405 lines
17 KiB
Python
405 lines
17 KiB
Python
|
import PIL
|
||
|
|
import argparse, os, sys, glob
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import numpy as np
|
||
|
|
import gradio as gr
|
||
|
|
from omegaconf import OmegaConf
|
||
|
|
from PIL import Image
|
||
|
|
from itertools import islice
|
||
|
|
from einops import rearrange, repeat
|
||
|
|
from torchvision.utils import make_grid
|
||
|
|
from torch import autocast
|
||
|
|
from contextlib import contextmanager, nullcontext
|
||
|
|
import mimetypes
|
||
|
|
import random
|
||
|
|
|
||
|
|
import k_diffusion as K
|
||
|
|
from ldm.util import instantiate_from_config
|
||
|
|
from ldm.models.diffusion.ddim import DDIMSampler
|
||
|
|
from ldm.models.diffusion.plms import PLMSSampler
|
||
|
|
|
||
|
|
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
|
||
|
|
mimetypes.init()
|
||
|
|
mimetypes.add_type('application/javascript', '.js')
|
||
|
|
|
||
|
|
# some of those options should not be changed at all because they would break the model, so I removed them from options.
|
||
|
|
opt_C = 4
|
||
|
|
opt_f = 8
|
||
|
|
|
||
|
|
parser = argparse.ArgumentParser()
|
||
|
|
parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None)
|
||
|
|
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("--n_rows", type=int, default=0, help="rows in the grid (default: n_samples)",)
|
||
|
|
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")
|
||
|
|
parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default='./GFPGAN')
|
||
|
|
opt = parser.parse_args()
|
||
|
|
|
||
|
|
GFPGAN_dir = opt.gfpgan_dir
|
||
|
|
|
||
|
|
|
||
|
|
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")
|
||
|
|
if "global_step" in pl_sd:
|
||
|
|
print(f"Global Step: {pl_sd['global_step']}")
|
||
|
|
sd = pl_sd["state_dict"]
|
||
|
|
model = instantiate_from_config(config.model)
|
||
|
|
m, u = model.load_state_dict(sd, strict=False)
|
||
|
|
if len(m) > 0 and verbose:
|
||
|
|
print("missing keys:")
|
||
|
|
print(m)
|
||
|
|
if len(u) > 0 and verbose:
|
||
|
|
print("unexpected keys:")
|
||
|
|
print(u)
|
||
|
|
|
||
|
|
model.cuda()
|
||
|
|
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 load_img(path):
|
||
|
|
return load_img_pil(Image.open(path))
|
||
|
|
|
||
|
|
|
||
|
|
class CFGDenoiser(nn.Module):
|
||
|
|
def __init__(self, model):
|
||
|
|
super().__init__()
|
||
|
|
self.inner_model = model
|
||
|
|
|
||
|
|
def forward(self, x, sigma, uncond, cond, cond_scale):
|
||
|
|
x_in = torch.cat([x] * 2)
|
||
|
|
sigma_in = torch.cat([sigma] * 2)
|
||
|
|
cond_in = torch.cat([uncond, cond])
|
||
|
|
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
|
||
|
|
return uncond + (cond - uncond) * cond_scale
|
||
|
|
|
||
|
|
|
||
|
|
def load_GFPGAN():
|
||
|
|
model_name = 'GFPGANv1.3'
|
||
|
|
model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
|
||
|
|
if not os.path.isfile(model_path):
|
||
|
|
raise Exception("GFPGAN model not found at path "+model_path)
|
||
|
|
|
||
|
|
sys.path.append(os.path.abspath(GFPGAN_dir))
|
||
|
|
from gfpgan import GFPGANer
|
||
|
|
|
||
|
|
return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
|
||
|
|
|
||
|
|
|
||
|
|
GFPGAN = None
|
||
|
|
if os.path.exists(GFPGAN_dir):
|
||
|
|
try:
|
||
|
|
GFPGAN = load_GFPGAN()
|
||
|
|
print("Loaded GFPGAN")
|
||
|
|
except Exception:
|
||
|
|
import traceback
|
||
|
|
print("Error loading GFPGAN:", file=sys.stderr)
|
||
|
|
print(traceback.format_exc(), file=sys.stderr)
|
||
|
|
|
||
|
|
config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
|
||
|
|
model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")
|
||
|
|
|
||
|
|
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
|
||
|
|
model = model.half().to(device)
|
||
|
|
|
||
|
|
|
||
|
|
def image_grid(imgs, rows):
|
||
|
|
cols = len(imgs) // rows
|
||
|
|
|
||
|
|
w, h = imgs[0].size
|
||
|
|
grid = Image.new('RGB', size=(cols * w, rows * h))
|
||
|
|
|
||
|
|
for i, img in enumerate(imgs):
|
||
|
|
grid.paste(img, box=(i % cols * w, i // cols * h))
|
||
|
|
|
||
|
|
return grid
|
||
|
|
|
||
|
|
def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
|
||
|
|
torch.cuda.empty_cache()
|
||
|
|
|
||
|
|
outpath = opt.outdir or "outputs/txt2img-samples"
|
||
|
|
|
||
|
|
if seed == -1:
|
||
|
|
seed = random.randrange(4294967294)
|
||
|
|
|
||
|
|
seed = int(seed)
|
||
|
|
|
||
|
|
is_PLMS = sampler_name == 'PLMS'
|
||
|
|
is_DDIM = sampler_name == 'DDIM'
|
||
|
|
is_Kdif = sampler_name == 'k-diffusion'
|
||
|
|
|
||
|
|
sampler = None
|
||
|
|
if is_PLMS:
|
||
|
|
sampler = PLMSSampler(model)
|
||
|
|
elif is_DDIM:
|
||
|
|
sampler = DDIMSampler(model)
|
||
|
|
elif is_Kdif:
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
raise Exception("Unknown sampler: " + sampler_name)
|
||
|
|
|
||
|
|
model_wrap = K.external.CompVisDenoiser(model)
|
||
|
|
|
||
|
|
os.makedirs(outpath, exist_ok=True)
|
||
|
|
|
||
|
|
batch_size = n_samples
|
||
|
|
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
|
||
|
|
|
||
|
|
assert prompt is not None
|
||
|
|
data = [batch_size * [prompt]]
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
start_code = None
|
||
|
|
if fixed_code:
|
||
|
|
start_code = torch.randn([n_samples, opt_C, height // opt_f, width // opt_f], device=device)
|
||
|
|
|
||
|
|
precision_scope = autocast if opt.precision == "autocast" else nullcontext
|
||
|
|
output_images = []
|
||
|
|
with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
|
||
|
|
all_samples = []
|
||
|
|
|
||
|
|
for n in range(n_iter):
|
||
|
|
for batch_index, prompts in enumerate(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)
|
||
|
|
shape = [opt_C, height // opt_f, width // opt_f]
|
||
|
|
|
||
|
|
current_seed = seed + n * len(data) + batch_index
|
||
|
|
torch.manual_seed(current_seed)
|
||
|
|
|
||
|
|
if is_Kdif:
|
||
|
|
sigmas = model_wrap.get_sigmas(ddim_steps)
|
||
|
|
x = torch.randn([n_samples, *shape], device=device) * sigmas[0] # for GPU draw
|
||
|
|
model_wrap_cfg = CFGDenoiser(model_wrap)
|
||
|
|
samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}, disable=False)
|
||
|
|
|
||
|
|
elif sampler is not None:
|
||
|
|
samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=start_code)
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
if not opt.skip_save or not opt.skip_grid:
|
||
|
|
for x_sample in x_samples_ddim:
|
||
|
|
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
|
||
|
|
x_sample = x_sample.astype(np.uint8)
|
||
|
|
|
||
|
|
if use_GFPGAN and GFPGAN is not None:
|
||
|
|
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
|
||
|
|
x_sample = restored_img
|
||
|
|
|
||
|
|
image = Image.fromarray(x_sample)
|
||
|
|
|
||
|
|
image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_')[:128]}.png"))
|
||
|
|
output_images.append(image)
|
||
|
|
base_count += 1
|
||
|
|
|
||
|
|
if not opt.skip_grid:
|
||
|
|
all_samples.append(x_sample)
|
||
|
|
|
||
|
|
if not opt.skip_grid:
|
||
|
|
# additionally, save as grid
|
||
|
|
grid = image_grid(output_images, rows=n_rows)
|
||
|
|
grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
|
||
|
|
grid_count += 1
|
||
|
|
|
||
|
|
|
||
|
|
if sampler is not None:
|
||
|
|
del sampler
|
||
|
|
|
||
|
|
info = f"""
|
||
|
|
{prompt}
|
||
|
|
Steps: {ddim_steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
|
||
|
|
""".strip()
|
||
|
|
|
||
|
|
return output_images, seed, info
|
||
|
|
|
||
|
|
|
||
|
|
dream_interface = gr.Interface(
|
||
|
|
dream,
|
||
|
|
inputs=[
|
||
|
|
gr.Textbox(label="Prompt", 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.Radio(label='Sampling method', choices=["DDIM", "PLMS", "k-diffusion"], value="k-diffusion"),
|
||
|
|
gr.Checkbox(label='Enable Fixed Code sampling', value=False),
|
||
|
|
gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
|
||
|
|
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
|
||
|
|
gr.Slider(minimum=1, maximum=16, step=1, label='Sampling iterations', value=1),
|
||
|
|
gr.Slider(minimum=1, maximum=4, step=1, label='Samples per iteration', value=1),
|
||
|
|
gr.Slider(minimum=1.0, maximum=15.0, 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(label="Images"),
|
||
|
|
gr.Number(label='Seed'),
|
||
|
|
gr.Textbox(label="Copy-paste generation parameters"),
|
||
|
|
],
|
||
|
|
title="Stable Diffusion Text-to-Image K",
|
||
|
|
description="Generate images from text with Stable Diffusion (using K-LMS)",
|
||
|
|
allow_flagging="never"
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
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()
|
||
|
|
|
||
|
|
outpath = opt.outdir or "outputs/img2img-samples"
|
||
|
|
|
||
|
|
if seed == -1:
|
||
|
|
seed = random.randrange(4294967294)
|
||
|
|
|
||
|
|
sampler = DDIMSampler(model)
|
||
|
|
|
||
|
|
model_wrap = K.external.CompVisDenoiser(model)
|
||
|
|
|
||
|
|
os.makedirs(outpath, exist_ok=True)
|
||
|
|
|
||
|
|
batch_size = n_samples
|
||
|
|
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
|
||
|
|
|
||
|
|
assert prompt is not None
|
||
|
|
data = [batch_size * [prompt]]
|
||
|
|
|
||
|
|
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
|
||
|
|
seedit = 0
|
||
|
|
|
||
|
|
image = init_img.convert("RGB")
|
||
|
|
w, h = image.size
|
||
|
|
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
|
||
|
|
x0 = init_latent
|
||
|
|
|
||
|
|
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():
|
||
|
|
all_samples = list()
|
||
|
|
for n in range(n_iter):
|
||
|
|
for batch_index, prompts in enumerate(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)
|
||
|
|
|
||
|
|
sigmas = model_wrap.get_sigmas(ddim_steps)
|
||
|
|
|
||
|
|
current_seed = seed + n * len(data) + batch_index
|
||
|
|
torch.manual_seed(current_seed)
|
||
|
|
|
||
|
|
noise = torch.randn_like(x0) * sigmas[ddim_steps - t_enc - 1] # for GPU draw
|
||
|
|
xi = x0 + noise
|
||
|
|
sigma_sched = sigmas[ddim_steps - t_enc - 1:]
|
||
|
|
# x = torch.randn([n_samples, *shape]).to(device) * sigmas[0] # for CPU draw
|
||
|
|
model_wrap_cfg = CFGDenoiser(model_wrap)
|
||
|
|
extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}
|
||
|
|
|
||
|
|
samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args=extra_args, disable=False)
|
||
|
|
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)
|
||
|
|
|
||
|
|
if not opt.skip_save:
|
||
|
|
for x_sample in x_samples_ddim:
|
||
|
|
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
|
||
|
|
image = Image.fromarray(x_sample.astype(np.uint8))
|
||
|
|
image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_')[:128]}.png"))
|
||
|
|
output_images.append(image)
|
||
|
|
base_count += 1
|
||
|
|
seedit += 1
|
||
|
|
|
||
|
|
if not opt.skip_grid:
|
||
|
|
all_samples.append(x_samples_ddim)
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
del sampler
|
||
|
|
return output_images, seed
|
||
|
|
|
||
|
|
|
||
|
|
# 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=50, 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=15.0, 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()
|