hypernetwork training mk1
This commit is contained in:
AUTOMATIC
parent
f7c787eb7c
commit
12c4d5c6b5
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@ -1,88 +0,0 @@
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import glob
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import os
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import sys
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import traceback
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import torch
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from ldm.util import default
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from modules import devices, shared
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import torch
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from torch import einsum
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from einops import rearrange, repeat
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class HypernetworkModule(torch.nn.Module):
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def __init__(self, dim, state_dict):
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super().__init__()
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self.linear1 = torch.nn.Linear(dim, dim * 2)
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self.linear2 = torch.nn.Linear(dim * 2, dim)
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self.load_state_dict(state_dict, strict=True)
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self.to(devices.device)
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def forward(self, x):
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return x + (self.linear2(self.linear1(x)))
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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, filename):
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self.filename = filename
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self.name = os.path.splitext(os.path.basename(filename))[0]
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self.layers = {}
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state_dict = torch.load(filename, map_location='cpu')
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for size, sd in state_dict.items():
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self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
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def load_hypernetworks(path):
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res = {}
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for filename in glob.iglob(path + '**/*.pt', recursive=True):
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try:
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hn = Hypernetwork(filename)
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res[hn.name] = hn
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except Exception:
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print(f"Error loading hypernetwork {filename}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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return res
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def attention_CrossAttention_forward(self, x, context=None, mask=None):
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h = self.heads
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q = self.to_q(x)
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context = default(context, x)
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hypernetwork = shared.selected_hypernetwork()
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hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
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if hypernetwork_layers is not None:
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k = self.to_k(hypernetwork_layers[0](context))
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v = self.to_v(hypernetwork_layers[1](context))
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else:
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k = self.to_k(context)
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v = self.to_v(context)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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if mask is not None:
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mask = rearrange(mask, 'b ... -> b (...)')
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, 'b j -> (b h) () j', h=h)
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sim.masked_fill_(~mask, max_neg_value)
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# attention, what we cannot get enough of
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attn = sim.softmax(dim=-1)
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out = einsum('b i j, b j d -> b i d', attn, v)
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out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
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return self.to_out(out)
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import datetime
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import glob
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import html
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import os
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import sys
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import traceback
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import tqdm
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import torch
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from ldm.util import default
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from modules import devices, shared, processing, sd_models
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import torch
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from torch import einsum
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from einops import rearrange, repeat
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import modules.textual_inversion.dataset
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class HypernetworkModule(torch.nn.Module):
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def __init__(self, dim, state_dict=None):
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super().__init__()
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self.linear1 = torch.nn.Linear(dim, dim * 2)
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self.linear2 = torch.nn.Linear(dim * 2, dim)
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if state_dict is not None:
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self.load_state_dict(state_dict, strict=True)
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else:
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self.linear1.weight.data.fill_(0.0001)
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self.linear1.bias.data.fill_(0.0001)
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self.linear2.weight.data.fill_(0.0001)
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self.linear2.bias.data.fill_(0.0001)
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self.to(devices.device)
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def forward(self, x):
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return x + (self.linear2(self.linear1(x)))
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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):
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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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for size in [320, 640, 768, 1280]:
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self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
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def weights(self):
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res = []
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for k, layers in self.layers.items():
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for layer in layers:
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layer.train()
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res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
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return res
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def save(self, filename):
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state_dict = {}
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for k, v in self.layers.items():
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state_dict[k] = (v[0].state_dict(), v[1].state_dict())
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state_dict['step'] = self.step
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state_dict['name'] = self.name
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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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torch.save(state_dict, filename)
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def load(self, filename):
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self.filename = filename
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if self.name is None:
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self.name = os.path.splitext(os.path.basename(filename))[0]
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state_dict = torch.load(filename, map_location='cpu')
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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.name = state_dict.get('name', self.name)
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self.step = state_dict.get('step', 0)
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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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def load_hypernetworks(path):
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res = {}
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for filename in glob.iglob(path + '**/*.pt', recursive=True):
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try:
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hn = Hypernetwork()
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hn.load(filename)
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res[hn.name] = hn
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except Exception:
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print(f"Error loading hypernetwork {filename}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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return res
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def attention_CrossAttention_forward(self, x, context=None, mask=None):
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h = self.heads
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q = self.to_q(x)
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context = default(context, x)
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hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None)
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if hypernetwork_layers is not None:
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hypernetwork_k, hypernetwork_v = hypernetwork_layers
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self.hypernetwork_k = hypernetwork_k
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self.hypernetwork_v = hypernetwork_v
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context_k = hypernetwork_k(context)
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context_v = hypernetwork_v(context)
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else:
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context_k = context
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context_v = context
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k = self.to_k(context_k)
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v = self.to_v(context_v)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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if mask is not None:
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mask = rearrange(mask, 'b ... -> b (...)')
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, 'b j -> (b h) () j', h=h)
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sim.masked_fill_(~mask, max_neg_value)
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# attention, what we cannot get enough of
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attn = sim.softmax(dim=-1)
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out = einsum('b i j, b j d -> b i d', attn, v)
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out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
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return self.to_out(out)
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def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
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assert hypernetwork_name, 'embedding not selected'
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shared.hypernetwork = shared.hypernetworks[hypernetwork_name]
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shared.state.textinfo = "Initializing hypernetwork training..."
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shared.state.job_count = steps
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filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
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log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
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if save_hypernetwork_every > 0:
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hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
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os.makedirs(hypernetwork_dir, exist_ok=True)
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else:
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hypernetwork_dir = None
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if create_image_every > 0:
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images_dir = os.path.join(log_directory, "images")
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os.makedirs(images_dir, exist_ok=True)
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else:
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images_dir = None
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cond_model = shared.sd_model.cond_stage_model
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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with torch.autocast("cuda"):
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
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hypernetwork = shared.hypernetworks[hypernetwork_name]
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weights = hypernetwork.weights()
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for weight in weights:
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weight.requires_grad = True
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optimizer = torch.optim.AdamW(weights, lr=learn_rate)
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losses = torch.zeros((32,))
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last_saved_file = "<none>"
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last_saved_image = "<none>"
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ititial_step = hypernetwork.step or 0
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if ititial_step > steps:
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return hypernetwork, filename
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pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
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for i, (x, text) in pbar:
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hypernetwork.step = i + ititial_step
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if hypernetwork.step > steps:
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break
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if shared.state.interrupted:
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break
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with torch.autocast("cuda"):
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c = cond_model([text])
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x = x.to(devices.device)
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loss = shared.sd_model(x.unsqueeze(0), c)[0]
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del x
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losses[hypernetwork.step % losses.shape[0]] = loss.item()
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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pbar.set_description(f"loss: {losses.mean():.7f}")
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if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
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last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
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hypernetwork.save(last_saved_file)
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if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
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last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
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preview_text = text if preview_image_prompt == "" else preview_image_prompt
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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prompt=preview_text,
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steps=20,
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do_not_save_grid=True,
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do_not_save_samples=True,
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)
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processed = processing.process_images(p)
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image = processed.images[0]
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shared.state.current_image = image
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image.save(last_saved_image)
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last_saved_image += f", prompt: {preview_text}"
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shared.state.job_no = hypernetwork.step
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shared.state.textinfo = f"""
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<p>
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Loss: {losses.mean():.7f}<br/>
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Step: {hypernetwork.step}<br/>
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Last prompt: {html.escape(text)}<br/>
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Last saved embedding: {html.escape(last_saved_file)}<br/>
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Last saved image: {html.escape(last_saved_image)}<br/>
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</p>
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"""
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checkpoint = sd_models.select_checkpoint()
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hypernetwork.sd_checkpoint = checkpoint.hash
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hypernetwork.sd_checkpoint_name = checkpoint.model_name
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hypernetwork.save(filename)
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return hypernetwork, filename
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@ -0,0 +1,43 @@
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import html
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import os
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import gradio as gr
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import modules.textual_inversion.textual_inversion
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import modules.textual_inversion.preprocess
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from modules import sd_hijack, shared
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def create_hypernetwork(name):
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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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hypernetwork = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
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hypernetwork.save(fn)
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shared.reload_hypernetworks()
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shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None)
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return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
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def train_hypernetwork(*args):
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initial_hypernetwork = shared.hypernetwork
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try:
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sd_hijack.undo_optimizations()
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hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args)
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res = f"""
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Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
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Hypernetwork saved to {html.escape(filename)}
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"""
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return res, ""
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except Exception:
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raise
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finally:
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shared.hypernetwork = initial_hypernetwork
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sd_hijack.apply_optimizations()
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@ -8,7 +8,7 @@ from torch import einsum
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from torch.nn.functional import silu
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import modules.textual_inversion.textual_inversion
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from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
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from modules import prompt_parser, devices, sd_hijack_optimizations, shared
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from modules.shared import opts, device, cmd_opts
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import ldm.modules.attention
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@ -32,6 +32,8 @@ def apply_optimizations():
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def undo_optimizations():
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from modules.hypernetwork import hypernetwork
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ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
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ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
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ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
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@ -45,8 +45,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
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q_in = self.to_q(x)
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context = default(context, x)
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hypernetwork = shared.selected_hypernetwork()
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hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
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hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None)
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if hypernetwork_layers is not None:
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k_in = self.to_k(hypernetwork_layers[0](context))
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@ -13,7 +13,7 @@ import modules.memmon
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import modules.sd_models
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import modules.styles
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import modules.devices as devices
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from modules import sd_samplers, hypernetwork
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from modules import sd_samplers
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from modules.paths import models_path, script_path, sd_path
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sd_model_file = os.path.join(script_path, 'model.ckpt')
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@ -28,6 +28,7 @@ parser.add_argument("--no-half", action='store_true', help="do not switch the mo
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parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
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parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
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parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
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parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
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parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
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parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
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parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
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@ -76,11 +77,15 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
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config_filename = cmd_opts.ui_settings_file
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hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
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def reload_hypernetworks():
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from modules.hypernetwork import hypernetwork
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hypernetworks.clear()
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hypernetworks.update(hypernetwork.load_hypernetworks(cmd_opts.hypernetwork_dir))
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def selected_hypernetwork():
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return hypernetworks.get(opts.sd_hypernetwork, None)
|
||||
hypernetworks = {}
|
||||
hypernetwork = None
|
||||
|
||||
|
||||
class State:
|
||||
|
|
|
|||
|
|
@ -22,7 +22,6 @@ def preprocess(*args):
|
|||
|
||||
|
||||
def train_embedding(*args):
|
||||
|
||||
try:
|
||||
sd_hijack.undo_optimizations()
|
||||
|
||||
|
|
|
|||
|
|
@ -37,6 +37,7 @@ import modules.generation_parameters_copypaste
|
|||
from modules import prompt_parser
|
||||
from modules.images import save_image
|
||||
import modules.textual_inversion.ui
|
||||
import modules.hypernetwork.ui
|
||||
|
||||
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
|
||||
mimetypes.init()
|
||||
|
|
@ -965,6 +966,18 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
with gr.Column():
|
||||
create_embedding = gr.Button(value="Create", variant='primary')
|
||||
|
||||
with gr.Group():
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
|
||||
|
||||
new_hypernetwork_name = gr.Textbox(label="Name")
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
create_hypernetwork = gr.Button(value="Create", variant='primary')
|
||||
|
||||
with gr.Group():
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
|
||||
|
||||
|
|
@ -986,6 +999,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
with gr.Group():
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
|
||||
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
|
||||
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
|
||||
learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
|
||||
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
|
||||
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
|
||||
|
|
@ -993,15 +1007,12 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
steps = gr.Number(label='Max steps', value=100000, precision=0)
|
||||
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
|
||||
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
|
||||
preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=2):
|
||||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
interrupt_training = gr.Button(value="Interrupt")
|
||||
train_embedding = gr.Button(value="Train", variant='primary')
|
||||
interrupt_training = gr.Button(value="Interrupt")
|
||||
train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
|
||||
train_embedding = gr.Button(value="Train Embedding", variant='primary')
|
||||
|
||||
with gr.Column():
|
||||
progressbar = gr.HTML(elem_id="ti_progressbar")
|
||||
|
|
@ -1027,6 +1038,18 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
]
|
||||
)
|
||||
|
||||
create_hypernetwork.click(
|
||||
fn=modules.hypernetwork.ui.create_hypernetwork,
|
||||
inputs=[
|
||||
new_hypernetwork_name,
|
||||
],
|
||||
outputs=[
|
||||
train_hypernetwork_name,
|
||||
ti_output,
|
||||
ti_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
run_preprocess.click(
|
||||
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
|
||||
_js="start_training_textual_inversion",
|
||||
|
|
@ -1062,12 +1085,33 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
]
|
||||
)
|
||||
|
||||
train_hypernetwork.click(
|
||||
fn=wrap_gradio_gpu_call(modules.hypernetwork.ui.train_hypernetwork, extra_outputs=[gr.update()]),
|
||||
_js="start_training_textual_inversion",
|
||||
inputs=[
|
||||
train_hypernetwork_name,
|
||||
learn_rate,
|
||||
dataset_directory,
|
||||
log_directory,
|
||||
steps,
|
||||
create_image_every,
|
||||
save_embedding_every,
|
||||
template_file,
|
||||
preview_image_prompt,
|
||||
],
|
||||
outputs=[
|
||||
ti_output,
|
||||
ti_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
interrupt_training.click(
|
||||
fn=lambda: shared.state.interrupt(),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
|
||||
def create_setting_component(key):
|
||||
def fun():
|
||||
return opts.data[key] if key in opts.data else opts.data_labels[key].default
|
||||
|
|
|
|||
|
|
@ -78,8 +78,7 @@ def apply_checkpoint(p, x, xs):
|
|||
|
||||
|
||||
def apply_hypernetwork(p, x, xs):
|
||||
hn = shared.hypernetworks.get(x, None)
|
||||
opts.data["sd_hypernetwork"] = hn.name if hn is not None else 'None'
|
||||
shared.hypernetwork = shared.hypernetworks.get(x, None)
|
||||
|
||||
|
||||
def format_value_add_label(p, opt, x):
|
||||
|
|
@ -199,7 +198,7 @@ class Script(scripts.Script):
|
|||
modules.processing.fix_seed(p)
|
||||
p.batch_size = 1
|
||||
|
||||
initial_hn = opts.sd_hypernetwork
|
||||
initial_hn = shared.hypernetwork
|
||||
|
||||
def process_axis(opt, vals):
|
||||
if opt.label == 'Nothing':
|
||||
|
|
@ -308,6 +307,6 @@ class Script(scripts.Script):
|
|||
# restore checkpoint in case it was changed by axes
|
||||
modules.sd_models.reload_model_weights(shared.sd_model)
|
||||
|
||||
opts.data["sd_hypernetwork"] = initial_hn
|
||||
shared.hypernetwork = initial_hn
|
||||
|
||||
return processed
|
||||
|
|
|
|||
|
|
@ -0,0 +1,27 @@
|
|||
a photo of a [filewords]
|
||||
a rendering of a [filewords]
|
||||
a cropped photo of the [filewords]
|
||||
the photo of a [filewords]
|
||||
a photo of a clean [filewords]
|
||||
a photo of a dirty [filewords]
|
||||
a dark photo of the [filewords]
|
||||
a photo of my [filewords]
|
||||
a photo of the cool [filewords]
|
||||
a close-up photo of a [filewords]
|
||||
a bright photo of the [filewords]
|
||||
a cropped photo of a [filewords]
|
||||
a photo of the [filewords]
|
||||
a good photo of the [filewords]
|
||||
a photo of one [filewords]
|
||||
a close-up photo of the [filewords]
|
||||
a rendition of the [filewords]
|
||||
a photo of the clean [filewords]
|
||||
a rendition of a [filewords]
|
||||
a photo of a nice [filewords]
|
||||
a good photo of a [filewords]
|
||||
a photo of the nice [filewords]
|
||||
a photo of the small [filewords]
|
||||
a photo of the weird [filewords]
|
||||
a photo of the large [filewords]
|
||||
a photo of a cool [filewords]
|
||||
a photo of a small [filewords]
|
||||
|
|
@ -0,0 +1 @@
|
|||
picture
|
||||
9
webui.py
9
webui.py
|
|
@ -74,6 +74,15 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
|
|||
return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
|
||||
|
||||
|
||||
def set_hypernetwork():
|
||||
shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None)
|
||||
|
||||
|
||||
shared.reload_hypernetworks()
|
||||
shared.opts.onchange("sd_hypernetwork", set_hypernetwork)
|
||||
set_hypernetwork()
|
||||
|
||||
|
||||
modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
|
||||
|
||||
shared.sd_model = modules.sd_models.load_model()
|
||||
|
|
|
|||
Loading…
Reference in New Issue