Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-18 16:54:33 +02:00
Code Issues Packages Projects Releases Wiki Activity

Cleanup

Browse Source
This commit is contained in:
Disty0
2025-06-14 00:54:18 +03:00
parent fb72c6f540
commit 2ba64abcde
3 changed files with 21 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/lora/lora_apply.py
+2
View File
@@ -155,6 +155,8 @@ def network_add_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.G
quant_conv=shared.opts.sdnq_quantize_conv_layers,
use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
use_quantized_matmul_conv=shared.opts.sdnq_use_quantized_matmul_conv,
quantize_with_gpu=shared.opts.sdnq_quantize_with_gpu,
dequantize_fp32=shared.opts.sdnq_dequantize_fp32,
param_name=getattr(self, 'network_layer_name', None),
)
self = self.to(device)
modules/model_quant.py
+4
View File
@@ -125,6 +125,8 @@ def create_sdnq_config(kwargs = None, allow_sdnq: bool = True, module: str = 'Mo
quant_conv=shared.opts.sdnq_quantize_conv_layers,
use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
use_quantized_matmul_conv=shared.opts.sdnq_use_quantized_matmul_conv,
quantize_with_gpu=shared.opts.sdnq_quantize_with_gpu,
dequantize_fp32=shared.opts.sdnq_dequantize_fp32,
)
log.debug(f'Quantization: module="{module}" type=sdnq dtype={weights_dtype}')
if kwargs is None:
@@ -339,6 +341,8 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc=True):
quant_conv=shared.opts.sdnq_quantize_conv_layers,
use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
use_quantized_matmul_conv=shared.opts.sdnq_use_quantized_matmul_conv,
quantize_with_gpu=shared.opts.sdnq_quantize_with_gpu,
dequantize_fp32=shared.opts.sdnq_dequantize_fp32,
param_name=op,
)
model.quantization_method = 'SDNQ'
modules/sdnq/__init__.py
+15 -5
View File
@@ -14,7 +14,7 @@ from .dequantizer import dequantizer_dict
from .forward import get_forward_func
def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_size=0, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, param_name=None, pre_mode=False):
def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_size=0, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, quantize_with_gpu=True, dequantize_fp32=False, param_name=None, pre_mode=False):
layer_class_name = layer.__class__.__name__
if layer_class_name in allowed_types:
is_conv_type = False
@@ -111,20 +111,20 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
elif pre_mode:
if shared.opts.device_map == "gpu":
return_device = devices.device
elif shared.opts.sdnq_quantize_with_gpu:
elif quantize_with_gpu:
return_device = devices.cpu
else:
return_device = layer.weight.device
else:
return_device = layer.weight.device
if not pre_mode:
if shared.opts.sdnq_quantize_with_gpu:
if quantize_with_gpu:
layer.weight.data = layer.weight.to(devices.device).to(dtype=torch.float32)
else:
layer.weight.data = layer.weight.to(dtype=torch.float32)
layer.weight.data, scale, zero_point = quantize_weight(layer.weight, reduction_axes, weights_dtype)
if not shared.opts.sdnq_dequantize_fp32 and not (use_quantized_matmul and not dtype_dict[weights_dtype]["is_integer"] and not use_tensorwise_fp8_matmul):
if not dequantize_fp32 and not (use_quantized_matmul and not dtype_dict[weights_dtype]["is_integer"] and not use_tensorwise_fp8_matmul):
scale = scale.to(torch_dtype)
if zero_point is not None:
zero_point = zero_point.to(torch_dtype)
@@ -158,7 +158,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
return layer
def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_size=0, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, param_name=None): # pylint: disable=unused-argument
def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_size=0, quant_conv=False, use_quantized_matmul=False, use_quantized_matmul_conv=False, quantize_with_gpu=True, dequantize_fp32=False, param_name=None): # pylint: disable=unused-argument
has_children = list(model.children())
if not has_children:
return model
@@ -172,6 +172,8 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
quant_conv=quant_conv,
use_quantized_matmul=use_quantized_matmul,
use_quantized_matmul_conv=use_quantized_matmul_conv,
quantize_with_gpu=quantize_with_gpu,
dequantize_fp32=dequantize_fp32,
param_name=module_param_name,
)
module = apply_sdnq_to_module(
@@ -182,6 +184,8 @@ def apply_sdnq_to_module(model, weights_dtype="int8", torch_dtype=None, group_si
quant_conv=quant_conv,
use_quantized_matmul=use_quantized_matmul,
use_quantized_matmul_conv=use_quantized_matmul_conv,
quantize_with_gpu=quantize_with_gpu,
dequantize_fp32=dequantize_fp32,
param_name=module_param_name,
)
return model
@@ -295,6 +299,8 @@ class SDNQQuantizer(DiffusersQuantizer):
quant_conv=self.quantization_config.quant_conv,
use_quantized_matmul=self.quantization_config.use_quantized_matmul,
use_quantized_matmul_conv=self.quantization_config.use_quantized_matmul_conv,
quantize_with_gpu=self.quantization_config.quantize_with_gpu,
dequantize_fp32=self.quantization_config.dequantize_fp32,
param_name=param_name,
pre_mode=True,
)
@@ -389,6 +395,8 @@ class SDNQConfig(QuantizationConfigMixin):
quant_conv: bool = False,
use_quantized_matmul: bool = False,
use_quantized_matmul_conv: bool = False,
quantize_with_gpu: bool = True,
dequantize_fp32: bool = False,
modules_to_not_convert: Optional[List[str]] = None,
**kwargs, # pylint: disable=unused-argument
):
@@ -398,6 +406,8 @@ class SDNQConfig(QuantizationConfigMixin):
self.quant_conv = quant_conv
self.use_quantized_matmul = use_quantized_matmul
self.use_quantized_matmul_conv = use_quantized_matmul_conv
self.quantize_with_gpu = quantize_with_gpu,
self.dequantize_fp32 = dequantize_fp32,
self.modules_to_not_convert = modules_to_not_convert
self.post_init()
self.is_integer = dtype_dict[self.weights_dtype]["is_integer"]