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import re
import torch
import os
import folder_paths
from comfy.clip_vision import clip_preprocess, Output
import comfy.utils
import comfy.model_management as model_management
try:
import torchvision.transforms.v2 as T
except ImportError:
import torchvision.transforms as T
def get_clipvision_file(preset):
preset = preset.lower()
clipvision_list = folder_paths.get_filename_list("clip_vision")
if preset.startswith("vit-g"):
pattern = r'(ViT.bigG.14.*39B.b160k|ipadapter.*sdxl|sdxl.*model\.(bin|safetensors))'
else:
pattern = r'(ViT.H.14.*s32B.b79K|ipadapter.*sd15|sd1.?5.*model\.(bin|safetensors))'
clipvision_file = [e for e in clipvision_list if re.search(pattern, e, re.IGNORECASE)]
clipvision_file = folder_paths.get_full_path("clip_vision", clipvision_file[0]) if clipvision_file else None
return clipvision_file
def get_ipadapter_file(preset, is_sdxl):
preset = preset.lower()
ipadapter_list = folder_paths.get_filename_list("ipadapter")
is_insightface = False
lora_pattern = None
if preset.startswith("light"):
if is_sdxl:
raise Exception("light model is not supported for SDXL")
pattern = r'sd15.light.v11\.(safetensors|bin)$'
# if v11 is not found, try with the old version
if not [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]:
pattern = r'sd15.light\.(safetensors|bin)$'
elif preset.startswith("standard"):
if is_sdxl:
pattern = r'ip.adapter.sdxl.vit.h\.(safetensors|bin)$'
else:
pattern = r'ip.adapter.sd15\.(safetensors|bin)$'
elif preset.startswith("vit-g"):
if is_sdxl:
pattern = r'ip.adapter.sdxl\.(safetensors|bin)$'
else:
pattern = r'sd15.vit.g\.(safetensors|bin)$'
elif preset.startswith("plus ("):
if is_sdxl:
pattern = r'plus.sdxl.vit.h\.(safetensors|bin)$'
else:
pattern = r'ip.adapter.plus.sd15\.(safetensors|bin)$'
elif preset.startswith("plus face"):
if is_sdxl:
pattern = r'plus.face.sdxl.vit.h\.(safetensors|bin)$'
else:
pattern = r'plus.face.sd15\.(safetensors|bin)$'
elif preset.startswith("full"):
if is_sdxl:
raise Exception("full face model is not supported for SDXL")
pattern = r'full.face.sd15\.(safetensors|bin)$'
elif preset.startswith("faceid portrait ("):
if is_sdxl:
pattern = r'portrait.sdxl\.(safetensors|bin)$'
else:
pattern = r'portrait.v11.sd15\.(safetensors|bin)$'
# if v11 is not found, try with the old version
if not [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]:
pattern = r'portrait.sd15\.(safetensors|bin)$'
is_insightface = True
elif preset.startswith("faceid portrait unnorm"):
if is_sdxl:
pattern = r'portrait.sdxl.unnorm\.(safetensors|bin)$'
else:
raise Exception("portrait unnorm model is not supported for SD1.5")
is_insightface = True
elif preset == "faceid":
if is_sdxl:
pattern = r'faceid.sdxl\.(safetensors|bin)$'
lora_pattern = r'faceid.sdxl.lora\.safetensors$'
else:
pattern = r'faceid.sd15\.(safetensors|bin)$'
lora_pattern = r'faceid.sd15.lora\.safetensors$'
is_insightface = True
elif preset.startswith("faceid plus -"):
if is_sdxl:
raise Exception("faceid plus model is not supported for SDXL")
pattern = r'faceid.plus.sd15\.(safetensors|bin)$'
lora_pattern = r'faceid.plus.sd15.lora\.safetensors$'
is_insightface = True
elif preset.startswith("faceid plus v2"):
if is_sdxl:
pattern = r'faceid.plusv2.sdxl\.(safetensors|bin)$'
lora_pattern = r'faceid.plusv2.sdxl.lora\.safetensors$'
else:
pattern = r'faceid.plusv2.sd15\.(safetensors|bin)$'
lora_pattern = r'faceid.plusv2.sd15.lora\.safetensors$'
is_insightface = True
# Community's models
elif preset.startswith("composition"):
if is_sdxl:
pattern = r'plus.composition.sdxl\.safetensors$'
else:
pattern = r'plus.composition.sd15\.safetensors$'
else:
raise Exception(f"invalid type '{preset}'")
ipadapter_file = [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]
ipadapter_file = folder_paths.get_full_path("ipadapter", ipadapter_file[0]) if ipadapter_file else None
return ipadapter_file, is_insightface, lora_pattern
def get_lora_file(pattern):
lora_list = folder_paths.get_filename_list("loras")
lora_file = [e for e in lora_list if re.search(pattern, e, re.IGNORECASE)]
lora_file = folder_paths.get_full_path("loras", lora_file[0]) if lora_file else None
return lora_file
def ipadapter_model_loader(file):
model = comfy.utils.load_torch_file(file, safe_load=True)
if file.lower().endswith(".safetensors"):
st_model = {"image_proj": {}, "ip_adapter": {}}
for key in model.keys():
if key.startswith("image_proj."):
st_model["image_proj"][key.replace("image_proj.", "")] = model[key]
elif key.startswith("ip_adapter."):
st_model["ip_adapter"][key.replace("ip_adapter.", "")] = model[key]
model = st_model
del st_model
if "adapter_modules" in model.keys():
model["ip_adapter"] = model["adapter_modules"]
del model["adapter_modules"]
if not "ip_adapter" in model.keys() or not model["ip_adapter"]:
raise Exception("invalid IPAdapter model {}".format(file))
if 'plusv2' in file.lower():
model["faceidplusv2"] = True
if 'unnorm' in file.lower():
model["portraitunnorm"] = True
return model
def insightface_loader(provider):
try:
from insightface.app import FaceAnalysis
except ImportError as e:
raise Exception(e)
path = os.path.join(folder_paths.models_dir, "insightface")
model = FaceAnalysis(name="antelopev2", root=path, providers=[provider + 'ExecutionProvider',])
model.prepare(ctx_id=0, det_size=(640, 640))
return model
def encode_image_masked(clip_vision, image, mask=None, batch_size=0, size=224):
model_management.load_model_gpu(clip_vision.patcher)
outputs = Output()
if batch_size == 0:
batch_size = image.shape[0]
elif batch_size > image.shape[0]:
batch_size = image.shape[0]
image_batch = torch.split(image, batch_size, dim=0)
for img in image_batch:
img = img.to(clip_vision.load_device)
pixel_values = clip_preprocess(img.to(clip_vision.load_device), size=size).float()
# TODO: support for multiple masks
if mask is not None:
pixel_values = pixel_values * mask.to(clip_vision.load_device)
out = clip_vision.model(pixel_values=pixel_values, intermediate_output=-2)
if not hasattr(outputs, "last_hidden_state"):
outputs["last_hidden_state"] = out[0].to(model_management.intermediate_device())
outputs["image_embeds"] = out[2].to(model_management.intermediate_device())
outputs["penultimate_hidden_states"] = out[1].to(model_management.intermediate_device())
else:
outputs["last_hidden_state"] = torch.cat((outputs["last_hidden_state"], out[0].to(model_management.intermediate_device())), dim=0)
outputs["image_embeds"] = torch.cat((outputs["image_embeds"], out[2].to(model_management.intermediate_device())), dim=0)
outputs["penultimate_hidden_states"] = torch.cat((outputs["penultimate_hidden_states"], out[1].to(model_management.intermediate_device())), dim=0)
del img, pixel_values, out
torch.cuda.empty_cache()
return outputs
def tensor_to_size(source, dest_size):
if isinstance(dest_size, torch.Tensor):
dest_size = dest_size.shape[0]
source_size = source.shape[0]
if source_size < dest_size:
shape = [dest_size - source_size] + [1]*(source.dim()-1)
source = torch.cat((source, source[-1:].repeat(shape)), dim=0)
elif source_size > dest_size:
source = source[:dest_size]
return source
def min_(tensor_list):
# return the element-wise min of the tensor list.
x = torch.stack(tensor_list)
mn = x.min(axis=0)[0]
return torch.clamp(mn, min=0)
def max_(tensor_list):
# return the element-wise max of the tensor list.
x = torch.stack(tensor_list)
mx = x.max(axis=0)[0]
return torch.clamp(mx, max=1)
# From https://github.com/Jamy-L/Pytorch-Contrast-Adaptive-Sharpening/
def contrast_adaptive_sharpening(image, amount):
img = T.functional.pad(image, (1, 1, 1, 1)).cpu()
a = img[..., :-2, :-2]
b = img[..., :-2, 1:-1]
c = img[..., :-2, 2:]
d = img[..., 1:-1, :-2]
e = img[..., 1:-1, 1:-1]
f = img[..., 1:-1, 2:]
g = img[..., 2:, :-2]
h = img[..., 2:, 1:-1]
i = img[..., 2:, 2:]
# Computing contrast
cross = (b, d, e, f, h)
mn = min_(cross)
mx = max_(cross)
diag = (a, c, g, i)
mn2 = min_(diag)
mx2 = max_(diag)
mx = mx + mx2
mn = mn + mn2
# Computing local weight
inv_mx = torch.reciprocal(mx)
amp = inv_mx * torch.minimum(mn, (2 - mx))
# scaling
amp = torch.sqrt(amp)
w = - amp * (amount * (1/5 - 1/8) + 1/8)
div = torch.reciprocal(1 + 4*w)
output = ((b + d + f + h)*w + e) * div
output = torch.nan_to_num(output)
output = output.clamp(0, 1)
return output
def tensor_to_image(tensor):
image = tensor.mul(255).clamp(0, 255).byte().cpu()
image = image[..., [2, 1, 0]].numpy()
return image
def image_to_tensor(image):
tensor = torch.clamp(torch.from_numpy(image).float() / 255., 0, 1)
tensor = tensor[..., [2, 1, 0]]
return tensor
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