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enable model cpu offload() #1

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Merged
merged 1 commit into from
Mar 20, 2025
Merged

enable model cpu offload() #1

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10 changes: 10 additions & 0 deletions src/diffusers/loaders/lora_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1978,6 +1978,16 @@ def _maybe_expand_transformer_param_shape_or_error_(
"The checkpoint seems to have been quantized with `bitsandbytes` (4bits). Install `bitsandbytes` to load quantized checkpoints."
)
elif is_bnb_4bit_quantized:
weight_on_cpu = False
if not module.weight.is_cuda:
weight_on_cpu = True
module_weight = dequantize_bnb_weight(
module.weight.cuda() if weight_on_cpu else module.weight,
state=module.weight.quant_state,
dtype=transformer.dtype,
).data
if weight_on_cpu:
module_weight = module_weight.cpu()
module_weight = dequantize_bnb_weight(module.weight, state=module.weight.quant_state).data
else:
module_weight = module.weight.data
Expand Down
20 changes: 12 additions & 8 deletions tests/quantization/bnb/test_4bit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,15 @@
import pytest
import safetensors.torch
from huggingface_hub import hf_hub_download

from diffusers import BitsAndBytesConfig, DiffusionPipeline, FluxTransformer2DModel, SD3Transformer2DModel
from PIL import Image

from diffusers import (
BitsAndBytesConfig,
DiffusionPipeline,
FluxControlPipeline,
FluxTransformer2DModel,
SD3Transformer2DModel,
)
from diffusers.utils import is_accelerate_version, logging
from diffusers.utils.testing_utils import (
CaptureLogger,
Expand Down Expand Up @@ -702,10 +709,7 @@ def setUp(self) -> None:
gc.collect()
torch.cuda.empty_cache()

self.pipeline_4bit = DiffusionPipeline.from_pretrained(
"eramth/flux-4bit",
torch_dtype=torch.float16,
)
self.pipeline_4bit = FluxControlPipeline.from_pretrained("eramth/flux-4bit", torch_dtype=torch.float16)
self.pipeline_4bit.enable_model_cpu_offload()

def tearDown(self):
Expand All @@ -719,6 +723,7 @@ def test_lora_loading(self):

output = self.pipeline_4bit(
prompt=self.prompt,
control_image=Image.new(mode="RGB", size=(256, 256)),
height=256,
width=256,
max_sequence_length=64,
Expand All @@ -727,8 +732,7 @@ def test_lora_loading(self):
generator=torch.Generator().manual_seed(42),
).images
out_slice = output[0, -3:, -3:, -1].flatten()
# TODO: update slice
expected_slice = np.array([0.5347, 0.5342, 0.5283, 0.5093, 0.4988, 0.5093, 0.5044, 0.5015, 0.4946])
expected_slice = np.array([0.1636, 0.1675, 0.1982, 0.1743, 0.1809, 0.1936, 0.1743, 0.2095, 0.2139])

max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice)
self.assertTrue(max_diff < 1e-3, msg=f"{out_slice=} != {expected_slice=}")
Expand Down