state_mesh_shardings = max_utils.setup_initial_state(
         model=pipeline.text_encoder_2,
         tx=tx,
         config=self.config,
         mesh=self.mesh,
         weights_init_fn=weights_init_fn,
-        model_params=params.get("text_encoder_2", None),
+        model_params=None,
         checkpoint_manager=self.checkpoint_manager,
         checkpoint_item=checkpoint_item_name,
         training=is_training,
     )
+    state = state.replace(params=params.get("text_encoder_2", None))
+    state = jax.device_put(state, state_mesh_shardings)
+    return state, state_mesh_shardings
 
   def restore_data_iterator_state(self, data_iterator):
     if (
@@ -302,15 +308,16 @@ def load_checkpoint(self, step=None, scheduler_class=None):
           tokenizer_path = os.path.join(tokenizer_path, "tokenizer")
         tokenizer_path = max_utils.download_blobs(tokenizer_path, "/tmp")
       tokenizer = CLIPTokenizer.from_pretrained(
-          tokenizer_path, subfolder="tokenizer", dtype=self.config.activations_dtype, weights_dtype=self.config.weights_dtype
+          tokenizer_path,
+          subfolder="tokenizer",
+          dtype=self.config.activations_dtype,
       )
 
       te_pretrained_config = CLIPTextConfig(**model_configs[0]["text_encoder_config"])
       text_encoder = FlaxCLIPTextModel(
           te_pretrained_config,
           seed=self.config.seed,
           dtype=self.config.activations_dtype,
-          weights_dtype=self.config.weights_dtype,
           _do_init=False,
       )
 

src/maxdiffusion/common_types.py

@@ -36,6 +36,7 @@
 
 BATCH = "activation_batch"
 LENGTH = "activation_length"
+EMBED = "activation_embed"
 HEAD = "activation_heads"
 D_KV = "activation_kv"
 KEEP_1 = "activation_keep_1"