Merge branch 'master' into token_updates
liamkerr
3 years ago
committed by
GitHub
commit
7308aeefd9
@ -0,0 +1,8 @@
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function start_training_textual_inversion(){
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requestProgress('ti')
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gradioApp().querySelector('#ti_error').innerHTML=''
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return args_to_array(arguments)
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}
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@ -0,0 +1,164 @@
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import math
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import torch
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from torch import einsum
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from ldm.util import default
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from einops import rearrange
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# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
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def split_cross_attention_forward_v1(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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k = self.to_k(context)
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v = self.to_v(context)
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del context, x
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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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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
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for i in range(0, q.shape[0], 2):
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end = i + 2
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s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
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s1 *= self.scale
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s2 = s1.softmax(dim=-1)
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del s1
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r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
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del s2
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r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
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del r1
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return self.to_out(r2)
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# taken from https://github.com/Doggettx/stable-diffusion
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def split_cross_attention_forward(self, x, context=None, mask=None):
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h = self.heads
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q_in = self.to_q(x)
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context = default(context, x)
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k_in = self.to_k(context) * self.scale
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v_in = self.to_v(context)
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del context, x
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
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del q_in, k_in, v_in
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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stats = torch.cuda.memory_stats(q.device)
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mem_active = stats['active_bytes.all.current']
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mem_reserved = stats['reserved_bytes.all.current']
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mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
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modifier = 3 if q.element_size() == 2 else 2.5
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mem_required = tensor_size * modifier
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steps = 1
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if mem_required > mem_free_total:
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steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
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# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
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# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
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if steps > 64:
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max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
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raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
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f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
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s2 = s1.softmax(dim=-1, dtype=q.dtype)
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del s1
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r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
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del s2
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del q, k, v
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r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
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del r1
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return self.to_out(r2)
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def nonlinearity_hijack(x):
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# swish
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t = torch.sigmoid(x)
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x *= t
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del t
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return x
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def cross_attention_attnblock_forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q1 = self.q(h_)
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k1 = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q1.shape
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q2 = q1.reshape(b, c, h*w)
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del q1
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q = q2.permute(0, 2, 1) # b,hw,c
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del q2
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k = k1.reshape(b, c, h*w) # b,c,hw
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del k1
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h_ = torch.zeros_like(k, device=q.device)
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stats = torch.cuda.memory_stats(q.device)
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mem_active = stats['active_bytes.all.current']
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mem_reserved = stats['reserved_bytes.all.current']
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mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
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mem_required = tensor_size * 2.5
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steps = 1
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if mem_required > mem_free_total:
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steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
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w2 = w1 * (int(c)**(-0.5))
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del w1
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w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
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del w2
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# attend to values
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v1 = v.reshape(b, c, h*w)
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w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
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del w3
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h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
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del v1, w4
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h2 = h_.reshape(b, c, h, w)
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del h_
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h3 = self.proj_out(h2)
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del h2
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h3 += x
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return h3
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@ -0,0 +1,76 @@
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import os
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import numpy as np
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import PIL
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import torch
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from PIL import Image
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from torch.utils.data import Dataset
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from torchvision import transforms
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import random
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import tqdm
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class PersonalizedBase(Dataset):
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def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
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self.placeholder_token = placeholder_token
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self.size = size
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self.width = width
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self.height = height
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self.flip = transforms.RandomHorizontalFlip(p=flip_p)
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self.dataset = []
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with open(template_file, "r") as file:
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lines = [x.strip() for x in file.readlines()]
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self.lines = lines
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assert data_root, 'dataset directory not specified'
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self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
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print("Preparing dataset...")
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for path in tqdm.tqdm(self.image_paths):
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image = Image.open(path)
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image = image.convert('RGB')
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image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
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filename = os.path.basename(path)
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filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')
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filename_tokens = [token for token in filename_tokens if token.isalpha()]
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npimage = np.array(image).astype(np.uint8)
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npimage = (npimage / 127.5 - 1.0).astype(np.float32)
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torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
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torchdata = torch.moveaxis(torchdata, 2, 0)
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init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
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self.dataset.append((init_latent, filename_tokens))
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self.length = len(self.dataset) * repeats
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self.initial_indexes = np.arange(self.length) % len(self.dataset)
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self.indexes = None
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self.shuffle()
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def shuffle(self):
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self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
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def __len__(self):
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return self.length
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def __getitem__(self, i):
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if i % len(self.dataset) == 0:
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self.shuffle()
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index = self.indexes[i % len(self.indexes)]
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x, filename_tokens = self.dataset[index]
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text = random.choice(self.lines)
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text = text.replace("[name]", self.placeholder_token)
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text = text.replace("[filewords]", ' '.join(filename_tokens))
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return x, text
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@ -0,0 +1,258 @@
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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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import tqdm
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import html
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import datetime
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from modules import shared, devices, sd_hijack, processing
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import modules.textual_inversion.dataset
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class Embedding:
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def __init__(self, vec, name, step=None):
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self.vec = vec
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self.name = name
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self.step = step
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self.cached_checksum = None
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def save(self, filename):
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embedding_data = {
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"string_to_token": {"*": 265},
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"string_to_param": {"*": self.vec},
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"name": self.name,
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"step": self.step,
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}
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torch.save(embedding_data, filename)
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def checksum(self):
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if self.cached_checksum is not None:
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return self.cached_checksum
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def const_hash(a):
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r = 0
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for v in a:
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r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
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return r
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self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
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return self.cached_checksum
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class EmbeddingDatabase:
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def __init__(self, embeddings_dir):
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self.ids_lookup = {}
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self.word_embeddings = {}
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self.dir_mtime = None
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self.embeddings_dir = embeddings_dir
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def register_embedding(self, embedding, model):
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self.word_embeddings[embedding.name] = embedding
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ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0]
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first_id = ids[0]
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if first_id not in self.ids_lookup:
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self.ids_lookup[first_id] = []
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self.ids_lookup[first_id].append((ids, embedding))
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return embedding
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def load_textual_inversion_embeddings(self):
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mt = os.path.getmtime(self.embeddings_dir)
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if self.dir_mtime is not None and mt <= self.dir_mtime:
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return
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self.dir_mtime = mt
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self.ids_lookup.clear()
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self.word_embeddings.clear()
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def process_file(path, filename):
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name = os.path.splitext(filename)[0]
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data = torch.load(path, map_location="cpu")
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# textual inversion embeddings
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if 'string_to_param' in data:
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param_dict = data['string_to_param']
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if hasattr(param_dict, '_parameters'):
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param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
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assert len(param_dict) == 1, 'embedding file has multiple terms in it'
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emb = next(iter(param_dict.items()))[1]
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# diffuser concepts
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elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
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assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
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emb = next(iter(data.values()))
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if len(emb.shape) == 1:
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emb = emb.unsqueeze(0)
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else:
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raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
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vec = emb.detach().to(devices.device, dtype=torch.float32)
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embedding = Embedding(vec, name)
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embedding.step = data.get('step', None)
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self.register_embedding(embedding, shared.sd_model)
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for fn in os.listdir(self.embeddings_dir):
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try:
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fullfn = os.path.join(self.embeddings_dir, fn)
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if os.stat(fullfn).st_size == 0:
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continue
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process_file(fullfn, fn)
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except Exception:
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print(f"Error loading emedding {fn}:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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continue
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print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
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def find_embedding_at_position(self, tokens, offset):
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token = tokens[offset]
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possible_matches = self.ids_lookup.get(token, None)
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if possible_matches is None:
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return None
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for ids, embedding in possible_matches:
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if tokens[offset:offset + len(ids)] == ids:
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return embedding
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return None
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def create_embedding(name, num_vectors_per_token):
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init_text = '*'
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cond_model = shared.sd_model.cond_stage_model
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embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
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ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
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embedded = embedding_layer(ids.to(devices.device)).squeeze(0)
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vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
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for i in range(num_vectors_per_token):
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vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
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fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
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assert not os.path.exists(fn), f"file {fn} already exists"
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embedding = Embedding(vec, name)
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embedding.step = 0
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embedding.save(fn)
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return fn
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def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
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assert embedding_name, 'embedding not selected'
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shared.state.textinfo = "Initializing textual inversion training..."
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shared.state.job_count = steps
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filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
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log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name)
|
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|
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if save_embedding_every > 0:
|
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embedding_dir = os.path.join(log_directory, "embeddings")
|
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os.makedirs(embedding_dir, exist_ok=True)
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else:
|
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embedding_dir = None
|
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|
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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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|
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cond_model = shared.sd_model.cond_stage_model
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|
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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=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
|
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|
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hijack = sd_hijack.model_hijack
|
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|
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embedding = hijack.embedding_db.word_embeddings[embedding_name]
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embedding.vec.requires_grad = True
|
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|
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optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
|
||||
|
||||
losses = torch.zeros((32,))
|
||||
|
||||
last_saved_file = "<none>"
|
||||
last_saved_image = "<none>"
|
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|
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ititial_step = embedding.step or 0
|
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if ititial_step > steps:
|
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return embedding, filename
|
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|
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pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
|
||||
for i, (x, text) in pbar:
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embedding.step = i + ititial_step
|
||||
|
||||
if embedding.step > steps:
|
||||
break
|
||||
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
|
||||
with torch.autocast("cuda"):
|
||||
c = cond_model([text])
|
||||
loss = shared.sd_model(x.unsqueeze(0), c)[0]
|
||||
|
||||
losses[embedding.step % losses.shape[0]] = loss.item()
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
pbar.set_description(f"loss: {losses.mean():.7f}")
|
||||
|
||||
if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
|
||||
last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
|
||||
embedding.save(last_saved_file)
|
||||
|
||||
if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
|
||||
last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
|
||||
|
||||
p = processing.StableDiffusionProcessingTxt2Img(
|
||||
sd_model=shared.sd_model,
|
||||
prompt=text,
|
||||
steps=20,
|
||||
do_not_save_grid=True,
|
||||
do_not_save_samples=True,
|
||||
)
|
||||
|
||||
processed = processing.process_images(p)
|
||||
image = processed.images[0]
|
||||
|
||||
shared.state.current_image = image
|
||||
image.save(last_saved_image)
|
||||
|
||||
last_saved_image += f", prompt: {text}"
|
||||
|
||||
shared.state.job_no = embedding.step
|
||||
|
||||
shared.state.textinfo = f"""
|
||||
<p>
|
||||
Loss: {losses.mean():.7f}<br/>
|
||||
Step: {embedding.step}<br/>
|
||||
Last prompt: {html.escape(text)}<br/>
|
||||
Last saved embedding: {html.escape(last_saved_file)}<br/>
|
||||
Last saved image: {html.escape(last_saved_image)}<br/>
|
||||
</p>
|
||||
"""
|
||||
|
||||
embedding.cached_checksum = None
|
||||
embedding.save(filename)
|
||||
|
||||
return embedding, filename
|
||||
|
||||
@ -0,0 +1,32 @@
|
||||
import html
|
||||
|
||||
import gradio as gr
|
||||
|
||||
import modules.textual_inversion.textual_inversion as ti
|
||||
from modules import sd_hijack, shared
|
||||
|
||||
|
||||
def create_embedding(name, nvpt):
|
||||
filename = ti.create_embedding(name, nvpt)
|
||||
|
||||
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
|
||||
|
||||
return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
|
||||
|
||||
|
||||
def train_embedding(*args):
|
||||
|
||||
try:
|
||||
sd_hijack.undo_optimizations()
|
||||
|
||||
embedding, filename = ti.train_embedding(*args)
|
||||
|
||||
res = f"""
|
||||
Training {'interrupted' if shared.state.interrupted else 'finished'} after {embedding.step} steps.
|
||||
Embedding saved to {html.escape(filename)}
|
||||
"""
|
||||
return res, ""
|
||||
except Exception:
|
||||
raise
|
||||
finally:
|
||||
sd_hijack.apply_optimizations()
|
||||
@ -0,0 +1,19 @@
|
||||
a painting, art by [name]
|
||||
a rendering, art by [name]
|
||||
a cropped painting, art by [name]
|
||||
the painting, art by [name]
|
||||
a clean painting, art by [name]
|
||||
a dirty painting, art by [name]
|
||||
a dark painting, art by [name]
|
||||
a picture, art by [name]
|
||||
a cool painting, art by [name]
|
||||
a close-up painting, art by [name]
|
||||
a bright painting, art by [name]
|
||||
a cropped painting, art by [name]
|
||||
a good painting, art by [name]
|
||||
a close-up painting, art by [name]
|
||||
a rendition, art by [name]
|
||||
a nice painting, art by [name]
|
||||
a small painting, art by [name]
|
||||
a weird painting, art by [name]
|
||||
a large painting, art by [name]
|
||||
@ -0,0 +1,19 @@
|
||||
a painting of [filewords], art by [name]
|
||||
a rendering of [filewords], art by [name]
|
||||
a cropped painting of [filewords], art by [name]
|
||||
the painting of [filewords], art by [name]
|
||||
a clean painting of [filewords], art by [name]
|
||||
a dirty painting of [filewords], art by [name]
|
||||
a dark painting of [filewords], art by [name]
|
||||
a picture of [filewords], art by [name]
|
||||
a cool painting of [filewords], art by [name]
|
||||
a close-up painting of [filewords], art by [name]
|
||||
a bright painting of [filewords], art by [name]
|
||||
a cropped painting of [filewords], art by [name]
|
||||
a good painting of [filewords], art by [name]
|
||||
a close-up painting of [filewords], art by [name]
|
||||
a rendition of [filewords], art by [name]
|
||||
a nice painting of [filewords], art by [name]
|
||||
a small painting of [filewords], art by [name]
|
||||
a weird painting of [filewords], art by [name]
|
||||
a large painting of [filewords], art by [name]
|
||||
@ -0,0 +1,27 @@
|
||||
a photo of a [name]
|
||||
a rendering of a [name]
|
||||
a cropped photo of the [name]
|
||||
the photo of a [name]
|
||||
a photo of a clean [name]
|
||||
a photo of a dirty [name]
|
||||
a dark photo of the [name]
|
||||
a photo of my [name]
|
||||
a photo of the cool [name]
|
||||
a close-up photo of a [name]
|
||||
a bright photo of the [name]
|
||||
a cropped photo of a [name]
|
||||
a photo of the [name]
|
||||
a good photo of the [name]
|
||||
a photo of one [name]
|
||||
a close-up photo of the [name]
|
||||
a rendition of the [name]
|
||||
a photo of the clean [name]
|
||||
a rendition of a [name]
|
||||
a photo of a nice [name]
|
||||
a good photo of a [name]
|
||||
a photo of the nice [name]
|
||||
a photo of the small [name]
|
||||
a photo of the weird [name]
|
||||
a photo of the large [name]
|
||||
a photo of a cool [name]
|
||||
a photo of a small [name]
|
||||
@ -0,0 +1,27 @@
|
||||
a photo of a [name], [filewords]
|
||||
a rendering of a [name], [filewords]
|
||||
a cropped photo of the [name], [filewords]
|
||||
the photo of a [name], [filewords]
|
||||
a photo of a clean [name], [filewords]
|
||||
a photo of a dirty [name], [filewords]
|
||||
a dark photo of the [name], [filewords]
|
||||
a photo of my [name], [filewords]
|
||||
a photo of the cool [name], [filewords]
|
||||
a close-up photo of a [name], [filewords]
|
||||
a bright photo of the [name], [filewords]
|
||||
a cropped photo of a [name], [filewords]
|
||||
a photo of the [name], [filewords]
|
||||
a good photo of the [name], [filewords]
|
||||
a photo of one [name], [filewords]
|
||||
a close-up photo of the [name], [filewords]
|
||||
a rendition of the [name], [filewords]
|
||||
a photo of the clean [name], [filewords]
|
||||
a rendition of a [name], [filewords]
|
||||
a photo of a nice [name], [filewords]
|
||||
a good photo of a [name], [filewords]
|
||||
a photo of the nice [name], [filewords]
|
||||
a photo of the small [name], [filewords]
|
||||
a photo of the weird [name], [filewords]
|
||||
a photo of the large [name], [filewords]
|
||||
a photo of a cool [name], [filewords]
|
||||
a photo of a small [name], [filewords]
|
||||
Loading…
Reference in New Issue