app.py

import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"

import os.path as osp
import time
import hashlib
import argparse
import shutil
import re
import random
from pathlib import Path
from typing import List

import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ImageEnhance
import PIL.Image as PImage
from torchvision.transforms.functional import to_tensor
from transformers import AutoTokenizer, T5EncoderModel, T5TokenizerFast
from huggingface_hub import hf_hub_download
import gradio as gr
import spaces

from models.infinity import Infinity
from models.basic import *
from utils.dynamic_resolution import dynamic_resolution_h_w, h_div_w_templates

torch._dynamo.config.cache_size_limit = 64

# Define a function to download weights if not present
def download_infinity_weights(weights_path):
    try:
        model_file = weights_path / 'infinity_2b_reg.pth'
        if not model_file.exists():
            hf_hub_download(repo_id="FoundationVision/Infinity", filename="infinity_2b_reg.pth", local_dir=str(weights_path))
        
        vae_file = weights_path / 'infinity_vae_d32reg.pth'
        if not vae_file.exists():
            hf_hub_download(repo_id="FoundationVision/Infinity", filename="infinity_vae_d32reg.pth", local_dir=str(weights_path))
        
    except Exception as e:
        print(f"Error downloading weights: {e}")

def extract_key_val(text):
    pattern = r'<(.+?):(.+?)>'
    matches = re.findall(pattern, text)
    key_val = {}
    for match in matches:
        key_val[match[0]] = match[1].lstrip()
    return key_val

def encode_prompt(text_tokenizer, text_encoder, prompt, enable_positive_prompt=False):
    if enable_positive_prompt:
        print(f'before positive_prompt aug: {prompt}')
        prompt = aug_with_positive_prompt(prompt)
        print(f'after positive_prompt aug: {prompt}')
    print(f'prompt={prompt}')
    captions = [prompt]
    tokens = text_tokenizer(text=captions, max_length=512, padding='max_length', truncation=True, return_tensors='pt')  # todo: put this into dataset
    input_ids = tokens.input_ids.cuda(non_blocking=True)
    mask = tokens.attention_mask.cuda(non_blocking=True)
    text_features = text_encoder(input_ids=input_ids, attention_mask=mask)['last_hidden_state'].float()
    lens: List[int] = mask.sum(dim=-1).tolist()
    cu_seqlens_k = F.pad(mask.sum(dim=-1).to(dtype=torch.int32).cumsum_(0), (1, 0))
    Ltext = max(lens)    
    kv_compact = []
    for len_i, feat_i in zip(lens, text_features.unbind(0)):
        kv_compact.append(feat_i[:len_i])
    kv_compact = torch.cat(kv_compact, dim=0)
    text_cond_tuple = (kv_compact, lens, cu_seqlens_k, Ltext)
    return text_cond_tuple

def aug_with_positive_prompt(prompt):
    for key in ['man', 'woman', 'men', 'women', 'boy', 'girl', 'child', 'person', 'human', 'adult', 'teenager', 'employee', 
                'employer', 'worker', 'mother', 'father', 'sister', 'brother', 'grandmother', 'grandfather', 'son', 'daughter']:
        if key in prompt:
            prompt = prompt + '. very smooth faces, good looking faces, face to the camera, perfect facial features'
            break
    return prompt

def enhance_image(image):
    for t in range(1):
        contrast_image = image.copy()
        contrast_enhancer = ImageEnhance.Contrast(contrast_image)
        contrast_image = contrast_enhancer.enhance(1.05)  # 增强对比度
        color_image = contrast_image.copy()
        color_enhancer = ImageEnhance.Color(color_image)
        color_image = color_enhancer.enhance(1.05)  # 增强饱和度
    return color_image

def gen_one_img(
    infinity_test, 
    vae, 
    text_tokenizer,
    text_encoder,
    prompt, 
    cfg_list=[],
    tau_list=[],
    negative_prompt='',
    scale_schedule=None,
    top_k=900,
    top_p=0.97,
    cfg_sc=3,
    cfg_exp_k=0.0,
    cfg_insertion_layer=-5,
    vae_type=0,
    gumbel=0,
    softmax_merge_topk=-1,
    gt_leak=-1,
    gt_ls_Bl=None,
    g_seed=None,
    sampling_per_bits=1,
    enable_positive_prompt=0,
):
    sstt = time.time()
    if not isinstance(cfg_list, list):
        cfg_list = [cfg_list] * len(scale_schedule)
    if not isinstance(tau_list, list):
        tau_list = [tau_list] * len(scale_schedule)
    text_cond_tuple = encode_prompt(text_tokenizer, text_encoder, prompt, enable_positive_prompt)
    if negative_prompt:
        negative_label_B_or_BLT = encode_prompt(text_tokenizer, text_encoder, negative_prompt)
    else:
        negative_label_B_or_BLT = None
    print(f'cfg: {cfg_list}, tau: {tau_list}')
    with torch.cuda.amp.autocast(enabled=True, dtype=torch.bfloat16, cache_enabled=True):
        stt = time.time()
        _, _, img_list = infinity_test.autoregressive_infer_cfg(
            vae=vae,
            scale_schedule=scale_schedule,
            label_B_or_BLT=text_cond_tuple, g_seed=g_seed,
            B=1, negative_label_B_or_BLT=negative_label_B_or_BLT, force_gt_Bhw=None,
            cfg_sc=cfg_sc, cfg_list=cfg_list, tau_list=tau_list, top_k=top_k, top_p=top_p,
            returns_vemb=1, ratio_Bl1=None, gumbel=gumbel, norm_cfg=False,
            cfg_exp_k=cfg_exp_k, cfg_insertion_layer=cfg_insertion_layer,
            vae_type=vae_type, softmax_merge_topk=softmax_merge_topk,
            ret_img=True, trunk_scale=1000,
            gt_leak=gt_leak, gt_ls_Bl=gt_ls_Bl, inference_mode=True,
            sampling_per_bits=sampling_per_bits,
        )
    print(f"cost: {time.time() - sstt}, infinity cost={time.time() - stt}")
    img = img_list[0]
    return img

def get_prompt_id(prompt):
    md5 = hashlib.md5()
    md5.update(prompt.encode('utf-8'))
    prompt_id = md5.hexdigest()
    return prompt_id

def save_slim_model(infinity_model_path, save_file=None, device='cpu', key='gpt_fsdp'):
    print('[Save slim model]')
    full_ckpt = torch.load(infinity_model_path, map_location=device)
    infinity_slim = full_ckpt['trainer'][key]
    # ema_state_dict = cpu_d['trainer'].get('gpt_ema_fsdp', state_dict)
    if not save_file:
        save_file = osp.splitext(infinity_model_path)[0] + '-slim.pth'
    print(f'Save to {save_file}')
    torch.save(infinity_slim, save_file)
    print('[Save slim model] done')
    return save_file

def load_tokenizer(t5_path =''):
    print(f'[Loading tokenizer and text encoder]')
    text_tokenizer: T5TokenizerFast = AutoTokenizer.from_pretrained(t5_path, revision=None, legacy=True)
    text_tokenizer.model_max_length = 512
    text_encoder: T5EncoderModel = T5EncoderModel.from_pretrained(t5_path, torch_dtype=torch.float16)
    text_encoder.to('cuda')
    text_encoder.eval()
    text_encoder.requires_grad_(False)
    return text_tokenizer, text_encoder

def load_infinity(
    rope2d_each_sa_layer, 
    rope2d_normalized_by_hw, 
    use_scale_schedule_embedding, 
    pn, 
    use_bit_label, 
    add_lvl_embeding_only_first_block, 
    model_path='', 
    scale_schedule=None, 
    vae=None, 
    device='cuda', 
    model_kwargs=None,
    text_channels=2048,
    apply_spatial_patchify=0,
    use_flex_attn=False,
    bf16=False,
):
    print(f'[Loading Infinity]')
    text_maxlen = 512
    with torch.cuda.amp.autocast(enabled=True, dtype=torch.bfloat16, cache_enabled=True), torch.no_grad():
        infinity_test: Infinity = Infinity(
            vae_local=vae, text_channels=text_channels, text_maxlen=text_maxlen,
            shared_aln=True, raw_scale_schedule=scale_schedule,
            checkpointing='full-block',
            customized_flash_attn=False,
            fused_norm=True,
            pad_to_multiplier=128,
            use_flex_attn=use_flex_attn,
            add_lvl_embeding_only_first_block=add_lvl_embeding_only_first_block,
            use_bit_label=use_bit_label,
            rope2d_each_sa_layer=rope2d_each_sa_layer,
            rope2d_normalized_by_hw=rope2d_normalized_by_hw,
            pn=pn,
            apply_spatial_patchify=apply_spatial_patchify,
            inference_mode=True,
            train_h_div_w_list=[1.0],
            **model_kwargs,
        ).to(device=device)
        print(f'[you selected Infinity with {model_kwargs=}] model size: {sum(p.numel() for p in infinity_test.parameters())/1e9:.2f}B, bf16={bf16}')

        if bf16:
            for block in infinity_test.unregistered_blocks:
                block.bfloat16()

        infinity_test.eval()
        infinity_test.requires_grad_(False)

        infinity_test.cuda()
        torch.cuda.empty_cache()

        print(f'[Load Infinity weights]')
        state_dict = torch.load(model_path, map_location=device)
        print(infinity_test.load_state_dict(state_dict))
        infinity_test.rng = torch.Generator(device=device)
        return infinity_test

def transform(pil_img, tgt_h, tgt_w):
    width, height = pil_img.size
    if width / height <= tgt_w / tgt_h:
        resized_width = tgt_w
        resized_height = int(tgt_w / (width / height))
    else:
        resized_height = tgt_h
        resized_width = int((width / height) * tgt_h)
    pil_img = pil_img.resize((resized_width, resized_height), resample=PImage.LANCZOS)
    # crop the center out
    arr = np.array(pil_img)
    crop_y = (arr.shape[0] - tgt_h) // 2
    crop_x = (arr.shape[1] - tgt_w) // 2
    im = to_tensor(arr[crop_y: crop_y + tgt_h, crop_x: crop_x + tgt_w])
    return im.add(im).add_(-1)

def joint_vi_vae_encode_decode(vae, image_path, scale_schedule, device, tgt_h, tgt_w):
    pil_image = Image.open(image_path).convert('RGB')
    inp = transform(pil_image, tgt_h, tgt_w)
    inp = inp.unsqueeze(0).to(device)
    scale_schedule = [(item[0], item[1], item[2]) for item in scale_schedule]
    t1 = time.time()
    h, z, _, all_bit_indices, _, infinity_input = vae.encode(inp, scale_schedule=scale_schedule)
    t2 = time.time()
    recons_img = vae.decode(z)[0]
    if len(recons_img.shape) == 4:
        recons_img = recons_img.squeeze(1)
    print(f'recons: z.shape: {z.shape}, recons_img shape: {recons_img.shape}')
    t3 = time.time()
    print(f'vae encode takes {t2-t1:.2f}s, decode takes {t3-t2:.2f}s')
    recons_img = (recons_img + 1) / 2
    recons_img = recons_img.permute(1, 2, 0).mul_(255).cpu().numpy().astype(np.uint8)
    gt_img = (inp[0] + 1) / 2
    gt_img = gt_img.permute(1, 2, 0).mul_(255).cpu().numpy().astype(np.uint8)
    print(recons_img.shape, gt_img.shape)
    return gt_img, recons_img, all_bit_indices

def load_visual_tokenizer(args):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    # load vae
    if args.vae_type in [16,18,20,24,32,64]:
        from models.bsq_vae.vae import vae_model
        schedule_mode = "dynamic"
        codebook_dim = args.vae_type
        codebook_size = 2**codebook_dim
        if args.apply_spatial_patchify:
            patch_size = 8
            encoder_ch_mult=[1, 2, 4, 4]
            decoder_ch_mult=[1, 2, 4, 4]
        else:
            patch_size = 16
            encoder_ch_mult=[1, 2, 4, 4, 4]
            decoder_ch_mult=[1, 2, 4, 4, 4]
        vae = vae_model(args.vae_path, schedule_mode, codebook_dim, codebook_size, patch_size=patch_size, 
                        encoder_ch_mult=encoder_ch_mult, decoder_ch_mult=decoder_ch_mult, test_mode=True).to(device)
    else:
        raise ValueError(f'vae_type={args.vae_type} not supported')
    return vae

def load_transformer(vae, args):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model_path = args.model_path
    if args.checkpoint_type == 'torch': 
        # copy large model to local; save slim to local; and copy slim to nas; load local slim model
        if osp.exists(args.cache_dir):
            local_model_path = osp.join(args.cache_dir, 'tmp', model_path.replace('/', '_'))
        else:
            local_model_path = model_path
        if args.enable_model_cache:
            slim_model_path = model_path.replace('ar-', 'slim-')
            local_slim_model_path = local_model_path.replace('ar-', 'slim-')
            os.makedirs(osp.dirname(local_slim_model_path), exist_ok=True)
            print(f'model_path: {model_path}, slim_model_path: {slim_model_path}')
            print(f'local_model_path: {local_model_path}, local_slim_model_path: {local_slim_model_path}')
            if not osp.exists(local_slim_model_path):
                if osp.exists(slim_model_path):
                    print(f'copy {slim_model_path} to {local_slim_model_path}')
                    shutil.copyfile(slim_model_path, local_slim_model_path)
                else:
                    if not osp.exists(local_model_path):
                        print(f'copy {model_path} to {local_model_path}')
                        shutil.copyfile(model_path, local_model_path)
                    save_slim_model(local_model_path, save_file=local_slim_model_path, device=device)
                    print(f'copy {local_slim_model_path} to {slim_model_path}')
                    if not osp.exists(slim_model_path):
                        shutil.copyfile(local_slim_model_path, slim_model_path)
                        os.remove(local_model_path)
                        os.remove(model_path)
            slim_model_path = local_slim_model_path
        else:
            slim_model_path = model_path
        print(f'load checkpoint from {slim_model_path}')

    if args.model_type == 'infinity_2b':
        kwargs_model = dict(depth=32, embed_dim=2048, num_heads=2048//128, drop_path_rate=0.1, mlp_ratio=4, block_chunks=8) # 2b model
    elif args.model_type == 'infinity_layer12':
        kwargs_model = dict(depth=12, embed_dim=768, num_heads=8, drop_path_rate=0.1, mlp_ratio=4, block_chunks=4)
    elif args.model_type == 'infinity_layer16':
        kwargs_model = dict(depth=16, embed_dim=1152, num_heads=12, drop_path_rate=0.1, mlp_ratio=4, block_chunks=4)
    elif args.model_type == 'infinity_layer24':
        kwargs_model = dict(depth=24, embed_dim=1536, num_heads=16, drop_path_rate=0.1, mlp_ratio=4, block_chunks=4)
    elif args.model_type == 'infinity_layer32':
        kwargs_model = dict(depth=32, embed_dim=2080, num_heads=20, drop_path_rate=0.1, mlp_ratio=4, block_chunks=4)
    elif args.model_type == 'infinity_layer40':
        kwargs_model = dict(depth=40, embed_dim=2688, num_heads=24, drop_path_rate=0.1, mlp_ratio=4, block_chunks=4)
    elif args.model_type == 'infinity_layer48':
        kwargs_model = dict(depth=48, embed_dim=3360, num_heads=28, drop_path_rate=0.1, mlp_ratio=4, block_chunks=4)
    infinity = load_infinity(
        rope2d_each_sa_layer=args.rope2d_each_sa_layer, 
        rope2d_normalized_by_hw=args.rope2d_normalized_by_hw,
        use_scale_schedule_embedding=args.use_scale_schedule_embedding,
        pn=args.pn,
        use_bit_label=args.use_bit_label, 
        add_lvl_embeding_only_first_block=args.add_lvl_embeding_only_first_block, 
        model_path=slim_model_path, 
        scale_schedule=None, 
        vae=vae, 
        device=device, 
        model_kwargs=kwargs_model,
        text_channels=args.text_channels,
        apply_spatial_patchify=args.apply_spatial_patchify,
        use_flex_attn=args.use_flex_attn,
        bf16=args.bf16,
    )
    return infinity

# Set up paths
weights_path = Path(__file__).parent / 'weights'
weights_path.mkdir(exist_ok=True)
download_infinity_weights(weights_path)

# Device setup
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32

# Define args
args = argparse.Namespace(
    pn='1M',
    model_path=str(weights_path / 'infinity_2b_reg.pth'),
    cfg_insertion_layer=0,
    vae_type=32,
    vae_path=str(weights_path / 'infinity_vae_d32reg.pth'),
    add_lvl_embeding_only_first_block=1,
    use_bit_label=1,
    model_type='infinity_2b',
    rope2d_each_sa_layer=1,
    rope2d_normalized_by_hw=2,
    use_scale_schedule_embedding=0,
    sampling_per_bits=1,
    text_encoder_ckpt=str(weights_path / 'flan-t5-xl'),
    text_channels=2048,
    apply_spatial_patchify=0,
    h_div_w_template=1.000,
    use_flex_attn=0,
    cache_dir='/dev/shm',
    checkpoint_type='torch',
    seed=0,
    bf16=1 if dtype == torch.bfloat16 else 0,
    save_file='tmp.jpg',
    enable_model_cache=False,
)

# Load models
text_tokenizer, text_encoder = load_tokenizer(t5_path="google/flan-t5-xl")
vae = load_visual_tokenizer(args)
infinity = load_transformer(vae, args)

# Define the image generation function
@spaces.GPU
def generate_image(prompt, cfg, tau, h_div_w, seed, enable_positive_prompt):
    try:
        args.prompt = prompt
        args.cfg = cfg
        args.tau = tau
        args.h_div_w = h_div_w
        args.seed = seed
        args.enable_positive_prompt = enable_positive_prompt
        
        # Find the closest h_div_w_template
        h_div_w_template_ = h_div_w_templates[np.argmin(np.abs(h_div_w_templates - h_div_w))]
        
        # Get scale_schedule based on h_div_w_template_
        scale_schedule = dynamic_resolution_h_w[h_div_w_template_][args.pn]['scales']
        scale_schedule = [(1, h, w) for (_, h, w) in scale_schedule]
        
        # Generate the image
        generated_image = gen_one_img(
            infinity,
            vae,
            text_tokenizer,
            text_encoder,
            prompt,
            g_seed=seed,
            gt_leak=0,
            gt_ls_Bl=None,
            cfg_list=cfg,
            tau_list=tau,
            scale_schedule=scale_schedule,
            cfg_insertion_layer=[args.cfg_insertion_layer],
            vae_type=args.vae_type,
            sampling_per_bits=args.sampling_per_bits,
            enable_positive_prompt=enable_positive_prompt,
        )
        
        # Convert the image to RGB and uint8
        image = generated_image.cpu().numpy()
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = np.uint8(image)
        
        return image
    except Exception as e:
        print(f"Error generating image: {e}")
        return None

# Set up Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("<h1><center>Infinity Image Generator</center></h1>")
    
    with gr.Row():
        with gr.Column():
            # Prompt Settings
            gr.Markdown("### Prompt Settings")
            prompt = gr.Textbox(label="Prompt", value="alien spaceship enterprise", placeholder="Enter your prompt here...")
            enable_positive_prompt = gr.Checkbox(label="Enable Positive Prompt", value=False, info="Enhance prompts with positive attributes for faces.")
            
            # Image Settings
            gr.Markdown("### Image Settings")
            with gr.Row():
                cfg = gr.Slider(label="CFG (Classifier-Free Guidance)", minimum=1, maximum=10, step=0.5, value=3, info="Controls the strength of the prompt.")
                tau = gr.Slider(label="Tau (Temperature)", minimum=0.1, maximum=1.0, step=0.1, value=0.5, info="Controls the randomness of the output.")
            with gr.Row():
                h_div_w = gr.Slider(label="Aspect Ratio (Height/Width)", minimum=0.5, maximum=2.0, step=0.1, value=1.0, info="Set the aspect ratio of the generated image.")
                seed = gr.Number(label="Seed", value=random.randint(0, 10000), info="Set a seed for reproducibility.")
            
            # Generate Button
            generate_button = gr.Button("Generate Image", variant="primary")
        
        with gr.Column():
            # Output Section
            gr.Markdown("### Generated Image")
            output_image = gr.Image(label="Generated Image", type="pil")
            gr.Markdown("**Tip:** Right-click the image to save it.")
    
    # Error Handling
    error_message = gr.Textbox(label="Error Message", visible=False)
    
    # Link the generate button to the image generation function
    generate_button.click(
        generate_image,
        inputs=[prompt, cfg, tau, h_div_w, seed, enable_positive_prompt],
        outputs=output_image
    )

# Launch the Gradio app
demo.launch()