-
Notifications
You must be signed in to change notification settings - Fork 57
/
app.py
139 lines (124 loc) · 6.43 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
from PIL import Image
import gradio as gr
from tools.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.tokenizer_image.vq_model import VQ_models
from autoregressive.serve.llm import LLM
from autoregressive.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("<h1 style='text-align: center'>Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation</h1>")
with gr.Tabs():
with gr.TabItem('Generate'):
with gr.Row():
with gr.Column():
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)