update files for flux finetuning and generation.

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?
+- **`2025/04/17`**: Flux Finetuning.
 - **`2025/02/12`**: Flux LoRA for inference.
 - **`2025/02/08`**: Flux schnell & dev inference.
 - **`2024/12/12`**: Load multiple LoRAs for inference.
@@ -76,6 +77,20 @@ For your first time running Maxdiffusion, we provide specific [instructions](doc
 
 After installation completes, run the training script.
 
+- **Flux**
+
+  Flux finetuning has only been tested on TPU v5p.
+
+  ```bash
+  python src/maxdiffusion/train_flux.py src/maxdiffusion/configs/base_flux_dev.yml run_name="test-flux-train" output_dir="gs://<your-gcs-bucket>/" save_final_checkpoint=True  jax_cache_dir="/tmp/jax_cache" max_train_steps=4500
+  ```
+
+  To generate images with a finetuned checkpoint, run:
+
+  ```bash
+  python src/maxdiffusion/generate_flux_pipeline.py src/maxdiffusion/configs/base_flux_dev.yml  run_name="test-flux-train" output_dir="gs://<your-gcs-bucket>/" jax_cache_dir="/tmp/jax_cache"
+  ```
+
 - **Stable Diffusion XL**
 
   ```bash

src/maxdiffusion/checkpointing/checkpointing_utils.py

@@ -143,7 +143,8 @@ def load_params_from_path(
 
   ckpt_path = os.path.join(config.checkpoint_dir, str(step), checkpoint_item)
   ckpt_path = epath.Path(ckpt_path)
-  ckpt_path = os.path.abspath(ckpt_path)
+  if not ckpt_path.as_uri().startswith("gs://"):
+    ckpt_path = os.path.abspath(ckpt_path)
 
   restore_args = ocp.checkpoint_utils.construct_restore_args(unboxed_abstract_params)
   restored = ckptr.restore(

src/maxdiffusion/configs/README.md

@@ -12,4 +12,12 @@ base_2_base.yml - used for training and inference using [stable-diffusion-2-base
 
 ## Stable Diffusion XL & SDXL Lightning
 
-base_xl.yml - used to run inference using [stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+base_xl.yml - used to run inference using [stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+
+base_xl_lightning.yml - used to run inference using [SDXL-Lightning](https://huggingface.co/ByteDance/SDXL-Lightning)
+
+## Flux
+
+base_flux_dev.yml - used for training and inference using [Flux Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)
+
+base_flux_schnell.yml - used for training and inference using [Flux Schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell)

src/maxdiffusion/configs/base_flux_dev.yml

@@ -177,7 +177,7 @@ hf_train_files: ''
 hf_access_token: ''
 image_column: 'image'
 caption_column: 'text'
-resolution: 512
+resolution: 1024
 center_crop: False
 random_flip: False
 # If cache_latents_text_encoder_outputs is True

src/maxdiffusion/generate_flux_pipeline.py

@@ -96,13 +96,13 @@ def run(config):
 
   t0 = time.perf_counter()
   with ExitStack():
-    imgs = pipeline(flux_params=flux_state, timesteps=50, vae_params=vae_state).block_until_ready()
+    imgs = pipeline(flux_params=flux_state, timesteps=config.num_inference_steps, vae_params=vae_state).block_until_ready()
   t1 = time.perf_counter()
   max_logging.log(f"Compile time: {t1 - t0:.1f}s.")
 
   t0 = time.perf_counter()
   with ExitStack():
-    imgs = pipeline(flux_params=flux_state, timesteps=50, vae_params=vae_state).block_until_ready()
+    imgs = pipeline(flux_params=flux_state, timesteps=config.num_inference_steps, vae_params=vae_state).block_until_ready()
   imgs = jax.experimental.multihost_utils.process_allgather(imgs, tiled=True)
   t1 = time.perf_counter()
   max_logging.log(f"Inference time: {t1 - t0:.1f}s.")

src/maxdiffusion/pipelines/flux/flux_pipeline.py

@@ -102,7 +102,7 @@ def vae_decode(self, latents, vae, state, config):
     return img
 
   def vae_encode(self, latents, vae, state):
-    img = vae.apply({"params": state["params"]}, latents, deterministic=True, method=vae.encode).latent_dist.mode()
+    img = vae.apply({"params": state.params}, latents, deterministic=True, method=vae.encode).latent_dist.mode()
     img = vae.config.scaling_factor * (img - vae.config.shift_factor)
     return img