layer options moves into create hnet ui
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discus0434
parent
7f8670c4ef
commit
42fbda83bb
5 changed files with 48 additions and 43 deletions
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@ -19,37 +19,21 @@ from modules.textual_inversion import textual_inversion
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from modules.textual_inversion.learn_schedule import LearnRateScheduler
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def parse_layer_structure(dim, state_dict):
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i = 0
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res = [1]
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while (key := "linear.{}.weight".format(i)) in state_dict:
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weight = state_dict[key]
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res.append(len(weight) // dim)
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i += 1
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return res
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class HypernetworkModule(torch.nn.Module):
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multiplier = 1.0
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layer_structure = None
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add_layer_norm = False
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def __init__(self, dim, state_dict=None):
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def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):
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super().__init__()
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if (state_dict is None or 'linear.0.weight' not in state_dict) and self.layer_structure is None:
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layer_structure = (1, 2, 1)
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else:
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if self.layer_structure is not None:
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assert self.layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
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assert self.layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
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layer_structure = self.layer_structure
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if layer_structure is not None:
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assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
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assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
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else:
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layer_structure = parse_layer_structure(dim, state_dict)
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linears = []
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for i in range(len(layer_structure) - 1):
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linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
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if self.add_layer_norm:
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if add_layer_norm:
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linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
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self.linear = torch.nn.Sequential(*linears)
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@ -77,38 +61,47 @@ class HypernetworkModule(torch.nn.Module):
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return x + self.linear(x) * self.multiplier
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def trainables(self):
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res = []
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layer_structure = []
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for layer in self.linear:
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res += [layer.weight, layer.bias]
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return res
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layer_structure += [layer.weight, layer.bias]
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return layer_structure
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def apply_strength(value=None):
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HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
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def apply_layer_structure(value=None):
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HypernetworkModule.layer_structure = value if value is not None else shared.opts.sd_hypernetwork_layer_structure
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def parse_layer_structure(dim, state_dict):
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i = 0
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layer_structure = [1]
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while (key := "linear.{}.weight".format(i)) in state_dict:
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weight = state_dict[key]
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layer_structure.append(len(weight) // dim)
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i += 1
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def apply_layer_norm(value=None):
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HypernetworkModule.add_layer_norm = value if value is not None else shared.opts.sd_hypernetwork_add_layer_norm
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return layer_structure
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class Hypernetwork:
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filename = None
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name = None
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def __init__(self, name=None, enable_sizes=None):
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def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False):
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self.filename = None
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self.name = name
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self.layers = {}
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self.step = 0
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self.sd_checkpoint = None
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self.sd_checkpoint_name = None
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self.layer_structure = layer_structure
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self.add_layer_norm = add_layer_norm
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for size in enable_sizes or []:
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self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
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self.layers[size] = (
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HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
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HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
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)
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def weights(self):
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res = []
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@ -128,6 +121,8 @@ class Hypernetwork:
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state_dict['step'] = self.step
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state_dict['name'] = self.name
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state_dict['layer_structure'] = self.layer_structure
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state_dict['is_layer_norm'] = self.add_layer_norm
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state_dict['sd_checkpoint'] = self.sd_checkpoint
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state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
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@ -142,10 +137,15 @@ class Hypernetwork:
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for size, sd in state_dict.items():
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if type(size) == int:
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self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
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self.layers[size] = (
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HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]),
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HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]),
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)
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self.name = state_dict.get('name', self.name)
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self.step = state_dict.get('step', 0)
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self.layer_structure = state_dict.get('layer_structure', None)
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self.add_layer_norm = state_dict.get('is_layer_norm', False)
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self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
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self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
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@ -9,11 +9,16 @@ from modules import sd_hijack, shared, devices
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from modules.hypernetworks import hypernetwork
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def create_hypernetwork(name, enable_sizes):
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def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False):
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fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
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assert not os.path.exists(fn), f"file {fn} already exists"
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hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes])
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hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
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name=name,
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enable_sizes=[int(x) for x in enable_sizes],
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layer_structure=layer_structure,
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add_layer_norm=add_layer_norm,
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)
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hypernet.save(fn)
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shared.reload_hypernetworks()
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@ -260,8 +260,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
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"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
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"sd_hypernetwork_layer_structure": OptionInfo(None, "Hypernetwork layer structure Default: (1,2,1).", gr.Dropdown, lambda: {"choices": [(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)]}),
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"sd_hypernetwork_add_layer_norm": OptionInfo(False, "Add layer normalization to hypernetwork architecture."),
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"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
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"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
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"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
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@ -1198,6 +1198,8 @@ def create_ui(wrap_gradio_gpu_call):
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with gr.Tab(label="Create hypernetwork"):
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new_hypernetwork_name = gr.Textbox(label="Name")
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new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
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new_hypernetwork_layer_structure = gr.Dropdown(label="Hypernetwork layer structure", choices=[(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)])
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new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
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with gr.Row():
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with gr.Column(scale=3):
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@ -1280,6 +1282,8 @@ def create_ui(wrap_gradio_gpu_call):
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inputs=[
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new_hypernetwork_name,
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new_hypernetwork_sizes,
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new_hypernetwork_layer_structure,
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new_hypernetwork_add_layer_norm,
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],
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outputs=[
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train_hypernetwork_name,
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2
webui.py
2
webui.py
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@ -85,8 +85,6 @@ def initialize():
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shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
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shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
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shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
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shared.opts.onchange("sd_hypernetwork_layer_structure", modules.hypernetworks.hypernetwork.apply_layer_structure)
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shared.opts.onchange("sd_hypernetwork_add_layer_norm", modules.hypernetworks.hypernetwork.apply_layer_norm)
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# make the program just exit at ctrl+c without waiting for anything
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def sigint_handler(sig, frame):
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