Update README

flux/README.md

@@ -167,8 +167,9 @@ python dreambooth.py \
     path/to/dreambooth/dataset/dog6
 ```
 
-
+Or you can directly use the pre-processed Hugging Face dataset
-Or you can directly use the pre-processed Hugging Face dataset [mlx-community/dreambooth-dog6](https://huggingface.co/datasets/mlx-community/dreambooth-dog6) for fine-tuning.
+[mlx-community/dreambooth-dog6](https://huggingface.co/datasets/mlx-community/dreambooth-dog6)
+for fine-tuning.
 
 ```shell
 python dreambooth.py \
@@ -210,3 +211,71 @@ speed during generation.
 
 [^1]: Refer to the [arXiv paper](https://arxiv.org/abs/2208.12242) for more details.
 [^2]: The images are from unsplash by https://unsplash.com/@alvannee .
+
+
+Distributed Computation
+------------------------
+
+The FLUX example supports distributed computation during both generation and
+training. See the [distributed communication
+documentation](https://ml-explore.github.io/mlx/build/html/usage/distributed.html)
+for information on how to set-up MLX for distributed communication. The rest of
+this section assumes you can launch distributed MLX programs using `mlx.launch
+--hostfile hostfile.json`.
+
+### Distributed Finetuning
+
+Distributed finetuning scales very well with FLUX and all one has to do is
+simply to adjust the gradient accumulation and iterations so that the batch
+size remains the same. For instance, to replicate the following training
+
+```shell
+python dreambooth.py \
+    --progress-prompt 'A photo of an sks dog lying on the sand at a beach in Greece' \
+    --progress-every 600 --iterations 1200 --learning-rate 0.0001 \
+    --lora-rank 4 --grad-accumulate 8 \
+    mlx-community/dreambooth-dog6
+```
+
+On 4 machines we simply run
+
+```shell
+mlx.launch --verbose --hostfile hostfile.json -- python dreambooth.py \
+    --progress-prompt 'A photo of an sks dog lying on the sand at a beach in Greece' \
+    --progress-every 150 --iterations 300 --learning-rate 0.0001 \
+    --lora-rank 4 --grad-accumulate 2 \
+    mlx-community/dreambooth-dog6
+```
+
+Note the iterations that changed to 300 from 1200 and the gradient accumulations to 2 from 8.
+
+### Distributed Inference
+
+Distributed inference can be divided in two different approaches. The first
+approach is the data-parallel approach, where each node generates its own
+images and shares them at the end. The second approach is the model-parallel
+approach where the model is shared across the nodes and they collaboratively
+generate the images.
+
+The `txt2image.py` script will attempt to choose the best approach depending on
+how many images are being generated across the nodes. The model-parallel
+approach can be forced by passing the argument `--force-shard`.
+
+For better performance in the model-parallel approach we suggest that you use a
+[thunderbolt
+ring](https://ml-explore.github.io/mlx/build/html/usage/distributed.html#getting-started-with-ring).
+
+All you have to do once again is use `mlx.launch` as follows
+
+```shell
+mlx.launch --verbose --hostfile hostfile.json -- \
+    python txt2image.py --model schnell \
+    --n-images 8 \
+    --image-size 512x512 \
+    --verbose \
+    'A photo of an astronaut riding a horse on Mars'
+```
+
+for model-parallel generation you may want to also pass `--env
+MLX_METAL_FAST_SYNCH=1` to `mlx.launch` which is an experimental setting that
+reduces the CPU/GPU synchronization overhead.

flux/txt2image.py

@@ -41,7 +41,7 @@ def load_adapter(flux, adapter_file, fuse=False):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description="Generate images from a textual prompt using stable diffusion"
+        description="Generate images from a textual prompt using FLUX"
     )
     parser.add_argument("prompt")
     parser.add_argument("--model", choices=["schnell", "dev"], default="schnell")