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eval script

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rromb 2022-06-05 19:22:43 +02:00
parent cec5968820
commit 36b5177221
1 changed files with 79 additions and 35 deletions
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114
scripts/txt2img.py
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@ -4,6 +4,7 @@ import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
@ -12,6 +13,11 @@ from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
@ -51,7 +57,7 @@ if __name__ == "__main__":
parser.add_argument(
"--ddim_steps",
type=int,
default=200,
default=50,
help="number of ddim sampling steps",
)
@ -91,8 +97,8 @@ if __name__ == "__main__":
parser.add_argument(
"--n_samples",
type=int,
default=4,
help="how many samples to produce for the given prompt",
default=8,
help="how many samples to produce for each given prompt. A.k.a batch size",
)
parser.add_argument(
@ -101,11 +107,35 @@ if __name__ == "__main__":
default=5.0,
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
"--dyn",
type=float,
help="dynamic thresholding from Imagen, in latent space (TODO: try in pixel space with intermediate decode)",
)
parser.add_argument(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--config",
type=str,
default="logs/f8-kl-clip-encoder-256x256-run1/configs/2022-06-01T22-11-40-project.yaml",
help="path to config which constructs model",
)
parser.add_argument(
"--ckpt",
type=str,
default="logs/f8-kl-clip-encoder-256x256-run1/checkpoints/last.ckpt",
help="path to checkpoint of model",
)
opt = parser.parse_args()
config = OmegaConf.load("configs/latent-diffusion/txt2img-1p4B-eval.yaml") # TODO: Optionally download from same location as ckpt and chnage this logic
model = load_model_from_config(config, "models/ldm/text2img-large/model.ckpt") # TODO: check path
config = OmegaConf.load(f"{opt.config}")
model = load_model_from_config(config, f"{opt.ckpt}")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
@ -118,48 +148,62 @@ if __name__ == "__main__":
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
prompt = opt.prompt
batch_size = opt.n_samples
if not opt.from_file:
prompt = opt.prompt
assert prompt is not None
data = [batch_size * [prompt]]
else:
print(f"reading prompts from {opt.from_file}")
with open(opt.from_file, "r") as f:
data = f.read().splitlines()
data = list(chunk(data, batch_size))
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
all_samples=list()
with torch.no_grad():
with model.ema_scope():
uc = None
if opt.scale != 1.0:
uc = model.get_learned_conditioning(opt.n_samples * [""])
for n in trange(opt.n_iter, desc="Sampling"):
c = model.get_learned_conditioning(opt.n_samples * [prompt])
shape = [4, opt.H//8, opt.W//8]
samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
conditioning=c,
batch_size=opt.n_samples,
shape=shape,
verbose=False,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
eta=opt.ddim_eta)
all_samples = list()
for prompts in tqdm(data, desc="data"):
uc = None
if opt.scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [""])
c = model.get_learned_conditioning(prompts)
shape = [4, opt.H//8, opt.W//8]
samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
conditioning=c,
batch_size=opt.n_samples,
shape=shape,
verbose=False,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
eta=opt.ddim_eta,
dynamic_threshold=opt.dyn)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0)
for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:04}.png"))
base_count += 1
all_samples.append(x_samples_ddim)
for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1
all_samples.append(x_samples_ddim)
# additionally, save as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=opt.n_samples)
# additionally, save as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=opt.n_samples)
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'{prompt.replace(" ", "-")}.png'))
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
grid_count += 1
print(f"Your samples are ready and waiting four you here: \n{outpath} \nEnjoy.")
print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")