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