127 lines
4.5 KiB
Python
127 lines
4.5 KiB
Python
import os
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import torch
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import torch.nn as nn
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import numpy as np
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import argparse
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import random
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from PIL import Image
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from tqdm import tqdm
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from safetensors.torch import save_file, load_file
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from upscaler import LatentUpscaler as Upscaler
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from vae import get_vae
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def parse_args():
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parser = argparse.ArgumentParser(description="Train latent interposer model")
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parser.add_argument("--steps", type=int, default=500000, help="No. of training steps")
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parser.add_argument('--bs', type=int, default=1, help="Batch size")
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parser.add_argument('--lr', default="1e-8", help="Learning rate")
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parser.add_argument("-n", "--save_every_n", type=int, dest="save", default=50000, help="Save model/sample periodically")
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parser.add_argument("-r", "--res", type=int, default=512, help="Source resolution")
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parser.add_argument("-f", "--fac", type=float, default=1.5, help="Upscale factor")
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parser.add_argument("-v", "--ver", choices=["v1","xl"], default="v1", help="SD version")
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parser.add_argument('--vae', help="Path to VAE (Optional)")
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parser.add_argument('--resume', help="Checkpoint to resume from")
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args = parser.parse_args()
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try:
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float(args.lr)
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except:
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parser.error("--lr must be a valid float eg. 0.001 or 1e-3")
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return args
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class Latent:
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def __init__(self, md5, ver, src_res, dst_res, dev):
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src = os.path.join(f"latents/{ver}_{src_res}px", f"{md5}.npy")
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dst = os.path.join(f"latents/{ver}_{dst_res}px", f"{md5}.npy")
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self.src = torch.from_numpy(np.load(src)).to(dev)
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self.dst = torch.from_numpy(np.load(dst)).to(dev)
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def load_latents(ver, src_res, dst_res, dev):
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print("Loading latents from disk")
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latents = []
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for i in tqdm(os.listdir(f"latents/{ver}_{src_res}px")):
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md5 = os.path.splitext(i)[0]
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latents.append(Latent(md5, ver, src_res, dst_res, dev))
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return latents
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vae = None
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def sample_decode(latent, filename, version):
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global vae
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if not vae:
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vae = get_vae(version, fp16=True)
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vae.to("cuda")
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latent = latent.half().to("cuda")
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out = vae.decode(latent).sample
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out = out.cpu().detach().numpy()
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out = np.squeeze(out, 0)
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out = out.transpose((1, 2, 0))
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out = np.clip(out, -1.0, 1.0)
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out = (out+1)/2 * 255
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out = out.astype(np.uint8)
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out = Image.fromarray(out)
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out.save(filename)
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if __name__ == "__main__":
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args = parse_args()
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target_dev = "cuda"
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dst_res = int(args.res*args.fac)
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latents = load_latents(args.ver, args.res, dst_res, target_dev)
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if not os.path.isdir("models"): os.mkdir("models")
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log = open(f"models/latent-upscaler_SD{args.ver}-x{args.fac}.csv", "w")
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if os.path.isfile(f"test_{args.ver}_{args.res}px.npy") and os.path.isfile(f"test_{args.ver}_{dst_res}px.npy"):
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ss_latent = torch.from_numpy(np.load(f"test_{args.ver}_{args.res}px.npy")).to(target_dev)
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st_latent = torch.from_numpy(np.load(f"test_{args.ver}_{dst_res}px.npy")).to(target_dev)
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else:
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sample_latent = random.choice(latents)
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ss_latent = sample_latent.src.to(target_dev)
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st_latent = sample_latent.dst.to(target_dev)
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model = Upscaler(args.fac)
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if args.resume:
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model.load_state_dict(load_file(args.resume))
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model.to(target_dev)
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criterion = torch.nn.MSELoss(size_average=False)
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optimizer = torch.optim.SGD(model.parameters(), lr=float(args.lr)/args.bs)
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for t in tqdm(range(int(args.steps/args.bs)), unit_scale=args.bs):
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step = t*args.bs
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# input batch
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lts = [random.choice(latents) for _ in range(args.bs)]
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src = torch.cat([x.src for x in lts],0)
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dst = torch.cat([x.dst for x in lts],0)
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y_pred = model(src) # forward
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loss = criterion(y_pred, dst) # loss
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# backward
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# print loss
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if step%1000 == 0:
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# test loss
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with torch.no_grad():
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t_pred = model(ss_latent)
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t_loss = criterion(t_pred, st_latent)
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tqdm.write(f"{step} - {loss.data.item()/args.bs:.2f}|{t_loss.data.item()/args.bs:.2f}")
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log.write(f"{step},{loss.data.item()/args.bs:.2f},{t_loss.data.item()/args.bs:.2f}\n")
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log.flush()
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# sample/save
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if step%args.save == 0:
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out = model(ss_latent)
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output_name = f"./models/latent-upscaler_SD{args.ver}-x{args.fac}_e{step/1000}k"
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sample_decode(out, f"{output_name}.png", args.ver)
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save_file(model.state_dict(), f"{output_name}.safetensors")
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# save final output
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output_name = f"./models/latent-upscaler_SD{args.ver}-x{args.fac}_e{step/1000}k"
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sample_decode(out, f"{output_name}.png", args.ver)
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save_file(model.state_dict(), f"{output_name}.safetensors")
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log.close()
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