Merge branch 'master' into stable
Jairo Correa
3 years ago
commit
ad0cc85d1f
@ -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 +1,90 @@
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import os.path
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import sys
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import traceback
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import PIL.Image
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import numpy as np
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import torch
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from basicsr.utils.download_util import load_file_from_url
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import modules.upscaler
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from modules import shared, modelloader
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from modules.paths import models_path
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from modules.scunet_model_arch import SCUNet as net
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class UpscalerScuNET(modules.upscaler.Upscaler):
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def __init__(self, dirname):
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self.name = "ScuNET"
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self.model_path = os.path.join(models_path, self.name)
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self.model_name = "ScuNET GAN"
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self.model_name2 = "ScuNET PSNR"
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self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
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self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
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self.user_path = dirname
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super().__init__()
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model_paths = self.find_models(ext_filter=[".pth"])
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scalers = []
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add_model2 = True
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for file in model_paths:
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if "http" in file:
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name = self.model_name
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else:
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name = modelloader.friendly_name(file)
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if name == self.model_name2 or file == self.model_url2:
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add_model2 = False
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try:
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scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
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scalers.append(scaler_data)
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except Exception:
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print(f"Error loading ScuNET model: {file}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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if add_model2:
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scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
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scalers.append(scaler_data2)
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self.scalers = scalers
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def do_upscale(self, img: PIL.Image, selected_file):
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torch.cuda.empty_cache()
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model = self.load_model(selected_file)
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if model is None:
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return img
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device = shared.device
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img = np.array(img)
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img = img[:, :, ::-1]
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img = np.moveaxis(img, 2, 0) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(shared.device)
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img = img.to(device)
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with torch.no_grad():
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output = model(img)
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output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
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output = 255. * np.moveaxis(output, 0, 2)
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output = output.astype(np.uint8)
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output = output[:, :, ::-1]
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torch.cuda.empty_cache()
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return PIL.Image.fromarray(output, 'RGB')
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def load_model(self, path: str):
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device = shared.device
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if "http" in path:
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filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
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progress=True)
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else:
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filename = path
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if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
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print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
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return None
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model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
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model.load_state_dict(torch.load(filename), strict=True)
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model.eval()
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for k, v in model.named_parameters():
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v.requires_grad = False
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model = model.to(device)
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return model
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@ -0,0 +1,265 @@
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# -*- coding: utf-8 -*-
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import numpy as np
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import torch
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import torch.nn as nn
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from einops import rearrange
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from einops.layers.torch import Rearrange
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from timm.models.layers import trunc_normal_, DropPath
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class WMSA(nn.Module):
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""" Self-attention module in Swin Transformer
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"""
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def __init__(self, input_dim, output_dim, head_dim, window_size, type):
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super(WMSA, self).__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.head_dim = head_dim
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self.scale = self.head_dim ** -0.5
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self.n_heads = input_dim // head_dim
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self.window_size = window_size
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self.type = type
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self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
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self.relative_position_params = nn.Parameter(
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torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
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self.linear = nn.Linear(self.input_dim, self.output_dim)
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trunc_normal_(self.relative_position_params, std=.02)
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self.relative_position_params = torch.nn.Parameter(
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self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
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2).transpose(
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0, 1))
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def generate_mask(self, h, w, p, shift):
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""" generating the mask of SW-MSA
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Args:
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shift: shift parameters in CyclicShift.
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Returns:
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attn_mask: should be (1 1 w p p),
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"""
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# supporting sqaure.
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attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
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if self.type == 'W':
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return attn_mask
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s = p - shift
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attn_mask[-1, :, :s, :, s:, :] = True
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attn_mask[-1, :, s:, :, :s, :] = True
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attn_mask[:, -1, :, :s, :, s:] = True
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attn_mask[:, -1, :, s:, :, :s] = True
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attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
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return attn_mask
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def forward(self, x):
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""" Forward pass of Window Multi-head Self-attention module.
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Args:
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x: input tensor with shape of [b h w c];
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attn_mask: attention mask, fill -inf where the value is True;
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Returns:
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output: tensor shape [b h w c]
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"""
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if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
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x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
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h_windows = x.size(1)
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w_windows = x.size(2)
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# sqaure validation
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# assert h_windows == w_windows
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x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
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qkv = self.embedding_layer(x)
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q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
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sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
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# Adding learnable relative embedding
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sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
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# Using Attn Mask to distinguish different subwindows.
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if self.type != 'W':
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attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
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sim = sim.masked_fill_(attn_mask, float("-inf"))
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probs = nn.functional.softmax(sim, dim=-1)
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output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
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output = rearrange(output, 'h b w p c -> b w p (h c)')
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output = self.linear(output)
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output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
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if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
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dims=(1, 2))
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return output
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def relative_embedding(self):
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cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
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relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
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# negative is allowed
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return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
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class Block(nn.Module):
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def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
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""" SwinTransformer Block
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"""
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super(Block, self).__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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assert type in ['W', 'SW']
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self.type = type
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if input_resolution <= window_size:
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self.type = 'W'
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self.ln1 = nn.LayerNorm(input_dim)
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self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.ln2 = nn.LayerNorm(input_dim)
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self.mlp = nn.Sequential(
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nn.Linear(input_dim, 4 * input_dim),
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nn.GELU(),
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nn.Linear(4 * input_dim, output_dim),
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)
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def forward(self, x):
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x = x + self.drop_path(self.msa(self.ln1(x)))
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x = x + self.drop_path(self.mlp(self.ln2(x)))
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return x
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class ConvTransBlock(nn.Module):
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def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
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""" SwinTransformer and Conv Block
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"""
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super(ConvTransBlock, self).__init__()
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self.conv_dim = conv_dim
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self.trans_dim = trans_dim
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self.head_dim = head_dim
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self.window_size = window_size
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self.drop_path = drop_path
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self.type = type
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self.input_resolution = input_resolution
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assert self.type in ['W', 'SW']
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if self.input_resolution <= self.window_size:
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self.type = 'W'
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self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
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self.type, self.input_resolution)
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self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
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self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
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self.conv_block = nn.Sequential(
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nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
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nn.ReLU(True),
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nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
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)
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def forward(self, x):
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conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
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conv_x = self.conv_block(conv_x) + conv_x
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trans_x = Rearrange('b c h w -> b h w c')(trans_x)
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trans_x = self.trans_block(trans_x)
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trans_x = Rearrange('b h w c -> b c h w')(trans_x)
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res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
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x = x + res
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return x
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class SCUNet(nn.Module):
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# def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
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def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
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super(SCUNet, self).__init__()
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if config is None:
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config = [2, 2, 2, 2, 2, 2, 2]
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self.config = config
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self.dim = dim
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self.head_dim = 32
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self.window_size = 8
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# drop path rate for each layer
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
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self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
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begin = 0
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self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution)
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for i in range(config[0])] + \
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[nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
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begin += config[0]
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self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution // 2)
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for i in range(config[1])] + \
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[nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
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begin += config[1]
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self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution // 4)
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for i in range(config[2])] + \
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[nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
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begin += config[2]
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self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution // 8)
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for i in range(config[3])]
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begin += config[3]
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self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
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[ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution // 4)
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for i in range(config[4])]
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begin += config[4]
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self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
|
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[ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution // 2)
|
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for i in range(config[5])]
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begin += config[5]
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self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
|
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[ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
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'W' if not i % 2 else 'SW', input_resolution)
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for i in range(config[6])]
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self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
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self.m_head = nn.Sequential(*self.m_head)
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self.m_down1 = nn.Sequential(*self.m_down1)
|
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self.m_down2 = nn.Sequential(*self.m_down2)
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self.m_down3 = nn.Sequential(*self.m_down3)
|
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self.m_body = nn.Sequential(*self.m_body)
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self.m_up3 = nn.Sequential(*self.m_up3)
|
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self.m_up2 = nn.Sequential(*self.m_up2)
|
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self.m_up1 = nn.Sequential(*self.m_up1)
|
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self.m_tail = nn.Sequential(*self.m_tail)
|
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# self.apply(self._init_weights)
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|
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def forward(self, x0):
|
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|
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h, w = x0.size()[-2:]
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paddingBottom = int(np.ceil(h / 64) * 64 - h)
|
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paddingRight = int(np.ceil(w / 64) * 64 - w)
|
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x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
|
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|
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x1 = self.m_head(x0)
|
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x2 = self.m_down1(x1)
|
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x3 = self.m_down2(x2)
|
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x4 = self.m_down3(x3)
|
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x = self.m_body(x4)
|
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x = self.m_up3(x + x4)
|
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x = self.m_up2(x + x3)
|
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x = self.m_up1(x + x2)
|
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x = self.m_tail(x + x1)
|
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|
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x = x[..., :h, :w]
|
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|
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return x
|
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|
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def _init_weights(self, m):
|
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if isinstance(m, nn.Linear):
|
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trunc_normal_(m.weight, std=.02)
|
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if m.bias is not None:
|
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nn.init.constant_(m.bias, 0)
|
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elif isinstance(m, nn.LayerNorm):
|
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nn.init.constant_(m.bias, 0)
|
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nn.init.constant_(m.weight, 1.0)
|
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@ -0,0 +1,156 @@
|
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import math
|
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import torch
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from torch import einsum
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|
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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
|
||||
|
||||
q = self.to_q(x)
|
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context = default(context, x)
|
||||
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))
|
||||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
|
||||
for i in range(0, q.shape[0], 2):
|
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end = i + 2
|
||||
s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
|
||||
s1 *= self.scale
|
||||
|
||||
s2 = s1.softmax(dim=-1)
|
||||
del s1
|
||||
|
||||
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
|
||||
del s2
|
||||
|
||||
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
||||
del r1
|
||||
|
||||
return self.to_out(r2)
|
||||
|
||||
|
||||
# taken from https://github.com/Doggettx/stable-diffusion
|
||||
def split_cross_attention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k_in = self.to_k(context) * self.scale
|
||||
v_in = self.to_v(context)
|
||||
del context, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
||||
|
||||
stats = torch.cuda.memory_stats(q.device)
|
||||
mem_active = stats['active_bytes.all.current']
|
||||
mem_reserved = stats['reserved_bytes.all.current']
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
||||
mem_free_torch = mem_reserved - mem_active
|
||||
mem_free_total = mem_free_cuda + mem_free_torch
|
||||
|
||||
gb = 1024 ** 3
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
|
||||
modifier = 3 if q.element_size() == 2 else 2.5
|
||||
mem_required = tensor_size * modifier
|
||||
steps = 1
|
||||
|
||||
if mem_required > mem_free_total:
|
||||
steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
|
||||
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
|
||||
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
|
||||
|
||||
if steps > 64:
|
||||
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
|
||||
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
|
||||
f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
|
||||
|
||||
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
|
||||
|
||||
s2 = s1.softmax(dim=-1, dtype=q.dtype)
|
||||
del s1
|
||||
|
||||
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
|
||||
del s2
|
||||
|
||||
del q, k, v
|
||||
|
||||
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
||||
del r1
|
||||
|
||||
return self.to_out(r2)
|
||||
|
||||
def cross_attention_attnblock_forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q1 = self.q(h_)
|
||||
k1 = self.k(h_)
|
||||
v = self.v(h_)
|
||||
|
||||
# compute attention
|
||||
b, c, h, w = q1.shape
|
||||
|
||||
q2 = q1.reshape(b, c, h*w)
|
||||
del q1
|
||||
|
||||
q = q2.permute(0, 2, 1) # b,hw,c
|
||||
del q2
|
||||
|
||||
k = k1.reshape(b, c, h*w) # b,c,hw
|
||||
del k1
|
||||
|
||||
h_ = torch.zeros_like(k, device=q.device)
|
||||
|
||||
stats = torch.cuda.memory_stats(q.device)
|
||||
mem_active = stats['active_bytes.all.current']
|
||||
mem_reserved = stats['reserved_bytes.all.current']
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
||||
mem_free_torch = mem_reserved - mem_active
|
||||
mem_free_total = mem_free_cuda + mem_free_torch
|
||||
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
|
||||
mem_required = tensor_size * 2.5
|
||||
steps = 1
|
||||
|
||||
if mem_required > mem_free_total:
|
||||
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
|
||||
|
||||
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
|
||||
w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
|
||||
w2 = w1 * (int(c)**(-0.5))
|
||||
del w1
|
||||
w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
|
||||
del w2
|
||||
|
||||
# attend to values
|
||||
v1 = v.reshape(b, c, h*w)
|
||||
w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
|
||||
del w3
|
||||
|
||||
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]
|
||||
del v1, w4
|
||||
|
||||
h2 = h_.reshape(b, c, h, w)
|
||||
del h_
|
||||
|
||||
h3 = self.proj_out(h2)
|
||||
del h2
|
||||
|
||||
h3 += x
|
||||
|
||||
return h3
|
||||
@ -0,0 +1,78 @@
|
||||
import os
|
||||
import numpy as np
|
||||
import PIL
|
||||
import torch
|
||||
from PIL import Image
|
||||
from torch.utils.data import Dataset
|
||||
from torchvision import transforms
|
||||
|
||||
import random
|
||||
import tqdm
|
||||
from modules import devices
|
||||
|
||||
|
||||
class PersonalizedBase(Dataset):
|
||||
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):
|
||||
|
||||
self.placeholder_token = placeholder_token
|
||||
|
||||
self.size = size
|
||||
self.width = width
|
||||
self.height = height
|
||||
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
|
||||
|
||||
self.dataset = []
|
||||
|
||||
with open(template_file, "r") as file:
|
||||
lines = [x.strip() for x in file.readlines()]
|
||||
|
||||
self.lines = lines
|
||||
|
||||
assert data_root, 'dataset directory not specified'
|
||||
|
||||
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
|
||||
print("Preparing dataset...")
|
||||
for path in tqdm.tqdm(self.image_paths):
|
||||
image = Image.open(path)
|
||||
image = image.convert('RGB')
|
||||
image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
|
||||
|
||||
filename = os.path.basename(path)
|
||||
filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')
|
||||
filename_tokens = [token for token in filename_tokens if token.isalpha()]
|
||||
|
||||
npimage = np.array(image).astype(np.uint8)
|
||||
npimage = (npimage / 127.5 - 1.0).astype(np.float32)
|
||||
|
||||
torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
|
||||
torchdata = torch.moveaxis(torchdata, 2, 0)
|
||||
|
||||
init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
|
||||
init_latent = init_latent.to(devices.cpu)
|
||||
|
||||
self.dataset.append((init_latent, filename_tokens))
|
||||
|
||||
self.length = len(self.dataset) * repeats
|
||||
|
||||
self.initial_indexes = np.arange(self.length) % len(self.dataset)
|
||||
self.indexes = None
|
||||
self.shuffle()
|
||||
|
||||
def shuffle(self):
|
||||
self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
|
||||
|
||||
def __len__(self):
|
||||
return self.length
|
||||
|
||||
def __getitem__(self, i):
|
||||
if i % len(self.dataset) == 0:
|
||||
self.shuffle()
|
||||
|
||||
index = self.indexes[i % len(self.indexes)]
|
||||
x, filename_tokens = self.dataset[index]
|
||||
|
||||
text = random.choice(self.lines)
|
||||
text = text.replace("[name]", self.placeholder_token)
|
||||
text = text.replace("[filewords]", ' '.join(filename_tokens))
|
||||
|
||||
return x, text
|
||||
@ -0,0 +1,75 @@
|
||||
import os
|
||||
from PIL import Image, ImageOps
|
||||
import tqdm
|
||||
|
||||
from modules import shared, images
|
||||
|
||||
|
||||
def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
|
||||
size = 512
|
||||
src = os.path.abspath(process_src)
|
||||
dst = os.path.abspath(process_dst)
|
||||
|
||||
assert src != dst, 'same directory specified as source and desitnation'
|
||||
|
||||
os.makedirs(dst, exist_ok=True)
|
||||
|
||||
files = os.listdir(src)
|
||||
|
||||
shared.state.textinfo = "Preprocessing..."
|
||||
shared.state.job_count = len(files)
|
||||
|
||||
if process_caption:
|
||||
shared.interrogator.load()
|
||||
|
||||
def save_pic_with_caption(image, index):
|
||||
if process_caption:
|
||||
caption = "-" + shared.interrogator.generate_caption(image)
|
||||
else:
|
||||
caption = ""
|
||||
|
||||
image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
|
||||
subindex[0] += 1
|
||||
|
||||
def save_pic(image, index):
|
||||
save_pic_with_caption(image, index)
|
||||
|
||||
if process_flip:
|
||||
save_pic_with_caption(ImageOps.mirror(image), index)
|
||||
|
||||
for index, imagefile in enumerate(tqdm.tqdm(files)):
|
||||
subindex = [0]
|
||||
filename = os.path.join(src, imagefile)
|
||||
img = Image.open(filename).convert("RGB")
|
||||
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
|
||||
ratio = img.height / img.width
|
||||
is_tall = ratio > 1.35
|
||||
is_wide = ratio < 1 / 1.35
|
||||
|
||||
if process_split and is_tall:
|
||||
img = img.resize((size, size * img.height // img.width))
|
||||
|
||||
top = img.crop((0, 0, size, size))
|
||||
save_pic(top, index)
|
||||
|
||||
bot = img.crop((0, img.height - size, size, img.height))
|
||||
save_pic(bot, index)
|
||||
elif process_split and is_wide:
|
||||
img = img.resize((size * img.width // img.height, size))
|
||||
|
||||
left = img.crop((0, 0, size, size))
|
||||
save_pic(left, index)
|
||||
|
||||
right = img.crop((img.width - size, 0, img.width, size))
|
||||
save_pic(right, index)
|
||||
else:
|
||||
img = images.resize_image(1, img, size, size)
|
||||
save_pic(img, index)
|
||||
|
||||
shared.state.nextjob()
|
||||
|
||||
if process_caption:
|
||||
shared.interrogator.send_blip_to_ram()
|
||||
@ -0,0 +1,271 @@
|
||||
import os
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import torch
|
||||
import tqdm
|
||||
import html
|
||||
import datetime
|
||||
|
||||
|
||||
from modules import shared, devices, sd_hijack, processing, sd_models
|
||||
import modules.textual_inversion.dataset
|
||||
|
||||
|
||||
class Embedding:
|
||||
def __init__(self, vec, name, step=None):
|
||||
self.vec = vec
|
||||
self.name = name
|
||||
self.step = step
|
||||
self.cached_checksum = None
|
||||
self.sd_checkpoint = None
|
||||
self.sd_checkpoint_name = None
|
||||
|
||||
def save(self, filename):
|
||||
embedding_data = {
|
||||
"string_to_token": {"*": 265},
|
||||
"string_to_param": {"*": self.vec},
|
||||
"name": self.name,
|
||||
"step": self.step,
|
||||
"sd_checkpoint": self.sd_checkpoint,
|
||||
"sd_checkpoint_name": self.sd_checkpoint_name,
|
||||
}
|
||||
|
||||
torch.save(embedding_data, filename)
|
||||
|
||||
def checksum(self):
|
||||
if self.cached_checksum is not None:
|
||||
return self.cached_checksum
|
||||
|
||||
def const_hash(a):
|
||||
r = 0
|
||||
for v in a:
|
||||
r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
|
||||
return r
|
||||
|
||||
self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
|
||||
return self.cached_checksum
|
||||
|
||||
|
||||
class EmbeddingDatabase:
|
||||
def __init__(self, embeddings_dir):
|
||||
self.ids_lookup = {}
|
||||
self.word_embeddings = {}
|
||||
self.dir_mtime = None
|
||||
self.embeddings_dir = embeddings_dir
|
||||
|
||||
def register_embedding(self, embedding, model):
|
||||
|
||||
self.word_embeddings[embedding.name] = embedding
|
||||
|
||||
ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0]
|
||||
|
||||
first_id = ids[0]
|
||||
if first_id not in self.ids_lookup:
|
||||
self.ids_lookup[first_id] = []
|
||||
|
||||
self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)
|
||||
|
||||
return embedding
|
||||
|
||||
def load_textual_inversion_embeddings(self):
|
||||
mt = os.path.getmtime(self.embeddings_dir)
|
||||
if self.dir_mtime is not None and mt <= self.dir_mtime:
|
||||
return
|
||||
|
||||
self.dir_mtime = mt
|
||||
self.ids_lookup.clear()
|
||||
self.word_embeddings.clear()
|
||||
|
||||
def process_file(path, filename):
|
||||
name = os.path.splitext(filename)[0]
|
||||
|
||||
data = torch.load(path, map_location="cpu")
|
||||
|
||||
# textual inversion embeddings
|
||||
if 'string_to_param' in data:
|
||||
param_dict = data['string_to_param']
|
||||
if hasattr(param_dict, '_parameters'):
|
||||
param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
|
||||
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
|
||||
emb = next(iter(param_dict.items()))[1]
|
||||
# diffuser concepts
|
||||
elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
|
||||
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
|
||||
|
||||
emb = next(iter(data.values()))
|
||||
if len(emb.shape) == 1:
|
||||
emb = emb.unsqueeze(0)
|
||||
else:
|
||||
raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
|
||||
|
||||
vec = emb.detach().to(devices.device, dtype=torch.float32)
|
||||
embedding = Embedding(vec, name)
|
||||
embedding.step = data.get('step', None)
|
||||
embedding.sd_checkpoint = data.get('hash', None)
|
||||
embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
|
||||
self.register_embedding(embedding, shared.sd_model)
|
||||
|
||||
for fn in os.listdir(self.embeddings_dir):
|
||||
try:
|
||||
fullfn = os.path.join(self.embeddings_dir, fn)
|
||||
|
||||
if os.stat(fullfn).st_size == 0:
|
||||
continue
|
||||
|
||||
process_file(fullfn, fn)
|
||||
except Exception:
|
||||
print(f"Error loading emedding {fn}:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
continue
|
||||
|
||||
print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
|
||||
|
||||
def find_embedding_at_position(self, tokens, offset):
|
||||
token = tokens[offset]
|
||||
possible_matches = self.ids_lookup.get(token, None)
|
||||
|
||||
if possible_matches is None:
|
||||
return None, None
|
||||
|
||||
for ids, embedding in possible_matches:
|
||||
if tokens[offset:offset + len(ids)] == ids:
|
||||
return embedding, len(ids)
|
||||
|
||||
return None, None
|
||||
|
||||
|
||||
def create_embedding(name, num_vectors_per_token, init_text='*'):
|
||||
cond_model = shared.sd_model.cond_stage_model
|
||||
embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
|
||||
|
||||
ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
|
||||
embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
|
||||
vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
|
||||
|
||||
for i in range(num_vectors_per_token):
|
||||
vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
|
||||
|
||||
fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
|
||||
assert not os.path.exists(fn), f"file {fn} already exists"
|
||||
|
||||
embedding = Embedding(vec, name)
|
||||
embedding.step = 0
|
||||
embedding.save(fn)
|
||||
|
||||
return fn
|
||||
|
||||
|
||||
def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
|
||||
assert embedding_name, 'embedding not selected'
|
||||
|
||||
shared.state.textinfo = "Initializing textual inversion training..."
|
||||
shared.state.job_count = steps
|
||||
|
||||
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
|
||||
|
||||
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name)
|
||||
|
||||
if save_embedding_every > 0:
|
||||
embedding_dir = os.path.join(log_directory, "embeddings")
|
||||
os.makedirs(embedding_dir, exist_ok=True)
|
||||
else:
|
||||
embedding_dir = None
|
||||
|
||||
if create_image_every > 0:
|
||||
images_dir = os.path.join(log_directory, "images")
|
||||
os.makedirs(images_dir, exist_ok=True)
|
||||
else:
|
||||
images_dir = None
|
||||
|
||||
cond_model = shared.sd_model.cond_stage_model
|
||||
|
||||
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
|
||||
with torch.autocast("cuda"):
|
||||
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)
|
||||
|
||||
hijack = sd_hijack.model_hijack
|
||||
|
||||
embedding = hijack.embedding_db.word_embeddings[embedding_name]
|
||||
embedding.vec.requires_grad = True
|
||||
|
||||
optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
|
||||
|
||||
losses = torch.zeros((32,))
|
||||
|
||||
last_saved_file = "<none>"
|
||||
last_saved_image = "<none>"
|
||||
|
||||
ititial_step = embedding.step or 0
|
||||
if ititial_step > steps:
|
||||
return embedding, filename
|
||||
|
||||
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
|
||||
for i, (x, text) in pbar:
|
||||
embedding.step = i + ititial_step
|
||||
|
||||
if embedding.step > steps:
|
||||
break
|
||||
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
|
||||
with torch.autocast("cuda"):
|
||||
c = cond_model([text])
|
||||
|
||||
x = x.to(devices.device)
|
||||
loss = shared.sd_model(x.unsqueeze(0), c)[0]
|
||||
del x
|
||||
|
||||
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>
|
||||
"""
|
||||
|
||||
checkpoint = sd_models.select_checkpoint()
|
||||
|
||||
embedding.sd_checkpoint = checkpoint.hash
|
||||
embedding.sd_checkpoint_name = checkpoint.model_name
|
||||
embedding.cached_checksum = None
|
||||
embedding.save(filename)
|
||||
|
||||
return embedding, filename
|
||||
|
||||
@ -0,0 +1,40 @@
|
||||
import html
|
||||
|
||||
import gradio as gr
|
||||
|
||||
import modules.textual_inversion.textual_inversion
|
||||
import modules.textual_inversion.preprocess
|
||||
from modules import sd_hijack, shared
|
||||
|
||||
|
||||
def create_embedding(name, initialization_text, nvpt):
|
||||
filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)
|
||||
|
||||
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 preprocess(*args):
|
||||
modules.textual_inversion.preprocess.preprocess(*args)
|
||||
|
||||
return "Preprocessing finished.", ""
|
||||
|
||||
|
||||
def train_embedding(*args):
|
||||
|
||||
try:
|
||||
sd_hijack.undo_optimizations()
|
||||
|
||||
embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
|
||||
|
||||
res = f"""
|
||||
Training {'interrupted' if shared.state.interrupted else 'finished'} at {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