Closed
Description
Describe the bug
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[5], line 13
10 model_path = "/mnt/models/AI-ModelScope/stable-diffusion-3.5-large-turbo"
11 torch_dtype = torch.float16
---> 13 transformer = SD3Transformer2DModel.from_pretrained(
14 model_path,
15 subfolder="transformer",
16 quantization_config=BitsAndBytesConfig(
17 load_in_4bit=True,
18 bnb_4bit_quant_type="nf4",
19 bnb_4bit_compute_dtype=torch.bfloat16,
20 bnb_4bit_use_double_quant=True
21 ),
22 torch_dtype=torch_dtype
23 )
24 text_encoder = CLIPTextModelWithProjection.from_pretrained(
25 model_path,
26 subfolder="text_encoder",
(...) 33 torch_dtype=torch_dtype
34 )
35 text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(
36 model_path,
37 subfolder="text_encoder_2",
(...) 44 torch_dtype=torch_dtype
45 )
File ~/python_project/StoryFusion/.venv/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py:114, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
111 if check_use_auth_token:
112 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 114 return fn(*args, **kwargs)
File ~/python_project/StoryFusion/.venv/lib/python3.12/site-packages/diffusers/models/modeling_utils.py:1206, in ModelMixin.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
1196 if hf_quantizer is not None:
1197 hf_quantizer.validate_environment(device_map=device_map)
1199 (
1200 model,
1201 missing_keys,
1202 unexpected_keys,
1203 mismatched_keys,
1204 offload_index,
1205 error_msgs,
-> 1206 ) = cls._load_pretrained_model(
1207 model,
1208 state_dict,
1209 resolved_model_file,
1210 pretrained_model_name_or_path,
1211 loaded_keys,
1212 ignore_mismatched_sizes=ignore_mismatched_sizes,
1213 low_cpu_mem_usage=low_cpu_mem_usage,
1214 device_map=device_map,
1215 offload_folder=offload_folder,
1216 offload_state_dict=offload_state_dict,
1217 dtype=torch_dtype,
1218 hf_quantizer=hf_quantizer,
1219 keep_in_fp32_modules=keep_in_fp32_modules,
1220 dduf_entries=dduf_entries,
1221 )
1222 loading_info = {
1223 "missing_keys": missing_keys,
1224 "unexpected_keys": unexpected_keys,
1225 "mismatched_keys": mismatched_keys,
1226 "error_msgs": error_msgs,
1227 }
1229 # Dispatch model with hooks on all devices if necessary
File ~/python_project/StoryFusion/.venv/lib/python3.12/site-packages/diffusers/models/modeling_utils.py:1465, in ModelMixin._load_pretrained_model(cls, model, state_dict, resolved_model_file, pretrained_model_name_or_path, loaded_keys, ignore_mismatched_sizes, assign_to_params_buffers, hf_quantizer, low_cpu_mem_usage, dtype, keep_in_fp32_modules, device_map, offload_state_dict, offload_folder, dduf_entries)
1457 mismatched_keys += _find_mismatched_keys(
1458 state_dict,
1459 model_state_dict,
1460 loaded_keys,
1461 ignore_mismatched_sizes,
1462 )
1464 if low_cpu_mem_usage:
-> 1465 offload_index, state_dict_index = load_model_dict_into_meta(
1466 model,
1467 state_dict,
1468 device_map=device_map,
1469 dtype=dtype,
1470 hf_quantizer=hf_quantizer,
1471 keep_in_fp32_modules=keep_in_fp32_modules,
1472 unexpected_keys=unexpected_keys,
1473 offload_folder=offload_folder,
1474 offload_index=offload_index,
1475 state_dict_index=state_dict_index,
1476 state_dict_folder=state_dict_folder,
1477 )
1478 else:
1479 if assign_to_params_buffers is None:
File ~/python_project/StoryFusion/.venv/lib/python3.12/site-packages/diffusers/models/model_loading_utils.py:298, in load_model_dict_into_meta(model, state_dict, dtype, model_name_or_path, hf_quantizer, keep_in_fp32_modules, device_map, unexpected_keys, offload_folder, offload_index, state_dict_index, state_dict_folder)
294 state_dict_index = offload_weight(param, param_name, state_dict_folder, state_dict_index)
295 elif is_quantized and (
296 hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=param_device)
297 ):
--> 298 hf_quantizer.create_quantized_param(
299 model, param, param_name, param_device, state_dict, unexpected_keys, dtype=dtype
300 )
301 else:
302 set_module_tensor_to_device(model, param_name, param_device, value=param, **set_module_kwargs)
TypeError: BnB4BitDiffusersQuantizer.create_quantized_param() got an unexpected keyword argument 'dtype'Reproduction
import warnings
import torch
from diffusers import SD3Transformer2DModel, BitsAndBytesConfig
from diffusers.pipelines import StableDiffusion3Pipeline
from transformers import CLIPTextModelWithProjection, T5EncoderModel
warnings.filterwarnings("ignore")
model_path = "/mnt/models/AI-ModelScope/stable-diffusion-3.5-large-turbo"
torch_dtype = torch.float16
transformer = SD3Transformer2DModel.from_pretrained(
model_path,
subfolder="transformer",
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True
),
torch_dtype=torch_dtype
)Logs
System Info
- 🤗 Diffusers version: 0.33.0.dev0
- Platform: Linux-6.8.0-51-generic-x86_64-with-glibc2.39
- Running on Google Colab?: No
- Python version: 3.12.3
- PyTorch version (GPU?): 2.5.1+cu124 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.29.2
- Transformers version: 4.49.0
- Accelerate version: 1.5.1
- PEFT version: 0.14.0
- Bitsandbytes version: 0.45.3
- Safetensors version: 0.5.3
- xFormers version: 0.0.28.post3
- Accelerator: Tesla V100-SXM2-16GB, 16384 MiB
Tesla V100-SXM2-16GB, 16384 MiB - Using GPU in script?:
- Using distributed or parallel set-up in script?:
Who can help?
No response