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import comfy.utils
import folder_paths
import torch
import logging
def load_hypernetwork_patch(path, strength):
sd = comfy.utils.load_torch_file(path, safe_load=True)
activation_func = sd.get('activation_func', 'linear')
is_layer_norm = sd.get('is_layer_norm', False)
use_dropout = sd.get('use_dropout', False)
activate_output = sd.get('activate_output', False)
last_layer_dropout = sd.get('last_layer_dropout', False)
valid_activation = {
"linear": torch.nn.Identity,
"relu": torch.nn.ReLU,
"leakyrelu": torch.nn.LeakyReLU,
"elu": torch.nn.ELU,
"swish": torch.nn.Hardswish,
"tanh": torch.nn.Tanh,
"sigmoid": torch.nn.Sigmoid,
"softsign": torch.nn.Softsign,
"mish": torch.nn.Mish,
}
if activation_func not in valid_activation:
logging.error("Unsupported Hypernetwork format, if you report it I might implement it. {} {} {} {} {} {}".format(path, activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout))
return None
out = {}
for d in sd:
try:
dim = int(d)
except:
continue
output = []
for index in [0, 1]:
attn_weights = sd[dim][index]
keys = attn_weights.keys()
linears = filter(lambda a: a.endswith(".weight"), keys)
linears = list(map(lambda a: a[:-len(".weight")], linears))
layers = []
i = 0
while i < len(linears):
lin_name = linears[i]
last_layer = (i == (len(linears) - 1))
penultimate_layer = (i == (len(linears) - 2))
lin_weight = attn_weights['{}.weight'.format(lin_name)]
lin_bias = attn_weights['{}.bias'.format(lin_name)]
layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0])
layer.load_state_dict({"weight": lin_weight, "bias": lin_bias})
layers.append(layer)
if activation_func != "linear":
if (not last_layer) or (activate_output):
layers.append(valid_activation[activation_func]())
if is_layer_norm:
i += 1
ln_name = linears[i]
ln_weight = attn_weights['{}.weight'.format(ln_name)]
ln_bias = attn_weights['{}.bias'.format(ln_name)]
ln = torch.nn.LayerNorm(ln_weight.shape[0])
ln.load_state_dict({"weight": ln_weight, "bias": ln_bias})
layers.append(ln)
if use_dropout:
if (not last_layer) and (not penultimate_layer or last_layer_dropout):
layers.append(torch.nn.Dropout(p=0.3))
i += 1
output.append(torch.nn.Sequential(*layers))
out[dim] = torch.nn.ModuleList(output)
class hypernetwork_patch:
def __init__(self, hypernet, strength):
self.hypernet = hypernet
self.strength = strength
def __call__(self, q, k, v, extra_options):
dim = k.shape[-1]
if dim in self.hypernet:
hn = self.hypernet[dim]
k = k + hn[0](k) * self.strength
v = v + hn[1](v) * self.strength
return q, k, v
def to(self, device):
for d in self.hypernet.keys():
self.hypernet[d] = self.hypernet[d].to(device)
return self
return hypernetwork_patch(out, strength)
class HypernetworkLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"hypernetwork_name": (folder_paths.get_filename_list("hypernetworks"), ),
"strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "load_hypernetwork"
CATEGORY = "loaders"
def load_hypernetwork(self, model, hypernetwork_name, strength):
hypernetwork_path = folder_paths.get_full_path("hypernetworks", hypernetwork_name)
model_hypernetwork = model.clone()
patch = load_hypernetwork_patch(hypernetwork_path, strength)
if patch is not None:
model_hypernetwork.set_model_attn1_patch(patch)
model_hypernetwork.set_model_attn2_patch(patch)
return (model_hypernetwork,)
NODE_CLASS_MAPPINGS = {
"HypernetworkLoader": HypernetworkLoader
}
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