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Merge branch 'comfyanonymous:master' into master

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patientx 2024-08-23 12:31:51 +03:00 committed by GitHub
commit c98e8a0a55
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3 changed files with 26 additions and 24 deletions
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20
comfy/lora.py
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@ -327,6 +327,26 @@ def model_lora_keys_unet(model, key_map={}):
return key_map
def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype):
dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype)
lora_diff *= alpha
weight_calc = weight + lora_diff.type(weight.dtype)
weight_norm = (
weight_calc.transpose(0, 1)
.reshape(weight_calc.shape[1], -1)
.norm(dim=1, keepdim=True)
.reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1))
.transpose(0, 1)
)
weight_calc *= (dora_scale / weight_norm).type(weight.dtype)
if strength != 1.0:
weight_calc -= weight
weight += strength * (weight_calc)
else:
weight[:] = weight_calc
return weight
def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
for p in patches:
strength = p[0]

9
comfy/model_management.py
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@ -44,11 +44,14 @@ cpu_state = CPUState.GPU
total_vram = 0
torch_version = torch.version.__version__
xpu_available = False
try:
torch_version = torch.version.__version__
xpu_available = (int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) <= 4)) and torch.xpu.is_available()
except:
pass
lowvram_available = True
xpu_available = int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) <= 4)
if args.deterministic:
logging.info("Using deterministic algorithms for pytorch")
torch.use_deterministic_algorithms(True, warn_only=True)

21
comfy/model_patcher.py
View File

@ -31,27 +31,6 @@ import comfy.lora
from comfy.types import UnetWrapperFunction
def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype):
dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype)
lora_diff *= alpha
weight_calc = weight + lora_diff.type(weight.dtype)
weight_norm = (
weight_calc.transpose(0, 1)
.reshape(weight_calc.shape[1], -1)
.norm(dim=1, keepdim=True)
.reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1))
.transpose(0, 1)
)
weight_calc *= (dora_scale / weight_norm).type(weight.dtype)
if strength != 1.0:
weight_calc -= weight
weight += strength * (weight_calc)
else:
weight[:] = weight_calc
return weight
def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):
to = model_options["transformer_options"].copy()