diff --git "a/pipeline.py" "b/pipeline.py"
new file mode 100644--- /dev/null
+++ "b/pipeline.py"
@@ -0,0 +1,3550 @@
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union, Tuple
+from collections import namedtuple
+import math
+import sys
+import os
+import logging
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization import QConfig, MinMaxObserver, PlaceholderObserver, QuantStub, DeQuantStub
+
+import mixdq_extension._C
+
+from transformers import (
+ CLIPImageProcessor,
+ CLIPTextModel,
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+ CLIPVisionModelWithProjection,
+)
+
+from diffusers import StableDiffusionXLPipeline
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import (
+ FromSingleFileMixin,
+ IPAdapterMixin,
+ StableDiffusionXLLoraLoaderMixin,
+ TextualInversionLoaderMixin,
+)
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.models.attention_processor import (
+ AttnProcessor2_0,
+ LoRAAttnProcessor2_0,
+ LoRAXFormersAttnProcessor,
+ XFormersAttnProcessor,
+)
+from diffusers.utils import (
+ USE_PEFT_BACKEND,
+ deprecate,
+ is_invisible_watermark_available,
+ is_torch_xla_available,
+ replace_example_docstring,
+ scale_lora_layers,
+ unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+
+import copy
+import itertools
+import warnings
+import functools
+import threading
+
+import torch.ao.nn.quantized as nnq
+from torch.ao.nn.intrinsic import _FusedModule
+
+from torch.ao.quantization.quantization_mappings import (
+ get_default_dynamic_quant_module_mappings,
+ get_default_static_quant_module_mappings,
+ get_default_static_quant_reference_module_mappings,
+ get_default_qat_module_mappings,
+ get_default_qconfig_propagation_list,
+ no_observer_set,
+ _has_special_act_post_process,
+ _get_special_act_post_process,
+)
+from torch.ao.quantization.utils import get_qparam_dict, has_no_children_ignoring_parametrizations
+from torch.ao.quantization.stubs import DeQuantStub, QuantWrapper
+from torch.ao.quantization.qconfig import (
+ _add_module_to_qconfig_obs_ctr,
+ default_dynamic_qconfig,
+ float16_dynamic_qconfig,
+ float_qparams_weight_only_qconfig,
+ float_qparams_weight_only_qconfig_4bit,
+ _activation_is_memoryless)
+from torch.nn.utils.parametrize import type_before_parametrizations
+from torch.ao.quantization.observer import _is_activation_post_process
+
+# TODO remove this once BC is no longer required to avoid a SEV
+from torch.ao.quantization.observer import ( # noqa: F401
+ _is_activation_post_process as is_activation_post_process
+)
+
+if is_invisible_watermark_available():
+ from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
+
+XLA_AVAILABLE = False
+
+
+logger = logging.getLogger(__name__) # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+ Examples:
+ ```py
+ >>> import torch
+ >>> from diffusers import StableDiffusionXLPipeline
+
+ >>> pipe = StableDiffusionXLPipeline.from_pretrained(
+ ... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+ ... )
+ >>> pipe = pipe.to("cuda")
+
+ >>> prompt = "a photo of an astronaut riding a horse on mars"
+ >>> image = pipe(prompt).images[0]
+ ```
+"""
+
+######################################################################################################
+# quant ops
+
+quantize_per_tensor = mixdq_extension._C.quantize_per_tensor_to_int8
+
+
+def qconv2d(
+ input_int,
+ weight_int,
+ weight_scale,
+ input_scale,
+ input_zp,
+ scale,
+ weight_sum_by_input_channels,
+ bias0,
+ bias=None,
+ stride=1,
+ padding=0,
+):
+ dilation = 1
+ return mixdq_extension._C.qconv2d_w8_a8_ohalf(
+ input_int, weight_int, weight_scale, input_scale, input_zp,
+ scale, weight_sum_by_input_channels, bias0,
+ bias, stride, padding, dilation
+ )
+
+
+qlinear = mixdq_extension._C.qlinear_w8_a8_ohalf
+
+# quant ops
+######################################################################################################
+
+
+######################################################################################################
+# the code below is for converting the NN model
+
+__all__ = [
+ "get_default_custom_config_dict",
+ "propagate_qconfig_",
+ "add_quant_dequant",
+ "prepare",
+ "quantize",
+ "quantize_dynamic",
+ "prepare_qat",
+ "quantize_qat",
+ "convert",
+ "swap_module",
+ 'QuantizedLinear',
+ 'QuantizedConv2d',
+]
+
+
+_DEFAULT_CUSTOM_CONFIG_DICT = {
+ 'float_to_observed_custom_module_class': {
+ nn.LSTM: nn.quantizable.LSTM,
+ nn.MultiheadAttention: nn.quantizable.MultiheadAttention,
+ },
+ 'observed_to_quantized_custom_module_class': {
+ nn.quantizable.LSTM: nn.quantized.LSTM,
+ nn.quantizable.MultiheadAttention: nn.quantized.MultiheadAttention,
+ }
+}
+
+_SPLIT = [1280, 1280, 1280, 1280, 640, 640, 640, 320, 320] # For SDXL-Turbo
+
+# global num
+_NUM = 0
+
+
+def get_default_custom_config_dict():
+ r"""Defines the default custom config dict.
+ """
+ return _DEFAULT_CUSTOM_CONFIG_DICT
+
+
+def _propagate_qconfig_helper(module, qconfig_dict,
+ qconfig_parent=None, prefix='', prepare_custom_config_dict=None):
+ r"""This is a helper function for `propagate_qconfig_`
+
+ Args:
+ module: input module
+ qconfig_dict: dictionary that maps from name of submodule to quantization
+ configuration
+ qconfig_parent: quantization config of parent module, we will fallback to
+ this config when there is no specified config for current
+ module
+ prefix: corresponding prefix of the current module, used as key in
+ qconfig_dict
+ prepare_custom_config_dict: dictionary for custom handling of modules
+ see docs for :func:`~torch.ao.quantization.prepare_fx`
+
+ Return:
+ None, module is modified inplace with qconfig attached
+ """
+
+ module_qconfig = qconfig_dict.get(
+ type_before_parametrizations(module), qconfig_parent)
+ module_qconfig = qconfig_dict.get(prefix, module_qconfig)
+ module_qconfig = getattr(module, 'qconfig', module_qconfig)
+
+ torch.ao.quantization.qconfig._assert_valid_qconfig(module_qconfig, module)
+
+ qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
+ module_qconfig, module)
+ module.qconfig = qconfig_with_device_check
+
+ for name, child in module.named_children():
+ module_prefix = prefix + '.' + name if prefix else name
+ # do no not propagate qconfig to child if child is non traceable
+ if prepare_custom_config_dict is None or not (
+ name in prepare_custom_config_dict.get(
+ "non_traceable_module_name", [])
+ or type(child) in prepare_custom_config_dict.get("non_traceable_module_class", [])
+ ):
+ _propagate_qconfig_helper(
+ child, qconfig_dict, qconfig_with_device_check, module_prefix
+ )
+
+
+def propagate_qconfig_(module, qconfig_dict=None, prepare_custom_config_dict=None):
+ r"""Propagate qconfig through the module hierarchy and assign `qconfig`
+ attribute on each leaf module
+
+ Args:
+ module: input module
+ qconfig_dict: dictionary that maps from name or type of submodule to
+ quantization configuration, qconfig applies to all submodules of a
+ given module unless qconfig for the submodules are specified (when
+ the submodule already has qconfig attribute)
+ prepare_custom_config_dict: dictionary for custom handling of modules
+ see docs for :func:`~torch.ao.quantization.prepare_fx`
+
+ Return:
+ None, module is modified inplace with qconfig attached
+ """
+ if qconfig_dict is None:
+ qconfig_dict = {}
+ if prepare_custom_config_dict is None:
+ prepare_custom_config_dict = {}
+ _propagate_qconfig_helper(
+ module, qconfig_dict, prepare_custom_config_dict=prepare_custom_config_dict)
+
+
+def _observer_forward_hook(self, input, output):
+ r"""Forward hook that calls observer on the output
+ """
+ return self.activation_post_process(output)
+
+
+def _observer_forward_pre_hook(self, input):
+ r"""Forward pre hook that calls observer on the output
+ """
+ return self.activation_post_process(input[0])
+
+
+def _register_activation_post_process_hook(module, pre_hook=False):
+ assert hasattr(module, 'activation_post_process'), \
+ 'Expect activation_post_process attribute already attached to the module'
+ if pre_hook:
+ handle = module.register_forward_pre_hook(
+ _observer_forward_pre_hook, prepend=True
+ )
+ else:
+ handle = module.register_forward_hook(
+ _observer_forward_hook, prepend=True
+ )
+
+
+def _add_observer_(module, qconfig_propagation_list=None, non_leaf_module_list=None, device=None, custom_module_class_mapping=None):
+ r"""Add observer for the leaf child of the module.
+
+ This function insert observer module to all leaf child module that
+ has a valid qconfig attribute.
+
+ Args:
+ module: input module with qconfig attributes for all the leaf modules that we want to quantize
+ qconfig_propagation_list: a list of quantizable modules that will have observers added to them
+ if they are leaf nodes
+ device: parent device, if any
+ non_leaf_module_list: list of non-leaf modules we want to add observer
+
+ Return:
+ None, module is modified inplace with added observer modules and forward_hooks
+ """
+ if qconfig_propagation_list is None:
+ qconfig_propagation_list = get_default_qconfig_propagation_list()
+
+ if custom_module_class_mapping is None:
+ custom_module_class_mapping = {}
+
+ # respect device affinity when adding observers
+ if device is None:
+ devices = _get_unique_devices_(module)
+ assert len(devices) <= 1, (
+ f"_add_observer_ only works with cpu or single-device CUDA modules, but got devices {devices}"
+ )
+ device = next(iter(devices)) if len(devices) > 0 else None
+
+ def get_activation_post_process(qconfig, device, special_act_post_process=None):
+ activation = qconfig.activation(
+ ) if special_act_post_process is None else special_act_post_process()
+ if device is not None:
+ activation.to(device)
+ return activation
+
+ def needs_observation(m):
+ return hasattr(m, 'qconfig') and m.qconfig is not None
+
+ def insert_activation_post_process(m, special_act_post_process=None):
+ """ Adds an activation post process module and register
+ a pre or post hook that calls the module
+ """
+ # We don't insert observer/fake_quantize for DeQuantStub
+ if needs_observation(m) and not isinstance(m, DeQuantStub):
+ # observer and hook will be gone after we swap the module
+ m.add_module('activation_post_process', get_activation_post_process(
+ m.qconfig, device, special_act_post_process))
+ # Register observer as the first entry in the hook list
+ # All post forward hooks are preserved and will be executed after the observer before convert
+ _register_activation_post_process_hook(
+ m, pre_hook=_activation_is_memoryless(m.qconfig))
+
+ for name, child in module.named_children():
+ # TODO remove Dropout special after codebase stable
+ if type_before_parametrizations(child) in [nn.Dropout]:
+ continue
+ elif issubclass(type_before_parametrizations(child), (nnq.FloatFunctional, nnq.QFunctional)):
+ if needs_observation(child):
+ assert hasattr(child, "activation_post_process"), (
+ f"functional class {type_before_parametrizations(child)} has no pre-defined `activation_post_process`"
+ )
+ child.activation_post_process = get_activation_post_process(
+ child.qconfig, device)
+ elif isinstance(child, _FusedModule):
+ # activation_post_process are now added directly to nn.Sequential/_FusedModule
+ if needs_observation(child):
+ insert_activation_post_process(child)
+ elif non_leaf_module_list is not None and type_before_parametrizations(child) in non_leaf_module_list:
+ if needs_observation(child):
+ insert_activation_post_process(child)
+ elif _has_special_act_post_process(child):
+ special_act_post_process = _get_special_act_post_process(child)
+ insert_activation_post_process(child, special_act_post_process)
+ elif needs_observation(child) and type_before_parametrizations(child) in custom_module_class_mapping:
+ observed_child = custom_module_class_mapping[type_before_parametrizations(
+ child)].from_float(child)
+ setattr(module, name, observed_child)
+ # TODO: These are the modules that cannot be observed
+ # Once there are more, we should move them to a separate list
+ if custom_module_class_mapping[type_before_parametrizations(child)] not in no_observer_set():
+ insert_activation_post_process(observed_child)
+ else:
+ _add_observer_(child, qconfig_propagation_list,
+ non_leaf_module_list, device, custom_module_class_mapping)
+
+ # Insert observers only for leaf nodes, note that this observer is for
+ # the output of the module, for input QuantStub will observe them
+ if has_no_children_ignoring_parametrizations(module) and not isinstance(module, torch.nn.Sequential) \
+ and type_before_parametrizations(module) in qconfig_propagation_list:
+ insert_activation_post_process(module)
+
+
+def _get_unique_devices_(module):
+ return {p.device for p in module.parameters()} | \
+ {p.device for p in module.buffers()}
+
+
+def add_quant_dequant(module):
+ r"""Wrap the leaf child module in QuantWrapper if it has a valid qconfig
+ Note that this function will modify the children of module inplace and it
+ can return a new module which wraps the input module as well.
+
+ Args:
+ module: input module with qconfig attributes for all the leaf modules
+ that we want to quantize
+
+ Return:
+ Either the inplace modified module with submodules wrapped in
+ `QuantWrapper` based on qconfig or a new `QuantWrapper` module which
+ wraps the input module, the latter case only happens when the input
+ module is a leaf module and we want to quantize it.
+ """
+ if has_no_children_ignoring_parametrizations(module) and hasattr(module, 'qconfig') and module.qconfig:
+ return QuantWrapper(module)
+
+ for name, child in module.named_children():
+ module._modules[name] = add_quant_dequant(child)
+ return module
+
+
+def prepare(model, inplace=False, allow_list=None,
+ observer_non_leaf_module_list=None,
+ prepare_custom_config_dict=None):
+ r"""Prepares a copy of the model for quantization calibration or quantization-aware training.
+
+ Quantization configuration should be assigned preemptively
+ to individual submodules in `.qconfig` attribute.
+
+ The model will be attached with observer or fake quant modules, and qconfig
+ will be propagated.
+
+ Args:
+ `model`: input model to be modified in-place
+ `inplace`: carry out model transformations in-place, the original module is mutated
+ `allow_list`: list of quantizable modules
+ `observer_non_leaf_module_list`: list of non-leaf modules we want to add observer
+ `prepare_custom_config_dict`: customization configuration dictionary for prepare function
+
+ .. code-block:: python
+
+ # Example of prepare_custom_config_dict:
+ prepare_custom_config_dict = {
+ # user will manually define the corresponding observed
+ # module class which has a from_float class method that converts
+ # float custom module to observed custom module
+ "float_to_observed_custom_module_class": {
+ CustomModule: ObservedCustomModule
+ }
+ }
+
+ """
+ torch._C._log_api_usage_once("quantization_api.quantize.prepare")
+ if prepare_custom_config_dict is None:
+ prepare_custom_config_dict = get_default_custom_config_dict()
+ custom_module_class_mapping = prepare_custom_config_dict.get(
+ "float_to_observed_custom_module_class", {})
+
+ if not inplace:
+ model = copy.deepcopy(model)
+
+ # TODO: remove allow_list
+ qconfig_propagation_list = allow_list
+ if allow_list is None:
+ qconfig_propagation_list = get_default_qconfig_propagation_list()
+ propagate_qconfig_(model, qconfig_dict=None)
+
+ # sanity check common API misusage
+ if not any(hasattr(m, 'qconfig') and m.qconfig for m in model.modules()):
+ warnings.warn("None of the submodule got qconfig applied. Make sure you "
+ "passed correct configuration through `qconfig_dict` or "
+ "by assigning the `.qconfig` attribute directly on submodules")
+
+ _add_observer_(
+ model, qconfig_propagation_list, observer_non_leaf_module_list,
+ custom_module_class_mapping=custom_module_class_mapping)
+ return model
+
+
+def _remove_activation_post_process(module):
+ # TODO: maybe we should change activation_post_process to _activation_post_process
+ # to prevent it from being used by user
+ if hasattr(module, 'activation_post_process') and \
+ _is_activation_post_process(module.activation_post_process):
+ delattr(module, 'activation_post_process')
+
+ # remove activation_post_process pre and post hooks
+ def remove_hooks(pre_hook=False):
+ hook_map = module._forward_pre_hooks if pre_hook else module._forward_hooks
+ observer_hook = _observer_forward_pre_hook if pre_hook else _observer_forward_hook
+ handle_ids_to_remove = set()
+ for handle_id, hook_fn in hook_map.items():
+ if hook_fn is observer_hook:
+ handle_ids_to_remove.add(handle_id)
+ for handle_id in handle_ids_to_remove:
+ hook_map.pop(handle_id)
+
+ remove_hooks(pre_hook=True)
+ remove_hooks(pre_hook=False)
+
+# TODO: rename to something more general
+
+
+def _remove_qconfig(module):
+ r"""Clean up the qconfig left in the module so that new qconfig can be
+ propagated.
+
+ Args:
+ module: module to be cleaned up
+ """
+ for child in module.children():
+ _remove_qconfig(child)
+
+ if hasattr(module, "qconfig"):
+ del module.qconfig
+
+ _remove_activation_post_process(module)
+
+
+def quantize(model, run_fn, run_args, mapping=None, inplace=False):
+ r"""Quantize the input float model with post training static quantization.
+
+ First it will prepare the model for calibration, then it calls
+ `run_fn` which will run the calibration step, after that we will
+ convert the model to a quantized model.
+
+ Args:
+ model: input float model
+ run_fn: a calibration function for calibrating the prepared model
+ run_args: positional arguments for `run_fn`
+ inplace: carry out model transformations in-place, the original module is mutated
+ mapping: correspondence between original module types and quantized counterparts
+
+ Return:
+ Quantized model.
+ """
+ torch._C._log_api_usage_once("quantization_api.quantize.quantize")
+ if mapping is None:
+ mapping = get_default_static_quant_module_mappings()
+ if not inplace:
+ model = copy.deepcopy(model)
+ model.eval()
+ prepare(model, inplace=True)
+ run_fn(model, *run_args)
+ convert(model, mapping, inplace=True)
+ return model
+
+
+def quantize_dynamic(model, qconfig_spec=None, dtype=torch.qint8,
+ mapping=None, inplace=False):
+ r"""Converts a float model to dynamic (i.e. weights-only) quantized model.
+
+ Replaces specified modules with dynamic weight-only quantized versions and output the quantized model.
+
+ For simplest usage provide `dtype` argument that can be float16 or qint8. Weight-only quantization
+ by default is performed for layers with large weights size - i.e. Linear and RNN variants.
+
+ Fine grained control is possible with `qconfig` and `mapping` that act similarly to `quantize()`.
+ If `qconfig` is provided, the `dtype` argument is ignored.
+
+ Args:
+ model: input model
+ qconfig_spec: Either:
+
+ - A dictionary that maps from name or type of submodule to quantization
+ configuration, qconfig applies to all submodules of a given
+ module unless qconfig for the submodules are specified (when the
+ submodule already has qconfig attribute). Entries in the dictionary
+ need to be QConfig instances.
+
+ - A set of types and/or submodule names to apply dynamic quantization to,
+ in which case the `dtype` argument is used to specify the bit-width
+
+ inplace: carry out model transformations in-place, the original module is mutated
+ mapping: maps type of a submodule to a type of corresponding dynamically quantized version
+ with which the submodule needs to be replaced
+
+ """
+ torch._C._log_api_usage_once("quantization_api.quantize.quantize_dynamic")
+ if qconfig_spec is None:
+ if dtype == torch.qint8:
+ qconfig_spec = {
+ nn.Linear: default_dynamic_qconfig,
+ nn.LSTM: default_dynamic_qconfig,
+ nn.GRU: default_dynamic_qconfig,
+ nn.LSTMCell: default_dynamic_qconfig,
+ nn.RNNCell: default_dynamic_qconfig,
+ nn.GRUCell: default_dynamic_qconfig,
+ }
+ elif dtype == torch.float16:
+ qconfig_spec = {
+ nn.Linear: float16_dynamic_qconfig,
+ nn.LSTM: float16_dynamic_qconfig,
+ nn.GRU: float16_dynamic_qconfig,
+ nn.LSTMCell: float16_dynamic_qconfig,
+ nn.RNNCell: float16_dynamic_qconfig,
+ nn.GRUCell: float16_dynamic_qconfig,
+ }
+ elif dtype == torch.quint8:
+ qconfig_spec = {
+ nn.EmbeddingBag: float_qparams_weight_only_qconfig,
+ nn.Embedding: float_qparams_weight_only_qconfig,
+ }
+ elif dtype == torch.quint4x2:
+ qconfig_spec = {
+ nn.EmbeddingBag: float_qparams_weight_only_qconfig_4bit,
+ }
+ else:
+ raise ValueError(
+ f"Don't know how to quantize with default settings for {dtype}. Provide full qconfig please")
+ elif isinstance(qconfig_spec, set):
+ if dtype is torch.qint8:
+ default_qconfig = default_dynamic_qconfig
+ elif dtype is torch.float16:
+ default_qconfig = float16_dynamic_qconfig
+ elif dtype is torch.quint8:
+ default_qconfig = float_qparams_weight_only_qconfig
+ elif dtype is torch.quint4x2:
+ default_qconfig = float_qparams_weight_only_qconfig_4bit
+ else:
+ raise RuntimeError(
+ 'Unknown dtype specified for quantize_dynamic: ', str(dtype))
+ qconfig_spec = dict(
+ zip(qconfig_spec, itertools.repeat(default_qconfig)))
+
+ if mapping is None:
+ mapping = get_default_dynamic_quant_module_mappings()
+
+ if not inplace:
+ model = copy.deepcopy(model)
+ model.eval()
+ propagate_qconfig_(model, qconfig_spec)
+ convert(model, mapping, inplace=True)
+ return model
+
+
+def prepare_qat(model, mapping=None, inplace=False):
+ r"""
+ Prepares a copy of the model for quantization calibration or
+ quantization-aware training and converts it to quantized version.
+
+ Quantization configuration should be assigned preemptively
+ to individual submodules in `.qconfig` attribute.
+
+ Args:
+ model: input model to be modified in-place
+ mapping: dictionary that maps float modules to quantized modules to be
+ replaced.
+ inplace: carry out model transformations in-place, the original module
+ is mutated
+ """
+ torch._C._log_api_usage_once("quantization_api.quantize.prepare_qat")
+ assert model.training, "prepare_qat only works on models in training mode"
+ if mapping is None:
+ mapping = get_default_qat_module_mappings()
+
+ if not inplace:
+ model = copy.deepcopy(model)
+
+ propagate_qconfig_(model, qconfig_dict=None)
+ convert(model, mapping=mapping, inplace=True, remove_qconfig=False)
+ prepare(model, observer_non_leaf_module_list=set(
+ mapping.values()), inplace=True)
+ return model
+
+
+def quantize_qat(model, run_fn, run_args, inplace=False):
+ r"""Do quantization aware training and output a quantized model
+
+ Args:
+ model: input model
+ run_fn: a function for evaluating the prepared model, can be a
+ function that simply runs the prepared model or a training
+ loop
+ run_args: positional arguments for `run_fn`
+
+ Return:
+ Quantized model.
+ """
+ torch._C._log_api_usage_once("quantization_api.quantize.quantize_qat")
+ if not inplace:
+ model = copy.deepcopy(model)
+ model.train()
+ prepare_qat(model, inplace=True)
+ run_fn(model, *run_args)
+ convert(model, inplace=True)
+ return model
+
+
+def convert(
+ module, mapping=None, inplace=False, remove_qconfig=True,
+ is_reference=False, convert_custom_config_dict=None, ckpt=None):
+ r"""Converts submodules in input module to a different module according to `mapping`
+ by calling `from_float` method on the target module class. And remove qconfig at the
+ end if remove_qconfig is set to True.
+
+ Args:
+ `module`: prepared and calibrated module
+ `mapping`: a dictionary that maps from source module type to target
+ module type, can be overwritten to allow swapping user defined
+ Modules
+ `inplace`: carry out model transformations in-place, the original module
+ is mutated
+ `convert_custom_config_dict`: custom configuration dictionary for convert function
+
+ .. code-block:: python
+
+ # Example of convert_custom_config_dict:
+ convert_custom_config_dict = {
+ # user will manually define the corresponding quantized
+ # module class which has a from_observed class method that converts
+ # observed custom module to quantized custom module
+ "observed_to_quantized_custom_module_class": {
+ ObservedCustomModule: QuantizedCustomModule
+ }
+ }
+
+ """
+ torch._C._log_api_usage_once("quantization_api.quantize.convert")
+ if not inplace:
+ module = copy.deepcopy(module)
+ _convert(
+ module, mapping, inplace=True, is_reference=is_reference,
+ convert_custom_config_dict=convert_custom_config_dict, ckpt=ckpt)
+ if remove_qconfig:
+ _remove_qconfig(module)
+ return module
+
+
+def _convert(
+ module, mapping=None, inplace=False,
+ is_reference=False, convert_custom_config_dict=None, ckpt=None):
+ r"""Converts submodules in input module to a different module according to `mapping`
+ by calling `from_float` method on the target module class
+
+ Args:
+ module: input module
+ mapping: a dictionary that maps from source module type to target
+ module type, can be overwritten to allow swapping user defined
+ Modules
+ inplace: carry out model transformations in-place, the original module
+ is mutated
+ is_reference: a flag to enable quantized reference module
+
+ """
+ if mapping is None:
+ mapping = get_default_static_quant_reference_module_mappings() if is_reference \
+ else get_default_static_quant_module_mappings()
+ if convert_custom_config_dict is None:
+ convert_custom_config_dict = get_default_custom_config_dict()
+ custom_module_class_mapping = convert_custom_config_dict.get(
+ "observed_to_quantized_custom_module_class", {})
+
+ if not inplace:
+ module = copy.deepcopy(module)
+ reassign = {}
+ for name, mod in module.named_children():
+ # both fused modules and observed custom modules are
+ # swapped as one unit
+ if not isinstance(mod, _FusedModule) and \
+ type_before_parametrizations(mod) not in custom_module_class_mapping:
+ _convert(mod, mapping, True, # inplace
+ is_reference, convert_custom_config_dict, ckpt=ckpt)
+ reassign[name] = swap_module(
+ mod, mapping, custom_module_class_mapping, ckpt=ckpt)
+
+ for key, value in reassign.items():
+ module._modules[key] = value
+
+ return module
+
+
+def swap_module(mod, mapping, custom_module_class_mapping, ckpt=None):
+ global _NUM
+ r"""Swaps the module if it has a quantized counterpart and it has an
+ `observer` attached.
+
+ Args:
+ mod: input module
+ mapping: a dictionary that maps from nn module to nnq module
+
+ Return:
+ The corresponding quantized module of `mod`
+ """
+ new_mod = mod
+ if hasattr(mod, 'qconfig') and mod.qconfig is not None:
+ swapped = False
+ if type_before_parametrizations(mod) in custom_module_class_mapping:
+ new_mod = custom_module_class_mapping[type_before_parametrizations(
+ mod)].from_observed(mod)
+ swapped = True
+ elif type_before_parametrizations(mod) in mapping:
+ qmod = mapping[type_before_parametrizations(mod)]
+ if hasattr(qmod, '_IS_REFERENCE') and qmod._IS_REFERENCE:
+ assert mod.qconfig is not None
+ weight_post_process = mod.qconfig.weight()
+ weight_post_process(mod.weight)
+ weight_qparams = get_qparam_dict(weight_post_process)
+ if 'up_blocks' in mod.module_name and 'conv_shortcut' in mod.module_name:
+ # _NUM = _NUM + 1
+ _split = _SPLIT[_NUM]
+ _NUM = _NUM + 1
+ # num = num + 1
+ else:
+ _split = 0
+ new_mod = qmod.from_float(mod, weight_qparams, split=_split)
+ else:
+ if 'up_blocks' in mod.module_name and 'conv_shortcut' in mod.module_name:
+ # _NUM = _NUM + 1
+ _split = _SPLIT[_NUM]
+ _NUM = _NUM + 1
+ # num = num + 1
+ print(f"split at {_split}")
+ else:
+ _split = 0
+ new_mod = qmod.from_float(mod, split=_split, ckpt=ckpt)
+ swapped = True
+
+ if swapped:
+ # Preserve module's pre forward hooks. They'll be called on quantized input
+ for pre_hook_fn in mod._forward_pre_hooks.values():
+ new_mod.register_forward_pre_hook(pre_hook_fn)
+ # Preserve module's post forward hooks except _observer_forward_hook
+ # After convert they'll work with quantized output
+ for hook_fn in mod._forward_hooks.values():
+ if hook_fn is not _observer_forward_hook:
+ new_mod.register_forward_hook(hook_fn)
+
+ # respect device affinity when swapping modules
+ devices = _get_unique_devices_(mod)
+ assert len(devices) <= 1, (
+ f"swap_module only works with cpu or single-device CUDA modules, but got devices {devices}"
+ )
+ device = next(iter(devices)) if len(devices) > 0 else None
+ if device:
+ new_mod.to(device)
+ return new_mod
+
+
+def _get_observer_dict(mod, target_dict, prefix=""):
+ r"""Traverse the modules and save all observers into dict.
+ This is mainly used for quantization accuracy debug
+ Args:
+ mod: the top module we want to save all observers
+ prefix: the prefix for the current module
+ target_dict: the dictionary used to save all the observers
+ """
+ def get_prefix(prefix):
+ return prefix if prefix == "" else prefix + '.'
+
+ if hasattr(mod, 'activation_post_process'):
+ target_dict[get_prefix(
+ prefix) + 'activation_post_process'] = mod.activation_post_process
+ for name, child in mod.named_children():
+ module_prefix = get_prefix(prefix) + name if prefix else name
+ _get_observer_dict(child, target_dict, module_prefix)
+
+
+# def filter_mod_name_prefix(mod_name):
+# if 'model.' in mod_name:
+# pos = mod_name.index('model.')
+# mod_name = mod_name[pos + 6:]
+# return mod_name
+
+
+# def register_qconfig_from_input_files(
+# unet,
+# # args,
+# w_bit=8,
+# a_bit=None,
+# bos=True,
+# bos_dict=None
+# ):
+# import yaml
+
+# bw_to_dtype = {
+# 8: torch.qint8,
+# 4: torch.quint4x2,
+# 2: torch.quint4x2, # !!!TODO: 2 is not supported, treat as 4
+# }
+
+# # load weight bits
+# # with open(w_config, 'r') as input_file:
+# if w_bit == 8:
+# mod_name_to_weight_width = w8_uniform_config
+# else:
+# raise RuntimeError("we only support int8 quantization")
+# # filter 'model.' from all names
+# mod_name_to_weight_width_copy = {}
+# for mod_name, bit_width in mod_name_to_weight_width.items():
+# new_name = filter_mod_name_prefix(mod_name)
+# mod_name_to_weight_width_copy[new_name] = bit_width
+# mod_name_to_weight_width = mod_name_to_weight_width_copy
+
+# # add qconfig to all modules whose name are in the yaml
+# mod_name_to_weight_width_copy = mod_name_to_weight_width
+# for name, mod in unet.named_modules():
+# if name in mod_name_to_weight_width:
+# assert not hasattr(mod, 'qconfig')
+# # get the corresponding bit-width of the layer
+# w_bitwidth = mod_name_to_weight_width[name]
+# w_dtype = bw_to_dtype[w_bitwidth]
+# act_preprocess = PlaceholderObserver.with_args(
+# dtype=torch.float16) # get the statistic info in the tensor
+# weight_process = PlaceholderObserver.with_args(dtype=w_dtype)
+# mod.qconfig = \
+# QConfig(activation=act_preprocess, weight=weight_process)
+
+# # init some parameters for each unquantized module
+# mod.module_name = name # set module name for each module
+# # record the bit_width of the weight
+# mod.w_bit = mod_name_to_weight_width[name]
+# if 'attn2' in name:
+# if 'to_k' in name or 'to_v' in name:
+# mod.bos = bos # set bos for corss attn layers
+# mod.bos_pre_computed = bos_dict[name]
+
+# del mod_name_to_weight_width_copy[name]
+# # check if there is any module not in the unet
+# if len(mod_name_to_weight_width_copy):
+# for name in mod_name_to_weight_width_copy.keys():
+# print(f"{name} not found in UNet!")
+# raise RuntimeError("Not all keys in weight yaml map to a module in "
+# "UNet.")
+
+# # load activation bits
+# if a_bit is None:
+# return
+
+# # with open(a_config, 'r') as input_file:
+# if a_bit == 8:
+# mod_name_to_act_width = a8_mixed_precision_config
+# else:
+# raise RuntimeError("we only support int8 quantization now")
+# # filter 'model.' from all names
+# mod_name_to_act_width_copy = {}
+# for mod_name, bit_width in mod_name_to_act_width.items():
+# new_name = filter_mod_name_prefix(mod_name)
+# mod_name_to_act_width_copy[new_name] = bit_width
+# mod_name_to_act_width = mod_name_to_act_width_copy
+
+# # add qconfig to all modules whose name are in the yaml
+# mod_name_to_act_width_copy = mod_name_to_act_width
+# for name, mod in unet.named_modules():
+# if name in mod_name_to_act_width:
+# a_bitwidth = mod_name_to_act_width[name]
+# a_dtype = bw_to_dtype[a_bitwidth]
+# act_preprocess = PlaceholderObserver.with_args(dtype=a_dtype)
+# if hasattr(mod, 'qconfig') and mod.qconfig:
+# assert isinstance(mod.qconfig, QConfig)
+# mod.qconfig = QConfig(weight=mod.qconfig.weight,
+# activation=act_preprocess)
+# else:
+# weight_process = PlaceholderObserver.with_args(
+# dtype=torch.float16)
+# mod.qconfig = QConfig(activation=act_preprocess,
+# weight=weight_process)
+
+# # init some parameters for each unquantized module
+# # record the bit_width of the act
+# mod.a_bit = mod_name_to_act_width[name]
+
+# del mod_name_to_act_width_copy[name]
+# # check if there is any module not in the unet
+# if len(mod_name_to_act_width_copy):
+# for name in mod_name_to_act_width_copy.keys():
+# print(f"{name} not found in UNet!")
+# raise RuntimeError("Not all keys in act yaml map to a module in "
+# "UNet.")
+
+
+# def convert_to_quantized(unet, ckpt):
+# # from quantize import convert
+# convert(unet,
+# mapping={nn.Linear: QuantizedLinear,
+# nn.Conv2d: QuantizedConv2d,
+# # QuantStub: Quantizer,
+# # DeQuantStub: DeQuantizer
+# },
+# inplace=True,
+# ckpt=ckpt)
+# # print("unet after quantization")
+# # print(unet)
+
+# the code above is for converting the NN model
+######################################################################################################
+
+
+######################################################################################################
+# mixdq utils
+
+def quantize_per_tensor_uint4(
+ input: torch.Tensor, scale, zero_point,
+):
+
+ # reshape the quant parameters for quantizing
+ scale = scale.view(-1, *([1] * (len(input.shape) - 1)))
+ zero_point = zero_point.view(-1, *([1] * (len(input.shape) - 1)))
+
+ # scale = scale.reshape()
+ scale_inv = 1.0 / scale
+ int_repr = torch.clamp(torch.round(input * scale_inv) +
+ zero_point, 0, 15).to(torch.uint8)
+ if len(input.shape) >= 4:
+ assert input.shape[1] % 2 == 0
+ return (int_repr[:, ::2, ...] << 4 | int_repr[:, 1::2, ...])
+ assert input.shape[-1] % 2 == 0
+ return (int_repr[..., ::2] << 4 | int_repr[..., 1::2])
+
+
+def unpack_uint4(input):
+ shape = input.shape
+ if len(shape) >= 4:
+ packed_dim = 2
+ new_shape = (input.shape[0], input.shape[1]*2, *input.shape[2:])
+ else:
+ packed_dim = -1
+ new_shape = (*input.shape[:-1], input.shape[-1]*2)
+ first_elements = (input >> 4).to(torch.uint8)
+ second_elements = (input & 0b1111).to(torch.uint8)
+ return torch.stack([first_elements, second_elements], dim=packed_dim).view(new_shape)
+
+
+def dequantize_per_tensor_uint4(
+ input, scale, zero_point,
+):
+ # reshape the quant parameters for dequantizing
+ scale = scale.view(-1, *([1] * (len(input.shape) - 1)))
+ zero_point = zero_point.view(-1, *([1] * (len(input.shape) - 1)))
+
+ input = unpack_uint4(input)
+ return (input.view(torch.uint8).to(torch.float32) - zero_point) * scale
+
+
+dtype_to_bw = {
+ torch.quint8: 8,
+ torch.quint4x2: 4,
+ torch.quint2x4: 2,
+ torch.float16: 16,
+}
+
+
+class QParam(namedtuple("QParam", ["qscheme", "dtype", "scales", "zero_points", "axis"], defaults=[torch.per_tensor_affine, torch.quint8, 1.0, 0.0, 0])):
+ @property
+ def zp_float(self):
+ return self.scales * self.zero_points
+ pass
+
+
+def create_qparams_from_dtype(
+ dtype,
+ device,
+ is_channel_wise=False,
+ num_kernels=None,
+ ckpt=None,
+ module_name=None,
+ bit_width=0,
+ quant_type=None,
+ split=0,
+):
+
+ if dtype == torch.float16:
+ return None
+ elif dtype in [torch.qint8, torch.quint8, torch.quint4x2]:
+ if quant_type == 'weight':
+ scales, zero_points, scales_0, zero_points_0 = get_quant_para(ckpt,
+ bit_width,
+ module_name,
+ quant_type='weight',
+ split=split,
+ device=device)
+ elif quant_type == 'act':
+ scales, zero_points, scales_0, zero_points_0 = get_quant_para(ckpt,
+ bit_width,
+ module_name,
+ quant_type='act',
+ split=split,
+ device=device)
+ else:
+ raise ValueError(f"Unsupported quantize dtype {dtype}")
+
+ if is_channel_wise:
+ assert num_kernels is not None
+ qparam = QParam(qscheme=torch.per_channel_affine,
+ scales=scales, zero_points=zero_points,
+ dtype=dtype, axis=0)
+ if split > 0:
+ qparam_0 = QParam(qscheme=torch.per_channel_affine,
+ scales=scales_0, zero_points=zero_points_0,
+ dtype=dtype, axis=0)
+ else:
+ qparam_0 = None
+
+ else:
+ qparam = QParam(qscheme=torch.per_tensor_affine,
+ scales=scales, zero_points=zero_points,
+ dtype=dtype)
+
+ if split > 0:
+ qparam_0 = QParam(qscheme=torch.per_tensor_affine,
+ scales=scales_0, zero_points=zero_points_0,
+ dtype=dtype)
+ else:
+ qparam_0 = None
+
+ return qparam, qparam_0
+
+
+def quantize_from_qparams(x: torch.Tensor, qparams: QParam):
+ if qparams.dtype == torch.quint4x2:
+ # TODO: support both per-channel and per-tensor
+ # assert qparams.qscheme == torch.per_tensor_affine
+ # print(x.shape)
+ return quantize_per_tensor_uint4(x, qparams.scales.to(x.device), qparams.zero_points.to(x.device))
+
+ if qparams.qscheme in [torch.per_tensor_affine]:
+ scales = qparams.scales
+ scales = scales.clone().detach().to(x.device) \
+ if isinstance(scales, torch.Tensor) \
+ else torch.tensor(scales, dtype=torch.float16, device=x.device)
+ zps = qparams.zero_points
+ zps = zps.clone().detach().to(x.device) \
+ if isinstance(zps, torch.Tensor) \
+ else torch.tensor(zps, dtype=torch.float16, device=x.device)
+
+ # Quantize only works on Float Tensor not Half. TODO: custom kernels
+ x = x.to(torch.float32)
+ x_quant = torch.quantize_per_tensor(x, scales, zps, qparams.dtype)
+ elif qparams.qscheme in [torch.per_channel_affine]:
+ scales = qparams.scales
+ assert isinstance(scales, torch.Tensor)
+ scales = scales.clone().detach().to(x.device)
+ zps = qparams.zero_points
+ assert isinstance(zps, torch.Tensor)
+ zps = zps.clone().detach().to(x.device)
+ assert qparams.axis < len(x.shape)
+ # Quantize only works on Float Tensor not Half TODO: custom kernels
+ x = x.to(torch.float32)
+ # print(scales.shape)
+ # if scales.shape == torch.Size([]):
+ # # torch.quantize_per_channel need the shape of scales and zps to be torch.size([N])
+ # scales = scales.reshape(1)
+ # zps = zps.reshape(1)
+ x_quant = torch.quantize_per_channel(x, scales, zps, axis=qparams.axis,
+ dtype=qparams.dtype)
+ else:
+ raise ValueError(f"Unknown qscheme {qparams.qscheme}")
+ return x_quant
+
+
+def dequantize_to_float16_linear(x: torch.Tensor, qparams: QParam):
+ if x.dtype == torch.float16:
+ return x
+ if x.dtype in [torch.quint8, torch.qint8]:
+ return x.dequantize().to(torch.float32)
+ elif x.dtype in [torch.int8]:
+ scale = (qparams.scales.view(-1, *
+ ([1] * (len(x.shape) - 1)))).cuda().float()
+ zero_points = (qparams.zero_points.view(-1, *
+ ([1] * (len(x.shape) - 1)))).cuda().float()
+
+ x = scale*(x - zero_points)
+ return x
+
+ assert x.dtype == torch.uint8 # the current way to support uint4
+ return dequantize_per_tensor_uint4(x, qparams.scales.to(x.device), qparams.zero_points.to(x.device)).to(torch.float16)
+
+
+def dequantize_to_float16(x: torch.Tensor, qparams: QParam):
+ if x.dtype == torch.float16:
+ return x
+ if x.dtype in [torch.quint8, torch.qint8]:
+ return x.dequantize().to(torch.float16)
+ elif x.dtype in [torch.int8]:
+ scale = (qparams.scales.view(-1, *([1] * (len(x.shape) - 1)))).cuda()
+ zero_points = (qparams.zero_points.view(-1, *
+ ([1] * (len(x.shape) - 1)))).cuda()
+
+ x = scale*(x - zero_points)
+ return x
+
+ assert x.dtype == torch.uint8 # the current way to support uint4
+ return dequantize_per_tensor_uint4(x, qparams.scales.to(x.device), qparams.zero_points.to(x.device)).to(torch.float16)
+
+
+def get_quant_para(ckpt, n_bit, module_name, quant_type, split=0, device=None):
+
+ if split == 0:
+ bit_idx = int(math.log2(n_bit)-1)
+
+ if quant_type == 'weight':
+ module_name = module_name + '.weight_quantizer'
+ assert module_name in ckpt.keys()
+ scales = ckpt[module_name]['delta_list'][bit_idx]
+ # sym quantization, zp=0
+ zero_point = ckpt[module_name]['zero_point_list'][bit_idx]
+ # print(zero_point)
+
+ elif quant_type == 'act':
+ module_name = module_name + '.act_quantizer'
+ assert module_name in ckpt.keys()
+ scales = ckpt[module_name]['delta_list'][bit_idx]
+ # change the data type from uint8 to int8
+ zero_point = ckpt[module_name]['zero_point_list'][bit_idx] - 128
+
+ return scales.to(device), zero_point.to(device), None, None
+
+ elif split > 0:
+ bit_idx = int(math.log2(n_bit)-1)
+
+ if quant_type == 'weight':
+ module_name = module_name + '.weight_quantizer'
+ assert module_name in ckpt.keys()
+ scales = ckpt[module_name]['delta_list'][bit_idx]
+ zero_point = ckpt[module_name]['zero_point_list'][bit_idx]
+
+ module_name = module_name + '_0'
+ assert module_name in ckpt.keys()
+ scales_0 = ckpt[module_name]['delta_list'][bit_idx]
+ zero_point_0 = ckpt[module_name]['zero_point_list'][bit_idx]
+ # print(zero_point, zero_point_0)
+
+ elif quant_type == 'act':
+ module_name = module_name + '.act_quantizer'
+
+ assert module_name in ckpt.keys()
+ scales = ckpt[module_name]['delta_list'][bit_idx]
+ zero_point = ckpt[module_name]['zero_point_list'][bit_idx]-128
+
+ module_name = module_name + '_0'
+ assert module_name in ckpt.keys()
+ scales_0 = ckpt[module_name]['delta_list'][bit_idx]
+ zero_point_0 = ckpt[module_name]['zero_point_list'][bit_idx]-128
+
+ return scales.to(device), zero_point.to(device), scales_0.to(device), zero_point_0.to(device)
+# mixdq utils
+######################################################################################################
+
+
+######################################################################################################
+# mixdq quantized module
+class QuantizedConv2d(nn.Module):
+
+ def __init__(self, in_channels: int, out_channels: int, kernel_size,
+ stride, padding, dilation, groups=1, bias=True,
+ device=None,
+ w_qparams=None, w_qparams_0=None, a_qparams=None,
+ a_qparams_0 = None, module_name=None, split=0) -> None:
+ super().__init__()
+
+ self.module_name = module_name
+ self.split = split # for shortcut layer
+
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.device = device
+ self.kernel_size = kernel_size
+ self.stride = stride
+ self.padding = padding
+ self.dilation = dilation
+ self.groups = groups
+
+ self.valid_for_acceleration = (
+ w_qparams is not None and \
+ a_qparams is not None and \
+ w_qparams.dtype in [torch.qint8, torch.quint8] and \
+ a_qparams.dtype in [torch.qint8, torch.quint8] and \
+ w_qparams.qscheme == torch.per_channel_affine and \
+ a_qparams.qscheme == torch.per_tensor_affine and \
+ torch.all(w_qparams.zero_points == 0.0).item() and \
+ (
+ split == 0 or (
+ w_qparams_0 is not None and \
+ a_qparams_0 is not None and \
+ w_qparams_0.dtype in [torch.qint8, torch.quint8] and \
+ a_qparams_0.dtype in [torch.qint8, torch.quint8] and \
+ w_qparams_0.qscheme == torch.per_channel_affine and \
+ a_qparams_0.qscheme == torch.per_tensor_affine and \
+ torch.all(w_qparams_0.zero_points == 0.0).item()
+ )
+ ) and \
+ (
+ len(set(self.stride)) == 1 and len(set(self.padding)) == 1 and \
+ len(set(self.dilation)) == 1 and self.dilation[0] == 1 and \
+ self.groups == 1
+ )
+ )
+ if self.valid_for_acceleration and (
+ self.in_channels % 4 != 0 or self.out_channels % 4 != 0):
+ logging.warning("Linear layer with in_features = "
+ f"{self.in_channels} and out_features = "
+ f"{self.out_channels} cannot use quantized kernel due to "
+ "misalignment. Falling back to FP kernels")
+ self.valid_for_acceleration = False
+
+ if self.valid_for_acceleration:
+ self.register_buffer("weight_scales",
+ w_qparams.scales.to(device).float())
+ self.register_buffer("weight_zero_points",
+ w_qparams.zero_points.to(device).float())
+ self.register_buffer("act_scales",
+ a_qparams.scales.to(device).float())
+ self.register_buffer("act_zero_points",
+ a_qparams.zero_points.to(device).float())
+ self.register_buffer("act_scales_inv", 1 / self.act_scales)
+ if self.split != 0:
+ self.register_buffer("weight_scales_0",
+ w_qparams_0.scales.to(device).float())
+ self.register_buffer("weight_zero_points_0",
+ w_qparams_0.zero_points.to(device).float())
+ self.register_buffer("act_scales_0",
+ a_qparams_0.scales.to(device).float())
+ self.register_buffer("act_zero_points_0",
+ a_qparams_0.zero_points.to(device).float())
+ self.register_buffer("act_scales_inv_0", 1 / self.act_scales_0)
+
+ @classmethod
+ def from_float(cls, float_mod, split=0, ckpt=None):
+
+ assert hasattr(float_mod, 'qconfig') and isinstance(float_mod.qconfig,
+ QConfig)
+ weight_process = float_mod.qconfig.weight()
+ w_dtype = weight_process.dtype
+ num_kernels = float_mod.weight.shape[0]
+ device=float_mod.weight.device
+ # init the w & a quant parameters
+ # split = 0
+ # if split == 0:
+ # init the quant parameters
+ w_qparams, w_qparams_0 = create_qparams_from_dtype(dtype=w_dtype,
+ device=device,
+ is_channel_wise=True,
+ num_kernels=num_kernels,
+ ckpt=ckpt,
+ module_name=float_mod.module_name,
+ quant_type='weight',
+ bit_width=float_mod.w_bit,
+ split=split)
+
+
+ act_process = float_mod.qconfig.activation()
+ act_dtype = act_process.dtype
+
+ # if split == 0:
+ if hasattr(float_mod, 'a_bit'):
+ # if we want to quantized the act
+ a_qparams, a_qparams_0 = create_qparams_from_dtype(dtype=act_dtype,
+ device=device,
+ is_channel_wise=False,
+ num_kernels=num_kernels,
+ ckpt=ckpt,
+ module_name=float_mod.module_name,
+ quant_type='act',
+ bit_width=float_mod.a_bit,
+ split=split)
+ else:
+ a_qparams = None
+ a_qparams_0 = None
+
+ new_mod = cls(float_mod.in_channels,
+ float_mod.out_channels,
+ float_mod.kernel_size,
+ float_mod.stride,
+ float_mod.padding,
+ float_mod.dilation,
+ float_mod.groups,
+ float_mod.bias is not None,
+ device=float_mod.weight.device,
+
+ w_qparams=w_qparams,
+ w_qparams_0 = w_qparams_0,
+ a_qparams=a_qparams,
+ a_qparams_0 = a_qparams_0,
+
+ module_name=float_mod.module_name,
+ split = split
+ )
+
+ weight = float_mod.weight.detach()
+
+ if split == 0:
+ if new_mod.valid_for_acceleration:
+ weight_int = torch.quantize_per_channel(
+ weight.float(),
+ new_mod.weight_scales,
+ new_mod.weight_zero_points,
+ axis=w_qparams.axis,
+ dtype=w_qparams.dtype).int_repr()
+
+ new_mod.register_buffer("weight_int", weight_int)
+ # auxiliary structure, used to quickly compute act_zp @ weight
+ if float_mod.padding[0] == 0:
+ weight_sum_per_output_channel = \
+ weight_int.float().sum(dim=[1,2,3])
+ new_mod.register_buffer("bias0",
+ weight_sum_per_output_channel*new_mod.act_zero_points)
+ new_mod.weight_sum_by_input_channels = None
+ else:
+ weight_sum_by_input_channels = \
+ weight_int.float().sum(dim=1, keepdim=True)
+ new_mod.register_buffer("weight_sum_by_input_channels",
+ weight_sum_by_input_channels)
+ new_mod.bias0 = None
+ new_mod.register_buffer("scale",
+ new_mod.weight_scales * new_mod.act_scales)
+ else:
+ new_mod.register_buffer("weight", weight)
+ if float_mod.bias is not None:
+ bias = float_mod.bias.detach()
+ new_mod.register_buffer("bias", bias)
+ else:
+ new_mod.bias = None
+
+ # for the weight of the shortcut
+ elif split > 0:
+ if new_mod.valid_for_acceleration:
+ weight_int = torch.quantize_per_channel(
+ weight[:, :split, ...].float(),
+ new_mod.weight_scales,
+ new_mod.weight_zero_points,
+ axis=w_qparams.axis,
+ dtype=w_qparams.dtype).int_repr()
+
+ weight_int_0 = torch.quantize_per_channel(
+ weight[:, split:, ...].float(),
+ new_mod.weight_scales_0,
+ new_mod.weight_zero_points_0,
+ axis=w_qparams_0.axis,
+ dtype=w_qparams_0.dtype).int_repr()
+
+ new_mod.register_buffer("weight_int", weight_int)
+ new_mod.register_buffer("weight_int_0", weight_int_0)
+
+ # auxiliary structure, used to quickly compute act_zp @ weight
+ if float_mod.padding[0] == 0:
+ weight_sum_per_output_channel = \
+ weight_int.float().sum(dim=[1,2,3])
+ new_mod.register_buffer("bias0",
+ weight_sum_per_output_channel * new_mod.act_zero_points)
+ weight_sum_per_output_channel_0 = \
+ weight_int_0.float().sum(dim=[1,2,3])
+ new_mod.register_buffer("bias0_0",
+ weight_sum_per_output_channel_0 * new_mod.act_zero_points_0)
+ new_mod.weight_sum_by_input_channels = None
+ new_mod.weight_sum_by_input_channels_0 = None
+ else:
+ weight_sum_by_input_channels = \
+ weight_int.float().sum(dim=1, keepdim=True)
+ new_mod.register_buffer("weight_sum_by_input_channels",
+ weight_sum_by_input_channels)
+ weight_sum_by_input_channels_0 = \
+ weight_int_0.float().sum(dim=1, keepdim=True)
+ new_mod.register_buffer("weight_sum_by_input_channels_0",
+ weight_sum_by_input_channels_0)
+ new_mod.bias0 = None
+ new_mod.bias0_0 = None
+ new_mod.register_buffer("scale",
+ new_mod.weight_scales * new_mod.act_scales)
+ new_mod.register_buffer("scale_0",
+ new_mod.weight_scales_0 * new_mod.act_scales_0)
+ else:
+ new_mod.register_buffer("weight", weight)
+
+ if float_mod.bias is not None:
+ bias = float_mod.bias.detach()
+ new_mod.register_buffer("bias", bias)
+ else:
+ new_mod.bias = None
+
+ return new_mod
+
+ def _get_name(self):
+ if self.valid_for_acceleration:
+ return "QuantizedConv2dW8A8"
+ return "QuantizedConv2dFPFallback"
+
+ def forward_fallback(self, x: torch.Tensor):
+ weight_recovered = \
+ self.weight_int.float() * self.weight_scales[:, None, None, None]
+ weight_recovered = weight_recovered.to(x.dtype)
+ bias = self.bias.to(x.dtype) if self.bias is not None else None
+
+ if self.split == 0:
+ return torch.nn.functional.conv2d(x,
+ weight_recovered,
+ bias,
+ self.stride,
+ self.padding,
+ self.dilation,
+ self.groups)
+ else:
+ weight_0_recovered = \
+ self.weight_int_0.float() * self.weight_scales_0[:, None, None, None]
+ weight_0_recovered = weight_0_recovered.to(x.dtype)
+ output = torch.nn.functional.conv2d(x[:, :self.split, :, :],
+ weight_recovered,
+ bias,
+ self.stride,
+ self.padding,
+ self.dilation,
+ self.groups)
+ output_0 = torch.nn.functional.conv2d(x[:, self.split:, :, :],
+ weight_0_recovered,
+ None,
+ self.stride,
+ self.padding,
+ self.dilation,
+ self.groups)
+ output = output + output_0
+ return output
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ if not self.valid_for_acceleration:
+ return F.conv2d(x, self.weight, self.bias, self.stride, self.padding,
+ self.dilation, self.groups)
+
+ if not x.dtype == torch.float16:
+ return self.forward_fallback(x)
+
+ if self.split == 0:
+ x_int = quantize_per_tensor(x,
+ self.act_scales_inv,
+ self.act_zero_points)
+ output = qconv2d(x_int, # input_int
+ self.weight_int, # weight_int,
+ self.weight_scales, # weight_scale
+ self.act_scales, # input_scale
+ self.act_zero_points, # input_zp
+ self.scale, # scale
+ self.weight_sum_by_input_channels,
+ # weight_sum_by_input_channels
+ self.bias0,
+ self.bias, # bias
+ self.stride[0], # stride
+ self.padding[0], # padding
+ )
+ return output
+ else:
+ x_int = quantize_per_tensor(x[:, :self.split, :, :],
+ self.act_scales_inv,
+ self.act_zero_points)
+ x_int_0 = quantize_per_tensor(x[:, self.split:, :, :],
+ self.act_scales_inv_0,
+ self.act_zero_points_0)
+ output = qconv2d(x_int, # input_int
+ self.weight_int, # weight_int,
+ self.weight_scales, # weight_scale
+ self.act_scales, # input_scale
+ self.act_zero_points, # input_zp
+ self.scale, # scale
+ self.weight_sum_by_input_channels,
+ # weight_sum_by_input_channels
+ self.bias0,
+ self.bias, # bias
+ self.stride[0], # stride
+ self.padding[0], # padding
+ )
+ output_0 = qconv2d(x_int_0, # input_int
+ self.weight_int_0, # weight_int,
+ self.weight_scales_0, # weight_scale
+ self.act_scales_0, # input_scale
+ self.act_zero_points_0, # input_zp
+ self.scale_0, # scale
+ self.weight_sum_by_input_channels_0,
+ # weight_sum_by_input_channels
+ self.bias0_0,
+ None, # bias. Here is none because bias
+ # need to be applied just once in output
+ self.stride[0], # stride
+ self.padding[0], # padding
+ )
+ output = output + output_0
+ return output
+
+
+class QuantizedLinear(nn.Module):
+ def __init__(self, in_features: int, out_features: int, bias: bool = True,
+ device=None, w_qparams=None, a_qparams=None, module_name=None) -> None:
+
+ super().__init__()
+ self.module_name = module_name
+ # print(module_name)
+
+ self.in_features = in_features
+ self.out_features = out_features
+ self.device = device
+ self.valid_for_acceleration = (
+ w_qparams is not None and \
+ a_qparams is not None and \
+ w_qparams.dtype in [torch.qint8, torch.quint8] and \
+ a_qparams.dtype in [torch.qint8, torch.quint8] and \
+ w_qparams.qscheme == torch.per_channel_affine and \
+ a_qparams.qscheme == torch.per_tensor_affine and \
+ torch.all(w_qparams.zero_points == 0.0).item()
+ )
+ if self.valid_for_acceleration and (
+ self.in_features % 4 != 0 or self.out_features % 4 != 0):
+ logging.warning("Linear layer with in_features = "
+ f"{self.in_features} and out_features = "
+ f"{self.out_features} cannot use quantized kernel due to "
+ "misalignment. Falling back to FP kernels")
+ self.valid_for_acceleration = False
+
+ if self.valid_for_acceleration:
+ self.register_buffer("weight_scales",
+ w_qparams.scales.to(device).float())
+ self.register_buffer("weight_zero_points",
+ w_qparams.zero_points.to(device).float())
+ self.register_buffer("act_scales",
+ a_qparams.scales.to(device).float())
+ self.register_buffer("act_zero_points",
+ a_qparams.zero_points.to(device).float())
+ self.register_buffer("act_scales_inv", 1 / self.act_scales)
+
+
+ @classmethod
+ def from_float(cls, float_mod, split=0, ckpt=None):
+ assert hasattr(float_mod, 'qconfig') and isinstance(float_mod.qconfig,
+ QConfig)
+ weight_process = float_mod.qconfig.weight()
+ w_dtype = weight_process.dtype
+ num_kernels = float_mod.weight.shape[0]
+ device=float_mod.weight.device
+
+ w_qparams, w_qparams_0 = create_qparams_from_dtype(dtype=w_dtype,
+ device=device,
+ is_channel_wise=True,
+ num_kernels=num_kernels,
+ ckpt=ckpt,
+ module_name=\
+ float_mod.module_name,
+ quant_type='weight',
+ bit_width=float_mod.w_bit,
+ split=split)
+
+
+ act_process = float_mod.qconfig.activation()
+ act_dtype = act_process.dtype
+
+ if hasattr(float_mod, 'a_bit'):
+ a_qparams, a_qparams_0 = create_qparams_from_dtype(dtype=act_dtype,
+ device=device,
+ is_channel_wise=False,
+ num_kernels=num_kernels,
+ ckpt=ckpt,
+ module_name=\
+ float_mod.module_name,
+ quant_type='act',
+ bit_width=float_mod.a_bit,
+ split=split)
+ else:
+ a_qparams = None
+ a_qparams_0 = None
+
+ new_mod = cls(float_mod.in_features,
+ float_mod.out_features,
+ float_mod.bias is not None,
+ device=float_mod.weight.device,
+ w_qparams=w_qparams,
+ a_qparams=a_qparams,
+ module_name = float_mod.module_name,
+ )
+
+ weight = float_mod.weight.detach()
+
+ if 'attn2' in float_mod.module_name:
+ if 'to_k' in float_mod.module_name or \
+ 'to_v' in float_mod.module_name:
+ new_mod.bos = float_mod.bos
+ # new_mod.bos_pre_computed =float_mod.bos_pre_computed.to(device)
+ new_mod.register_buffer("bos_pre_computed", float_mod.bos_pre_computed)
+ # the input of the org_weight is key_first_token
+ # new_mod.register_buffer("org_weight", weight)
+
+ if new_mod.valid_for_acceleration:
+ weight_int = torch.quantize_per_channel(
+ weight.float(),
+ new_mod.weight_scales,
+ new_mod.weight_zero_points,
+ axis=w_qparams.axis,
+ dtype=w_qparams.dtype).int_repr()
+
+ new_mod.register_buffer("weight_int", weight_int)
+
+ # auxiliary structure, used to quickly compute act_zp @ weight
+ weight_sum_by_input_channels = weight_int.float().sum(dim=1)
+ new_mod.register_buffer("weight_sum_by_input_channels",
+ weight_sum_by_input_channels)
+ new_mod.register_buffer("scale",
+ new_mod.weight_scales*new_mod.act_scales)
+ new_mod.register_buffer("bias0",
+ weight_sum_by_input_channels * new_mod.act_zero_points)
+ else:
+ new_mod.register_buffer("weight", weight)
+ if float_mod.bias is not None:
+ bias = float_mod.bias.detach()
+ new_mod.register_buffer("bias", bias)
+ else:
+ new_mod.bias = None
+ return new_mod
+
+ def _get_name(self):
+ if self.valid_for_acceleration:
+ return "QuantizedLinearW8A8"
+ return "QuantizedLinearFPFallback"
+
+ def forward_fallback(self, x):
+ weight_recovered = self.weight_int.float()* self.weight_scales[:, None]
+ weight_recovered = weight_recovered.to(x.dtype)
+ return F.linear(x,
+ weight_recovered,
+ self.bias.to(x.dtype) if self.bias is not None else None)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ if not self.valid_for_acceleration:
+ return F.linear(x, self.weight, self.bias)
+
+ if not x.dtype == torch.float16:
+ return self.forward_fallback(x)
+
+ if not hasattr(self, 'bos') or not self.bos:
+ x_int = quantize_per_tensor(x,
+ self.act_scales_inv,
+ self.act_zero_points)
+ output = qlinear(
+ x_int, # input_int
+ self.weight_int, # weight_int
+ self.weight_scales, # weight_scale
+ self.act_scales, # input_scale
+ self.act_zero_points, # input_zp
+ self.weight_sum_by_input_channels,
+ # weight_sum_by_input_channels
+ self.scale,
+ self.bias0,
+ self.bias # bias (None or tensor)
+ )
+ return output
+ else:
+ # use bos and quantize the activation
+ x_except_first_token = quantize_per_tensor(x[:,1:,:],
+ self.act_scales_inv,
+ self.act_zero_points)
+ out_except_first_token = qlinear(x_except_first_token,
+ self.weight_int,
+ self.weight_scales,
+ self.act_scales,
+ self.act_zero_points,
+ self.weight_sum_by_input_channels,
+ self.scale,
+ self.bias0,
+ self.bias)
+ out_first_token = self.bos_pre_computed.expand(x.shape[0], -1, -1)
+ output =torch.cat([out_first_token, out_except_first_token], dim=1)
+ return output
+
+# mixdq quantized module
+######################################################################################################
+
+
+######################################################################################################
+# func for sdxl pipeline
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+ """
+ Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+ Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+ """
+ std_text = noise_pred_text.std(
+ dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+ std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+ # rescale the results from guidance (fixes overexposure)
+ noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+ # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+ noise_cfg = guidance_rescale * noise_pred_rescaled + \
+ (1 - guidance_rescale) * noise_cfg
+ return noise_cfg
+
+
+def retrieve_timesteps(
+ scheduler,
+ num_inference_steps: Optional[int] = None,
+ device: Optional[Union[str, torch.device]] = None,
+ timesteps: Optional[List[int]] = None,
+ **kwargs,
+):
+ """
+ Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+ custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+ Args:
+ scheduler (`SchedulerMixin`):
+ The scheduler to get timesteps from.
+ num_inference_steps (`int`):
+ The number of diffusion steps used when generating samples with a pre-trained model. If used,
+ `timesteps` must be `None`.
+ device (`str` or `torch.device`, *optional*):
+ The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+ timesteps (`List[int]`, *optional*):
+ Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
+ timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
+ must be `None`.
+
+ Returns:
+ `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+ second element is the number of inference steps.
+ """
+ if timesteps is not None:
+ accepts_timesteps = "timesteps" in set(
+ inspect.signature(scheduler.set_timesteps).parameters.keys())
+ if not accepts_timesteps:
+ raise ValueError(
+ f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+ f" timestep schedules. Please check whether you are using the correct scheduler."
+ )
+ scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+ timesteps = scheduler.timesteps
+ num_inference_steps = len(timesteps)
+ else:
+ scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+ timesteps = scheduler.timesteps
+ return timesteps, num_inference_steps
+
+# func for sdxl pipeline
+######################################################################################################
+
+
+######################################################################################################
+# quant inference
+def filter_mod_name_prefix(mod_name):
+ if 'model.' in mod_name:
+ pos = mod_name.index('model.')
+ mod_name = mod_name[pos + 6:]
+ return mod_name
+
+
+def register_qconfig_from_input_files(
+ unet,
+ # args,
+ w_bit=8,
+ a_bit=None,
+ bos=True,
+ bos_dict=None
+):
+ import yaml
+
+ bw_to_dtype = {
+ 8: torch.qint8,
+ 4: torch.quint4x2,
+ 2: torch.quint4x2, # !!!TODO: 2 is not supported, treat as 4
+ }
+
+ # load weight bits
+ # with open(w_config, 'r') as input_file:
+ if w_bit == 8:
+ mod_name_to_weight_width = w8_uniform_config
+ else:
+ raise RuntimeError("we only support int8 quantization")
+ # filter 'model.' from all names
+ mod_name_to_weight_width_copy = {}
+ for mod_name, bit_width in mod_name_to_weight_width.items():
+ new_name = filter_mod_name_prefix(mod_name)
+ mod_name_to_weight_width_copy[new_name] = bit_width
+ mod_name_to_weight_width = mod_name_to_weight_width_copy
+
+ # add qconfig to all modules whose name are in the yaml
+ mod_name_to_weight_width_copy = mod_name_to_weight_width
+ for name, mod in unet.named_modules():
+ if name in mod_name_to_weight_width:
+ assert not hasattr(mod, 'qconfig')
+ # get the corresponding bit-width of the layer
+ w_bitwidth = mod_name_to_weight_width[name]
+ w_dtype = bw_to_dtype[w_bitwidth]
+ act_preprocess = PlaceholderObserver.with_args(
+ dtype=torch.float16) # get the statistic info in the tensor
+ weight_process = PlaceholderObserver.with_args(dtype=w_dtype)
+ mod.qconfig = \
+ QConfig(activation=act_preprocess, weight=weight_process)
+
+ # init some parameters for each unquantized module
+ mod.module_name = name # set module name for each module
+ # record the bit_width of the weight
+ mod.w_bit = mod_name_to_weight_width[name]
+ if 'attn2' in name:
+ if 'to_k' in name or 'to_v' in name:
+ mod.bos = bos # set bos for corss attn layers
+ mod.bos_pre_computed = bos_dict[name]
+
+ del mod_name_to_weight_width_copy[name]
+ # check if there is any module not in the unet
+ if len(mod_name_to_weight_width_copy):
+ for name in mod_name_to_weight_width_copy.keys():
+ print(f"{name} not found in UNet!")
+ raise RuntimeError("Not all keys in weight yaml map to a module in "
+ "UNet.")
+
+ # load activation bits
+ if a_bit is None:
+ return
+
+ # with open(a_config, 'r') as input_file:
+ if a_bit == 8:
+ mod_name_to_act_width = a8_mixed_precision_config
+ else:
+ raise RuntimeError("we only support int8 quantization now")
+ # filter 'model.' from all names
+ mod_name_to_act_width_copy = {}
+ for mod_name, bit_width in mod_name_to_act_width.items():
+ new_name = filter_mod_name_prefix(mod_name)
+ mod_name_to_act_width_copy[new_name] = bit_width
+ mod_name_to_act_width = mod_name_to_act_width_copy
+
+ # add qconfig to all modules whose name are in the yaml
+ mod_name_to_act_width_copy = mod_name_to_act_width
+ for name, mod in unet.named_modules():
+ if name in mod_name_to_act_width:
+ a_bitwidth = mod_name_to_act_width[name]
+ a_dtype = bw_to_dtype[a_bitwidth]
+ act_preprocess = PlaceholderObserver.with_args(dtype=a_dtype)
+ if hasattr(mod, 'qconfig') and mod.qconfig:
+ assert isinstance(mod.qconfig, QConfig)
+ mod.qconfig = QConfig(weight=mod.qconfig.weight,
+ activation=act_preprocess)
+ else:
+ weight_process = PlaceholderObserver.with_args(
+ dtype=torch.float16)
+ mod.qconfig = QConfig(activation=act_preprocess,
+ weight=weight_process)
+
+ # init some parameters for each unquantized module
+ # record the bit_width of the act
+ mod.a_bit = mod_name_to_act_width[name]
+
+ del mod_name_to_act_width_copy[name]
+ # check if there is any module not in the unet
+ if len(mod_name_to_act_width_copy):
+ for name in mod_name_to_act_width_copy.keys():
+ print(f"{name} not found in UNet!")
+ raise RuntimeError("Not all keys in act yaml map to a module in "
+ "UNet.")
+
+
+def convert_to_quantized(unet, ckpt):
+ # from quantize import convert
+ convert(unet,
+ mapping={nn.Linear: QuantizedLinear,
+ nn.Conv2d: QuantizedConv2d,
+ },
+ inplace=True,
+ ckpt=ckpt)
+
+
+# def compile_opt(pipeline, args):
+# if args.run_pipeline:
+# pipeline = pipeline.to('cuda')
+# else:
+# pipeline.unet = pipeline.unet.to("cuda")
+
+# if args.compile_tool == 'pt2':
+# pipeline.unet = torch.compile(pipeline.unet)
+# elif args.compile_tool == 'onediff':
+# from onediff.infer_compiler import oneflow_compile
+# pipeline.unet = oneflow_compile(pipeline.unet)
+# elif args.compile_tool == 'sfast':
+# # apply stable-fast
+# from sfast.compilers.diffusion_pipeline_compiler import compile_unet
+# from sfast.compilers.diffusion_pipeline_compiler import CompilationConfig
+
+# sfast_config = CompilationConfig.Default()
+# sfast_config.enable_triton = True
+# sfast_config.enable_cuda_graph = False
+# sfast_config.enable_jit = True
+
+# pipeline.unet = compile_unet(pipeline.unet, sfast_config)
+# else:
+# print(f"Unknown compile tool {args.compile_tool}")
+# return pipeline
+
+
+def cuda_graph_opt(unet):
+
+ def hash_arg(arg):
+ if isinstance(arg, torch.Tensor):
+ arg_device = arg.device
+ arg_device_type = arg_device.type
+ return (arg_device_type, arg_device.index, arg.dtype, arg.shape,
+ arg.item()
+ if arg_device_type == 'cpu' and arg.numel() == 1 else None)
+ if isinstance(arg, (str, int, float, bytes, bool)):
+ return arg
+ if isinstance(arg, (tuple, list)):
+ return tuple(map(hash_arg, arg))
+ if isinstance(arg, dict):
+ return tuple(
+ sorted(((hash_arg(k), hash_arg(v)) for k, v in arg.items()),
+ key=lambda x: x[0]))
+ return type(arg)
+
+ def copy_args(arg):
+ if isinstance(arg, tuple):
+ return tuple(map(copy_args, arg))
+ if isinstance(arg, list):
+ return list(map(copy_args, arg))
+ if isinstance(arg, dict):
+ d_ = dict()
+ for k, v in arg.items():
+ d_[k] = copy_args(v)
+ return d_
+ if isinstance(arg, (str, int, float, bytes, bool)):
+ return arg
+ if isinstance(arg, torch.Tensor):
+ return arg.detach().clone()
+ if arg is None:
+ return None
+ raise ValueError(f"Unknown argument type {arg}")
+
+ def copy_args_to_dest(dest_arg, src_arg):
+ if isinstance(src_arg, (tuple, list)):
+ for i, x in enumerate(src_arg):
+ copy_args_to_dest(dest_arg[i], x)
+ if isinstance(src_arg, dict):
+ for k, v in src_arg.items():
+ copy_args_to_dest(dest_arg[k], v)
+ if isinstance(src_arg, (str, int, float, bytes, bool)) \
+ or src_arg is None:
+ pass # should be the same with dest_arg
+ if isinstance(src_arg, torch.Tensor):
+ dest_arg.copy_(src_arg)
+
+ def create_forward_with_cuda_graph(net):
+ lock = threading.Lock()
+ cached_cuda_graphs = {}
+
+ wrapped = net.forward
+
+ @functools.wraps(wrapped)
+ def forward_with_cuda_graph(*args, **kwargs):
+ key = (hash_arg(args), hash_arg(kwargs))
+ if not (key in cached_cuda_graphs):
+ with lock:
+ if not (key in cached_cuda_graphs):
+ args_, kwargs_ = copy_args((args, kwargs))
+
+ s = torch.cuda.Stream()
+ s.wait_stream(torch.cuda.current_stream())
+
+ with torch.no_grad():
+ with torch.cuda.stream(s):
+ for _ in range(3):
+ static_output = wrapped(*args_, **kwargs_)
+
+ g = torch.cuda.CUDAGraph()
+ with torch.no_grad():
+ with torch.cuda.graph(g):
+ static_output = wrapped(*args_, **kwargs_)
+
+ cached_cuda_graphs[key] = (
+ (args_, kwargs_),
+ g,
+ static_output
+ )
+ static_inputs, graph, static_output = cached_cuda_graphs[key]
+ args_, kwargs_ = static_inputs
+
+ copy_args_to_dest((args_, kwargs_), (args, kwargs))
+ graph.replay()
+ return static_output
+
+ forward_with_cuda_graph.__self__ = net
+ forward_with_cuda_graph._cached = cached_cuda_graphs
+ return forward_with_cuda_graph
+
+ unet.forward = create_forward_with_cuda_graph(unet)
+
+ # # change static parts of unet to cuda graph
+ # for mod in unet.down_blocks:
+ # mod.forward = create_forward_with_cuda_graph(mod)
+ # if unet.mid_block is not None:
+ # unet.mid_block.forward = create_forward_with_cuda_graph(unet.mid_block)
+ # for mod in unet.up_blocks:
+ # mod.forward = create_forward_with_cuda_graph(mod)
+ return unet
+
+
+def make_memory_friendly(bytes):
+
+ MBs = bytes / (1024*1024)
+
+ B = bytes % 1024
+ bytes = bytes // 1024
+ kB = bytes % 1024
+ bytes = bytes // 1024
+ MB = bytes % 1024
+ GB = bytes // 1024
+
+ return f"{GB} G {MB} M {B} {kB} K {B} Bytes ({MBs} MBs)"
+
+
+class MixDQ_SDXLTurbo_Pipeline_W8A8(
+ DiffusionPipeline,
+ FromSingleFileMixin,
+ StableDiffusionXLLoraLoaderMixin,
+ TextualInversionLoaderMixin,
+ IPAdapterMixin,
+):
+ r"""
+ Pipeline for text-to-image generation using Stable Diffusion XL.
+
+ This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+ library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+ In addition the pipeline inherits the following loading methods:
+ - *LoRA*: [`loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`]
+ - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]
+
+ as well as the following saving methods:
+ - *LoRA*: [`loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`]
+
+ Args:
+ vae ([`AutoencoderKL`]):
+ Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+ text_encoder ([`CLIPTextModel`]):
+ Frozen text-encoder. Stable Diffusion XL uses the text portion of
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+ the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+ text_encoder_2 ([` CLIPTextModelWithProjection`]):
+ Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+ specifically the
+ [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+ variant.
+ tokenizer (`CLIPTokenizer`):
+ Tokenizer of class
+ [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+ tokenizer_2 (`CLIPTokenizer`):
+ Second Tokenizer of class
+ [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+ unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+ scheduler ([`SchedulerMixin`]):
+ A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+ [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+ force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
+ Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
+ `stabilityai/stable-diffusion-xl-base-1-0`.
+ add_watermarker (`bool`, *optional*):
+ Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
+ watermark output images. If not defined, it will default to True if the package is installed, otherwise no
+ watermarker will be used.
+ """
+
+ model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
+ _optional_components = [
+ "tokenizer",
+ "tokenizer_2",
+ "text_encoder",
+ "text_encoder_2",
+ "image_encoder",
+ "feature_extractor",
+ ]
+ _callback_tensor_inputs = [
+ "latents",
+ "prompt_embeds",
+ "negative_prompt_embeds",
+ "add_text_embeds",
+ "add_time_ids",
+ "negative_pooled_prompt_embeds",
+ "negative_add_time_ids",
+ ]
+
+ def __init__(
+ self,
+ vae: AutoencoderKL,
+ text_encoder: CLIPTextModel,
+ text_encoder_2: CLIPTextModelWithProjection,
+ tokenizer: CLIPTokenizer,
+ tokenizer_2: CLIPTokenizer,
+ unet: UNet2DConditionModel,
+ scheduler: KarrasDiffusionSchedulers,
+ image_encoder: CLIPVisionModelWithProjection = None,
+ feature_extractor: CLIPImageProcessor = None,
+ force_zeros_for_empty_prompt: bool = True,
+ add_watermarker: Optional[bool] = None,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vae=vae,
+ text_encoder=text_encoder,
+ text_encoder_2=text_encoder_2,
+ tokenizer=tokenizer,
+ tokenizer_2=tokenizer_2,
+ unet=unet,
+ scheduler=scheduler,
+ image_encoder=image_encoder,
+ feature_extractor=feature_extractor,
+ )
+ self.register_to_config(
+ force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+ self.vae_scale_factor = 2 ** (
+ len(self.vae.config.block_out_channels) - 1)
+ self.image_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor)
+
+ self.default_sample_size = self.unet.config.sample_size
+
+ add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
+
+ if add_watermarker:
+ self.watermark = StableDiffusionXLWatermarker()
+ else:
+ self.watermark = None
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
+ def enable_vae_slicing(self):
+ r"""
+ Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+ compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+ """
+ self.vae.enable_slicing()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
+ def disable_vae_slicing(self):
+ r"""
+ Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+ computing decoding in one step.
+ """
+ self.vae.disable_slicing()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
+ def enable_vae_tiling(self):
+ r"""
+ Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+ compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+ processing larger images.
+ """
+ self.vae.enable_tiling()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
+ def disable_vae_tiling(self):
+ r"""
+ Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+ computing decoding in one step.
+ """
+ self.vae.disable_tiling()
+
+ def encode_prompt(
+ self,
+ prompt: str,
+ prompt_2: Optional[str] = None,
+ device: Optional[torch.device] = None,
+ num_images_per_prompt: int = 1,
+ do_classifier_free_guidance: bool = True,
+ negative_prompt: Optional[str] = None,
+ negative_prompt_2: Optional[str] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ lora_scale: Optional[float] = None,
+ clip_skip: Optional[int] = None,
+ ):
+ r"""
+ Encodes the prompt into text encoder hidden states.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ prompt to be encoded
+ prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+ used in both text-encoders
+ device: (`torch.device`):
+ torch device
+ num_images_per_prompt (`int`):
+ number of images that should be generated per prompt
+ do_classifier_free_guidance (`bool`):
+ whether to use classifier free guidance or not
+ negative_prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation. If not defined, one has to pass
+ `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+ less than `1`).
+ negative_prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+ `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+ prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+ provided, text embeddings will be generated from `prompt` input argument.
+ negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+ argument.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
+ lora_scale (`float`, *optional*):
+ A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+ clip_skip (`int`, *optional*):
+ Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+ the output of the pre-final layer will be used for computing the prompt embeddings.
+ """
+ device = device or self._execution_device
+
+ # set lora scale so that monkey patched LoRA
+ # function of text encoder can correctly access it
+ if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
+ self._lora_scale = lora_scale
+
+ # dynamically adjust the LoRA scale
+ if self.text_encoder is not None:
+ if not USE_PEFT_BACKEND:
+ adjust_lora_scale_text_encoder(
+ self.text_encoder, lora_scale)
+ else:
+ scale_lora_layers(self.text_encoder, lora_scale)
+
+ if self.text_encoder_2 is not None:
+ if not USE_PEFT_BACKEND:
+ adjust_lora_scale_text_encoder(
+ self.text_encoder_2, lora_scale)
+ else:
+ scale_lora_layers(self.text_encoder_2, lora_scale)
+
+ prompt = [prompt] if isinstance(prompt, str) else prompt
+
+ if prompt is not None:
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ # Define tokenizers and text encoders
+ tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [
+ self.tokenizer_2]
+ text_encoders = (
+ [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [
+ self.text_encoder_2]
+ )
+
+ if prompt_embeds is None:
+ prompt_2 = prompt_2 or prompt
+ prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+ # textual inversion: procecss multi-vector tokens if necessary
+ prompt_embeds_list = []
+ prompts = [prompt, prompt_2]
+ for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+ if isinstance(self, TextualInversionLoaderMixin):
+ prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+ text_inputs = tokenizer(
+ prompt,
+ padding="max_length",
+ max_length=tokenizer.model_max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ text_input_ids = text_inputs.input_ids
+ untruncated_ids = tokenizer(
+ prompt, padding="longest", return_tensors="pt").input_ids
+
+ if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+ text_input_ids, untruncated_ids
+ ):
+ removed_text = tokenizer.batch_decode(
+ untruncated_ids[:, tokenizer.model_max_length - 1: -1])
+ logger.warning(
+ "The following part of your input was truncated because CLIP can only handle sequences up to"
+ f" {tokenizer.model_max_length} tokens: {removed_text}"
+ )
+
+ prompt_embeds = text_encoder(
+ text_input_ids.to(device), output_hidden_states=True)
+
+ # We are only ALWAYS interested in the pooled output of the final text encoder
+ pooled_prompt_embeds = prompt_embeds[0]
+ if clip_skip is None:
+ prompt_embeds = prompt_embeds.hidden_states[-2]
+ else:
+ # "2" because SDXL always indexes from the penultimate layer.
+ prompt_embeds = prompt_embeds.hidden_states[-(
+ clip_skip + 2)]
+
+ prompt_embeds_list.append(prompt_embeds)
+
+ prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+ # get unconditional embeddings for classifier free guidance
+ zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+ if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+ negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+ negative_pooled_prompt_embeds = torch.zeros_like(
+ pooled_prompt_embeds)
+ elif do_classifier_free_guidance and negative_prompt_embeds is None:
+ negative_prompt = negative_prompt or ""
+ negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+ # normalize str to list
+ negative_prompt = batch_size * \
+ [negative_prompt] if isinstance(
+ negative_prompt, str) else negative_prompt
+ negative_prompt_2 = (
+ batch_size *
+ [negative_prompt_2] if isinstance(
+ negative_prompt_2, str) else negative_prompt_2
+ )
+
+ uncond_tokens: List[str]
+ if prompt is not None and type(prompt) is not type(negative_prompt):
+ raise TypeError(
+ f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+ f" {type(prompt)}."
+ )
+ elif batch_size != len(negative_prompt):
+ raise ValueError(
+ f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+ f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+ " the batch size of `prompt`."
+ )
+ else:
+ uncond_tokens = [negative_prompt, negative_prompt_2]
+
+ negative_prompt_embeds_list = []
+ for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+ if isinstance(self, TextualInversionLoaderMixin):
+ negative_prompt = self.maybe_convert_prompt(
+ negative_prompt, tokenizer)
+
+ max_length = prompt_embeds.shape[1]
+ uncond_input = tokenizer(
+ negative_prompt,
+ padding="max_length",
+ max_length=max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ negative_prompt_embeds = text_encoder(
+ uncond_input.input_ids.to(device),
+ output_hidden_states=True,
+ )
+ # We are only ALWAYS interested in the pooled output of the final text encoder
+ negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+ negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+ negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+ negative_prompt_embeds = torch.concat(
+ negative_prompt_embeds_list, dim=-1)
+
+ if self.text_encoder_2 is not None:
+ prompt_embeds = prompt_embeds.to(
+ dtype=self.text_encoder_2.dtype, device=device)
+ else:
+ prompt_embeds = prompt_embeds.to(
+ dtype=self.unet.dtype, device=device)
+
+ bs_embed, seq_len, _ = prompt_embeds.shape
+ # duplicate text embeddings for each generation per prompt, using mps friendly method
+ prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+ prompt_embeds = prompt_embeds.view(
+ bs_embed * num_images_per_prompt, seq_len, -1)
+
+ if do_classifier_free_guidance:
+ # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+ seq_len = negative_prompt_embeds.shape[1]
+
+ if self.text_encoder_2 is not None:
+ negative_prompt_embeds = negative_prompt_embeds.to(
+ dtype=self.text_encoder_2.dtype, device=device)
+ else:
+ negative_prompt_embeds = negative_prompt_embeds.to(
+ dtype=self.unet.dtype, device=device)
+
+ negative_prompt_embeds = negative_prompt_embeds.repeat(
+ 1, num_images_per_prompt, 1)
+ negative_prompt_embeds = negative_prompt_embeds.view(
+ batch_size * num_images_per_prompt, seq_len, -1)
+
+ pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+ bs_embed * num_images_per_prompt, -1
+ )
+ if do_classifier_free_guidance:
+ negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+ bs_embed * num_images_per_prompt, -1
+ )
+
+ if self.text_encoder is not None:
+ if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+ # Retrieve the original scale by scaling back the LoRA layers
+ unscale_lora_layers(self.text_encoder, lora_scale)
+
+ if self.text_encoder_2 is not None:
+ if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+ # Retrieve the original scale by scaling back the LoRA layers
+ unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+ return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
+ def encode_image(self, image, device, num_images_per_prompt):
+ dtype = next(self.image_encoder.parameters()).dtype
+
+ if not isinstance(image, torch.Tensor):
+ image = self.feature_extractor(
+ image, return_tensors="pt").pixel_values
+
+ image = image.to(device=device, dtype=dtype)
+ image_embeds = self.image_encoder(image).image_embeds
+ image_embeds = image_embeds.repeat_interleave(
+ num_images_per_prompt, dim=0)
+
+ uncond_image_embeds = torch.zeros_like(image_embeds)
+ return image_embeds, uncond_image_embeds
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+ def prepare_extra_step_kwargs(self, generator, eta):
+ # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+ # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+ # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+ # and should be between [0, 1]
+
+ accepts_eta = "eta" in set(inspect.signature(
+ self.scheduler.step).parameters.keys())
+ extra_step_kwargs = {}
+ if accepts_eta:
+ extra_step_kwargs["eta"] = eta
+
+ # check if the scheduler accepts generator
+ accepts_generator = "generator" in set(
+ inspect.signature(self.scheduler.step).parameters.keys())
+ if accepts_generator:
+ extra_step_kwargs["generator"] = generator
+ return extra_step_kwargs
+
+ def check_inputs(
+ self,
+ prompt,
+ prompt_2,
+ height,
+ width,
+ callback_steps,
+ negative_prompt=None,
+ negative_prompt_2=None,
+ prompt_embeds=None,
+ negative_prompt_embeds=None,
+ pooled_prompt_embeds=None,
+ negative_pooled_prompt_embeds=None,
+ callback_on_step_end_tensor_inputs=None,
+ ):
+ if height % 8 != 0 or width % 8 != 0:
+ raise ValueError(
+ f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+ if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
+ raise ValueError(
+ f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+ f" {type(callback_steps)}."
+ )
+
+ if callback_on_step_end_tensor_inputs is not None and not all(
+ k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+ ):
+ raise ValueError(
+ f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+ )
+
+ if prompt is not None and prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+ " only forward one of the two."
+ )
+ elif prompt_2 is not None and prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+ " only forward one of the two."
+ )
+ elif prompt is None and prompt_embeds is None:
+ raise ValueError(
+ "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+ )
+ elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+ raise ValueError(
+ f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+ elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+ raise ValueError(
+ f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+ if negative_prompt is not None and negative_prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+ f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+ )
+ elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+ f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+ )
+
+ if prompt_embeds is not None and negative_prompt_embeds is not None:
+ if prompt_embeds.shape != negative_prompt_embeds.shape:
+ raise ValueError(
+ "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+ f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+ f" {negative_prompt_embeds.shape}."
+ )
+
+ if prompt_embeds is not None and pooled_prompt_embeds is None:
+ raise ValueError(
+ "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+ )
+
+ if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
+ raise ValueError(
+ "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
+ )
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+ def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+ shape = (batch_size, num_channels_latents, height //
+ self.vae_scale_factor, width // self.vae_scale_factor)
+ if isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ if latents is None:
+ latents = randn_tensor(
+ shape, generator=generator, device=device, dtype=dtype)
+ else:
+ latents = latents.to(device)
+
+ # scale the initial noise by the standard deviation required by the scheduler
+ latents = latents * self.scheduler.init_noise_sigma
+ return latents
+
+ def _get_add_time_ids(
+ self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
+ ):
+ add_time_ids = list(
+ original_size + crops_coords_top_left + target_size)
+
+ passed_add_embed_dim = (
+ self.unet.config.addition_time_embed_dim *
+ len(add_time_ids) + text_encoder_projection_dim
+ )
+ expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
+
+ if expected_add_embed_dim != passed_add_embed_dim:
+ raise ValueError(
+ f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+ )
+
+ add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+ return add_time_ids
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
+ def upcast_vae(self):
+ dtype = self.vae.dtype
+ self.vae.to(dtype=torch.float32)
+ use_torch_2_0_or_xformers = isinstance(
+ self.vae.decoder.mid_block.attentions[0].processor,
+ (
+ AttnProcessor2_0,
+ XFormersAttnProcessor,
+ LoRAXFormersAttnProcessor,
+ LoRAAttnProcessor2_0,
+ ),
+ )
+ # if xformers or torch_2_0 is used attention block does not need
+ # to be in float32 which can save lots of memory
+ if use_torch_2_0_or_xformers:
+ self.vae.post_quant_conv.to(dtype)
+ self.vae.decoder.conv_in.to(dtype)
+ self.vae.decoder.mid_block.to(dtype)
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
+ def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
+ r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
+
+ The suffixes after the scaling factors represent the stages where they are being applied.
+
+ Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
+ that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
+
+ Args:
+ s1 (`float`):
+ Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
+ mitigate "oversmoothing effect" in the enhanced denoising process.
+ s2 (`float`):
+ Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
+ mitigate "oversmoothing effect" in the enhanced denoising process.
+ b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
+ b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
+ """
+ if not hasattr(self, "unet"):
+ raise ValueError("The pipeline must have `unet` for using FreeU.")
+ self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
+ def disable_freeu(self):
+ """Disables the FreeU mechanism if enabled."""
+ self.unet.disable_freeu()
+
+ # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
+ def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+ """
+ See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+ Args:
+ timesteps (`torch.Tensor`):
+ generate embedding vectors at these timesteps
+ embedding_dim (`int`, *optional*, defaults to 512):
+ dimension of the embeddings to generate
+ dtype:
+ data type of the generated embeddings
+
+ Returns:
+ `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+ """
+ assert len(w.shape) == 1
+ w = w * 1000.0
+
+ half_dim = embedding_dim // 2
+ emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+ emb = w.to(dtype)[:, None] * emb[None, :]
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+ if embedding_dim % 2 == 1: # zero pad
+ emb = torch.nn.functional.pad(emb, (0, 1))
+ assert emb.shape == (w.shape[0], embedding_dim)
+ return emb
+
+ def quantize_unet(
+ self,
+ w_bit=None,
+ a_bit=None,
+ bos=False,
+ # cuda_graph_only=True,
+ # run_pipeline=True,
+ # compile=False,
+ ):
+ r"""
+ This function helps quantize the UNet in the SDXL Pipeline
+ Now we only support quantization with the setting W8A8
+
+ Args:
+ w_bit: (`str`):
+ the bit width of weight
+ a_bit: (`str`):
+ the bit width of activation
+ bos: (`bool`):
+ if to use bos technique
+ cuda_graph_only: (`bool`):
+ if to use cuda_graph
+ run_pipeline: (`bool`):
+ run the full pipeline or just the unet
+ """
+ # load the quant para and the pre-computed bos tensor
+ from huggingface_hub import hf_hub_download
+
+ path = hf_hub_download(
+ repo_id="Stein-Fun/mixdq_test",
+ filename="bos_pre_computed.pt",
+ revision="version_0",
+ )
+ bos_dict = torch.load(path, map_location='cpu')
+
+ path = hf_hub_download(
+ repo_id="Stein-Fun/mixdq_test",
+ filename="quant_para_wsym_fp16.pt",
+ revision="version_0",
+ )
+ ckpt = torch.load(path, map_location='cpu')
+
+ register_qconfig_from_input_files(
+ self.unet,
+ # args,
+ w_bit=w_bit,
+ a_bit=a_bit,
+ bos=bos,
+ bos_dict=bos_dict
+ )
+ convert_to_quantized(self.unet, ckpt)
+
+ def set_cuda_graph(
+ self,
+ run_pipeline = True,
+ compile = False,
+ ):
+
+ if run_pipeline:
+ self.to('cuda')
+ else:
+ self.unet.to("cuda")
+
+ batch_size = 1
+ prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
+
+ # start = time.time()
+ if run_pipeline:
+ def run_once():
+ latents = self(prompt=[prompt]*batch_size,
+ guidance_scale=0.0,
+ num_inference_steps=1).images[0]
+ return latents
+ else:
+ sample_shape = (
+ batch_size * 1,
+ self.unet.config.in_channels,
+ self.unet.config.sample_size,
+ self.unet.config.sample_size,
+ )
+
+ encoder_embedding_shape = (
+ batch_size * 1,
+ 77, # just an example,
+ 2048,
+ )
+
+ device=torch.device('cuda')
+ example_sample = torch.rand(*sample_shape, device=device,
+ dtype=torch.float16)
+ example_embedding = torch.rand(*encoder_embedding_shape,
+ device=device, dtype=torch.float16)
+ timestep = torch.tensor(999., device=device)
+ text_embeds = torch.rand(batch_size, 1280, device=device,
+ dtype=torch.float16)
+ time_ids = torch.tensor([[512.,512.,0.,0.,512.,512.]], dtype=torch.float16,
+ device=device)
+ time_ids = torch.concat([time_ids] * batch_size)
+
+ def run_once():
+ with torch.no_grad():
+ latents = self.unet(sample=example_sample,
+ timestep=timestep,
+ encoder_hidden_states=example_embedding,
+ added_cond_kwargs={
+ 'time_ids': time_ids,
+ 'text_embeds': text_embeds
+ },
+ return_dict=False)[0]
+ return latents
+
+ # if cuda_graph_only:
+ print("apply the cuda graph!")
+ if compile:
+ logging.warning("--compile and --cuda_graph_only should not be used"
+ " together, cuda_graph_only is ignored.")
+ else:
+ self.unet = cuda_graph_opt(self.unet)
+ print("start to warm up!")
+ print("run the pipeline") if run_pipeline else print("run the unet")
+ latents = run_once()
+ # if run_pipeline and output_type=='pil':
+ # latents.save('result_01.png')
+ print("finish warming up!")
+
+
+ def run_for_test(
+ self,
+ device,
+ prompt: str = "A black and white photo of an older man skiing.",
+ batch_size: int = 1,
+ output_type: str = "pil",
+ run_pipeline: bool = False,
+ memory_snapshot_name: str=None,
+ profile: bool=False,
+ profile_tool: str="torch_profiler",
+ path: str = "result.png"
+ ):
+ r"""
+ run for test the memory reduction after quantization on GPUs
+
+ Args:
+ device: (`torch.device`):
+ torch device, 'CUDA' is supported only
+ prompt: (`str` or `List[str]`, *optional*):
+ prompt to be encoded
+ batch_size: (`int`):
+ the batch size of inputs
+ output_type (`str`, *optional*, defaults to `"pil"`):
+ The output format of the generate image. Choose between
+ [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+ batch_size: (`int`):
+ the batch size of inputs
+ run_pipeline: (`bool`):
+ if to run the whole pipeline or just run the UNet
+ profile: (`bool`):
+ if to profile to test the lantency of the inference
+ profile_tool: (`str`):
+ choose the profiler: "torch_profiler" or "nsys"
+ path: (`str`):
+ the path to save the output image
+ """
+ if run_pipeline:
+ self.to('cuda')
+ else:
+ self.unet.to("cuda")
+
+ model_memory = torch.cuda.memory_allocated()
+ print("Static (weights) memory usage:", make_memory_friendly(model_memory))
+
+
+ # start = time.time()
+ if run_pipeline:
+ def run_once():
+ latents = self(prompt=[prompt]*batch_size,
+ guidance_scale=0.0,
+ num_inference_steps=1,
+ output_type=output_type).images[0]
+ return latents
+ else:
+ sample_shape = (
+ batch_size * 1,
+ self.unet.config.in_channels,
+ self.unet.config.sample_size,
+ self.unet.config.sample_size,
+ )
+
+ encoder_embedding_shape = (
+ batch_size * 1,
+ 77, # just an example,
+ 2048,
+ )
+
+ device=torch.device('cuda')
+ example_sample = torch.rand(*sample_shape, device=device,
+ dtype=torch.float16)
+ example_embedding = torch.rand(*encoder_embedding_shape,
+ device=device, dtype=torch.float16)
+ timestep = torch.tensor(999., device=device)
+ text_embeds = torch.rand(batch_size, 1280, device=device,
+ dtype=torch.float16)
+ time_ids = torch.tensor([[512.,512.,0.,0.,512.,512.]], dtype=torch.float16,
+ device=device)
+ time_ids = torch.concat([time_ids] * batch_size)
+
+ def run_once():
+ with torch.no_grad():
+ latents = self.unet(sample=example_sample,
+ timestep=timestep,
+ encoder_hidden_states=example_embedding,
+ added_cond_kwargs={
+ 'time_ids': time_ids,
+ 'text_embeds': text_embeds
+ },
+ return_dict=False)[0]
+ return latents
+
+ # latents = run_once()
+ # latents = run_once()
+
+ # if run_pipeline:
+ # latents.save('result_00.png')
+
+ # if cuda_graph_only:
+ # if compile:
+ # logging.warning("--compile and --cuda_graph_only should not be used"
+ # " together, cuda_graph_only is ignored.")
+ # else:
+ # self.unet = cuda_graph_opt(self.unet)
+ # print("start to warm up!")
+ # latents = run_once()
+ # # if run_pipeline and output_type=='pil':
+ # # latents.save('result_01.png')
+ # print("finish warming up!")
+
+ # print("quant inference!")
+ # latents = run_once()
+ # if run_pipeline and output_type=='pil':
+ # latents.save(path)
+ # else:
+ latents = run_once()
+ if run_pipeline and output_type=='pil':
+ latents.save(path)
+
+ peak_memory = torch.cuda.max_memory_allocated()
+ print("Dynamic (acts) memory usage:",
+ make_memory_friendly(peak_memory - model_memory))
+ print("Peak (total) memory usage:", make_memory_friendly(peak_memory))
+
+ if memory_snapshot_name is not None:
+ torch.cuda.memory._dump_snapshot(memory_snapshot_name)
+
+ if profile:
+ if profile_tool == "nsys":
+ torch.cuda.cudart().cudaProfilerStart()
+ for iter in range(3):
+ torch.cuda.nvtx.range_push(f"iter_{iter}")
+ run_once()
+ torch.cuda.nvtx.range_pop()
+ torch.cuda.cudart().cudaProfilerStop()
+ elif profile_tool == "torch_profiler":
+ with torch.profiler.profile(
+ on_trace_ready=torch.profiler.tensorboard_trace_handler('./log/sdxl'),
+ with_stack=True
+ ):
+ for _ in range(3):
+ run_once()
+ else:
+ print(f"Unknown profile_tool {profile_tool}, use nsys or torch_profiler")
+
+ # if output_type=="pil":
+ # image = latents
+
+ # image.save("result.png")
+ return latents
+
+
+ @property
+ def guidance_scale(self):
+ return self._guidance_scale
+
+ @property
+ def guidance_rescale(self):
+ return self._guidance_rescale
+
+ @property
+ def clip_skip(self):
+ return self._clip_skip
+
+ # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+ # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+ # corresponds to doing no classifier free guidance.
+ @property
+ def do_classifier_free_guidance(self):
+ return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+ @property
+ def cross_attention_kwargs(self):
+ return self._cross_attention_kwargs
+
+ @property
+ def denoising_end(self):
+ return self._denoising_end
+
+ @property
+ def num_timesteps(self):
+ return self._num_timesteps
+
+ @torch.no_grad()
+ @replace_example_docstring(EXAMPLE_DOC_STRING)
+ def __call__(
+ self,
+ prompt: Union[str, List[str]] = None,
+ prompt_2: Optional[Union[str, List[str]]] = None,
+ height: Optional[int] = None,
+ width: Optional[int] = None,
+ num_inference_steps: int = 50,
+ timesteps: List[int] = None,
+ denoising_end: Optional[float] = None,
+ guidance_scale: float = 5.0,
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ negative_prompt_2: Optional[Union[str, List[str]]] = None,
+ num_images_per_prompt: Optional[int] = 1,
+ eta: float = 0.0,
+ generator: Optional[Union[torch.Generator,
+ List[torch.Generator]]] = None,
+ latents: Optional[torch.FloatTensor] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ ip_adapter_image: Optional[PipelineImageInput] = None,
+ output_type: Optional[str] = "pil",
+ return_dict: bool = True,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ guidance_rescale: float = 0.0,
+ original_size: Optional[Tuple[int, int]] = None,
+ crops_coords_top_left: Tuple[int, int] = (0, 0),
+ target_size: Optional[Tuple[int, int]] = None,
+ negative_original_size: Optional[Tuple[int, int]] = None,
+ negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+ negative_target_size: Optional[Tuple[int, int]] = None,
+ clip_skip: Optional[int] = None,
+ callback_on_step_end: Optional[Callable[[
+ int, int, Dict], None]] = None,
+ callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+ **kwargs,
+ ):
+ r"""
+ Function invoked when calling the pipeline for generation.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+ instead.
+ prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+ used in both text-encoders
+ height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+ The height in pixels of the generated image. This is set to 1024 by default for the best results.
+ Anything below 512 pixels won't work well for
+ [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+ and checkpoints that are not specifically fine-tuned on low resolutions.
+ width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+ The width in pixels of the generated image. This is set to 1024 by default for the best results.
+ Anything below 512 pixels won't work well for
+ [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+ and checkpoints that are not specifically fine-tuned on low resolutions.
+ num_inference_steps (`int`, *optional*, defaults to 50):
+ The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+ expense of slower inference.
+ timesteps (`List[int]`, *optional*):
+ Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+ in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+ passed will be used. Must be in descending order.
+ denoising_end (`float`, *optional*):
+ When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+ completed before it is intentionally prematurely terminated. As a result, the returned sample will
+ still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+ scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+ "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+ Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+ guidance_scale (`float`, *optional*, defaults to 5.0):
+ Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+ `guidance_scale` is defined as `w` of equation 2. of [Imagen
+ Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+ 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+ usually at the expense of lower image quality.
+ negative_prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation. If not defined, one has to pass
+ `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+ less than `1`).
+ negative_prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+ `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+ num_images_per_prompt (`int`, *optional*, defaults to 1):
+ The number of images to generate per prompt.
+ eta (`float`, *optional*, defaults to 0.0):
+ Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+ [`schedulers.DDIMScheduler`], will be ignored for others.
+ generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+ One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+ to make generation deterministic.
+ latents (`torch.FloatTensor`, *optional*):
+ Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+ generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+ tensor will ge generated by sampling using the supplied random `generator`.
+ prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+ provided, text embeddings will be generated from `prompt` input argument.
+ negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+ argument.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
+ ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+ output_type (`str`, *optional*, defaults to `"pil"`):
+ The output format of the generate image. Choose between
+ [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+ of a plain tuple.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+ `self.processor` in
+ [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ guidance_rescale (`float`, *optional*, defaults to 0.0):
+ Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+ Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+ [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+ Guidance rescale factor should fix overexposure when using zero terminal SNR.
+ original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+ `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+ explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+ `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+ `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+ `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ For most cases, `target_size` should be set to the desired height and width of the generated image. If
+ not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+ section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+ micro-conditioning as explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+ information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+ negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+ To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+ micro-conditioning as explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+ information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+ negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ To negatively condition the generation process based on a target image resolution. It should be as same
+ as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+ information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+ callback_on_step_end (`Callable`, *optional*):
+ A function that calls at the end of each denoising steps during the inference. The function is called
+ with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+ callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+ `callback_on_step_end_tensor_inputs`.
+ callback_on_step_end_tensor_inputs (`List`, *optional*):
+ The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+ will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+ `._callback_tensor_inputs` attribute of your pipeline class.
+
+ Examples:
+
+ Returns:
+ [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+ [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+ `tuple`. When returning a tuple, the first element is a list with the generated images.
+ """
+
+ callback = kwargs.pop("callback", None)
+ callback_steps = kwargs.pop("callback_steps", None)
+
+ if callback is not None:
+ deprecate(
+ "callback",
+ "1.0.0",
+ "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+ )
+ if callback_steps is not None:
+ deprecate(
+ "callback_steps",
+ "1.0.0",
+ "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+ )
+
+ # 0. Default height and width to unet
+ height = height or self.default_sample_size * self.vae_scale_factor
+ width = width or self.default_sample_size * self.vae_scale_factor
+
+ original_size = original_size or (height, width)
+ target_size = target_size or (height, width)
+
+ # 1. Check inputs. Raise error if not correct
+ self.check_inputs(
+ prompt,
+ prompt_2,
+ height,
+ width,
+ callback_steps,
+ negative_prompt,
+ negative_prompt_2,
+ prompt_embeds,
+ negative_prompt_embeds,
+ pooled_prompt_embeds,
+ negative_pooled_prompt_embeds,
+ callback_on_step_end_tensor_inputs,
+ )
+
+ self._guidance_scale = guidance_scale
+ self._guidance_rescale = guidance_rescale
+ self._clip_skip = clip_skip
+ self._cross_attention_kwargs = cross_attention_kwargs
+ self._denoising_end = denoising_end
+
+ # 2. Define call parameters
+ if prompt is not None and isinstance(prompt, str):
+ batch_size = 1
+ elif prompt is not None and isinstance(prompt, list):
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ device = self._execution_device
+
+ # 3. Encode input prompt
+ lora_scale = (
+ self.cross_attention_kwargs.get(
+ "scale", None) if self.cross_attention_kwargs is not None else None
+ )
+
+ (
+ prompt_embeds,
+ negative_prompt_embeds,
+ pooled_prompt_embeds,
+ negative_pooled_prompt_embeds,
+ ) = self.encode_prompt(
+ prompt=prompt,
+ prompt_2=prompt_2,
+ device=device,
+ num_images_per_prompt=num_images_per_prompt,
+ do_classifier_free_guidance=self.do_classifier_free_guidance,
+ negative_prompt=negative_prompt,
+ negative_prompt_2=negative_prompt_2,
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ pooled_prompt_embeds=pooled_prompt_embeds,
+ negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+ lora_scale=lora_scale,
+ clip_skip=self.clip_skip,
+ )
+
+ # 4. Prepare timesteps
+ timesteps, num_inference_steps = retrieve_timesteps(
+ self.scheduler, num_inference_steps, device, timesteps)
+
+ # 5. Prepare latent variables
+ num_channels_latents = self.unet.config.in_channels
+ latents = self.prepare_latents(
+ batch_size * num_images_per_prompt,
+ num_channels_latents,
+ height,
+ width,
+ prompt_embeds.dtype,
+ device,
+ generator,
+ latents,
+ )
+
+ # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+ extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+ # 7. Prepare added time ids & embeddings
+ add_text_embeds = pooled_prompt_embeds
+ if self.text_encoder_2 is None:
+ text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+ else:
+ text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+ add_time_ids = self._get_add_time_ids(
+ original_size,
+ crops_coords_top_left,
+ target_size,
+ dtype=prompt_embeds.dtype,
+ text_encoder_projection_dim=text_encoder_projection_dim,
+ )
+ if negative_original_size is not None and negative_target_size is not None:
+ negative_add_time_ids = self._get_add_time_ids(
+ negative_original_size,
+ negative_crops_coords_top_left,
+ negative_target_size,
+ dtype=prompt_embeds.dtype,
+ text_encoder_projection_dim=text_encoder_projection_dim,
+ )
+ else:
+ negative_add_time_ids = add_time_ids
+
+ if self.do_classifier_free_guidance:
+ prompt_embeds = torch.cat(
+ [negative_prompt_embeds, prompt_embeds], dim=0)
+ add_text_embeds = torch.cat(
+ [negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+ add_time_ids = torch.cat(
+ [negative_add_time_ids, add_time_ids], dim=0)
+
+ prompt_embeds = prompt_embeds.to(device)
+ add_text_embeds = add_text_embeds.to(device)
+ add_time_ids = add_time_ids.to(device).repeat(
+ batch_size * num_images_per_prompt, 1)
+
+ if ip_adapter_image is not None:
+ image_embeds, negative_image_embeds = self.encode_image(
+ ip_adapter_image, device, num_images_per_prompt)
+ if self.do_classifier_free_guidance:
+ image_embeds = torch.cat([negative_image_embeds, image_embeds])
+ image_embeds = image_embeds.to(device)
+
+ # 8. Denoising loop
+ num_warmup_steps = max(
+ len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+ # 8.1 Apply denoising_end
+ if (
+ self.denoising_end is not None
+ and isinstance(self.denoising_end, float)
+ and self.denoising_end > 0
+ and self.denoising_end < 1
+ ):
+ discrete_timestep_cutoff = int(
+ round(
+ self.scheduler.config.num_train_timesteps
+ - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+ )
+ )
+ num_inference_steps = len(
+ list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+ timesteps = timesteps[:num_inference_steps]
+
+ # 9. Optionally get Guidance Scale Embedding
+ timestep_cond = None
+ if self.unet.config.time_cond_proj_dim is not None:
+ guidance_scale_tensor = torch.tensor(
+ self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+ timestep_cond = self.get_guidance_scale_embedding(
+ guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+ ).to(device=device, dtype=latents.dtype)
+
+ self._num_timesteps = len(timesteps)
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i, t in enumerate(timesteps):
+ # expand the latents if we are doing classifier free guidance
+ latent_model_input = torch.cat(
+ [latents] * 2) if self.do_classifier_free_guidance else latents
+
+ latent_model_input = self.scheduler.scale_model_input(
+ latent_model_input, t)
+
+ # predict the noise residual
+ added_cond_kwargs = {
+ "text_embeds": add_text_embeds, "time_ids": add_time_ids}
+ if ip_adapter_image is not None:
+ added_cond_kwargs["image_embeds"] = image_embeds
+ noise_pred = self.unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=prompt_embeds,
+ timestep_cond=timestep_cond,
+ cross_attention_kwargs=self.cross_attention_kwargs,
+ added_cond_kwargs=added_cond_kwargs,
+ return_dict=False,
+ )[0]
+
+ # perform guidance
+ if self.do_classifier_free_guidance:
+ noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+ noise_pred = noise_pred_uncond + self.guidance_scale * \
+ (noise_pred_text - noise_pred_uncond)
+
+ if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+ # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+ noise_pred = rescale_noise_cfg(
+ noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents = self.scheduler.step(
+ noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+ if callback_on_step_end is not None:
+ callback_kwargs = {}
+ for k in callback_on_step_end_tensor_inputs:
+ callback_kwargs[k] = locals()[k]
+ callback_outputs = callback_on_step_end(
+ self, i, t, callback_kwargs)
+
+ latents = callback_outputs.pop("latents", latents)
+ prompt_embeds = callback_outputs.pop(
+ "prompt_embeds", prompt_embeds)
+ negative_prompt_embeds = callback_outputs.pop(
+ "negative_prompt_embeds", negative_prompt_embeds)
+ add_text_embeds = callback_outputs.pop(
+ "add_text_embeds", add_text_embeds)
+ negative_pooled_prompt_embeds = callback_outputs.pop(
+ "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+ )
+ add_time_ids = callback_outputs.pop(
+ "add_time_ids", add_time_ids)
+ negative_add_time_ids = callback_outputs.pop(
+ "negative_add_time_ids", negative_add_time_ids)
+
+ # call the callback, if provided
+ if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, t, latents)
+
+ # if XLA_AVAILABLE:
+ # xm.mark_step()
+
+ if not output_type == "latent":
+ # make sure the VAE is in float32 mode, as it overflows in float16
+ needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+ if needs_upcasting:
+ self.upcast_vae()
+ latents = latents.to(
+ next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+ image = self.vae.decode(
+ latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+ # cast back to fp16 if needed
+ if needs_upcasting:
+ self.vae.to(dtype=torch.float16)
+ else:
+ image = latents
+
+ if not output_type == "latent":
+ # apply watermark if available
+ if self.watermark is not None:
+ image = self.watermark.apply_watermark(image)
+
+ image = self.image_processor.postprocess(
+ image, output_type=output_type)
+
+ # Offload all models
+ self.maybe_free_model_hooks()
+
+ if not return_dict:
+ return (image,)
+
+ return StableDiffusionXLPipelineOutput(images=image)
+
+
+# for quant inference
+######################################################################################################
+
+
+######################################################################################################
+# mixed precision config
+a8_mixed_precision_config = \
+ {
+ 'add_embedding.linear_1': 8, 'add_embedding.linear_2': 8, 'down_blocks.0.downsamplers.0.conv': 8, 'down_blocks.0.resnets.0.conv1': 8, 'down_blocks.0.resnets.0.time_emb_proj': 8, 'down_blocks.0.resnets.1.conv1': 8, 'down_blocks.0.resnets.1.conv2': 8, 'down_blocks.0.resnets.1.time_emb_proj': 8, 'down_blocks.1.attentions.0.proj_in': 8, 'down_blocks.1.attentions.0.proj_out': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_k': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_v': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.ff.net.2': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_k': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_out.0': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_q': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_v': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_k': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_out.0': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_q': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_v': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.ff.net.0.proj': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.ff.net.2': 8,
+ 'down_blocks.1.attentions.1.proj_in': 8, 'down_blocks.1.attentions.1.proj_out': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_k': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_q': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_v': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.ff.net.2': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn1.to_k': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn1.to_out.0': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn1.to_q': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn1.to_v': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn2.to_k': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn2.to_out.0': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn2.to_q': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn2.to_v': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.ff.net.0.proj': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.ff.net.2': 8, 'down_blocks.1.downsamplers.0.conv': 8, 'down_blocks.1.resnets.0.conv1': 8, 'down_blocks.1.resnets.0.conv2': 8, 'down_blocks.1.resnets.0.conv_shortcut': 8, 'down_blocks.1.resnets.0.time_emb_proj': 8, 'down_blocks.1.resnets.1.conv1': 8, 'down_blocks.1.resnets.1.conv2': 8, 'down_blocks.1.resnets.1.time_emb_proj': 8,
+ 'down_blocks.2.attentions.0.proj_in': 8, 'down_blocks.2.attentions.0.proj_out': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.ff.net.0.proj': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.ff.net.2': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn2.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn2.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn2.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn2.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.ff.net.0.proj': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.ff.net.2': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn2.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn2.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn2.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn2.to_v': 8,
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+ 'up_blocks.0.attentions.2.transformer_blocks.7.ff.net.0.proj': 8, 'up_blocks.0.attentions.2.transformer_blocks.7.ff.net.2': 8, 'up_blocks.0.attentions.2.transformer_blocks.8.attn1.to_k': 8, 'up_blocks.0.attentions.2.transformer_blocks.8.attn1.to_out.0': 8, 'up_blocks.0.attentions.2.transformer_blocks.8.attn1.to_q': 8, 'up_blocks.0.attentions.2.transformer_blocks.8.attn1.to_v': 8, 'up_blocks.0.attentions.2.transformer_blocks.8.attn2.to_k': 8, 'up_blocks.0.attentions.2.transformer_blocks.8.attn2.to_out.0': 8, 'up_blocks.0.attentions.2.transformer_blocks.8.attn2.to_q': 8, 'up_blocks.0.attentions.2.transformer_blocks.8.attn2.to_v': 8, 'up_blocks.0.attentions.2.transformer_blocks.8.ff.net.0.proj': 8, 'up_blocks.0.attentions.2.transformer_blocks.8.ff.net.2': 8, 'up_blocks.0.attentions.2.transformer_blocks.9.attn1.to_k': 8, 'up_blocks.0.attentions.2.transformer_blocks.9.attn1.to_out.0': 8, 'up_blocks.0.attentions.2.transformer_blocks.9.attn1.to_q': 8, 'up_blocks.0.attentions.2.transformer_blocks.9.attn1.to_v': 8, 'up_blocks.0.attentions.2.transformer_blocks.9.attn2.to_k': 8, 'up_blocks.0.attentions.2.transformer_blocks.9.attn2.to_out.0': 8, 'up_blocks.0.attentions.2.transformer_blocks.9.attn2.to_q': 8, 'up_blocks.0.attentions.2.transformer_blocks.9.attn2.to_v': 8, 'up_blocks.0.attentions.2.transformer_blocks.9.ff.net.0.proj': 8, 'up_blocks.0.attentions.2.transformer_blocks.9.ff.net.2': 8, 'up_blocks.0.resnets.0.conv1': 8, 'up_blocks.0.resnets.0.conv2': 8, 'up_blocks.0.resnets.0.conv_shortcut': 8, 'up_blocks.0.resnets.0.time_emb_proj': 8, 'up_blocks.0.resnets.1.conv1': 8, 'up_blocks.0.resnets.1.conv2': 8, 'up_blocks.0.resnets.1.conv_shortcut': 8, 'up_blocks.0.resnets.1.time_emb_proj': 8,
+ 'up_blocks.0.resnets.2.conv1': 8, 'up_blocks.0.resnets.2.conv2': 8, 'up_blocks.0.resnets.2.conv_shortcut': 8, 'up_blocks.0.resnets.2.time_emb_proj': 8, 'up_blocks.0.upsamplers.0.conv': 8, 'up_blocks.1.attentions.0.proj_in': 8, 'up_blocks.1.attentions.0.proj_out': 8, 'up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_k': 8, 'up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0': 8, 'up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q': 8, 'up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_v': 8, 'up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k': 8, 'up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0': 8, 'up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q': 8, 'up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v': 8, 'up_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj': 8, 'up_blocks.1.attentions.0.transformer_blocks.0.ff.net.2': 8, 'up_blocks.1.attentions.0.transformer_blocks.1.attn1.to_k': 8, 'up_blocks.1.attentions.0.transformer_blocks.1.attn1.to_out.0': 8, 'up_blocks.1.attentions.0.transformer_blocks.1.attn1.to_q': 8, 'up_blocks.1.attentions.0.transformer_blocks.1.attn1.to_v': 8, 'up_blocks.1.attentions.0.transformer_blocks.1.attn2.to_k': 8, 'up_blocks.1.attentions.0.transformer_blocks.1.attn2.to_out.0': 8, 'up_blocks.1.attentions.0.transformer_blocks.1.attn2.to_q': 8, 'up_blocks.1.attentions.0.transformer_blocks.1.attn2.to_v': 8, 'up_blocks.1.attentions.0.transformer_blocks.1.ff.net.0.proj': 8, 'up_blocks.1.attentions.0.transformer_blocks.1.ff.net.2': 8, 'up_blocks.1.attentions.1.proj_in': 8, 'up_blocks.1.attentions.1.proj_out': 8, 'up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_k': 8,
+ 'up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0': 8, 'up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_q': 8, 'up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_v': 8, 'up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k': 8, 'up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0': 8, 'up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q': 8, 'up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v': 8, 'up_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj': 8, 'up_blocks.1.attentions.1.transformer_blocks.0.ff.net.2': 8, 'up_blocks.1.attentions.1.transformer_blocks.1.attn1.to_k': 8, 'up_blocks.1.attentions.1.transformer_blocks.1.attn1.to_out.0': 8, 'up_blocks.1.attentions.1.transformer_blocks.1.attn1.to_q': 8, 'up_blocks.1.attentions.1.transformer_blocks.1.attn1.to_v': 8, 'up_blocks.1.attentions.1.transformer_blocks.1.attn2.to_k': 8, 'up_blocks.1.attentions.1.transformer_blocks.1.attn2.to_out.0': 8, 'up_blocks.1.attentions.1.transformer_blocks.1.attn2.to_q': 8, 'up_blocks.1.attentions.1.transformer_blocks.1.attn2.to_v': 8, 'up_blocks.1.attentions.1.transformer_blocks.1.ff.net.0.proj': 8, 'up_blocks.1.attentions.1.transformer_blocks.1.ff.net.2': 8, 'up_blocks.1.attentions.2.proj_in': 8, 'up_blocks.1.attentions.2.proj_out': 8, 'up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_k': 8, 'up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_out.0': 8, 'up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_q': 8, 'up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_v': 8, 'up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_k': 8, 'up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0': 8, 'up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_q': 8, 'up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_v': 8, 'up_blocks.1.attentions.2.transformer_blocks.0.ff.net.0.proj': 8,
+ 'up_blocks.1.attentions.2.transformer_blocks.0.ff.net.2': 8, 'up_blocks.1.attentions.2.transformer_blocks.1.attn1.to_k': 8, 'up_blocks.1.attentions.2.transformer_blocks.1.attn1.to_out.0': 8, 'up_blocks.1.attentions.2.transformer_blocks.1.attn1.to_q': 8, 'up_blocks.1.attentions.2.transformer_blocks.1.attn1.to_v': 8, 'up_blocks.1.attentions.2.transformer_blocks.1.attn2.to_k': 8, 'up_blocks.1.attentions.2.transformer_blocks.1.attn2.to_out.0': 8, 'up_blocks.1.attentions.2.transformer_blocks.1.attn2.to_q': 8, 'up_blocks.1.attentions.2.transformer_blocks.1.attn2.to_v': 8, 'up_blocks.1.attentions.2.transformer_blocks.1.ff.net.0.proj': 8, 'up_blocks.1.attentions.2.transformer_blocks.1.ff.net.2': 8, 'up_blocks.1.resnets.0.conv1': 8, 'up_blocks.1.resnets.0.conv2': 8, 'up_blocks.1.resnets.0.conv_shortcut': 8, 'up_blocks.1.resnets.0.time_emb_proj': 8, 'up_blocks.1.resnets.1.conv1': 8, 'up_blocks.1.resnets.1.conv2': 8, 'up_blocks.1.resnets.1.conv_shortcut': 8, 'up_blocks.1.resnets.1.time_emb_proj': 8, 'up_blocks.1.resnets.2.conv1': 8, 'up_blocks.1.resnets.2.conv2': 8, 'up_blocks.1.resnets.2.conv_shortcut': 8, 'up_blocks.1.resnets.2.time_emb_proj': 8, 'up_blocks.1.upsamplers.0.conv': 8, 'up_blocks.2.resnets.0.conv1': 8, 'up_blocks.2.resnets.0.conv2': 8, 'up_blocks.2.resnets.0.conv_shortcut': 8, 'up_blocks.2.resnets.0.time_emb_proj': 8, 'up_blocks.2.resnets.1.conv1': 8, 'up_blocks.2.resnets.1.conv2': 8,
+ 'up_blocks.2.resnets.1.conv_shortcut': 8, 'up_blocks.2.resnets.1.time_emb_proj': 8, 'up_blocks.2.resnets.2.conv1': 8, 'up_blocks.2.resnets.2.conv2': 8, 'up_blocks.2.resnets.2.time_emb_proj': 8,
+ }
+
+w8_uniform_config = \
+ {
+ 'conv_in': 8, 'time_embedding.linear_1': 8, 'time_embedding.linear_2': 8, 'add_embedding.linear_1': 8, 'add_embedding.linear_2': 8, 'down_blocks.0.resnets.0.conv1': 8, 'down_blocks.0.resnets.0.time_emb_proj': 8, 'down_blocks.0.resnets.0.conv2': 8, 'down_blocks.0.resnets.1.conv1': 8, 'down_blocks.0.resnets.1.time_emb_proj': 8, 'down_blocks.0.resnets.1.conv2': 8, 'down_blocks.0.downsamplers.0.conv': 8, 'down_blocks.1.attentions.0.proj_in': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_k': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_v': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj': 8, 'down_blocks.1.attentions.0.transformer_blocks.0.ff.net.2': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_q': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_k': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_v': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_out.0': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_q': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_k': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_v': 8,
+ 'down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_out.0': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.ff.net.0.proj': 8, 'down_blocks.1.attentions.0.transformer_blocks.1.ff.net.2': 8, 'down_blocks.1.attentions.0.proj_out': 8, 'down_blocks.1.attentions.1.proj_in': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_q': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_k': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_v': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj': 8, 'down_blocks.1.attentions.1.transformer_blocks.0.ff.net.2': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn1.to_q': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn1.to_k': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn1.to_v': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn1.to_out.0': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn2.to_q': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn2.to_k': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn2.to_v': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.attn2.to_out.0': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.ff.net.0.proj': 8, 'down_blocks.1.attentions.1.transformer_blocks.1.ff.net.2': 8, 'down_blocks.1.attentions.1.proj_out': 8, 'down_blocks.1.resnets.0.conv1': 8, 'down_blocks.1.resnets.0.time_emb_proj': 8, 'down_blocks.1.resnets.0.conv2': 8, 'down_blocks.1.resnets.0.conv_shortcut': 8,
+ 'down_blocks.1.resnets.1.conv1': 8, 'down_blocks.1.resnets.1.time_emb_proj': 8, 'down_blocks.1.resnets.1.conv2': 8, 'down_blocks.1.downsamplers.0.conv': 8, 'down_blocks.2.attentions.0.proj_in': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.ff.net.0.proj': 8, 'down_blocks.2.attentions.0.transformer_blocks.0.ff.net.2': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn2.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn2.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn2.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.attn2.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.ff.net.0.proj': 8, 'down_blocks.2.attentions.0.transformer_blocks.1.ff.net.2': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn2.to_q': 8,
+ 'down_blocks.2.attentions.0.transformer_blocks.2.attn2.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn2.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.attn2.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.ff.net.0.proj': 8, 'down_blocks.2.attentions.0.transformer_blocks.2.ff.net.2': 8, 'down_blocks.2.attentions.0.transformer_blocks.3.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.3.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.3.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.3.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.3.attn2.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.3.attn2.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.3.attn2.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.3.attn2.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.3.ff.net.0.proj': 8, 'down_blocks.2.attentions.0.transformer_blocks.3.ff.net.2': 8, 'down_blocks.2.attentions.0.transformer_blocks.4.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.4.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.4.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.4.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.4.attn2.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.4.attn2.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.4.attn2.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.4.attn2.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.4.ff.net.0.proj': 8, 'down_blocks.2.attentions.0.transformer_blocks.4.ff.net.2': 8, 'down_blocks.2.attentions.0.transformer_blocks.5.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.5.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.5.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.5.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.5.attn2.to_q': 8,
+ 'down_blocks.2.attentions.0.transformer_blocks.5.attn2.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.5.attn2.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.5.attn2.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.5.ff.net.0.proj': 8, 'down_blocks.2.attentions.0.transformer_blocks.5.ff.net.2': 8, 'down_blocks.2.attentions.0.transformer_blocks.6.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.6.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.6.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.6.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.6.attn2.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.6.attn2.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.6.attn2.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.6.attn2.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.6.ff.net.0.proj': 8, 'down_blocks.2.attentions.0.transformer_blocks.6.ff.net.2': 8, 'down_blocks.2.attentions.0.transformer_blocks.7.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.7.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.7.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.7.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.7.attn2.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.7.attn2.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.7.attn2.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.7.attn2.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.7.ff.net.0.proj': 8, 'down_blocks.2.attentions.0.transformer_blocks.7.ff.net.2': 8, 'down_blocks.2.attentions.0.transformer_blocks.8.attn1.to_q': 8, 'down_blocks.2.attentions.0.transformer_blocks.8.attn1.to_k': 8, 'down_blocks.2.attentions.0.transformer_blocks.8.attn1.to_v': 8, 'down_blocks.2.attentions.0.transformer_blocks.8.attn1.to_out.0': 8, 'down_blocks.2.attentions.0.transformer_blocks.8.attn2.to_q': 8,
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+ }