update

Author: lmxyy

nunchaku/lora/flux/compose.py

@@ -64,7 +64,7 @@ def compose_lora(
     >>> # Compose two LoRA weights with different strengths
     >>> lora_paths = [("lora1.safetensors", 0.8), ("lora2.safetensors", 0.6)]
     >>> composed = compose_lora(lora_paths, "composed_lora.safetensors")
-    
+
     >>> # Compose from weight dictionaries
     >>> lora_dicts = [({"layer.weight": torch.randn(10, 20)}, 1.0)]
     >>> composed = compose_lora(lora_dicts)

nunchaku/lora/flux/diffusers_converter.py

@@ -60,7 +60,7 @@ def handle_kohya_lora(state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Te
     - `transformer_blocks_` → `transformer_blocks.`
     - `lora_down` → `lora_A`
     - `lora_up` → `lora_B`
-    
+
     And many other component-specific transformations.
 
     Examples
@@ -145,7 +145,7 @@ def to_diffusers(input_lora: str | dict[str, torch.Tensor], output_path: str | N
     --------
     >>> # Convert from file path
     >>> diffusers_weights = to_diffusers("path/to/lora.safetensors")
-    
+
     >>> # Convert from weight dictionary
     >>> weights = {"transformer.layer.lora_A.weight": torch.randn(10, 20)}
     >>> diffusers_weights = to_diffusers(weights, "output.safetensors")

nunchaku/lora/flux/nunchaku_converter.py

@@ -1,4 +1,3 @@
-# convert the diffusers lora to nunchaku format
 """
 Nunchaku LoRA format converter for Flux models.
 
@@ -22,7 +21,7 @@
 - `pack_lowrank_weight`/`unpack_lowrank_weight`: Low-rank weight transformations
 - `fuse_vectors`: Vector fusion for bias terms
 """
-"""Convert LoRA weights to Nunchaku format."""
+
 import logging
 import os
 

nunchaku/lora/flux/packer.py

@@ -90,6 +90,7 @@ class MmaWeightPackerBase:
         If tile sizes are not properly divisible.
         If pack size is not in the valid range [1, 4].
     """
+
     def __init__(self, bits: int, warp_n: int, comp_n: int = None, comp_k: int = None):
         self.bits = bits
         assert self.bits in (1, 4, 8, 16, 32), "weight bits should be 1, 4, 8, 16, or 32."
@@ -226,6 +227,7 @@ class NunchakuWeightPacker(MmaWeightPackerBase):
     pad_lowrank_weight(weight, down)
         Pad low-rank weight tensors to required dimensions.
     """
+
     def __init__(self, bits: int, warp_n: int = 128):
         super().__init__(bits=bits, warp_n=warp_n)
         self.num_k_unrolls = 2

nunchaku/lora/flux/utils.py

@@ -16,7 +16,7 @@ def is_nunchaku_format(lora: str | dict[str, torch.Tensor]) -> bool:
     """
     Check if LoRA weights are in Nunchaku format.
 
-    This function determines whether the provided LoRA weights are already in 
+    This function determines whether the provided LoRA weights are already in
     Nunchaku format by checking for specific key patterns that are characteristic
     of Nunchaku-formatted LoRA weights.
 
@@ -36,7 +36,7 @@ def is_nunchaku_format(lora: str | dict[str, torch.Tensor]) -> bool:
     >>> # Check format from file path
     >>> is_nunchaku_format("path/to/lora.safetensors")
     True
-    
+
     >>> # Check format from weight dictionary
     >>> weights = {"transformer_blocks.0.mlp_fc.weight": torch.randn(128, 256)}
     >>> is_nunchaku_format(weights)

nunchaku/models/text_encoders/t5_encoder.py

@@ -37,10 +37,7 @@
 log_level = os.getenv("LOG_LEVEL", "INFO").upper()
 
 # Configure logging
-logging.basicConfig(
-    level=getattr(logging, log_level, logging.INFO),
-    format="%(asctime)s - %(levelname)s - %(message)s"
-)
+logging.basicConfig(level=getattr(logging, log_level, logging.INFO), format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger(__name__)