Delete inversion

inversion/invert.py

@@ -1,87 +0,0 @@
-import sys
-import os 
-sys.path.append(os.path.abspath(os.path.join("", "..")))
-import torch
-import torchvision
-import warnings
-warnings.filterwarnings("ignore")
-from PIL import Image
-from lora_w2w import LoRAw2w
-from utils import load_models, inference, save_model_w2w, save_model_for_diffusers
-from inversion import invert
-import argparse
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--device", default="cuda:0", type=str)
-    parser.add_argument("--mean_path", default="/files/mean.pt", type=str, help="Path to file with parameter means")
-    parser.add_argument("--std_path", default="/files/std.pt", type=str, help="Path to file with parameter standard deviations.")
-    parser.add_argument("--v_path", default="/files/V.pt", type=str, help="Path to V orthogonal projection/unprojection matrix.")
-    parser.add_argument("--dim_path", default="/files/weight_dimensions.pt", type=str, help="Path to file with dimensions of LoRA layers. Used for saving in Diffusers pipeline format.")
-    parser.add_argument("--imfolder", default="/inversion/images/real_image/real/", type=str, help="Path to folder containing image.")
-    parser.add_argument("--mask_path", default=None, type=str, help="Path to mask file.")
-    parser.add_argument("--epochs", default=400, type=int)
-    parser.add_argument("--lr", default= 1e-1, type=float)
-    parser.add_argument("--weight_decay", default= 1e-10, type=float)
-    parser.add_argument("--dim", default= 10000, type=int, help="Number of principal component coefficients to optimize.")
-    parser.add_argument("--diffusers_format", default=False, action="store_true", help="Whether to save in mode that can be loaded in Diffusers pipeline")
-    parser.add_argument("--save_name", default="/files/inversion1.pt", type=str, help="Output path + filename.")
-
-
-
-    ### variables
-    args = parser.parse_args()
-    device = args.device
-    mean_path = args.mean_path
-    std_path = args.std_path
-    v_path = args.v_path
-    dim_path = args.dim_path
-    imfolder = args.imfolder
-    mask_path = args.mask_path
-    epochs = args.epochs
-    lr = args.lr
-    weight_decay = args.weight_decay
-    dim = args.dim
-    diffusers_format = args.diffusers_format
-    save_name = args.save_name
-
-
-    ### load models
-    unet, vae, text_encoder, tokenizer, noise_scheduler = load_models(device)
-
-    ### load files
-    mean = torch.load(mean_path).bfloat16().to(device)
-    std = torch.load(std_path).bfloat16().to(device)
-    v = torch.load(v_path).bfloat16().to(device)
-    weight_dimensions = torch.load(dim_path)
-
-    ### initialize network
-
-    proj = torch.zeros(1,dim).bfloat16().to(device)
-    network = LoRAw2w( proj, mean, std, v[:,:dim], 
-                        unet,
-                        rank=1,
-                        multiplier=1.0,
-                        alpha=27.0,
-                        train_method="xattn-strict"
-                    ).to(device, torch.bfloat16)
-    ### run inversion 
-    network = invert(network=network, unet=unet, vae=vae, 
-                     text_encoder=text_encoder, tokenizer=tokenizer, 
-                     prompt = "sks person", noise_scheduler = noise_scheduler, epochs=epochs, 
-                     image_path = imfolder, mask_path = mask_path, device = device)
-    
-    
-    ### save model
-
-    if diffusers_format:
-        save_model_for_diffusers(network,std, mean, v, weight_dimensions,
-                                path=save_name)
-    else: 
-        save_model_w2w(network, path=save_name)
-
-
-
-if __name__ == "__main__":
-    main()

inversion/run_inversion.sh

@@ -1,22 +0,0 @@
-python inversion/invert.py \
-    --device="cuda:0"  \
-    --mean_path="files/mean.pt" \
-    --std_path="files/std.pt" \
-    --v_path="files/V.pt" \
-    --std_path="files/std.pt" \
-    --dim_path="files/weight_dimensions.pt" \
-    --imfolder="inversion/images/real_image/real/" \
-    --mask_path="inversion/images/real_image/mask.png" \
-    --epochs=400 \
-    --lr=1e-1 \
-    --weight_decay=1e-10 \
-    --dim=10000 \
-    --save_name="files/real_inversion1.pt"
-
-
-
-
-            
-            
-            
-