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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) | |