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import gradio as gr | |
from share_btn import community_icon_html, loading_icon_html | |
from tqdm.auto import tqdm | |
import argparse, os, sys, glob | |
import cv2 | |
import torch | |
import numpy as np | |
from omegaconf import OmegaConf | |
from PIL import Image | |
from tqdm import trange | |
from imwatermark import WatermarkEncoder | |
from itertools import islice | |
from einops import rearrange | |
from torchvision.utils import make_grid | |
import time | |
from pytorch_lightning import seed_everything | |
from torch import autocast | |
from contextlib import contextmanager, nullcontext | |
sys.path.append("./stable-diffusion/") | |
from ldm.util import instantiate_from_config | |
from ldm.models.diffusion.psld import DDIMSampler | |
from ldm.models.diffusion.plms import PLMSSampler | |
from ldm.models.diffusion.dpm_solver import DPMSolverSampler | |
# from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker | |
from transformers import AutoFeatureExtractor | |
## lr | |
import torchvision | |
import pdb | |
# os.environ['CUDA_VISIBLE_DEVICES']='1' | |
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") | |
import subprocess | |
## | |
# load safety model | |
safety_model_id = "CompVis/stable-diffusion-safety-checker" | |
safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id) | |
# safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id) | |
def chunk(it, size): | |
it = iter(it) | |
return iter(lambda: tuple(islice(it, size)), ()) | |
def numpy_to_pil(images): | |
""" | |
Convert a numpy image or a batch of images to a PIL image. | |
""" | |
if images.ndim == 3: | |
images = images[None, ...] | |
images = (images * 255).round().astype("uint8") | |
pil_images = [Image.fromarray(image) for image in images] | |
return pil_images | |
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 = model.to(device) | |
model.eval() | |
return model | |
def put_watermark(img, wm_encoder=None): | |
if wm_encoder is not None: | |
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) | |
img = wm_encoder.encode(img, 'dwtDct') | |
img = Image.fromarray(img[:, :, ::-1]) | |
return img | |
def load_replacement(x): | |
try: | |
hwc = x.shape | |
y = Image.open("assets/rick.jpeg").convert("RGB").resize((hwc[1], hwc[0])) | |
y = (np.array(y)/255.0).astype(x.dtype) | |
assert y.shape == x.shape | |
return y | |
except Exception: | |
return x | |
def check_safety(x_image): | |
safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt") | |
x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values) | |
assert x_checked_image.shape[0] == len(has_nsfw_concept) | |
for i in range(len(has_nsfw_concept)): | |
if has_nsfw_concept[i]: | |
x_checked_image[i] = load_replacement(x_checked_image[i]) | |
return x_checked_image, has_nsfw_concept | |
parser = argparse.ArgumentParser() | |
parser.add_argument( | |
"--prompt", | |
type=str, | |
nargs="?", | |
default="", | |
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_false', | |
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=200, | |
help="number of ddim sampling steps", | |
) | |
parser.add_argument( | |
"--plms", | |
action='store_true', | |
help="use plms sampling", | |
) | |
parser.add_argument( | |
"--dpm_solver", | |
action='store_true', | |
help="use dpm_solver 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=1, | |
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=1, | |
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" | |
) | |
## | |
parser.add_argument( | |
"--dps_path", | |
type=str, | |
default='diffusion-posterior-sampling/', | |
help="DPS codebase path", | |
) | |
parser.add_argument( | |
"--task_config", | |
type=str, | |
default='configs/inpainting_config.yaml', | |
help="task config yml file", | |
) | |
parser.add_argument( | |
"--diffusion_config", | |
type=str, | |
default='configs/diffusion_config.yaml', | |
help="diffusion config yml file", | |
) | |
parser.add_argument( | |
"--model_config", | |
type=str, | |
default='configs/model_config.yaml', | |
help="model config yml file", | |
) | |
parser.add_argument( | |
"--gamma", | |
type=float, | |
default=1e-1, | |
help="inpainting error", | |
) | |
parser.add_argument( | |
"--omega", | |
type=float, | |
default=1.0, | |
help="measurement error", | |
) | |
parser.add_argument( | |
"--inpainting", | |
type=int, | |
default=1, | |
help="inpainting", | |
) | |
parser.add_argument( | |
"--general_inverse", | |
type=int, | |
default=0, | |
help="general inverse", | |
) | |
parser.add_argument( | |
"--file_id", | |
type=str, | |
default='00014.png', | |
help='input image', | |
) | |
parser.add_argument( | |
"--skip_low_res", | |
action='store_true', | |
help='downsample result to 256', | |
) | |
parser.add_argument( | |
"--ffhq256", | |
action='store_true', | |
help='load SD weights trained on FFHQ', | |
) | |
parser.add_argument( | |
"--sd_path", | |
type=str, | |
default='stable-diffusion/', | |
help="SD codebase path", | |
) | |
## | |
opt,_ = parser.parse_known_args() | |
# pdb.set_trace() | |
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" | |
## | |
if opt.ffhq256: | |
print("Using FFHQ 256 finetuned model...") | |
opt.config = "models/ldm/ffhq256/config.yaml" | |
opt.ckpt = "models/ldm/ffhq256/model.ckpt" | |
sys.path.append(opt.sd_path) | |
opt.outdir = opt.sd_path+opt.outdir | |
opt.config = opt.sd_path+opt.config | |
opt.ckpt = opt.sd_path+opt.ckpt | |
## | |
seed_everything(opt.seed) | |
# pdb.set_trace() | |
print(f"Is CUDA available: {torch.cuda.is_available()}") | |
# True | |
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}") | |
# Tesla T4 | |
config = OmegaConf.load(f"{opt.config}") | |
if os.path.exists(opt.ckpt): | |
model = load_model_from_config(config, f"{opt.ckpt}") | |
else: | |
print('No pretrained weights found in ', opt.ckpt) | |
# print('Falling back to random weights...') | |
# model = instantiate_from_config(config.model) | |
print('Downloading stable diffusion pretrained weights') | |
subprocess.call(['sh', './download.sh']) | |
model = load_model_from_config(config, "model.ckpt") | |
model = model.to(device) | |
if opt.dpm_solver: | |
sampler = DPMSolverSampler(model) | |
elif opt.plms: | |
sampler = PLMSSampler(model) | |
else: | |
# pdb.set_trace() | |
sampler = DDIMSampler(model) | |
os.makedirs(opt.outdir, exist_ok=True) | |
outpath = opt.outdir | |
print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...") | |
wm = "StableDiffusionV1" | |
wm_encoder = WatermarkEncoder() | |
wm_encoder.set_watermark('bytes', wm.encode('utf-8')) | |
batch_size = opt.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]] | |
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 | |
def read_content(file_path: str) -> str: | |
"""read the content of target file | |
""" | |
with open(file_path, 'r', encoding='utf-8') as f: | |
content = f.read() | |
return content | |
# def predict(dict, prompt=""): | |
# init_image = dict["image"].convert("RGB").resize((512, 512)) | |
# mask = dict["mask"].convert("RGB").resize((512, 512)) | |
# output = pipe(prompt = prompt, image=init_image, mask_image=mask,guidance_scale=7.5) | |
# return output.images[0], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
######################################################### | |
# Sampler | |
######################################################### | |
def predict(ddim_steps, gamma, gluing_kernel_size, gluing_kernel_sigma, omega, dict, prompt=""): | |
opt.ddim_steps = ddim_steps | |
opt.gamma = gamma | |
opt.omega = omega | |
if opt.gamma==0 and opt.omega==0: | |
opt.inpainting = 0 | |
opt.general_inverse = 0 | |
opt.prompt = prompt | |
init_image = dict["image"].convert("RGB").resize((512, 512)) | |
# pdb.set_trace() | |
mask = dict["mask"].convert("RGB").resize((512, 512)) | |
# convert input image to array in [-1, 1] | |
init_image = torch.tensor(2 * (np.asarray(init_image) / 255) - 1, device=device) | |
mask = torch.tensor((np.asarray(mask) / 255), device=device) | |
init_image = init_image.type(torch.float32) | |
# mask = mask.type(torch.float32) | |
# add one dimension for the batch and bring channels first | |
init_image = init_image.permute(2, 0, 1).unsqueeze(0) | |
mask = mask.permute(2, 0, 1).unsqueeze(0) | |
mask[mask>=0.5] = 1.0 | |
mask[mask<0.5] = 0.0 | |
mask = 1-mask | |
# check if the gadio takes the mask only or the masker image as arguments? | |
######################################################### | |
## DPS configs | |
######################################################### | |
sys.path.append(opt.dps_path) | |
import yaml | |
from guided_diffusion.measurements import get_noise, get_operator | |
from util.img_utils import clear_color, mask_generator | |
import torch.nn.functional as f | |
import matplotlib.pyplot as plt | |
def load_yaml(file_path: str) -> dict: | |
with open(file_path) as f: | |
config = yaml.load(f, Loader=yaml.FullLoader) | |
return config | |
model_config=opt.dps_path+opt.model_config | |
diffusion_config=opt.dps_path+opt.diffusion_config | |
task_config=opt.dps_path+opt.task_config | |
# pdb.set_trace() | |
# Load configurations | |
model_config = load_yaml(model_config) | |
diffusion_config = load_yaml(diffusion_config) | |
task_config = load_yaml(task_config) | |
task_config['measurement']['mask_opt']['image_size']=opt.H | |
# Prepare Operator and noise | |
measure_config = task_config['measurement'] | |
operator = get_operator(device=device, **measure_config['operator']) | |
noiser = get_noise(**measure_config['noise']) | |
# Exception) In case of inpainting, we need to generate a mask | |
if measure_config['operator']['name'] == 'inpainting': | |
mask_gen = mask_generator( | |
**measure_config['mask_opt'] | |
) | |
# print(init_image.shape) | |
# Exception) In case of inpainging, | |
if measure_config['operator'] ['name'] == 'inpainting': | |
dps_mask = mask_gen(init_image) # dps mask | |
# dps_mask = torch.ones_like(org_image) # no mask | |
dps_mask[:,0,:,:] = mask[:,0,:,:] | |
dps_mask = dps_mask[:, 0, :, :].unsqueeze(dim=0) | |
# Forward measurement model (Ax + n) | |
y = operator.forward(init_image, mask=dps_mask) | |
y_n = noiser(y) | |
else: | |
# Forward measurement model (Ax + n) | |
y = operator.forward(init_image) | |
y_n = noiser(y) | |
mask = None | |
######################################################### | |
# pdb.set_trace() | |
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) | |
precision_scope = autocast if opt.precision=="autocast" else nullcontext | |
with precision_scope("cuda"): | |
with model.ema_scope(): | |
uc = None | |
if opt.ffhq256: | |
shape = [opt.C, opt.H // opt.f, opt.W // opt.f] | |
samples_ddim, _ = sampler.sample(S=opt.ddim_steps, | |
batch_size=opt.n_samples, | |
shape=shape, | |
verbose=False, | |
eta=opt.ddim_eta, | |
x_T=start_code, | |
ip_mask = mask, | |
measurements = y_n, | |
operator = operator, | |
gamma = opt.gamma, | |
inpainting = opt.inpainting, | |
omega = opt.omega, | |
general_inverse=opt.general_inverse, | |
noiser=noiser, | |
ffhq256=opt.ffhq256) | |
else: | |
# pdb.set_trace() | |
if opt.scale != 1.0 : | |
uc = model.get_learned_conditioning(batch_size * [""]) | |
if isinstance(opt.prompt, tuple): | |
opt.prompt = list(opt.prompt) | |
c = model.get_learned_conditioning(opt.prompt) | |
shape = [opt.C, opt.H // opt.f, opt.W // opt.f] | |
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, | |
x_T=start_code, | |
ip_mask = mask, | |
measurements = y_n, | |
operator = operator, | |
gamma = opt.gamma, | |
inpainting = opt.inpainting, | |
omega = opt.omega, | |
general_inverse=opt.general_inverse, | |
noiser=noiser) | |
x_samples_ddim = model.decode_first_stage(samples_ddim) | |
# pdb.set_trace() | |
# final step | |
if gluing_kernel_size > 0 and gluing_kernel_sigma > 0: | |
blur = torchvision.transforms.GaussianBlur(gluing_kernel_size, sigma=gluing_kernel_sigma) | |
mask = blur(mask) | |
x_samples_ddim = mask * init_image + (1-mask) * x_samples_ddim | |
x_samples_ddim1 = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) | |
x_samples_ddim1 = x_samples_ddim1.cpu().permute(0, 2, 3, 1).numpy() | |
x_checked_image_torch = torch.from_numpy(x_samples_ddim1).permute(0, 3, 1, 2) | |
x_sample1 = 255. * rearrange(x_checked_image_torch[0].cpu().numpy(), 'c h w -> h w c') | |
## no need to enc-dec again | |
encoded_z_0 = model.encode_first_stage(x_samples_ddim.float()) | |
encoded_z_0 = model.get_first_stage_encoding(encoded_z_0) | |
x_samples_ddim = model.decode_first_stage(encoded_z_0) | |
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) | |
x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy() | |
# x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim) | |
# x_checked_image_torch = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2) | |
# pdb.set_trace() | |
x_checked_image_torch = torch.from_numpy(x_samples_ddim).permute(0, 3, 1, 2) | |
x_sample2 = 255. * rearrange(x_checked_image_torch[0].cpu().numpy(), 'c h w -> h w c') | |
# img = Image.fromarray(x_sample2.astype(np.uint8)) | |
# img = put_watermark(img, wm_encoder) | |
image1 = x_sample1.astype("uint8") | |
image2 = x_sample2.astype("uint8") | |
# pdb.set_trace() | |
return image1, image2, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
css = ''' | |
.container {max-width: 1150px;margin: auto;padding-top: 1.5rem} | |
#image_upload{min-height:400px} | |
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px} | |
#mask_radio .gr-form{background:transparent; border: none} | |
#word_mask{margin-top: .75em !important} | |
#word_mask textarea:disabled{opacity: 0.3} | |
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5} | |
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white} | |
.dark .footer {border-color: #303030} | |
.dark .footer>p {background: #0b0f19} | |
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%} | |
#image_upload .touch-none{display: flex} | |
@keyframes spin { | |
from { | |
transform: rotate(0deg); | |
} | |
to { | |
transform: rotate(360deg); | |
} | |
} | |
#share-btn-container { | |
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; | |
} | |
#share-btn { | |
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important; | |
} | |
#share-btn * { | |
all: unset; | |
} | |
#share-btn-container div:nth-child(-n+2){ | |
width: auto !important; | |
min-height: 0px !important; | |
} | |
#share-btn-container .wrap { | |
display: none !important; | |
} | |
''' | |
image_blocks = gr.Blocks(css=css) | |
with image_blocks as demo: | |
gr.HTML(read_content("header.html")) | |
with gr.Group(): | |
with gr.Box(): | |
with gr.Row(): | |
with gr.Column(): | |
image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload").style(height=400) | |
ddim_steps = gr.Slider(minimum = 1, maximum = 1000, step = 1, label = 'Number of diffusion steps (e.g. 400)', value=400) | |
gamma = gr.Slider(minimum = 0, maximum = 1, step=0.01, label = 'Gluing factor (e.g. 1e-1)', value=1e-1) | |
gluing_kernel_size = gr.Slider(minimum = 0, maximum = 100, step=1, label = 'Gluing kernel size (e.g. 15)', value=15) | |
gluing_kernel_sigma = gr.Slider(minimum = 0, maximum = 25, step=1, label = 'Gluing kernel sigma (e.g. 7)', value=7) | |
omega = gr.Slider(minimum = 0, maximum = 2, step=0.1, label = 'Measurement factor (e.g. 1)', value=1) | |
with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True): | |
prompt = gr.Textbox(placeholder = 'Your prompt (leave empty for posterior sampling)', show_label=False, elem_id="input-text") | |
btn = gr.Button("Inpaint!").style( | |
margin=False, | |
rounded=(False, True, True, False), | |
full_width=False, | |
) | |
with gr.Column(): | |
image_out1 = gr.Image(label="Output1", elem_id="output-img-1").style(height=400) | |
with gr.Group(elem_id="share-btn-container"): | |
community_icon = gr.HTML(community_icon_html, visible=False) | |
loading_icon = gr.HTML(loading_icon_html, visible=False) | |
image_out2 = gr.Image(label="Output2", elem_id="output-img-2").style(height=400) | |
with gr.Group(elem_id="share-btn-container"): | |
community_icon = gr.HTML(community_icon_html, visible=False) | |
loading_icon = gr.HTML(loading_icon_html, visible=False) | |
btn.click(fn=predict, inputs=[ddim_steps, gamma, gluing_kernel_size, gluing_kernel_sigma, omega, image, prompt], outputs=[image_out1, image_out2, community_icon, loading_icon]) | |
gr.HTML( | |
""" | |
<div class="footer"> | |
<p>Image Inpainting by <a href="https://huggingface.co/spaces/PSLD/PSLD" style="text-decoration: underline;" target="_blank">PSLD</a> - Generative Foundation Model by <a href="https://huggingface.co/runwayml" style="text-decoration: underline;" target="_blank">RunwayML</a> - Gradio Demo by 🤗 Hugging Face | |
</p> | |
</div> | |
<div class="acknowledgments"> | |
<p><h4>LICENSE</h4> | |
The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license.</a></p> | |
<p><h4>Biases and content acknowledgment of Stable Diffusion</h4> | |
Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" style="text-decoration: underline;" target="_blank">model card.</a></p> | |
<p><h4>Limitations of PSLD</h4> | |
Our evaluation is based on <a href="https://huggingface.co/runwayml" style="text-decoration: underline;" target="_blank">Stable Diffusion v-1.5</a> which was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>. | |
Biases in this dataset and the generative foundation model will be implicitly affecting our algorithm. Our method | |
can work with any latent diffusion model and we expect new foundation models trained on better datasets like <a href="https://www.datacomp.ai/" style="text-decoration: underline;" target="_blank">DataComp</a> | |
to mitigate these issues. | |
</p> | |
</div> | |
""" | |
) | |
image_blocks.queue(max_size=100, api_open=False) | |
image_blocks.launch() |