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Martin Kristiansen
2023-07-27 10:54:01 -04:00
parent 2183dba5c5
commit 218b6d0546
148 changed files with 5486 additions and 6296 deletions
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66
scripts/orig_scripts/img2img.py
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@@ -18,7 +18,7 @@ from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.invoke.devices import choose_torch_device
from ldm.invoke.devices import choose_torch_device
def chunk(it, size):
@@ -55,7 +55,7 @@ def load_img(path):
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1.
return 2.0 * image - 1.0
def main():
@@ -66,33 +66,24 @@ def main():
type=str,
nargs="?",
default="a painting of a virus monster playing guitar",
help="the prompt to render"
help="the prompt to render",
)
parser.add_argument(
"--init-img",
type=str,
nargs="?",
help="path to the input image"
)
parser.add_argument("--init-img", type=str, nargs="?", help="path to the input image")
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/img2img-samples"
"--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/img2img-samples"
)
parser.add_argument(
"--skip_grid",
action='store_true',
action="store_true",
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
"--skip_save",
action='store_true',
action="store_true",
help="do not save indiviual samples. For speed measurements.",
)
@@ -105,12 +96,12 @@ def main():
parser.add_argument(
"--plms",
action='store_true',
action="store_true",
help="use plms sampling",
)
parser.add_argument(
"--fixed_code",
action='store_true',
action="store_true",
help="if enabled, uses the same starting code across all samples ",
)
@@ -187,11 +178,7 @@ def main():
help="the seed (for reproducible sampling)",
)
parser.add_argument(
"--precision",
type=str,
help="evaluate at this precision",
choices=["full", "autocast"],
default="autocast"
"--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast"
)
opt = parser.parse_args()
@@ -232,18 +219,18 @@ def main():
assert os.path.isfile(opt.init_img)
init_image = load_img(opt.init_img).to(device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=opt.ddim_steps, ddim_eta=opt.ddim_eta, verbose=False)
assert 0. <= opt.strength <= 1., 'can only work with strength in [0.0, 1.0]'
assert 0.0 <= opt.strength <= 1.0, "can only work with strength in [0.0, 1.0]"
t_enc = int(opt.strength * opt.ddim_steps)
print(f"target t_enc is {t_enc} steps")
precision_scope = autocast if opt.precision == "autocast" else nullcontext
if device.type in ['mps', 'cpu']:
precision_scope = nullcontext # have to use f32 on mps
if device.type in ["mps", "cpu"]:
precision_scope = nullcontext # have to use f32 on mps
with torch.no_grad():
with precision_scope(device.type):
with model.ema_scope():
@@ -259,37 +246,42 @@ def main():
c = model.get_learned_conditioning(prompts)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * batch_size).to(device))
# decode it
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,)
samples = sampler.decode(
z_enc,
c,
t_enc,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
)
x_samples = model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
if not opt.skip_save:
for x_sample in x_samples:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
x_sample = 255.0 * 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"))
os.path.join(sample_path, f"{base_count:05}.png")
)
base_count += 1
all_samples.append(x_samples)
if not opt.skip_grid:
# additionally, save as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = rearrange(grid, "n b c h w -> (n b) c h w")
grid = make_grid(grid, nrow=n_rows)
# 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 = 255.0 * 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
toc = time.time()
print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
f" \nEnjoy.")
print(f"Your samples are ready and waiting for you here: \n{outpath} \n" f" \nEnjoy.")
if __name__ == "__main__":