│ /usr/local/lib/python3.10/dist-packages/accelerate/utils/modeling.py:872 in │
│ load_checkpoint_in_model │
│ │
│ 869 │ """ │
│ 870 │ tied_params = find_tied_parameters(model) │
│ 871 │ if offload_folder is None and device_map is not None and "disk" in device_map.values │
│ ❱ 872 │ │ raise ValueError( │
│ 873 │ │ │ "At least one of the model submodule will be offloaded to disk, please pass │
│ 874 │ │ ) │
│ 875 │ elif offload_folder is not None and device_map is not None and "disk" in device_map. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ValueError: At least one of the model submodule will be offloaded to disk, please pass along an offload_folder.
When running on colab, modified demo code
I actually have been playing with both XL and M models to see speed vs quality differences with the models.
So I now loaded XL model again during the same session.
I have been flush()ing and del ing the pipes and everything.
Anyway, line giving me errors is:
for image in images:
flush()
pipe = IFImg2ImgPipeline.from_pretrained(
"DeepFloyd/IF-I-XL-v1.0",
text_encoder=None,
variant="fp16",
torch_dtype=torch.float16,
device_map="auto",
offload_folder = '/content/offload' #THIS IS APPARENTLY IGNORED? SHOULD IT BE IGNORED?
)
│ /usr/local/lib/python3.10/dist-packages/accelerate/utils/modeling.py:872 in │ │ load_checkpoint_in_model │ │ │ │ 869 │ """ │ │ 870 │ tied_params = find_tied_parameters(model) │ │ 871 │ if offload_folder is None and device_map is not None and "disk" in device_map.values │ │ ❱ 872 │ │ raise ValueError( │ │ 873 │ │ │ "At least one of the model submodule will be offloaded to disk, please pass │ │ 874 │ │ ) │ │ 875 │ elif offload_folder is not None and device_map is not None and "disk" in device_map. │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ ValueError: At least one of the model submodule will be offloaded to disk, please pass along an
offload_folder.When running on colab, modified demo code
I actually have been playing with both XL and M models to see speed vs quality differences with the models.
So I now loaded XL model again during the same session. I have been flush()ing and del ing the pipes and everything. Anyway, line giving me errors is:
text_encoder = T5EncoderModel.from_pretrained( "DeepFloyd/IF-I-XL-v1.0", subfolder="text_encoder", device_map="auto", load_in_8bit=True, variant="8bit" )
pipe = IFImg2ImgPipeline.from_pretrained( "DeepFloyd/IF-I-XL-v1.0", text_encoder=text_encoder, unet=None, device_map="auto" ) prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
free some memory
del pipe del text_encoder
for image in images: flush() pipe = IFImg2ImgPipeline.from_pretrained( "DeepFloyd/IF-I-XL-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16, device_map="auto", offload_folder = '/content/offload' #THIS IS APPARENTLY IGNORED? SHOULD IT BE IGNORED? )