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"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"import torch\n",
"torch.cuda.set_device(0)\n",
"import cv2\n",
"import numpy as np\n",
"from tools.run_infinity import *\n",
"\n",
"model_path='weights/infinity_2b_reg.pth'\n",
"vae_path='weights/infinity_vae_d32_reg.pth'\n",
"text_encoder_ckpt = 'weights/flan-t5-xl'\n",
"args=argparse.Namespace(\n",
" pn='1M',\n",
" model_path=model_path,\n",
" cfg_insertion_layer=0,\n",
" vae_type=32,\n",
" vae_path=vae_path,\n",
" add_lvl_embeding_only_first_block=1,\n",
" use_bit_label=1,\n",
" model_type='infinity_2b',\n",
" rope2d_each_sa_layer=1,\n",
" rope2d_normalized_by_hw=2,\n",
" use_scale_schedule_embedding=0,\n",
" sampling_per_bits=1,\n",
" text_encoder_ckpt=text_encoder_ckpt,\n",
" text_channels=2048,\n",
" apply_spatial_patchify=0,\n",
" h_div_w_template=1.000,\n",
" use_flex_attn=0,\n",
" cache_dir='/dev/shm',\n",
" checkpoint_type='torch',\n",
" seed=0,\n",
" bf16=1,\n",
" save_file='tmp.jpg'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load text encoder\n",
"text_tokenizer, text_encoder = load_tokenizer(t5_path=args.text_encoder_ckpt)\n",
"# load vae\n",
"vae = load_visual_tokenizer(args)\n",
"# load infinity\n",
"infinity = load_transformer(vae, args)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prompt = \"\"\"alien spaceship enterprise\"\"\"\n",
"cfg = 3\n",
"tau = 0.5\n",
"h_div_w = 1/1 # aspect ratio, height:width\n",
"seed = random.randint(0, 10000)\n",
"enable_positive_prompt=0\n",
"\n",
"h_div_w_template_ = h_div_w_templates[np.argmin(np.abs(h_div_w_templates-h_div_w))]\n",
"scale_schedule = dynamic_resolution_h_w[h_div_w_template_][args.pn]['scales']\n",
"scale_schedule = [(1, h, w) for (_, h, w) in scale_schedule]\n",
"generated_image = gen_one_img(\n",
" infinity,\n",
" vae,\n",
" text_tokenizer,\n",
" text_encoder,\n",
" prompt,\n",
" g_seed=seed,\n",
" gt_leak=0,\n",
" gt_ls_Bl=None,\n",
" cfg_list=cfg,\n",
" tau_list=tau,\n",
" scale_schedule=scale_schedule,\n",
" cfg_insertion_layer=[args.cfg_insertion_layer],\n",
" vae_type=args.vae_type,\n",
" sampling_per_bits=args.sampling_per_bits,\n",
" enable_positive_prompt=enable_positive_prompt,\n",
")\n",
"args.save_file = 'ipynb_tmp.jpg'\n",
"os.makedirs(osp.dirname(osp.abspath(args.save_file)), exist_ok=True)\n",
"cv2.imwrite(args.save_file, generated_image.cpu().numpy())\n",
"print(f'Save to {osp.abspath(args.save_file)}')"
]
}
],
"metadata": {
"fileId": "8ac263ab-b18c-41dc-b409-0fb0f32525f0",
"filePath": "/mnt/bn/foundation-vision/hanjian.thu123/infinity/infinity/tools/interactive_infer.ipynb",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.2"
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"nbformat": 4,
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