Merge remote-tracking branch 'origin/main'
This commit is contained in:
Patrick Esser
commit
85868a5d34
4 changed files with 131 additions and 99 deletions
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@ -1,6 +1,7 @@
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"""make variations of input image"""
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import argparse, os, sys, glob
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import PIL
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import torch
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import numpy as np
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from omegaconf import OmegaConf
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@ -9,6 +10,8 @@ from tqdm import tqdm, trange
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from itertools import islice
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from einops import rearrange, repeat
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from torchvision.utils import make_grid
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from torch import autocast
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from contextlib import nullcontext
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import time
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from pytorch_lightning import seed_everything
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@ -43,8 +46,12 @@ def load_model_from_config(config, ckpt, verbose=False):
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def load_img(path):
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image = np.array(Image.open(path).convert("RGB"))
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image = image.astype(np.float32) / 255.0
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image = Image.open(path).convert("RGB")
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w, h = image.size
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print(f"loaded input image of size ({w}, {h}) from {path}")
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h), resample=PIL.Image.LANCZOS)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return 2.*image - 1.
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@ -119,20 +126,6 @@ def main():
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help="sample this often",
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)
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parser.add_argument(
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"--H",
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type=int,
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default=256,
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help="image height, in pixel space",
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)
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parser.add_argument(
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"--W",
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type=int,
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default=256,
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help="image width, in pixel space",
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)
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parser.add_argument(
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"--C",
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type=int,
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@ -149,7 +142,7 @@ def main():
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parser.add_argument(
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"--n_samples",
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type=int,
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default=8,
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default=2,
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help="how many samples to produce for each given prompt. A.k.a batch size",
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)
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@ -170,7 +163,7 @@ def main():
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parser.add_argument(
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"--strength",
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type=float,
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default=0.3,
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default=0.75,
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help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image",
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)
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@ -197,6 +190,14 @@ def main():
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default=42,
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help="the seed (for reproducible sampling)",
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)
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parser.add_argument(
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"--precision",
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type=str,
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help="evaluate at this precision",
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choices=["full", "autocast"],
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default="autocast"
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)
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opt = parser.parse_args()
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seed_everything(opt.seed)
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@ -244,51 +245,53 @@ def main():
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t_enc = int(opt.strength * opt.ddim_steps)
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print(f"target t_enc is {t_enc} steps")
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precision_scope = autocast if opt.precision == "autocast" else nullcontext
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with torch.no_grad():
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with model.ema_scope():
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tic = time.time()
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all_samples = list()
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for n in trange(opt.n_iter, desc="Sampling"):
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for prompts in tqdm(data, desc="data"):
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uc = None
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if opt.scale != 1.0:
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uc = model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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with precision_scope("cuda"):
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with model.ema_scope():
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tic = time.time()
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all_samples = list()
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for n in trange(opt.n_iter, desc="Sampling"):
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for prompts in tqdm(data, desc="data"):
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uc = None
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if opt.scale != 1.0:
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uc = model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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# encode (scaled latent)
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z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))
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# decode it
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samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
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unconditional_conditioning=uc,)
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# encode (scaled latent)
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z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))
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# decode it
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samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
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unconditional_conditioning=uc,)
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x_samples = model.decode_first_stage(samples)
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x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
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x_samples = model.decode_first_stage(samples)
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x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
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if not opt.skip_save:
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for x_sample in x_samples:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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Image.fromarray(x_sample.astype(np.uint8)).save(
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os.path.join(sample_path, f"{base_count:05}.png"))
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base_count += 1
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all_samples.append(x_samples)
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if not opt.skip_save:
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for x_sample in x_samples:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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Image.fromarray(x_sample.astype(np.uint8)).save(
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os.path.join(sample_path, f"{base_count:05}.png"))
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base_count += 1
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all_samples.append(x_samples)
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if not opt.skip_grid:
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# additionally, save as grid
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grid = torch.stack(all_samples, 0)
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grid = rearrange(grid, 'n b c h w -> (n b) c h w')
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grid = make_grid(grid, nrow=n_rows)
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if not opt.skip_grid:
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# additionally, save as grid
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grid = torch.stack(all_samples, 0)
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grid = rearrange(grid, 'n b c h w -> (n b) c h w')
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grid = make_grid(grid, nrow=n_rows)
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# to image
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grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
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Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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# to image
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grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
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Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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toc = time.time()
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toc = time.time()
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print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
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f"Sampling took {toc - tic}s, i.e. produced {opt.n_iter * opt.n_samples / (toc - tic):.2f} samples/sec."
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f"Sampling took {toc - tic}s, i.e., produced {opt.n_iter * opt.n_samples / (toc - tic):.2f} samples/sec."
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f" \nEnjoy.")
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@ -3,7 +3,7 @@ import torch
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import fire
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def prune_it(p):
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def prune_it(p, keep_only_ema=False):
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print(f"prunin' in path: {p}")
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size_initial = os.path.getsize(p)
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nsd = dict()
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@ -16,12 +16,30 @@ def prune_it(p):
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print(f"removing optimizer states for path {p}")
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if "global_step" in sd:
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print(f"This is global step {sd['global_step']}.")
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fn = f"{os.path.splitext(p)[0]}-pruned.ckpt"
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if keep_only_ema:
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sd = nsd["state_dict"].copy()
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# infer ema keys
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ema_keys = {k: "model_ema." + k[6:].replace(".", "") for k in sd.keys() if k.startswith("model.")}
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new_sd = dict()
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for k in sd:
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if k in ema_keys:
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new_sd[k] = sd[ema_keys[k]]
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elif not k.startswith("model_ema.") or k in ["model_ema.num_updates", "model_ema.decay"]:
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new_sd[k] = sd[k]
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assert len(new_sd) == len(sd) - len(ema_keys)
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nsd["state_dict"] = new_sd
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fn = f"{os.path.splitext(p)[0]}-pruned.ckpt" if not keep_only_ema else f"{os.path.splitext(p)[0]}-ema-pruned.ckpt"
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print(f"saving pruned checkpoint at: {fn}")
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torch.save(nsd, fn)
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newsize = os.path.getsize(fn)
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print(f"New ckpt size: {newsize*1e-9:.2f} GB. "
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f"Saved {(size_initial - newsize)*1e-9:.2f} GB by removing optimizer states")
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MSG = f"New ckpt size: {newsize*1e-9:.2f} GB. " + \
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f"Saved {(size_initial - newsize)*1e-9:.2f} GB by removing optimizer states"
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if keep_only_ema:
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MSG += " and non-EMA weights"
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print(MSG)
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if __name__ == "__main__":
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@ -9,6 +9,8 @@ from einops import rearrange
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from torchvision.utils import make_grid
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import time
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from pytorch_lightning import seed_everything
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from torch import autocast
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from contextlib import contextmanager, nullcontext
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from ldm.util import instantiate_from_config
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from ldm.models.diffusion.ddim import DDIMSampler
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@ -178,6 +180,13 @@ def main():
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default=42,
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help="the seed (for reproducible sampling)",
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)
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parser.add_argument(
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"--precision",
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type=str,
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help="evaluate at this precision",
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choices=["full", "autocast"],
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default="autocast"
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)
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opt = parser.parse_args()
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seed_everything(opt.seed)
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@ -217,53 +226,55 @@ def main():
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if opt.fixed_code:
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start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
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precision_scope = autocast if opt.precision=="autocast" else nullcontext
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with torch.no_grad():
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with model.ema_scope():
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tic = time.time()
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all_samples = list()
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for n in trange(opt.n_iter, desc="Sampling"):
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for prompts in tqdm(data, desc="data"):
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uc = None
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if opt.scale != 1.0:
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uc = model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
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samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
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conditioning=c,
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batch_size=opt.n_samples,
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shape=shape,
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verbose=False,
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unconditional_guidance_scale=opt.scale,
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unconditional_conditioning=uc,
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eta=opt.ddim_eta,
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dynamic_threshold=opt.dyn,
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x_T=start_code)
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with precision_scope("cuda"):
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with model.ema_scope():
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tic = time.time()
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all_samples = list()
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for n in trange(opt.n_iter, desc="Sampling"):
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for prompts in tqdm(data, desc="data"):
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uc = None
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if opt.scale != 1.0:
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uc = model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
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samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
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conditioning=c,
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batch_size=opt.n_samples,
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shape=shape,
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verbose=False,
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unconditional_guidance_scale=opt.scale,
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unconditional_conditioning=uc,
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eta=opt.ddim_eta,
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dynamic_threshold=opt.dyn,
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x_T=start_code)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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if not opt.skip_save:
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for x_sample in x_samples_ddim:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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Image.fromarray(x_sample.astype(np.uint8)).save(
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os.path.join(sample_path, f"{base_count:05}.png"))
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base_count += 1
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all_samples.append(x_samples_ddim)
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if not opt.skip_save:
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for x_sample in x_samples_ddim:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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Image.fromarray(x_sample.astype(np.uint8)).save(
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os.path.join(sample_path, f"{base_count:05}.png"))
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base_count += 1
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all_samples.append(x_samples_ddim)
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if not opt.skip_grid:
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# additionally, save as grid
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grid = torch.stack(all_samples, 0)
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grid = rearrange(grid, 'n b c h w -> (n b) c h w')
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grid = make_grid(grid, nrow=n_rows)
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if not opt.skip_grid:
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# additionally, save as grid
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grid = torch.stack(all_samples, 0)
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grid = rearrange(grid, 'n b c h w -> (n b) c h w')
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grid = make_grid(grid, nrow=n_rows)
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# to image
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grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
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Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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# to image
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grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
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Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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toc = time.time()
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toc = time.time()
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print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
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f"Sampling took {toc - tic}s, i.e. produced {opt.n_iter * opt.n_samples / (toc - tic):.2f} samples/sec."
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