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Fix output issue

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harubaru 2022-08-20 11:17:34 -07:00 committed by GitHub
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1 changed files with 254 additions and 159 deletions
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scripts/txt2img.py
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@ -1,3 +1,5 @@
import PIL
import gradio as gr
import argparse, os, sys, glob
import torch
import numpy as np
@ -5,7 +7,7 @@ from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
from einops import rearrange, repeat
from torchvision.utils import make_grid
import time
from pytorch_lightning import seed_everything
@ -16,6 +18,71 @@ from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
parser = argparse.ArgumentParser()
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/img2img-samples"
)
parser.add_argument(
"--skip_grid",
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',
help="do not save indiviual samples. For speed measurements.",
)
parser.add_argument(
"--C",
type=int,
default=4,
help="latent channels",
)
parser.add_argument(
"--f",
type=int,
default=8,
help="downsampling factor, most often 8 or 16",
)
parser.add_argument(
"--n_rows",
type=int,
default=0,
help="rows in the grid (default: n_samples)",
)
parser.add_argument(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--config",
type=str,
default="configs/stable-diffusion/v1-inference.yaml",
help="path to config which constructs model",
)
parser.add_argument(
"--ckpt",
type=str,
default="models/ldm/stable-diffusion-v1/model.ckpt",
help="path to checkpoint of model",
)
parser.add_argument(
"--precision",
type=str,
help="evaluate at this precision",
choices=["full", "autocast"],
default="autocast"
)
opt = parser.parse_args()
def chunk(it, size):
it = iter(it)
@ -41,167 +108,48 @@ def load_model_from_config(config, ckpt, verbose=False):
model.eval()
return model
def load_img_pil(img_pil):
image = img_pil.convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h})")
w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
print(f"cropped image to size ({w}, {h})")
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.
def main():
parser = argparse.ArgumentParser()
def load_img(path):
return load_img_pil(Image.open(path))
parser.add_argument(
"--prompt",
type=str,
nargs="?",
default="a painting of a virus monster playing guitar",
help="the prompt to render"
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/txt2img-samples"
)
parser.add_argument(
"--skip_grid",
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',
help="do not save individual samples. For speed measurements.",
)
parser.add_argument(
"--ddim_steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
parser.add_argument(
"--plms",
action='store_true',
help="use plms sampling",
)
parser.add_argument(
"--laion400m",
action='store_true',
help="uses the LAION400M model",
)
parser.add_argument(
"--fixed_code",
action='store_true',
help="if enabled, uses the same starting code across samples ",
)
parser.add_argument(
"--ddim_eta",
type=float,
default=0.0,
help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
"--n_iter",
type=int,
default=2,
help="sample this often",
)
parser.add_argument(
"--H",
type=int,
default=512,
help="image height, in pixel space",
)
parser.add_argument(
"--W",
type=int,
default=512,
help="image width, in pixel space",
)
parser.add_argument(
"--C",
type=int,
default=4,
help="latent channels",
)
parser.add_argument(
"--f",
type=int,
default=8,
help="downsampling factor",
)
parser.add_argument(
"--n_samples",
type=int,
default=3,
help="how many samples to produce for each given prompt. A.k.a. batch size",
)
parser.add_argument(
"--n_rows",
type=int,
default=0,
help="rows in the grid (default: n_samples)",
)
parser.add_argument(
"--scale",
type=float,
default=7.5,
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--config",
type=str,
default="configs/stable-diffusion/v1-inference.yaml",
help="path to config which constructs model",
)
parser.add_argument(
"--ckpt",
type=str,
default="models/ldm/stable-diffusion-v1/model.ckpt",
help="path to checkpoint of model",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="the seed (for reproducible sampling)",
)
parser.add_argument(
"--precision",
type=str,
help="evaluate at this precision",
choices=["full", "autocast"],
default="autocast"
)
opt = parser.parse_args()
config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")
if opt.laion400m:
print("Falling back to LAION 400M model...")
opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
opt.ckpt = "models/ldm/text2img-large/model.ckpt"
opt.outdir = "outputs/txt2img-samples-laion400m"
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.half().to(device)
seed_everything(opt.seed)
def dream(prompt: str, ddim_steps: int, plms: bool, fixed_code: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
torch.cuda.empty_cache()
config = OmegaConf.load(f"{opt.config}")
model = load_model_from_config(config, f"{opt.ckpt}")
opt.H = height
opt.W = width
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
rng_seed = seed_everything(seed)
if opt.plms:
if plms:
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
opt.outdir = "outputs/txt2img-samples"
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
batch_size = opt.n_samples
batch_size = n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
prompt = opt.prompt
assert prompt is not None
data = [batch_size * [prompt]]
@ -217,32 +165,33 @@ def main():
grid_count = len(os.listdir(outpath)) - 1
start_code = None
if opt.fixed_code:
start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
if fixed_code:
start_code = torch.randn([n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
precision_scope = autocast if opt.precision=="autocast" else nullcontext
output_images = []
with torch.no_grad():
with precision_scope("cuda"):
with model.ema_scope():
tic = time.time()
all_samples = list()
for n in trange(opt.n_iter, desc="Sampling"):
for n in trange(n_iter, desc="Sampling"):
for prompts in tqdm(data, desc="data"):
uc = None
if opt.scale != 1.0:
if cfg_scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
samples_ddim, _ = sampler.sample(S=ddim_steps,
conditioning=c,
batch_size=opt.n_samples,
batch_size=n_samples,
shape=shape,
verbose=False,
unconditional_guidance_scale=opt.scale,
unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,
eta=opt.ddim_eta,
eta=ddim_eta,
x_T=start_code)
x_samples_ddim = model.decode_first_stage(samples_ddim)
@ -252,7 +201,8 @@ def main():
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"))
os.path.join(sample_path, f"{base_count:05}-{rng_seed}_{prompt.replace(' ', '_')[:128]}.png"))
output_images.append(Image.fromarray(x_sample.astype(np.uint8)))
base_count += 1
if not opt.skip_grid:
@ -270,10 +220,155 @@ def main():
grid_count += 1
toc = time.time()
del sampler
return output_images, rng_seed
print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
f" \nEnjoy.")
def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int):
torch.cuda.empty_cache()
rng_seed = seed_everything(seed)
sampler = DDIMSampler(model)
if __name__ == "__main__":
main()
opt.outdir = "outputs/img2img-samples"
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
batch_size = n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
prompt = 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
image = init_img.convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h})")
w, h = map(lambda x: x - x % 32, (width, height)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
print(f"cropped image to size ({w}, {h})")
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
output_images = []
precision_scope = autocast if opt.precision == "autocast" else nullcontext
with torch.no_grad():
with precision_scope("cuda"):
init_image = 2.*image - 1.
init_image = init_image.to(device)
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=ddim_steps, ddim_eta=ddim_eta, verbose=False)
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(denoising_strength * ddim_steps)
print(f"target t_enc is {t_enc} steps")
with model.ema_scope():
tic = time.time()
all_samples = list()
for n in trange(n_iter, desc="Sampling"):
for prompts in tqdm(data, desc="data"):
uc = None
if cfg_scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
# encode (scaled latent)
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=cfg_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')
Image.fromarray(x_sample.astype(np.uint8)).save(
os.path.join(sample_path, f"{base_count:05}-{rng_seed}_{prompt.replace(' ', '_')[:128]}.png"))
output_images.append(Image.fromarray(x_sample.astype(np.uint8)))
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 = 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'))
Image.fromarray(grid.astype(np.uint8))
grid_count += 1
toc = time.time()
del sampler
return output_images, rng_seed
dream_interface = gr.Interface(
dream,
inputs=[
gr.Textbox(placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
gr.Checkbox(label='Enable PLMS sampling', value=False),
gr.Checkbox(label='Enable Fixed Code sampling', value=False),
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
gr.Slider(minimum=1, maximum=8, step=1, label='Sampling iterations', value=2),
gr.Slider(minimum=1, maximum=8, step=1, label='Samples per iteration', value=2),
gr.Slider(minimum=1.0, maximum=20.0, step=0.5, label='Classifier Free Guidance Scale', value=7.0),
gr.Number(label='Seed', value=-1),
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
],
outputs=[
gr.Gallery(),
gr.Number(label='Seed')
],
title="Stable Diffusion Text-to-Image",
description="Generate images from text with Stable Diffusion",
)
# prompt, init_img, ddim_steps, plms, ddim_eta, n_iter, n_samples, cfg_scale, denoising_strength, seed
img2img_interface = gr.Interface(
translation,
inputs=[
gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
gr.Image(value="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg", source="upload", interactive=True, type="pil"),
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
gr.Slider(minimum=1, maximum=8, step=1, label='Sampling iterations', value=2),
gr.Slider(minimum=1, maximum=8, step=1, label='Samples per iteration', value=2),
gr.Slider(minimum=1.0, maximum=20.0, step=0.5, label='Classifier Free Guidance Scale', value=7.0),
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
gr.Number(label='Seed', value=-1),
gr.Slider(minimum=64, maximum=2048, step=64, label="Resize Height", value=512),
gr.Slider(minimum=64, maximum=2048, step=64, label="Resize Width", value=512),
],
outputs=[
gr.Gallery(),
gr.Number(label='Seed')
],
title="Stable Diffusion Image-to-Image",
description="Generate images from images with Stable Diffusion",
)
demo = gr.TabbedInterface(interface_list=[dream_interface, img2img_interface], tab_names=["Dream", "Image Translation"])
demo.launch()