Quantizer configs are mutated in place, leaking modules_to_not_convert across loads #15124

Describe the bug

BitsAndBytes (4-bit and 8-bit), TorchAO, Quanto, and ModelOpt quantizers each alias a modules_to_not_convert-style list directly from self.quantization_config and then call .extend() on it, instead of copying it first:

• BitsAndBytes 4/8-bit: bnb_quantizer.py (_process_model_before_weight_loading)
• TorchAO: torchao_quantizer.py (_process_model_before_weight_loading)
• Quanto: quanto_quantizer.py (_process_model_before_weight_loading)
• ModelOpt: modelopt_quantizer.py (_process_model_before_weight_loading), which also writes module entries into modelopt_config["quant_cfg"]

Reusing the same quantization_config object across two from_pretrained calls leaks state between loads — keep_in_fp32_modules and CPU/disk-offloaded module names accumulate on the shared config object across calls, and in PipelineQuantizationConfig the same quant_kwargs list / quant_mapping object is shared across components (including transformers text encoders), so the leak isn't confined to one component. The accumulated list also gets written into config.json on save.

Reproduction

import torch
from diffusers import BitsAndBytesConfig
from diffusers.quantizers.bitsandbytes.bnb_quantizer import BnB8BitDiffusersQuantizer

skip_modules = ["proj_out"]
config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_skip_modules=skip_modules)
model = torch.nn.Sequential(torch.nn.Linear(4, 4), torch.nn.Linear(4, 4))

for _ in range(2):
    quantizer = BnB8BitDiffusersQuantizer(config)
    quantizer._process_model_before_weight_loading(model, device_map=None, keep_in_fp32_modules=["norm_out"])

print(config.llm_int8_skip_modules)
# ['proj_out', 'norm_out', 'norm_out'] after two loads - should stay ['proj_out']

System Info

• diffusers @ main, commit aaf57d0
• Python 3.12, Linux

Who can help?

(leaving blank — no specific quantization reviewer listed in the template's tag guide)