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: model_config, model_path = models[name] print(model_config, model_path) model = load_model(model_config, model_path, device=device) cache[name] = model return model models = { "diffusion_db_128ch_1timesteps_openclip_vith14": (get_model_config('ddgan_ddb_v2'), download('diffusion_db_128ch_1timesteps_openclip_vith14.th')), "diffusion_db_192ch_2timesteps_openclip_vith14": (get_model_config('ddgan_ddb_v3'), download('diffusion_db_192ch_2timesteps_openclip_vith14.th')), } default = "diffusion_db_128ch_1timesteps_openclip_vith14" 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 = """ DDGAN """ 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) iface.queue(concurrency_count=8, max_size=100).launch(max_threads=8, debug=True)