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bitsandbytes

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sayakpaul
2025-04-12 12:41:25 +05:30
parent 61287def48
commit 7b09d62e72
1 changed files with 16 additions and 16 deletions
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docs/source/en/quantization/bitsandbytes.md
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@@ -49,7 +49,7 @@ For Ada and higher-series GPUs. we recommend changing `torch_dtype` to `torch.bf
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig
from diffusers import FluxTransformer2DModel
from diffusers import AutoModel
from transformers import T5EncoderModel
quant_config = TransformersBitsAndBytesConfig(load_in_8bit=True,)
@@ -63,7 +63,7 @@ text_encoder_2_8bit = T5EncoderModel.from_pretrained(
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True,)
transformer_8bit = FluxTransformer2DModel.from_pretrained(
transformer_8bit = AutoModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
quantization_config=quant_config,
@@ -74,7 +74,7 @@ transformer_8bit = FluxTransformer2DModel.from_pretrained(
By default, all the other modules such as `torch.nn.LayerNorm` are converted to `torch.float16`. You can change the data type of these modules with the `torch_dtype` parameter.
```diff
transformer_8bit = FluxTransformer2DModel.from_pretrained(
transformer_8bit = AutoModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
quantization_config=quant_config,
@@ -133,7 +133,7 @@ For Ada and higher-series GPUs. we recommend changing `torch_dtype` to `torch.bf
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig
from diffusers import FluxTransformer2DModel
from diffusers import AutoModel
from transformers import T5EncoderModel
quant_config = TransformersBitsAndBytesConfig(load_in_4bit=True,)
@@ -147,7 +147,7 @@ text_encoder_2_4bit = T5EncoderModel.from_pretrained(
quant_config = DiffusersBitsAndBytesConfig(load_in_4bit=True,)
transformer_4bit = FluxTransformer2DModel.from_pretrained(
transformer_4bit = AutoModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
quantization_config=quant_config,
@@ -158,7 +158,7 @@ transformer_4bit = FluxTransformer2DModel.from_pretrained(
By default, all the other modules such as `torch.nn.LayerNorm` are converted to `torch.float16`. You can change the data type of these modules with the `torch_dtype` parameter.
```diff
transformer_4bit = FluxTransformer2DModel.from_pretrained(
transformer_4bit = AutoModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
quantization_config=quant_config,
@@ -217,11 +217,11 @@ print(model.get_memory_footprint())
Quantized models can be loaded from the [`~ModelMixin.from_pretrained`] method without needing to specify the `quantization_config` parameters:
```py
from diffusers import FluxTransformer2DModel, BitsAndBytesConfig
from diffusers import AutoModel, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
model_4bit = FluxTransformer2DModel.from_pretrained(
model_4bit = AutoModel.from_pretrained(
"hf-internal-testing/flux.1-dev-nf4-pkg", subfolder="transformer"
)
```
@@ -243,13 +243,13 @@ An "outlier" is a hidden state value greater than a certain threshold, and these
To find the best threshold for your model, we recommend experimenting with the `llm_int8_threshold` parameter in [`BitsAndBytesConfig`]:
```py
from diffusers import FluxTransformer2DModel, BitsAndBytesConfig
from diffusers import AutoModel, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(
load_in_8bit=True, llm_int8_threshold=10,
)
model_8bit = FluxTransformer2DModel.from_pretrained(
model_8bit = AutoModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
quantization_config=quantization_config,
@@ -305,7 +305,7 @@ NF4 is a 4-bit data type from the [QLoRA](https://hf.co/papers/2305.14314) paper
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig
from diffusers import FluxTransformer2DModel
from diffusers import AutoModel
from transformers import T5EncoderModel
quant_config = TransformersBitsAndBytesConfig(
@@ -325,7 +325,7 @@ quant_config = DiffusersBitsAndBytesConfig(
bnb_4bit_quant_type="nf4",
)
transformer_4bit = FluxTransformer2DModel.from_pretrained(
transformer_4bit = AutoModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
quantization_config=quant_config,
@@ -343,7 +343,7 @@ Nested quantization is a technique that can save additional memory at no additio
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig
from diffusers import FluxTransformer2DModel
from diffusers import AutoModel
from transformers import T5EncoderModel
quant_config = TransformersBitsAndBytesConfig(
@@ -363,7 +363,7 @@ quant_config = DiffusersBitsAndBytesConfig(
bnb_4bit_use_double_quant=True,
)
transformer_4bit = FluxTransformer2DModel.from_pretrained(
transformer_4bit = AutoModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
quantization_config=quant_config,
@@ -379,7 +379,7 @@ Once quantized, you can dequantize a model to its original precision, but this m
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig
from diffusers import FluxTransformer2DModel
from diffusers import AutoModel
from transformers import T5EncoderModel
quant_config = TransformersBitsAndBytesConfig(
@@ -399,7 +399,7 @@ quant_config = DiffusersBitsAndBytesConfig(
bnb_4bit_use_double_quant=True,
)
transformer_4bit = FluxTransformer2DModel.from_pretrained(
transformer_4bit = AutoModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
quantization_config=quant_config,