mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
95 lines
3.6 KiB
Python
95 lines
3.6 KiB
Python
import time
|
|
from contextlib import contextmanager
|
|
from pathlib import Path
|
|
|
|
import accelerate
|
|
import torch
|
|
from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
|
|
from safetensors.torch import load_file, save_file
|
|
|
|
from invokeai.backend.quantization.bnb_nf4 import quantize_model_nf4
|
|
|
|
|
|
@contextmanager
|
|
def log_time(name: str):
|
|
"""Helper context manager to log the time taken by a block of code."""
|
|
start = time.time()
|
|
try:
|
|
yield None
|
|
finally:
|
|
end = time.time()
|
|
print(f"'{name}' took {end - start:.4f} secs")
|
|
|
|
|
|
def main():
|
|
# Load the FLUX transformer model onto the meta device.
|
|
model_path = Path(
|
|
"/data/invokeai/models/.download_cache/black-forest-labs_flux.1-schnell/FLUX.1-schnell/transformer/"
|
|
)
|
|
|
|
with log_time("Intialize FLUX transformer on meta device"):
|
|
model_config = FluxTransformer2DModel.load_config(model_path, local_files_only=True)
|
|
with accelerate.init_empty_weights():
|
|
empty_model = FluxTransformer2DModel.from_config(model_config)
|
|
assert isinstance(empty_model, FluxTransformer2DModel)
|
|
|
|
# TODO(ryand): We may want to add some modules to not quantize here (e.g. the proj_out layer). See the accelerate
|
|
# `get_keys_to_not_convert(...)` function for a heuristic to determine which modules to not quantize.
|
|
modules_to_not_convert: set[str] = set()
|
|
|
|
model_nf4_path = model_path / "bnb_nf4"
|
|
if model_nf4_path.exists():
|
|
# The quantized model already exists, load it and return it.
|
|
print(f"A pre-quantized model already exists at '{model_nf4_path}'. Attempting to load it...")
|
|
|
|
# Replace the linear layers with NF4 quantized linear layers (still on the meta device).
|
|
with log_time("Replace linear layers with NF4 layers"), accelerate.init_empty_weights():
|
|
model = quantize_model_nf4(
|
|
empty_model, modules_to_not_convert=modules_to_not_convert, compute_dtype=torch.bfloat16
|
|
)
|
|
|
|
with log_time("Load state dict into model"):
|
|
sd = load_file(model_nf4_path / "model.safetensors")
|
|
model.load_state_dict(sd, strict=True, assign=True)
|
|
|
|
with log_time("Move model to cuda"):
|
|
model = model.to("cuda")
|
|
|
|
print(f"Successfully loaded pre-quantized model from '{model_nf4_path}'.")
|
|
|
|
else:
|
|
# The quantized model does not exist, quantize the model and save it.
|
|
print(f"No pre-quantized model found at '{model_nf4_path}'. Quantizing the model...")
|
|
|
|
with log_time("Replace linear layers with NF4 layers"), accelerate.init_empty_weights():
|
|
model = quantize_model_nf4(
|
|
empty_model, modules_to_not_convert=modules_to_not_convert, compute_dtype=torch.bfloat16
|
|
)
|
|
|
|
with log_time("Load state dict into model"):
|
|
# Load sharded state dict.
|
|
files = list(model_path.glob("*.safetensors"))
|
|
state_dict = dict()
|
|
for file in files:
|
|
sd = load_file(file)
|
|
state_dict.update(sd)
|
|
|
|
model.load_state_dict(state_dict, strict=True, assign=True)
|
|
|
|
with log_time("Move model to cuda and quantize"):
|
|
model = model.to("cuda")
|
|
|
|
with log_time("Save quantized model"):
|
|
model_nf4_path.mkdir(parents=True, exist_ok=True)
|
|
output_path = model_nf4_path / "model.safetensors"
|
|
save_file(model.state_dict(), output_path)
|
|
|
|
print(f"Successfully quantized and saved model to '{output_path}'.")
|
|
|
|
assert isinstance(model, FluxTransformer2DModel)
|
|
return model
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|