fix merge conflicts

src/flux/cli.py

@@ -5,15 +5,13 @@
 from glob import iglob
 
 import torch
+from cuda import cudart
 from fire import Fire
 from transformers import pipeline
 
 from flux.sampling import denoise, get_noise, get_schedule, prepare, unpack
-from flux.util import configs, load_ae, load_clip, load_flow_model, load_t5, save_image
-
 from flux.trt.trt_manager import TRTManager
-from cuda import cudart
-
+from flux.util import configs, load_ae, load_clip, load_flow_model, load_t5, save_image
 
 NSFW_THRESHOLD = 0.85
 
@@ -29,9 +27,7 @@ class SamplingOptions:
 
 
 def parse_prompt(options: SamplingOptions) -> SamplingOptions | None:
-    user_question = (
-        "Next prompt (write /h for help, /q to quit and leave empty to repeat):\n"
-    )
+    user_question = "Next prompt (write /h for help, /q to quit and leave empty to repeat):\n"
     usage = (
         "Usage: Either write your prompt directly, leave this field empty "
         "to repeat the prompt or write a command starting with a slash:\n"
@@ -137,9 +133,7 @@ def main(
         trt: use TensorRT backend for optimized inference
         kwargs: additional arguments for TensorRT support
     """
-    nsfw_classifier = pipeline(
-        "image-classification", model="Falconsai/nsfw_image_detection", device=device
-    )
+    nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=device)
 
     if name not in configs:
         available = ", ".join(configs.keys())
@@ -158,11 +152,7 @@ def main(
         os.makedirs(output_dir)
         idx = 0
     else:
-        fns = [
-            fn
-            for fn in iglob(output_name.format(idx="*"))
-            if re.search(r"img_[0-9]+\.jpg$", fn)
-        ]
+        fns = [fn for fn in iglob(output_name.format(idx="*")) if re.search(r"img_[0-9]+\.jpg$", fn)]
         if len(fns) > 0:
             idx = max(int(fn.split("_")[-1].split(".")[0]) for fn in fns) + 1
         else:
@@ -260,9 +250,7 @@ def main(
             torch.cuda.empty_cache()
             t5, clip = t5.to(torch_device), clip.to(torch_device)
         inp = prepare(t5, clip, x, prompt=opts.prompt)
-        timesteps = get_schedule(
-            opts.num_steps, inp["img"].shape[1], shift=(name != "flux-schnell")
-        )
+        timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=(name != "flux-schnell"))
 
         # offload TEs to CPU, load model to gpu
         if offload: