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import os
os.environ["USE_NATIVE"] = "1"
import math
import torch
import torchvision
import gradio as gr
from PIL import Image
import torchvision
from test_ddgan import load_model, sample
from model_configs import get_model_config
from subprocess import call
def download(filename):
return "models/" + filename
device = 'cuda' if torch.cuda.is_available() else 'cpu'
cache = {}
def load(name):
if name in cache:
return cache[name]
else:
cfg_name = models[name]
model_config = get_model_config(cfg_name)
model_path = download(name + ".th")
model = load_model(model_config, model_path, device=device)
cache[name] = model
return model
models = {
"diffusion_db_128ch_1timesteps_openclip_vith14": "ddgan_ddb_v2",
"diffusion_db_192ch_2timesteps_openclip_vith14": 'ddgan_ddb_v3',
}
default = "diffusion_db_128ch_1timesteps_openclip_vith14"
load(default)
def gen(md, model_name, md2, text, seed, nb_samples, width, height):
print("load ", model_name)
model = load(model_name)
print(model)
torch.manual_seed(int(seed))
nb_samples = int(nb_samples)
height = int(height)
width = int(width)
with torch.no_grad():
cond = model.text_encoder([text]*nb_samples)
if text == "":
cond[0].normal_()
cond[1].normal_()
cond[0][1:] = cond[0][0:1]
cond[1][1:] = cond[1][0:1]
x_init = torch.randn(nb_samples, 3, height, width).to(device)
print(x_init.shape)
fake_sample = sample(model, x_init=x_init, cond=cond)
fake_sample = (fake_sample + 1) / 2
grid = torchvision.utils.make_grid(fake_sample, nrow=4)
grid = grid.permute(1, 2, 0).cpu().numpy()
grid = (grid*255).astype("uint8")
return Image.fromarray(grid)
text = """
Text-to-Image Denoising Diffusion GANs is a text-to-image model
based on Denoising Diffusion GANs <https://arxiv.org/abs/2112.07804>.
The code is based on their official code <<https://nvlabs.github.io/denoising-diffusion-gan/>,
which is updated to support text conditioning. Many thanks to the authors of DDGAN for releasing
the code.
The provided models are trained on DiffusionDB <https://arxiv.org/abs/2210.14896>, which is a dataset that was synthetically
generated with Stable Diffusion, many thanks to the authors for releasing the dataset.
Models were trained on JURECA-DC supercomputer at Jülich Supercomputing Centre (JSC), many thanks for the compute provided to train the models.
"""
iface = gr.Interface(
fn=gen,
inputs=[
gr.Markdown(text),
# text caption
gr.Dropdown(list(models.keys()), value=default),
gr.Markdown("If text caption is empty, random CLIP embeddings will be used as input"),
gr.Textbox(
lines=1,
placeholder="Enter text caption here, or leave empty",
value="Painting of a hamster king with a crown and a cape in a magical forest."
),
gr.Number(value=0), # seed
gr.Number(value=4), # nb_samples
gr.Number(value=256), # width
gr.Number(value=256),# height
],
outputs="image"
)
iface.launch(debug=True)
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