[Single File] Add GGUF support (#9964)

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* Update src/diffusers/quantizers/gguf/utils.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

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* Update docs/source/en/quantization/gguf.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

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---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

.github/workflows/nightly_tests.yml

@@ -357,6 +357,8 @@ jobs:
         config:
           - backend: "bitsandbytes"
             test_location: "bnb"
+          - backend: "gguf"
+            test_location: "gguf"
     runs-on:
       group: aws-g6e-xlarge-plus
     container:

docs/source/en/_toctree.yml

@@ -157,6 +157,8 @@
     title: Getting Started
   - local: quantization/bitsandbytes
     title: bitsandbytes
+  - local: quantization/gguf
+    title: gguf
   - local: quantization/torchao
     title: torchao
   title: Quantization Methods

docs/source/en/api/quantization.md

@@ -28,6 +28,9 @@ Learn how to quantize models in the [Quantization](../quantization/overview) gui
 
 [[autodoc]] BitsAndBytesConfig
 
+## GGUFQuantizationConfig
+
+[[autodoc]] GGUFQuantizationConfig
 ## TorchAoConfig
 
 [[autodoc]] TorchAoConfig

docs/source/en/quantization/gguf.md

+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+-->
+
+# GGUF
+
+The GGUF file format is typically used to store models for inference with [GGML](https://github.com/ggerganov/ggml) and supports a variety of block wise quantization options. Diffusers supports loading checkpoints prequantized and saved in the GGUF format via `from_single_file` loading with Model classes. Loading GGUF checkpoints via Pipelines is currently not supported.
+
+The following example will load the [FLUX.1 DEV](https://huggingface.co/black-forest-labs/FLUX.1-dev) transformer model using the GGUF Q2_K quantization variant.
+
+Before starting please install gguf in your environment
+
+```shell
+pip install -U gguf
+```
+
+Since GGUF is a single file format, use [`~FromSingleFileMixin.from_single_file`] to load the model and pass in the [`GGUFQuantizationConfig`].
+
+When using GGUF checkpoints, the quantized weights remain in a low memory `dtype`(typically `torch.unint8`) and are dynamically dequantized and cast to the configured `compute_dtype` during each module's forward pass through the model. The `GGUFQuantizationConfig` allows you to set the `compute_dtype`. 
+
+The functions used for dynamic dequantizatation are based on the great work done by [city96](https://github.com/city96/ComfyUI-GGUF), who created the Pytorch ports of the original (`numpy`)[https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/gguf/quants.py] implementation by [compilade](https://github.com/compilade).
+
+```python
+import torch
+
+from diffusers import FluxPipeline, FluxTransformer2DModel, GGUFQuantizationConfig
+
+ckpt_path = (
+    "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf"
+)
+transformer = FluxTransformer2DModel.from_single_file(
+    ckpt_path,
+    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
+    torch_dtype=torch.bfloat16,
+)
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    transformer=transformer,
+    generator=torch.manual_seed(0),
+    torch_dtype=torch.bfloat16,
+)
+pipe.enable_model_cpu_offload()
+prompt = "A cat holding a sign that says hello world"
+image = pipe(prompt).images[0]
+image.save("flux-gguf.png")
+```
+
+## Supported Quantization Types
+
+- BF16
+- Q4_0
+- Q4_1
+- Q5_0
+- Q5_1
+- Q8_0
+- Q2_K
+- Q3_K
+- Q4_K
+- Q5_K
+- Q6_K
+

docs/source/en/quantization/overview.md

@@ -17,7 +17,7 @@ Quantization techniques focus on representing data with less information while a
 
 <Tip>
 
-Interested in adding a new quantization method to Transformers? Refer to the [Contribute new quantization method guide](https://huggingface.co/docs/transformers/main/en/quantization/contribute) to learn more about adding a new quantization method.
+Interested in adding a new quantization method to Diffusers? Refer to the [Contribute new quantization method guide](https://huggingface.co/docs/transformers/main/en/quantization/contribute) to learn more about adding a new quantization method.
 
 </Tip>
 
@@ -32,4 +32,9 @@ If you are new to the quantization field, we recommend you to check out these be
 
 ## When to use what?
 
-Diffusers supports [bitsandbytes](https://huggingface.co/docs/bitsandbytes/main/en/index) and [torchao](https://github.com/pytorch/ao). Refer to this [table](https://huggingface.co/docs/transformers/main/en/quantization/overview#when-to-use-what) to help you determine which quantization backend to use.
\ No newline at end of file
+Diffusers currently supports the following quantization methods.
+- [BitsandBytes]()
+- [TorchAO]()
+- [GGUF]()
+
+[This resource](https://huggingface.co/docs/transformers/main/en/quantization/overview#when-to-use-what) provides a good overview of the pros and cons of different quantization techniques.

src/diffusers/__init__.py

@@ -31,7 +31,7 @@ _import_structure = {
     "loaders": ["FromOriginalModelMixin"],
     "models": [],
     "pipelines": [],
-    "quantizers.quantization_config": ["BitsAndBytesConfig", "TorchAoConfig"],
+    "quantizers.quantization_config": ["BitsAndBytesConfig", "GGUFQuantizationConfig", "TorchAoConfig"],
     "schedulers": [],
     "utils": [
         "OptionalDependencyNotAvailable",
@@ -569,7 +569,7 @@ else:
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     from .configuration_utils import ConfigMixin
-    from .quantizers.quantization_config import BitsAndBytesConfig, TorchAoConfig
+    from .quantizers.quantization_config import BitsAndBytesConfig, GGUFQuantizationConfig, TorchAoConfig
 
     try:
         if not is_onnx_available():

src/diffusers/loaders/single_file_model.py

@@ -17,8 +17,10 @@ import re
 from contextlib import nullcontext
 from typing import Optional
 
+import torch
 from huggingface_hub.utils import validate_hf_hub_args
 
+from ..quantizers import DiffusersAutoQuantizer
 from ..utils import deprecate, is_accelerate_available, logging
 from .single_file_utils import (
     SingleFileComponentError,
@@ -214,6 +216,8 @@ class FromOriginalModelMixin:
         subfolder = kwargs.pop("subfolder", None)
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
+        quantization_config = kwargs.pop("quantization_config", None)
+        device = kwargs.pop("device", None)
 
         if isinstance(pretrained_model_link_or_path_or_dict, dict):
             checkpoint = pretrained_model_link_or_path_or_dict
@@ -227,6 +231,12 @@ class FromOriginalModelMixin:
                 local_files_only=local_files_only,
                 revision=revision,
             )
+        if quantization_config is not None:
+            hf_quantizer = DiffusersAutoQuantizer.from_config(quantization_config)
+            hf_quantizer.validate_environment()
+
+        else:
+            hf_quantizer = None
 
         mapping_functions = SINGLE_FILE_LOADABLE_CLASSES[mapping_class_name]
 
@@ -309,8 +319,36 @@ class FromOriginalModelMixin:
         with ctx():
             model = cls.from_config(diffusers_model_config)
 
+        # Check if `_keep_in_fp32_modules` is not None
+        use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (
+            (torch_dtype == torch.float16) or hasattr(hf_quantizer, "use_keep_in_fp32_modules")
+        )
+        if use_keep_in_fp32_modules:
+            keep_in_fp32_modules = cls._keep_in_fp32_modules
+            if not isinstance(keep_in_fp32_modules, list):
+                keep_in_fp32_modules = [keep_in_fp32_modules]
+
+        else:
+            keep_in_fp32_modules = []
+
+        if hf_quantizer is not None:
+            hf_quantizer.preprocess_model(
+                model=model,
+                device_map=None,
+                state_dict=diffusers_format_checkpoint,
+                keep_in_fp32_modules=keep_in_fp32_modules,
+            )
+
         if is_accelerate_available():
-            unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
+            param_device = torch.device(device) if device else torch.device("cpu")
+            unexpected_keys = load_model_dict_into_meta(
+                model,
+                diffusers_format_checkpoint,
+                dtype=torch_dtype,
+                device=param_device,
+                hf_quantizer=hf_quantizer,
+                keep_in_fp32_modules=keep_in_fp32_modules,
+            )
 
         else:
             _, unexpected_keys = model.load_state_dict(diffusers_format_checkpoint, strict=False)
@@ -324,7 +362,11 @@ class FromOriginalModelMixin:
                 f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
             )
 
-        if torch_dtype is not None:
+        if hf_quantizer is not None:
+            hf_quantizer.postprocess_model(model)
+            model.hf_quantizer = hf_quantizer
+
+        if torch_dtype is not None and hf_quantizer is None:
             model.to(torch_dtype)
 
         model.eval()

src/diffusers/loaders/single_file_utils.py

@@ -81,8 +81,14 @@ CHECKPOINT_KEY_NAMES = {
     "open_clip_sd3": "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight",
     "stable_cascade_stage_b": "down_blocks.1.0.channelwise.0.weight",
     "stable_cascade_stage_c": "clip_txt_mapper.weight",
-    "sd3": "model.diffusion_model.joint_blocks.0.context_block.adaLN_modulation.1.bias",
-    "sd35_large": "model.diffusion_model.joint_blocks.37.x_block.mlp.fc1.weight",
+    "sd3": [
+        "joint_blocks.0.context_block.adaLN_modulation.1.bias",
+        "model.diffusion_model.joint_blocks.0.context_block.adaLN_modulation.1.bias",
+    ],
+    "sd35_large": [
+        "joint_blocks.37.x_block.mlp.fc1.weight",
+        "model.diffusion_model.joint_blocks.37.x_block.mlp.fc1.weight",
+    ],
     "animatediff": "down_blocks.0.motion_modules.0.temporal_transformer.transformer_blocks.0.attention_blocks.0.pos_encoder.pe",
     "animatediff_v2": "mid_block.motion_modules.0.temporal_transformer.norm.bias",
     "animatediff_sdxl_beta": "up_blocks.2.motion_modules.0.temporal_transformer.norm.weight",
@@ -542,13 +548,20 @@ def infer_diffusers_model_type(checkpoint):
     ):
         model_type = "stable_cascade_stage_b"
 
-    elif CHECKPOINT_KEY_NAMES["sd3"] in checkpoint and checkpoint[CHECKPOINT_KEY_NAMES["sd3"]].shape[-1] == 9216:
-        if checkpoint["model.diffusion_model.pos_embed"].shape[1] == 36864:
+    elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["sd3"]) and any(
+        checkpoint[key].shape[-1] == 9216 if key in checkpoint else False for key in CHECKPOINT_KEY_NAMES["sd3"]
+    ):
+        if "model.diffusion_model.pos_embed" in checkpoint:
+            key = "model.diffusion_model.pos_embed"
+        else:
+            key = "pos_embed"
+
+        if checkpoint[key].shape[1] == 36864:
             model_type = "sd3"
-        elif checkpoint["model.diffusion_model.pos_embed"].shape[1] == 147456:
+        elif checkpoint[key].shape[1] == 147456:
             model_type = "sd35_medium"
 
-    elif CHECKPOINT_KEY_NAMES["sd35_large"] in checkpoint:
+    elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["sd35_large"]):
         model_type = "sd35_large"
 
     elif CHECKPOINT_KEY_NAMES["animatediff"] in checkpoint:

src/diffusers/models/model_loading_utils.py

@@ -17,6 +17,7 @@
 import importlib
 import inspect
 import os
+from array import array
 from collections import OrderedDict
 from pathlib import Path
 from typing import List, Optional, Union
@@ -26,6 +27,7 @@ import torch
 from huggingface_hub.utils import EntryNotFoundError
 
 from ..utils import (
+    GGUF_FILE_EXTENSION,
     SAFE_WEIGHTS_INDEX_NAME,
     SAFETENSORS_FILE_EXTENSION,
     WEIGHTS_INDEX_NAME,
@@ -33,6 +35,8 @@ from ..utils import (
     _get_model_file,
     deprecate,
     is_accelerate_available,
+    is_gguf_available,
+    is_torch_available,
     is_torch_version,
     logging,
 )
@@ -139,6 +143,8 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[
         file_extension = os.path.basename(checkpoint_file).split(".")[-1]
         if file_extension == SAFETENSORS_FILE_EXTENSION:
             return safetensors.torch.load_file(checkpoint_file, device="cpu")
+        elif file_extension == GGUF_FILE_EXTENSION:
+            return load_gguf_checkpoint(checkpoint_file)
         else:
             weights_only_kwarg = {"weights_only": True} if is_torch_version(">=", "1.13") else {}
             return torch.load(
@@ -211,13 +217,14 @@ def load_model_dict_into_meta(
                     set_module_kwargs["dtype"] = dtype
 
         # bnb params are flattened.
+        # gguf quants have a different shape based on the type of quantization applied
         if empty_state_dict[param_name].shape != param.shape:
             if (
                 is_quantized
                 and hf_quantizer.pre_quantized
                 and hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=device)
             ):
-                hf_quantizer.check_quantized_param_shape(param_name, empty_state_dict[param_name].shape, param.shape)
+                hf_quantizer.check_quantized_param_shape(param_name, empty_state_dict[param_name], param)
             else:
                 model_name_or_path_str = f"{model_name_or_path} " if model_name_or_path is not None else ""
                 raise ValueError(
@@ -396,3 +403,78 @@ def _fetch_index_file_legacy(
                 index_file = None
 
     return index_file
+
+
+def _gguf_parse_value(_value, data_type):
+    if not isinstance(data_type, list):
+        data_type = [data_type]
+    if len(data_type) == 1:
+        data_type = data_type[0]
+        array_data_type = None
+    else:
+        if data_type[0] != 9:
+            raise ValueError("Received multiple types, therefore expected the first type to indicate an array.")
+        data_type, array_data_type = data_type
+
+    if data_type in [0, 1, 2, 3, 4, 5, 10, 11]:
+        _value = int(_value[0])
+    elif data_type in [6, 12]:
+        _value = float(_value[0])
+    elif data_type in [7]:
+        _value = bool(_value[0])
+    elif data_type in [8]:
+        _value = array("B", list(_value)).tobytes().decode()
+    elif data_type in [9]:
+        _value = _gguf_parse_value(_value, array_data_type)
+    return _value
+
+
+def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False):
+    """
+    Load a GGUF file and return a dictionary of parsed parameters containing tensors, the parsed tokenizer and config
+    attributes.
+
+    Args:
+        gguf_checkpoint_path (`str`):
+            The path the to GGUF file to load
+        return_tensors (`bool`, defaults to `True`):
+            Whether to read the tensors from the file and return them. Not doing so is faster and only loads the
+            metadata in memory.
+    """
+
+    if is_gguf_available() and is_torch_available():
+        import gguf
+        from gguf import GGUFReader
+
+        from ..quantizers.gguf.utils import SUPPORTED_GGUF_QUANT_TYPES, GGUFParameter
+    else:
+        logger.error(
+            "Loading a GGUF checkpoint in PyTorch, requires both PyTorch and GGUF>=0.10.0 to be installed. Please see "
+            "https://pytorch.org/ and https://github.com/ggerganov/llama.cpp/tree/master/gguf-py for installation instructions."
+        )
+        raise ImportError("Please install torch and gguf>=0.10.0 to load a GGUF checkpoint in PyTorch.")
+
+    reader = GGUFReader(gguf_checkpoint_path)
+
+    parsed_parameters = {}
+    for tensor in reader.tensors:
+        name = tensor.name
+        quant_type = tensor.tensor_type
+
+        # if the tensor is a torch supported dtype do not use GGUFParameter
+        is_gguf_quant = quant_type not in [gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16]
+        if is_gguf_quant and quant_type not in SUPPORTED_GGUF_QUANT_TYPES:
+            _supported_quants_str = "\n".join([str(type) for type in SUPPORTED_GGUF_QUANT_TYPES])
+            raise ValueError(
+                (
+                    f"{name} has a quantization type: {str(quant_type)} which is unsupported."
+                    "\n\nCurrently the following quantization types are supported: \n\n"
+                    f"{_supported_quants_str}"
+                    "\n\nTo request support for this quantization type please open an issue here: https://github.com/huggingface/diffusers"
+                )
+            )
+
+        weights = torch.from_numpy(tensor.data.copy())
+        parsed_parameters[name] = GGUFParameter(weights, quant_type=quant_type) if is_gguf_quant else weights
+
+    return parsed_parameters

src/diffusers/models/modeling_utils.py

@@ -1038,14 +1038,14 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                     dtype_present_in_args = True
                     break
 
-        # Checks if the model has been loaded in 4-bit or 8-bit with BNB
-        if getattr(self, "quantization_method", None) == QuantizationMethod.BITS_AND_BYTES:
+        if getattr(self, "is_quantized", False):
             if dtype_present_in_args:
                 raise ValueError(
-                    "You cannot cast a bitsandbytes model in a new `dtype`. Make sure to load the model using `from_pretrained` using the"
-                    " desired `dtype` by passing the correct `torch_dtype` argument."
+                    "Casting a quantized model to a new `dtype` is unsupported. To set the dtype of unquantized layers, please "
+                    "use the `torch_dtype` argument when loading the model using `from_pretrained` or `from_single_file`"
                 )
 
+        if getattr(self, "quantization_method", None) == QuantizationMethod.BITS_AND_BYTES:
             if getattr(self, "is_loaded_in_8bit", False):
                 raise ValueError(
                     "`.to` is not supported for `8-bit` bitsandbytes models. Please use the model as it is, since the"

src/diffusers/models/transformers/transformer_flux.py

@@ -524,7 +524,6 @@ class FluxTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOrig
                     )
                 else:
                     hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
-
         hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
 
         for index_block, block in enumerate(self.single_transformer_blocks):

src/diffusers/quantizers/auto.py

@@ -15,23 +15,33 @@
 Adapted from
 https://github.com/huggingface/transformers/blob/c409cd81777fb27aadc043ed3d8339dbc020fb3b/src/transformers/quantizers/auto.py
 """
+
 import warnings
 from typing import Dict, Optional, Union
 
 from .bitsandbytes import BnB4BitDiffusersQuantizer, BnB8BitDiffusersQuantizer
-from .quantization_config import BitsAndBytesConfig, QuantizationConfigMixin, QuantizationMethod, TorchAoConfig
+from .gguf import GGUFQuantizer
+from .quantization_config import (
+    BitsAndBytesConfig,
+    GGUFQuantizationConfig,
+    QuantizationConfigMixin,
+    QuantizationMethod,
+    TorchAoConfig,
+)
 from .torchao import TorchAoHfQuantizer
 
 
 AUTO_QUANTIZER_MAPPING = {
     "bitsandbytes_4bit": BnB4BitDiffusersQuantizer,
     "bitsandbytes_8bit": BnB8BitDiffusersQuantizer,
+    "gguf": GGUFQuantizer,
     "torchao": TorchAoHfQuantizer,
 }
 
 AUTO_QUANTIZATION_CONFIG_MAPPING = {
     "bitsandbytes_4bit": BitsAndBytesConfig,
     "bitsandbytes_8bit": BitsAndBytesConfig,
+    "gguf": GGUFQuantizationConfig,
     "torchao": TorchAoConfig,
 }
 

src/diffusers/quantizers/bitsandbytes/bnb_quantizer.py

@@ -204,7 +204,10 @@ class BnB4BitDiffusersQuantizer(DiffusersQuantizer):
 
         module._parameters[tensor_name] = new_value
 
-    def check_quantized_param_shape(self, param_name, current_param_shape, loaded_param_shape):
+    def check_quantized_param_shape(self, param_name, current_param, loaded_param):
+        current_param_shape = current_param.shape
+        loaded_param_shape = loaded_param.shape
+
         n = current_param_shape.numel()
         inferred_shape = (n,) if "bias" in param_name else ((n + 1) // 2, 1)
         if loaded_param_shape != inferred_shape:

src/diffusers/quantizers/gguf/__init__.py

+from .gguf_quantizer import GGUFQuantizer

src/diffusers/quantizers/gguf/gguf_quantizer.py

+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+
+from ..base import DiffusersQuantizer
+
+
+if TYPE_CHECKING:
+    from ...models.modeling_utils import ModelMixin
+
+
+from ...utils import (
+    get_module_from_name,
+    is_accelerate_available,
+    is_accelerate_version,
+    is_gguf_available,
+    is_gguf_version,
+    is_torch_available,
+    logging,
+)
+
+
+if is_torch_available() and is_gguf_available():
+    import torch
+
+    from .utils import (
+        GGML_QUANT_SIZES,
+        GGUFParameter,
+        _dequantize_gguf_and_restore_linear,
+        _quant_shape_from_byte_shape,
+        _replace_with_gguf_linear,
+    )
+
+
+logger = logging.get_logger(__name__)
+
+
+class GGUFQuantizer(DiffusersQuantizer):
+    use_keep_in_fp32_modules = True
+
+    def __init__(self, quantization_config, **kwargs):
+        super().__init__(quantization_config, **kwargs)
+
+        self.compute_dtype = quantization_config.compute_dtype
+        self.pre_quantized = quantization_config.pre_quantized
+        self.modules_to_not_convert = quantization_config.modules_to_not_convert
+
+        if not isinstance(self.modules_to_not_convert, list):
+            self.modules_to_not_convert = [self.modules_to_not_convert]
+
+    def validate_environment(self, *args, **kwargs):
+        if not is_accelerate_available() or is_accelerate_version("<", "0.26.0"):
+            raise ImportError(
+                "Loading GGUF Parameters requires `accelerate` installed in your enviroment: `pip install 'accelerate>=0.26.0'`"
+            )
+        if not is_gguf_available() or is_gguf_version("<", "0.10.0"):
+            raise ImportError(
+                "To load GGUF format files you must have `gguf` installed in your environment: `pip install gguf>=0.10.0`"
+            )
+
+    # Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.adjust_max_memory
+    def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
+        # need more space for buffers that are created during quantization
+        max_memory = {key: val * 0.90 for key, val in max_memory.items()}
+        return max_memory
+
+    def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
+        if target_dtype != torch.uint8:
+            logger.info(f"target_dtype {target_dtype} is replaced by `torch.uint8` for GGUF quantization")
+        return torch.uint8
+
+    def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
+        if torch_dtype is None:
+            torch_dtype = self.compute_dtype
+        return torch_dtype
+
+    def check_quantized_param_shape(self, param_name, current_param, loaded_param):
+        loaded_param_shape = loaded_param.shape
+        current_param_shape = current_param.shape
+        quant_type = loaded_param.quant_type
+
+        block_size, type_size = GGML_QUANT_SIZES[quant_type]
+
+        inferred_shape = _quant_shape_from_byte_shape(loaded_param_shape, type_size, block_size)
+        if inferred_shape != current_param_shape:
+            raise ValueError(
+                f"{param_name} has an expected quantized shape of: {inferred_shape}, but receieved shape: {loaded_param_shape}"
+            )
+
+        return True
+
+    def check_if_quantized_param(
+        self,
+        model: "ModelMixin",
+        param_value: Union["GGUFParameter", "torch.Tensor"],
+        param_name: str,
+        state_dict: Dict[str, Any],
+        **kwargs,
+    ) -> bool:
+        if isinstance(param_value, GGUFParameter):
+            return True
+
+        return False
+
+    def create_quantized_param(
+        self,
+        model: "ModelMixin",
+        param_value: Union["GGUFParameter", "torch.Tensor"],
+        param_name: str,
+        target_device: "torch.device",
+        state_dict: Optional[Dict[str, Any]] = None,
+        unexpected_keys: Optional[List[str]] = None,
+    ):
+        module, tensor_name = get_module_from_name(model, param_name)
+        if tensor_name not in module._parameters and tensor_name not in module._buffers:
+            raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.")
+
+        if tensor_name in module._parameters:
+            module._parameters[tensor_name] = param_value.to(target_device)
+        if tensor_name in module._buffers:
+            module._buffers[tensor_name] = param_value.to(target_device)
+
+    def _process_model_before_weight_loading(
+        self,
+        model: "ModelMixin",
+        device_map,
+        keep_in_fp32_modules: List[str] = [],
+        **kwargs,
+    ):
+        state_dict = kwargs.get("state_dict", None)
+
+        self.modules_to_not_convert.extend(keep_in_fp32_modules)
+        self.modules_to_not_convert = [module for module in self.modules_to_not_convert if module is not None]
+
+        _replace_with_gguf_linear(
+            model, self.compute_dtype, state_dict, modules_to_not_convert=self.modules_to_not_convert
+        )
+
+    def _process_model_after_weight_loading(self, model: "ModelMixin", **kwargs):
+        return model
+
+    @property
+    def is_serializable(self):
+        return False
+
+    @property
+    def is_trainable(self) -> bool:
+        return False
+
+    def _dequantize(self, model):
+        is_model_on_cpu = model.device.type == "cpu"
+        if is_model_on_cpu:
+            logger.info(
+                "Model was found to be on CPU (could happen as a result of `enable_model_cpu_offload()`). So, moving it to GPU. After dequantization, will move the model back to CPU again to preserve the previous device."
+            )
+            model.to(torch.cuda.current_device())
+
+        model = _dequantize_gguf_and_restore_linear(model, self.modules_to_not_convert)
+        if is_model_on_cpu:
+            model.to("cpu")
+        return model

src/diffusers/quantizers/gguf/utils.py

+# Copyright 2024 The HuggingFace Team and City96. All rights reserved.
+# #
+# # Licensed under the Apache License, Version 2.0 (the "License");
+# # you may not use this file except in compliance with the License.
+# # You may obtain a copy of the License at
+# #
+# #     http://www.apache.org/licenses/LICENSE-2.0
+# #
+# # Unless required by applicable law or agreed to in writing, software
+# # distributed under the License is distributed on an "AS IS" BASIS,
+# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# # See the License for the specific language governing permissions and
+# # limitations under the License.
+
+
+import inspect
+from contextlib import nullcontext
+
+import gguf
+import torch
+import torch.nn as nn
+
+from ...utils import is_accelerate_available
+
+
+if is_accelerate_available():
+    import accelerate
+    from accelerate import init_empty_weights
+    from accelerate.hooks import add_hook_to_module, remove_hook_from_module
+
+
+# Copied from diffusers.quantizers.bitsandbytes.utils._create_accelerate_new_hook
+def _create_accelerate_new_hook(old_hook):
+    r"""
+    Creates a new hook based on the old hook. Use it only if you know what you are doing ! This method is a copy of:
+    https://github.com/huggingface/peft/blob/748f7968f3a31ec06a1c2b0328993319ad9a150a/src/peft/utils/other.py#L245 with
+    some changes
+    """
+    old_hook_cls = getattr(accelerate.hooks, old_hook.__class__.__name__)
+    old_hook_attr = old_hook.__dict__
+    filtered_old_hook_attr = {}
+    old_hook_init_signature = inspect.signature(old_hook_cls.__init__)
+    for k in old_hook_attr.keys():
+        if k in old_hook_init_signature.parameters:
+            filtered_old_hook_attr[k] = old_hook_attr[k]
+    new_hook = old_hook_cls(**filtered_old_hook_attr)
+    return new_hook
+
+
+def _replace_with_gguf_linear(model, compute_dtype, state_dict, prefix="", modules_to_not_convert=[]):
+    def _should_convert_to_gguf(state_dict, prefix):
+        weight_key = prefix + "weight"
+        return weight_key in state_dict and isinstance(state_dict[weight_key], GGUFParameter)
+
+    has_children = list(model.children())
+    if not has_children:
+        return
+
+    for name, module in model.named_children():
+        module_prefix = prefix + name + "."
+        _replace_with_gguf_linear(module, compute_dtype, state_dict, module_prefix, modules_to_not_convert)
+
+        if (
+            isinstance(module, nn.Linear)
+            and _should_convert_to_gguf(state_dict, module_prefix)
+            and name not in modules_to_not_convert
+        ):
+            ctx = init_empty_weights if is_accelerate_available() else nullcontext
+            with ctx():
+                model._modules[name] = GGUFLinear(
+                    module.in_features,
+                    module.out_features,
+                    module.bias is not None,
+                    compute_dtype=compute_dtype,
+                )
+            model._modules[name].source_cls = type(module)
+            # Force requires_grad to False to avoid unexpected errors
+            model._modules[name].requires_grad_(False)
+
+    return model
+
+
+def _dequantize_gguf_and_restore_linear(model, modules_to_not_convert=[]):
+    for name, module in model.named_children():
+        if isinstance(module, GGUFLinear) and name not in modules_to_not_convert:
+            device = module.weight.device
+            bias = getattr(module, "bias", None)
+
+            ctx = init_empty_weights if is_accelerate_available() else nullcontext
+            with ctx():
+                new_module = nn.Linear(
+                    module.in_features,
+                    module.out_features,
+                    module.bias is not None,
+                    device=device,
+                )
+            new_module.weight = nn.Parameter(dequantize_gguf_tensor(module.weight))
+            if bias is not None:
+                new_module.bias = bias
+
+            # Create a new hook and attach it in case we use accelerate
+            if hasattr(module, "_hf_hook"):
+                old_hook = module._hf_hook
+                new_hook = _create_accelerate_new_hook(old_hook)
+
+                remove_hook_from_module(module)
+                add_hook_to_module(new_module, new_hook)
+
+            new_module.to(device)
+            model._modules[name] = new_module
+
+        has_children = list(module.children())
+        if has_children:
+            _dequantize_gguf_and_restore_linear(module, modules_to_not_convert)
+
+    return model
+
+
+# dequantize operations based on torch ports of GGUF dequantize_functions
+# from City96
+# more info: https://github.com/city96/ComfyUI-GGUF/blob/main/dequant.py
+
+
+QK_K = 256
+K_SCALE_SIZE = 12
+
+
+def to_uint32(x):
+    x = x.view(torch.uint8).to(torch.int32)
+    return (x[:, 0] | x[:, 1] << 8 | x[:, 2] << 16 | x[:, 3] << 24).unsqueeze(1)
+
+
+def split_block_dims(blocks, *args):
+    n_max = blocks.shape[1]
+    dims = list(args) + [n_max - sum(args)]
+    return torch.split(blocks, dims, dim=1)
+
+
+def get_scale_min(scales):
+    n_blocks = scales.shape[0]
+    scales = scales.view(torch.uint8)
+    scales = scales.reshape((n_blocks, 3, 4))
+
+    d, m, m_d = torch.split(scales, scales.shape[-2] // 3, dim=-2)
+
+    sc = torch.cat([d & 0x3F, (m_d & 0x0F) | ((d >> 2) & 0x30)], dim=-1)
+    min = torch.cat([m & 0x3F, (m_d >> 4) | ((m >> 2) & 0x30)], dim=-1)
+
+    return (sc.reshape((n_blocks, 8)), min.reshape((n_blocks, 8)))
+
+
+def dequantize_blocks_Q8_0(blocks, block_size, type_size, dtype=None):
+    d, x = split_block_dims(blocks, 2)
+    d = d.view(torch.float16).to(dtype)
+    x = x.view(torch.int8)
+    return d * x
+
+
+def dequantize_blocks_Q5_1(blocks, block_size, type_size, dtype=None):
+    n_blocks = blocks.shape[0]
+
+    d, m, qh, qs = split_block_dims(blocks, 2, 2, 4)
+    d = d.view(torch.float16).to(dtype)
+    m = m.view(torch.float16).to(dtype)
+    qh = to_uint32(qh)
+
+    qh = qh.reshape((n_blocks, 1)) >> torch.arange(32, device=d.device, dtype=torch.int32).reshape(1, 32)
+    ql = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor(
+        [0, 4], device=d.device, dtype=torch.uint8
+    ).reshape(1, 1, 2, 1)
+    qh = (qh & 1).to(torch.uint8)
+    ql = (ql & 0x0F).reshape((n_blocks, -1))
+
+    qs = ql | (qh << 4)
+    return (d * qs) + m
+
+
+def dequantize_blocks_Q5_0(blocks, block_size, type_size, dtype=None):
+    n_blocks = blocks.shape[0]
+
+    d, qh, qs = split_block_dims(blocks, 2, 4)
+    d = d.view(torch.float16).to(dtype)
+    qh = to_uint32(qh)
+
+    qh = qh.reshape(n_blocks, 1) >> torch.arange(32, device=d.device, dtype=torch.int32).reshape(1, 32)
+    ql = qs.reshape(n_blocks, -1, 1, block_size // 2) >> torch.tensor(
+        [0, 4], device=d.device, dtype=torch.uint8
+    ).reshape(1, 1, 2, 1)
+
+    qh = (qh & 1).to(torch.uint8)
+    ql = (ql & 0x0F).reshape(n_blocks, -1)
+
+    qs = (ql | (qh << 4)).to(torch.int8) - 16
+    return d * qs
+
+
+def dequantize_blocks_Q4_1(blocks, block_size, type_size, dtype=None):
+    n_blocks = blocks.shape[0]
+
+    d, m, qs = split_block_dims(blocks, 2, 2)
+    d = d.view(torch.float16).to(dtype)
+    m = m.view(torch.float16).to(dtype)
+
+    qs = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor(
+        [0, 4], device=d.device, dtype=torch.uint8
+    ).reshape(1, 1, 2, 1)
+    qs = (qs & 0x0F).reshape(n_blocks, -1)
+
+    return (d * qs) + m
+
+
+def dequantize_blocks_Q4_0(blocks, block_size, type_size, dtype=None):
+    n_blocks = blocks.shape[0]
+
+    d, qs = split_block_dims(blocks, 2)
+    d = d.view(torch.float16).to(dtype)
+
+    qs = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor(
+        [0, 4], device=d.device, dtype=torch.uint8
+    ).reshape((1, 1, 2, 1))
+    qs = (qs & 0x0F).reshape((n_blocks, -1)).to(torch.int8) - 8
+    return d * qs
+
+
+def dequantize_blocks_Q6_K(blocks, block_size, type_size, dtype=None):
+    n_blocks = blocks.shape[0]
+
+    (
+        ql,
+        qh,
+        scales,
+        d,
+    ) = split_block_dims(blocks, QK_K // 2, QK_K // 4, QK_K // 16)
+
+    scales = scales.view(torch.int8).to(dtype)
+    d = d.view(torch.float16).to(dtype)
+    d = (d * scales).reshape((n_blocks, QK_K // 16, 1))
+
+    ql = ql.reshape((n_blocks, -1, 1, 64)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 2, 1)
+    )
+    ql = (ql & 0x0F).reshape((n_blocks, -1, 32))
+    qh = qh.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 4, 1)
+    )
+    qh = (qh & 0x03).reshape((n_blocks, -1, 32))
+    q = (ql | (qh << 4)).to(torch.int8) - 32
+    q = q.reshape((n_blocks, QK_K // 16, -1))
+
+    return (d * q).reshape((n_blocks, QK_K))
+
+
+def dequantize_blocks_Q5_K(blocks, block_size, type_size, dtype=None):
+    n_blocks = blocks.shape[0]
+
+    d, dmin, scales, qh, qs = split_block_dims(blocks, 2, 2, K_SCALE_SIZE, QK_K // 8)
+
+    d = d.view(torch.float16).to(dtype)
+    dmin = dmin.view(torch.float16).to(dtype)
+
+    sc, m = get_scale_min(scales)
+
+    d = (d * sc).reshape((n_blocks, -1, 1))
+    dm = (dmin * m).reshape((n_blocks, -1, 1))
+
+    ql = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 2, 1)
+    )
+    qh = qh.reshape((n_blocks, -1, 1, 32)) >> torch.arange(0, 8, device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 8, 1)
+    )
+    ql = (ql & 0x0F).reshape((n_blocks, -1, 32))
+    qh = (qh & 0x01).reshape((n_blocks, -1, 32))
+    q = ql | (qh << 4)
+
+    return (d * q - dm).reshape((n_blocks, QK_K))
+
+
+def dequantize_blocks_Q4_K(blocks, block_size, type_size, dtype=None):
+    n_blocks = blocks.shape[0]
+
+    d, dmin, scales, qs = split_block_dims(blocks, 2, 2, K_SCALE_SIZE)
+    d = d.view(torch.float16).to(dtype)
+    dmin = dmin.view(torch.float16).to(dtype)
+
+    sc, m = get_scale_min(scales)
+
+    d = (d * sc).reshape((n_blocks, -1, 1))
+    dm = (dmin * m).reshape((n_blocks, -1, 1))
+
+    qs = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 2, 1)
+    )
+    qs = (qs & 0x0F).reshape((n_blocks, -1, 32))
+
+    return (d * qs - dm).reshape((n_blocks, QK_K))
+
+
+def dequantize_blocks_Q3_K(blocks, block_size, type_size, dtype=None):
+    n_blocks = blocks.shape[0]
+
+    hmask, qs, scales, d = split_block_dims(blocks, QK_K // 8, QK_K // 4, 12)
+    d = d.view(torch.float16).to(dtype)
+
+    lscales, hscales = scales[:, :8], scales[:, 8:]
+    lscales = lscales.reshape((n_blocks, 1, 8)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(
+        (1, 2, 1)
+    )
+    lscales = lscales.reshape((n_blocks, 16))
+    hscales = hscales.reshape((n_blocks, 1, 4)) >> torch.tensor(
+        [0, 2, 4, 6], device=d.device, dtype=torch.uint8
+    ).reshape((1, 4, 1))
+    hscales = hscales.reshape((n_blocks, 16))
+    scales = (lscales & 0x0F) | ((hscales & 0x03) << 4)
+    scales = scales.to(torch.int8) - 32
+
+    dl = (d * scales).reshape((n_blocks, 16, 1))
+
+    ql = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 4, 1)
+    )
+    qh = hmask.reshape(n_blocks, -1, 1, 32) >> torch.arange(0, 8, device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 8, 1)
+    )
+    ql = ql.reshape((n_blocks, 16, QK_K // 16)) & 3
+    qh = (qh.reshape((n_blocks, 16, QK_K // 16)) & 1) ^ 1
+    q = ql.to(torch.int8) - (qh << 2).to(torch.int8)
+
+    return (dl * q).reshape((n_blocks, QK_K))
+
+
+def dequantize_blocks_Q2_K(blocks, block_size, type_size, dtype=None):
+    n_blocks = blocks.shape[0]
+
+    scales, qs, d, dmin = split_block_dims(blocks, QK_K // 16, QK_K // 4, 2)
+    d = d.view(torch.float16).to(dtype)
+    dmin = dmin.view(torch.float16).to(dtype)
+
+    # (n_blocks, 16, 1)
+    dl = (d * (scales & 0xF)).reshape((n_blocks, QK_K // 16, 1))
+    ml = (dmin * (scales >> 4)).reshape((n_blocks, QK_K // 16, 1))
+
+    shift = torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 1, 4, 1))
+
+    qs = (qs.reshape((n_blocks, -1, 1, 32)) >> shift) & 3
+    qs = qs.reshape((n_blocks, QK_K // 16, 16))
+    qs = dl * qs - ml
+
+    return qs.reshape((n_blocks, -1))
+
+
+def dequantize_blocks_BF16(blocks, block_size, type_size, dtype=None):
+    return (blocks.view(torch.int16).to(torch.int32) << 16).view(torch.float32)
+
+
+GGML_QUANT_SIZES = gguf.GGML_QUANT_SIZES
+dequantize_functions = {
+    gguf.GGMLQuantizationType.BF16: dequantize_blocks_BF16,
+    gguf.GGMLQuantizationType.Q8_0: dequantize_blocks_Q8_0,
+    gguf.GGMLQuantizationType.Q5_1: dequantize_blocks_Q5_1,
+    gguf.GGMLQuantizationType.Q5_0: dequantize_blocks_Q5_0,
+    gguf.GGMLQuantizationType.Q4_1: dequantize_blocks_Q4_1,
+    gguf.GGMLQuantizationType.Q4_0: dequantize_blocks_Q4_0,
+    gguf.GGMLQuantizationType.Q6_K: dequantize_blocks_Q6_K,
+    gguf.GGMLQuantizationType.Q5_K: dequantize_blocks_Q5_K,
+    gguf.GGMLQuantizationType.Q4_K: dequantize_blocks_Q4_K,
+    gguf.GGMLQuantizationType.Q3_K: dequantize_blocks_Q3_K,
+    gguf.GGMLQuantizationType.Q2_K: dequantize_blocks_Q2_K,
+}
+SUPPORTED_GGUF_QUANT_TYPES = list(dequantize_functions.keys())
+
+
+def _quant_shape_from_byte_shape(shape, type_size, block_size):
+    return (*shape[:-1], shape[-1] // type_size * block_size)
+
+
+def dequantize_gguf_tensor(tensor):
+    if not hasattr(tensor, "quant_type"):
+        return tensor
+
+    quant_type = tensor.quant_type
+    dequant_fn = dequantize_functions[quant_type]
+
+    block_size, type_size = GGML_QUANT_SIZES[quant_type]
+
+    tensor = tensor.view(torch.uint8)
+    shape = _quant_shape_from_byte_shape(tensor.shape, type_size, block_size)
+
+    n_blocks = tensor.numel() // type_size
+    blocks = tensor.reshape((n_blocks, type_size))
+
+    dequant = dequant_fn(blocks, block_size, type_size)
+    dequant = dequant.reshape(shape)
+
+    return dequant.as_tensor()
+
+
+class GGUFParameter(torch.nn.Parameter):
+    def __new__(cls, data, requires_grad=False, quant_type=None):
+        data = data if data is not None else torch.empty(0)
+        self = torch.Tensor._make_subclass(cls, data, requires_grad)
+        self.quant_type = quant_type
+
+        return self
+
+    def as_tensor(self):
+        return torch.Tensor._make_subclass(torch.Tensor, self, self.requires_grad)
+
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+
+        result = super().__torch_function__(func, types, args, kwargs)
+
+        # When converting from original format checkpoints we often use splits, cats etc on tensors
+        # this method ensures that the returned tensor type from those operations remains GGUFParameter
+        # so that we preserve quant_type information
+        quant_type = None
+        for arg in args:
+            if isinstance(arg, list) and (arg[0], GGUFParameter):
+                quant_type = arg[0].quant_type
+                break
+            if isinstance(arg, GGUFParameter):
+                quant_type = arg.quant_type
+                break
+        if isinstance(result, torch.Tensor):
+            return cls(result, quant_type=quant_type)
+        # Handle tuples and lists
+        elif isinstance(result, (tuple, list)):
+            # Preserve the original type (tuple or list)
+            wrapped = [cls(x, quant_type=quant_type) if isinstance(x, torch.Tensor) else x for x in result]
+            return type(result)(wrapped)
+        else:
+            return result
+
+
+class GGUFLinear(nn.Linear):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        bias=False,
+        compute_dtype=None,
+        device=None,
+    ) -> None:
+        super().__init__(in_features, out_features, bias, device)
+        self.compute_dtype = compute_dtype
+
+    def forward(self, inputs):
+        weight = dequantize_gguf_tensor(self.weight)
+        weight = weight.to(self.compute_dtype)
+        bias = self.bias.to(self.compute_dtype)
+
+        output = torch.nn.functional.linear(inputs, weight, bias)
+        return output

src/diffusers/quantizers/quantization_config.py

@@ -43,6 +43,7 @@ logger = logging.get_logger(__name__)
 
 class QuantizationMethod(str, Enum):
     BITS_AND_BYTES = "bitsandbytes"
+    GGUF = "gguf"
     TORCHAO = "torchao"
 
 
@@ -394,6 +395,29 @@ class BitsAndBytesConfig(QuantizationConfigMixin):
         return serializable_config_dict
 
 
+@dataclass
+class GGUFQuantizationConfig(QuantizationConfigMixin):
+    """This is a config class for GGUF Quantization techniques.
+
+    Args:
+        compute_dtype: (`torch.dtype`, defaults to `torch.float32`):
+            This sets the computational type which might be different than the input type. For example, inputs might be
+            fp32, but computation can be set to bf16 for speedups.
+
+    """
+
+    def __init__(self, compute_dtype: Optional["torch.dtype"] = None):
+        self.quant_method = QuantizationMethod.GGUF
+        self.compute_dtype = compute_dtype
+        self.pre_quantized = True
+
+        # TODO: (Dhruv) Add this as an init argument when we can support loading unquantized checkpoints.
+        self.modules_to_not_convert = None
+
+        if self.compute_dtype is None:
+            self.compute_dtype = torch.float32
+
+
 @dataclass
 class TorchAoConfig(QuantizationConfigMixin):
     """This is a config class for torchao quantization/sparsity techniques.

src/diffusers/utils/__init__.py

@@ -23,6 +23,7 @@ from .constants import (
     DEPRECATED_REVISION_ARGS,
     DIFFUSERS_DYNAMIC_MODULE_NAME,
     FLAX_WEIGHTS_NAME,
+    GGUF_FILE_EXTENSION,
     HF_MODULES_CACHE,
     HUGGINGFACE_CO_RESOLVE_ENDPOINT,
     MIN_PEFT_VERSION,
@@ -66,6 +67,8 @@ from .import_utils import (
     is_bs4_available,
     is_flax_available,
     is_ftfy_available,
+    is_gguf_available,
+    is_gguf_version,
     is_google_colab,
     is_inflect_available,
     is_invisible_watermark_available,

src/diffusers/utils/constants.py

@@ -34,6 +34,7 @@ ONNX_WEIGHTS_NAME = "model.onnx"
 SAFETENSORS_WEIGHTS_NAME = "diffusion_pytorch_model.safetensors"
 SAFE_WEIGHTS_INDEX_NAME = "diffusion_pytorch_model.safetensors.index.json"
 SAFETENSORS_FILE_EXTENSION = "safetensors"
+GGUF_FILE_EXTENSION = "gguf"
 ONNX_EXTERNAL_WEIGHTS_NAME = "weights.pb"
 HUGGINGFACE_CO_RESOLVE_ENDPOINT = os.environ.get("HF_ENDPOINT", "https://huggingface.co")
 DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules"

src/diffusers/utils/import_utils.py

@@ -339,6 +339,14 @@ if _imageio_available:
     except importlib_metadata.PackageNotFoundError:
         _imageio_available = False
 
+_is_gguf_available = importlib.util.find_spec("gguf") is not None
+if _is_gguf_available:
+    try:
+        _gguf_version = importlib_metadata.version("gguf")
+        logger.debug(f"Successfully import gguf version {_gguf_version}")
+    except importlib_metadata.PackageNotFoundError:
+        _is_gguf_available = False
+
 
 _is_torchao_available = importlib.util.find_spec("torchao") is not None
 if _is_torchao_available:
@@ -469,6 +477,10 @@ def is_imageio_available():
     return _imageio_available
 
 
+def is_gguf_available():
+    return _is_gguf_available
+
+
 def is_torchao_available():
     return _is_torchao_available
 
@@ -607,8 +619,13 @@ IMAGEIO_IMPORT_ERROR = """
 """
 
 # docstyle-ignore
+GGUF_IMPORT_ERROR = """
+{0} requires the gguf library but it was not found in your environment. You can install it with pip: `pip install gguf`
+"""
+
 TORCHAO_IMPORT_ERROR = """
-{0} requires the torchao library but it was not found in your environment. You can install it with pip: `pip install torchao`
+{0} requires the torchao library but it was not found in your environment. You can install it with pip: `pip install
+torchao`
 """
 
 BACKENDS_MAPPING = OrderedDict(
@@ -636,6 +653,7 @@ BACKENDS_MAPPING = OrderedDict(
         ("bitsandbytes", (is_bitsandbytes_available, BITSANDBYTES_IMPORT_ERROR)),
         ("sentencepiece", (is_sentencepiece_available, SENTENCEPIECE_IMPORT_ERROR)),
         ("imageio", (is_imageio_available, IMAGEIO_IMPORT_ERROR)),
+        ("gguf", (is_gguf_available, GGUF_IMPORT_ERROR)),
         ("torchao", (is_torchao_available, TORCHAO_IMPORT_ERROR)),
     ]
 )
@@ -793,6 +811,21 @@ def is_bitsandbytes_version(operation: str, version: str):
     return compare_versions(parse(_bitsandbytes_version), operation, version)
 
 
+def is_gguf_version(operation: str, version: str):
+    """
+    Compares the current Accelerate version to a given reference with an operation.
+
+    Args:
+        operation (`str`):
+            A string representation of an operator, such as `">"` or `"<="`
+        version (`str`):
+            A version string
+    """
+    if not _is_gguf_available:
+        return False
+    return compare_versions(parse(_gguf_version), operation, version)
+
+
 def is_k_diffusion_version(operation: str, version: str):
     """
     Compares the current k-diffusion version to a given reference with an operation.