FLUX: fix pre-commit lints

flux/mlx_flux/dreambooth.py

@@ -1,16 +1,17 @@
 # Copyright © 2024 Apple Inc.
 
 import argparse
+import time
+from functools import partial
+from pathlib import Path
+
 import mlx.core as mx
 import mlx.nn as nn
 import mlx.optimizers as optim
 import numpy as np
-import time
-from PIL import Image
-from functools import partial
 from mlx.nn.utils import average_gradients
 from mlx.utils import tree_flatten, tree_map, tree_reduce
-from pathlib import Path
+from PIL import Image
 
 from .datasets import load_dataset
 from .flux import FluxPipeline
@@ -187,7 +188,6 @@ if __name__ == "__main__":
     optimizer = optim.Adam(learning_rate=lr_schedule)
     state = [flux.flow.state, optimizer.state, mx.random.state]
 
-
     @partial(mx.compile, inputs=state, outputs=state)
     def single_step(x, t5_feat, clip_feat, guidance):
         loss, grads = nn.value_and_grad(flux.flow, flux.training_loss)(
@@ -198,14 +198,12 @@ if __name__ == "__main__":
 
         return loss
 
-
     @partial(mx.compile, inputs=state, outputs=state)
     def compute_loss_and_grads(x, t5_feat, clip_feat, guidance):
         return nn.value_and_grad(flux.flow, flux.training_loss)(
             x, t5_feat, clip_feat, guidance
         )
 
-
     @partial(mx.compile, inputs=state, outputs=state)
     def compute_loss_and_accumulate_grads(x, t5_feat, clip_feat, guidance, prev_grads):
         loss, grads = nn.value_and_grad(flux.flow, flux.training_loss)(
@@ -214,7 +212,6 @@ if __name__ == "__main__":
         grads = tree_map(lambda a, b: a + b, prev_grads, grads)
         return loss, grads
 
-
     @partial(mx.compile, inputs=state, outputs=state)
     def grad_accumulate_and_step(x, t5_feat, clip_feat, guidance, prev_grads):
         loss, grads = nn.value_and_grad(flux.flow, flux.training_loss)(
@@ -230,7 +227,6 @@ if __name__ == "__main__":
 
         return loss
 
-
     # We simply route to the appropriate step based on whether we have
     # gradients from a previous step and whether we should be performing an
     # update or simply computing and accumulating gradients in this step.
@@ -253,7 +249,6 @@ if __name__ == "__main__":
                     x, t5_feat, clip_feat, guidance, prev_grads
                 )
 
-
     dataset = load_dataset(args.dataset)
     trainer = Trainer(flux, dataset, args)
     trainer.encode_dataset()
@@ -273,7 +268,7 @@ if __name__ == "__main__":
 
         if (i + 1) % 10 == 0:
             toc = time.time()
-            peak_mem = mx.metal.get_peak_memory() / 1024 ** 3
+            peak_mem = mx.metal.get_peak_memory() / 1024**3
             print(
                 f"Iter: {i + 1} Loss: {sum(losses) / 10:.3f} "
                 f"It/s: {10 / (toc - tic):.3f} "

flux/mlx_flux/txt2image.py

@@ -1,6 +1,7 @@
 # Copyright © 2024 Apple Inc.
 
 import argparse
+
 import mlx.core as mx
 import mlx.nn as nn
 import numpy as np
@@ -96,7 +97,7 @@ def main():
     # First we get and eval the conditioning
     conditioning = next(latents)
     mx.eval(conditioning)
-    peak_mem_conditioning = mx.metal.get_peak_memory() / 1024 ** 3
+    peak_mem_conditioning = mx.metal.get_peak_memory() / 1024**3
     mx.metal.reset_peak_memory()
 
     # The following is not necessary but it may help in memory constrained
@@ -111,15 +112,15 @@ def main():
     # The following is not necessary but it may help in memory constrained
     # systems by reusing the memory kept by the flow transformer.
     del flux.flow
-    peak_mem_generation = mx.metal.get_peak_memory() / 1024 ** 3
+    peak_mem_generation = mx.metal.get_peak_memory() / 1024**3
     mx.metal.reset_peak_memory()
 
     # Decode them into images
     decoded = []
     for i in tqdm(range(0, args.n_images, args.decoding_batch_size)):
-        decoded.append(flux.decode(x_t[i: i + args.decoding_batch_size], latent_size))
+        decoded.append(flux.decode(x_t[i : i + args.decoding_batch_size], latent_size))
         mx.eval(decoded[-1])
-    peak_mem_decoding = mx.metal.get_peak_memory() / 1024 ** 3
+    peak_mem_decoding = mx.metal.get_peak_memory() / 1024**3
     peak_mem_overall = max(
         peak_mem_conditioning, peak_mem_generation, peak_mem_decoding
     )

flux/setup.py

@@ -39,14 +39,14 @@ setup(
     url="https://github.com/ml-explore/mlx-examples",
     license="MIT",
     install_requires=requirements,
-
     # Package configuration
-    packages=find_namespace_packages(include=["mlx_flux", "mlx_flux.*"]),  # 明确指定包含的包
+    packages=find_namespace_packages(
+        include=["mlx_flux", "mlx_flux.*"]
+    ),  # 明确指定包含的包
     package_data={
         "mlx_flux": ["*.py"],
     },
     include_package_data=True,
-
     python_requires=">=3.8",
     entry_points={
         "console_scripts": [