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Merge branch 'comfyanonymous:master' into master

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patientx 2024-09-04 09:29:56 +03:00 committed by GitHub
commit 64f428801e
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3 changed files with 29 additions and 7 deletions
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2
comfy/cli_args.py
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@ -92,6 +92,8 @@ class LatentPreviewMethod(enum.Enum):
parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
cache_group = parser.add_mutually_exclusive_group()
cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")

32
comfy/lora.py
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@ -528,20 +528,40 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "glora":
if v[4] is not None:
alpha = v[4] / v[0].shape[0]
else:
alpha = 1.0
dora_scale = v[5]
old_glora = False
if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]:
rank = v[0].shape[0]
old_glora = True
if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]:
if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]:
pass
else:
old_glora = False
rank = v[1].shape[0]
a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype)
a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype)
b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype)
b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype)
if v[4] is not None:
alpha = v[4] / rank
else:
alpha = 1.0
try:
lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape)
if old_glora:
lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora
else:
if weight.dim() > 2:
lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
else:
lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
lora_diff += torch.mm(b1, b2).reshape(weight.shape)
if dora_scale is not None:
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
else:

2
latent_preview.py
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@ -9,7 +9,7 @@ import folder_paths
import comfy.utils
import logging
MAX_PREVIEW_RESOLUTION = 512
MAX_PREVIEW_RESOLUTION = args.preview_size
def preview_to_image(latent_image):
latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1) # change scale from -1..1 to 0..1