from pydoc import describe
import gradio as gr
import torch
from omegaconf import OmegaConf
import sys
sys.path.append(".")
sys.path.append('./taming-transformers')
sys.path.append('./latent-diffusion')
from taming.models import vqgan
from ldm.util import instantiate_from_config
from huggingface_hub import hf_hub_download
model_path_e = hf_hub_download(repo_id="multimodalart/compvis-latent-diffusion-text2img-large", filename="txt2img-f8-large.ckpt")
#@title Import stuff
import argparse, os, sys, glob
import numpy as np
from PIL import Image
from einops import rearrange
from torchvision.utils import make_grid
import transformers
import gc
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from open_clip import tokenizer
import open_clip
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cuda")
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.half().cuda()
model.eval()
return model
def load_safety_model(clip_model):
"""load the safety model"""
import autokeras as ak # pylint: disable=import-outside-toplevel
from tensorflow.keras.models import load_model # pylint: disable=import-outside-toplevel
from os.path import expanduser # pylint: disable=import-outside-toplevel
home = expanduser("~")
cache_folder = home + "/.cache/clip_retrieval/" + clip_model.replace("/", "_")
if clip_model == "ViT-L/14":
model_dir = cache_folder + "/clip_autokeras_binary_nsfw"
dim = 768
elif clip_model == "ViT-B/32":
model_dir = cache_folder + "/clip_autokeras_nsfw_b32"
dim = 512
else:
raise ValueError("Unknown clip model")
if not os.path.exists(model_dir):
os.makedirs(cache_folder, exist_ok=True)
from urllib.request import urlretrieve # pylint: disable=import-outside-toplevel
path_to_zip_file = cache_folder + "/clip_autokeras_binary_nsfw.zip"
if clip_model == "ViT-L/14":
url_model = "https://raw.githubusercontent.com/LAION-AI/CLIP-based-NSFW-Detector/main/clip_autokeras_binary_nsfw.zip"
elif clip_model == "ViT-B/32":
url_model = (
"https://raw.githubusercontent.com/LAION-AI/CLIP-based-NSFW-Detector/main/clip_autokeras_nsfw_b32.zip"
)
else:
raise ValueError("Unknown model {}".format(clip_model))
urlretrieve(url_model, path_to_zip_file)
import zipfile # pylint: disable=import-outside-toplevel
with zipfile.ZipFile(path_to_zip_file, "r") as zip_ref:
zip_ref.extractall(cache_folder)
loaded_model = load_model(model_dir, custom_objects=ak.CUSTOM_OBJECTS)
loaded_model.predict(np.random.rand(10 ** 3, dim).astype("float32"), batch_size=10 ** 3)
return loaded_model
def is_unsafe(safety_model, embeddings, threshold=1):
"""find unsafe embeddings"""
nsfw_values = safety_model.predict(embeddings, batch_size=embeddings.shape[0])
x = np.array([e[0] for e in nsfw_values])
return True if x > threshold else False
config = OmegaConf.load("latent-diffusion/configs/latent-diffusion/txt2img-1p4B-eval.yaml")
model = load_model_from_config(config,model_path_e)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
#NSFW CLIP Filter
safety_model = load_safety_model("ViT-B/32")
clip_model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='openai')
def run(prompt, steps, width, height, images, scale):
opt = argparse.Namespace(
prompt = prompt,
outdir='latent-diffusion/outputs',
ddim_steps = int(steps),
ddim_eta = 0,
n_iter = 1,
W=int(width),
H=int(height),
n_samples=int(images),
scale=scale,
plms=True
)
if opt.plms:
opt.ddim_eta = 0
sampler = PLMSSampler(model)
else:
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
prompt = opt.prompt
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
all_samples=list()
all_samples_images=list()
with torch.no_grad():
with torch.cuda.amp.autocast():
with model.ema_scope():
uc = None
if opt.scale > 0:
uc = model.get_learned_conditioning(opt.n_samples * [""])
for n in range(opt.n_iter):
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)
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_vector = Image.fromarray(x_sample.astype(np.uint8))
image_preprocess = preprocess(image_vector).unsqueeze(0)
with torch.no_grad():
image_features = clip_model.encode_image(image_preprocess)
image_features /= image_features.norm(dim=-1, keepdim=True)
query = image_features.cpu().detach().numpy().astype("float32")
unsafe = is_unsafe(safety_model,query,1)
if(not unsafe):
all_samples_images.append(image_vector)
else:
return(None,None,"Sorry, potential NSFW content was detected on your outputs by our NSFW detection model. Try again with different prompts. If you feel your prompt was not supposed to give NSFW outputs, this may be due to a bias in the model. Read more about biases in the Biases Acknowledgment section below.")
#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)
# 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=2)
# 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'))
return(Image.fromarray(grid.astype(np.uint8)),all_samples_images,None)
image = gr.outputs.Image(type="pil", label="Your result")
css = ".output-image{height: 528px !important} .output-carousel .output-image{height:272px !important} a{text-decoration: underline}"
iface = gr.Interface(fn=run, inputs=[
gr.inputs.Textbox(label="Prompt - try adding increments to your prompt such as 'oil on canvas', 'a painting', 'a book cover'",default="chalk pastel drawing of a dog wearing a funny hat"),
gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate",default=45,maximum=50,minimum=1,step=1),
gr.inputs.Radio(label="Width", choices=[32,64,128,256],default=256),
gr.inputs.Radio(label="Height", choices=[32,64,128,256],default=256),
gr.inputs.Slider(label="Images - How many images you wish to generate", default=2, step=1, minimum=1, maximum=4),
gr.inputs.Slider(label="Diversity scale - How different from one another you wish the images to be",default=5.0, minimum=1.0, maximum=15.0),
#gr.inputs.Slider(label="ETA - between 0 and 1. Lower values can provide better quality, higher values can be more diverse",default=0.0,minimum=0.0, maximum=1.0,step=0.1),
],
outputs=[image,gr.outputs.Carousel(label="Individual images",components=["image"]),gr.outputs.Textbox(label="Error")],
css=css,
title="Generate images from text with Latent Diffusion LAION-400M",
description="",
article="Biases acknowledgment
Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exarcbates societal biases. According to the
Latent Diffusion paper:
\"Deep learning modules tend to reproduce or exacerbate biases that are already present in the data\". The model was trained on an unfiltered version the LAION-400M dataset, which scrapped non-curated image-text-pairs from the internet (the exception being the the removal of illegal content) and is meant to be used for research purposes, such as this one.
You can read more on LAION's websiteWho owns the images produced by this demo?
")
iface.launch(enable_queue=True)