style: make linter happy (#192)

* style: make linter happy

* upgrade the version

Author: lmxyy

nodes/models/flux.py

@@ -98,19 +98,19 @@ def forward(
         controlnet_block_samples = None if control is None else [y.to(x.dtype) for y in control["input"]]
         controlnet_single_block_samples = None if control is None else [y.to(x.dtype) for y in control["output"]]
         if getattr(model, "_is_cached", False):
-            # A more robust caching strategy 
+            # A more robust caching strategy
             cache_invalid = False
-            
-            # Check if timestamps have changed or are out of valid range 
+
+            # Check if timestamps have changed or are out of valid range
             if self._prev_timestep is None:
                 cache_invalid = True
-            elif abs(self._prev_timestep - timestep_float) > 1e-5:  # Add a tolerance 
+            elif abs(self._prev_timestep - timestep_float) > 1e-5:  # Add a tolerance
                 cache_invalid = True
-            
+
             if cache_invalid:
                 self._cache_context = create_cache_context()
-            
-            # Update the previous timestamp 
+
+            # Update the previous timestamp
             self._prev_timestep = timestep_float
             with cache_context(self._cache_context):
                 out = model(

pyproject.toml

@@ -16,7 +16,7 @@ line-length = 120
 [project]
 name = "ComfyUI-nunchaku"
 description = "Nunchaku ComfyUI Node. Nunchaku is the inference engine that supports SVDQuant. SVDQuant is a new post-training training quantization paradigm for diffusion models, which quantize both the weights and activations of FLUX.1 to 4 bits, achieving 3.5× memory and 8.7× latency reduction on a 16GB laptop 4090 GPU. See more details: https://github.com/mit-han-lab/nunchaku"
-version = "0.3.0dev1"
+version = "0.3.0dev2"
 license = { file = "LICENSE.txt" }
 dependencies = []
 requires-python = ">=3.10, <3.13"