Spaces:
Runtime error
Runtime error
| import time | |
| import argparse | |
| import torch | |
| from torchvision.utils import save_image | |
| from tokenizer.tokenizer_image.vq_model import VQ_models | |
| from serve.gpt_model import GPT_models | |
| from serve.llm import LLM | |
| from vllm import SamplingParams | |
| def main(args): | |
| # Setup PyTorch: | |
| torch.manual_seed(args.seed) | |
| torch.backends.cudnn.deterministic = True | |
| torch.backends.cudnn.benchmark = False | |
| torch.set_grad_enabled(False) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # create and load model | |
| vq_model = VQ_models[args.vq_model]( | |
| codebook_size=args.codebook_size, | |
| codebook_embed_dim=args.codebook_embed_dim) | |
| vq_model.to(device) | |
| vq_model.eval() | |
| checkpoint = torch.load(args.vq_ckpt, map_location="cpu") | |
| vq_model.load_state_dict(checkpoint["model"]) | |
| del checkpoint | |
| print(f"image tokenizer is loaded") | |
| # Labels to condition the model with (feel free to change): | |
| class_labels = [207, 360, 387, 974, 88, 979, 417, 279] | |
| latent_size = args.image_size // args.downsample_size | |
| qzshape = [len(class_labels), args.codebook_embed_dim, latent_size, latent_size] | |
| prompt_token_ids = [[cind] for cind in class_labels] | |
| if args.cfg_scale > 1.0: | |
| prompt_token_ids.extend([[args.num_classes] for _ in range(len(prompt_token_ids))]) | |
| # Create an LLM. | |
| llm = LLM( | |
| args=args, | |
| model='autoregressive/serve/fake_json/{}.json'.format(args.gpt_model), | |
| gpu_memory_utilization=0.9, | |
| skip_tokenizer_init=True) | |
| print(f"gpt model is loaded") | |
| # Create a sampling params object. | |
| sampling_params = SamplingParams( | |
| temperature=args.temperature, top_p=args.top_p, top_k=args.top_k, | |
| max_tokens=latent_size ** 2) | |
| # Generate texts from the prompts. The output is a list of RequestOutput objects | |
| # that contain the prompt, generated text, and other information. | |
| t1 = time.time() | |
| outputs = llm.generate( | |
| prompt_token_ids=prompt_token_ids, | |
| sampling_params=sampling_params, | |
| use_tqdm=False) | |
| sampling_time = time.time() - t1 | |
| print(f"gpt sampling takes about {sampling_time:.2f} seconds.") | |
| # decode to image | |
| index_sample = torch.tensor([output.outputs[0].token_ids for output in outputs], device=device) | |
| if args.cfg_scale > 1.0: | |
| index_sample = index_sample[:len(class_labels)] | |
| t2 = time.time() | |
| samples = vq_model.decode_code(index_sample, qzshape) # output value is between [-1, 1] | |
| decoder_time = time.time() - t2 | |
| print(f"decoder takes about {decoder_time:.2f} seconds.") | |
| # Save and display images: | |
| save_image(samples, "sample_{}.png".format(args.gpt_type), nrow=4, normalize=True, value_range=(-1, 1)) | |
| print(f"image is saved to sample_{args.gpt_type}.png") | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--gpt-model", type=str, choices=list(GPT_models.keys()), default="GPT-B") | |
| parser.add_argument("--gpt-ckpt", type=str, required=True, help="ckpt path for gpt model") | |
| parser.add_argument("--gpt-type", type=str, choices=['c2i', 't2i'], default="c2i", help="class-conditional or text-conditional") | |
| parser.add_argument("--from-fsdp", action='store_true') | |
| parser.add_argument("--cls-token-num", type=int, default=1, help="max token number of condition input") | |
| parser.add_argument("--precision", type=str, default='bf16', choices=["none", "fp16", "bf16"]) | |
| parser.add_argument("--compile", action='store_true', default=False) | |
| parser.add_argument("--vq-model", type=str, choices=list(VQ_models.keys()), default="VQ-16") | |
| parser.add_argument("--vq-ckpt", type=str, required=True, help="ckpt path for vq model") | |
| parser.add_argument("--codebook-size", type=int, default=16384, help="codebook size for vector quantization") | |
| parser.add_argument("--codebook-embed-dim", type=int, default=8, help="codebook dimension for vector quantization") | |
| parser.add_argument("--image-size", type=int, choices=[256, 384, 512], default=384) | |
| parser.add_argument("--downsample-size", type=int, choices=[8, 16], default=16) | |
| parser.add_argument("--num-classes", type=int, default=1000) | |
| parser.add_argument("--cfg-scale", type=float, default=4.0) | |
| parser.add_argument("--seed", type=int, default=0) | |
| parser.add_argument("--top-k", type=int, default=2000,help="top-k value to sample with") | |
| parser.add_argument("--temperature", type=float, default=1.0, help="temperature value to sample with") | |
| parser.add_argument("--top-p", type=float, default=1.0, help="top-p value to sample with") | |
| args = parser.parse_args() | |
| main(args) | |