from PIL import Image import gradio as gr from imagenet_en_cn import IMAGENET_1K_CLASSES from huggingface_hub import hf_hub_download import torch torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True torch.set_float32_matmul_precision('high') setattr(torch.nn.Linear, 'reset_parameters', lambda self: None) setattr(torch.nn.LayerNorm, 'reset_parameters', lambda self: None) from vllm import SamplingParams import time import argparse from tokenizer_image.vq_model import VQ_models # from models.generate import generate from serve.llm import LLM from serve.sampler import Sampler device = "cuda" model2ckpt = { "GPT-XL": ("vq_ds16_c2i.pt", "c2i_XL_384.pt", 384), "GPT-B": ("vq_ds16_c2i.pt", "c2i_B_256.pt", 256), } def load_model(args): ckpt_folder = "./" vq_ckpt, gpt_ckpt, image_size = model2ckpt[args.gpt_model] hf_hub_download(repo_id="FoundationVision/LlamaGen", filename=vq_ckpt, local_dir=ckpt_folder) hf_hub_download(repo_id="FoundationVision/LlamaGen", filename=gpt_ckpt, local_dir=ckpt_folder) # 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(f"{ckpt_folder}{vq_ckpt}", map_location="cpu") vq_model.load_state_dict(checkpoint["model"]) del checkpoint print(f"image tokenizer is loaded") # Create an LLM. args.image_size = image_size args.gpt_ckpt = f"{ckpt_folder}{gpt_ckpt}" llm = LLM( args=args, model='serve/fake_json/{}.json'.format(args.gpt_model), gpu_memory_utilization=0.6, skip_tokenizer_init=True) print(f"gpt model is loaded") return vq_model, llm, image_size def infer(cfg_scale, top_k, top_p, temperature, class_label, seed): llm.llm_engine.model_executor.driver_worker.model_runner.model.sampler = Sampler(cfg_scale) args.cfg_scale = cfg_scale n = 4 latent_size = image_size // args.downsample_size # Labels to condition the model with (feel free to change): class_labels = [class_label for _ in range(n)] qzshape = [len(class_labels), args.codebook_embed_dim, latent_size, latent_size] prompt_token_ids = [[cind] for cind in class_labels] if cfg_scale > 1.0: prompt_token_ids.extend([[args.num_classes] for _ in range(len(prompt_token_ids))]) # Create a sampling params object. sampling_params = SamplingParams( temperature=temperature, top_p=top_p, top_k=top_k, max_tokens=latent_size ** 2) t1 = time.time() torch.manual_seed(seed) 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.") index_sample = torch.tensor([output.outputs[0].token_ids for output in outputs], device=device) if 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.") # Convert to PIL.Image format: samples = samples.mul(127.5).add_(128.0).clamp_(0, 255).permute(0, 2, 3, 1).to("cpu", torch.uint8).numpy() samples = [Image.fromarray(sample) for sample in samples] return samples parser = argparse.ArgumentParser() parser.add_argument("--gpt-model", type=str, default="GPT-XL") 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("--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("--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("--cfg-interval", type=float, default=-1) 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() vq_model, llm, image_size = load_model(args) with gr.Blocks() as demo: gr.Markdown("

Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

") with gr.Tabs(): with gr.TabItem('Generate'): with gr.Row(): with gr.Column(): # with gr.Row(): # image_size = gr.Radio(choices=[384], value=384, label='Peize Model Resolution') with gr.Row(): i1k_class = gr.Dropdown( list(IMAGENET_1K_CLASSES.values()), value='llama [羊驼]', type="index", label='ImageNet-1K Class' ) cfg_scale = gr.Slider(minimum=1, maximum=25, step=0.1, value=4.0, label='Classifier-free Guidance Scale') top_k = gr.Slider(minimum=1, maximum=16384, step=1, value=4000, label='Top-K') top_p = gr.Slider(minimum=0., maximum=1.0, step=0.1, value=1.0, label="Top-P") temperature = gr.Slider(minimum=0., maximum=1.0, step=0.1, value=1.0, label='Temperature') seed = gr.Slider(minimum=0, maximum=1000, step=1, value=42, label='Seed') # seed = gr.Number(value=0, label='Seed') button = gr.Button("Generate", variant="primary") with gr.Column(): output = gr.Gallery(label='Generated Images', height=700) button.click(infer, inputs=[cfg_scale, top_k, top_p, temperature, i1k_class, seed], outputs=[output]) demo.queue() demo.launch(debug=True)