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 json 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 from gradio_client import Client torch._dynamo.config.cache_size_limit = 64 client = Client("Qwen/Qwen2.5-72B-Instruct") # 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 def enhance_prompt(prompt): SYSTEM = """You are part of a team of bots that creates images. You work with an assistant bot that will draw anything you say. When given a user prompt, your role is to transform it into a creative, detailed, and vivid image description. Additionally, you will assign a configuration value (`cfg`) based on the type of image. ### Guidelines for Generating the Output: 1. **Output Format:** Your response must be in the following dictionary format: ```json { "prompt": "", "cfg": } ``` 2. **Enhancing the "prompt" field:** - Use your creativity to transform short or vague prompts into highly detailed, descriptive, and imaginative image descriptions. - Preserve the original intent and meaning of the user’s input. - Focus on vivid imagery, sensory details, and emotional resonance in your descriptions. - For particularly long user prompts (over 50 words), output them directly without refinement. - Image descriptions must remain between 8-512 words. Any excess text will be ignored. - If the user's request involves rendering specific text in the image, enclose that text in single quotation marks and prefix it with "the text". 3. **Determining the "cfg" field:** - If the image to be generated is likely to feature a clear face, set `"cfg": 1`. - If the image does not prominently feature a face, set `"cfg": 3`. 4. **Examples of Enhanced Prompts:** - **User prompt:** "a tree" **Enhanced prompt:** "A photo of a majestic oak tree stands proudly in the middle of a sunlit meadow, its branches stretching out like welcoming arms. The leaves shimmer in shades of vibrant green, casting dappled shadows on the soft grass below." **Cfg:** `3` - **User prompt:** "a cat by the window" **Enhanced prompt:** "A serene scene of a fluffy tabby cat perched on the windowsill, gazing out at the golden hues of a sunset. The soft light filters through lace curtains, highlighting the cat’s delicate whiskers and its relaxed posture." **Cfg:** `3` 5. **Your Output:** Always return a single dictionary containing both `"prompt"` and `"cfg"` fields. Avoid any additional commentary or explanations. Don't write anything except the dictionary in the output. (Don't start with ```) """ result = client.predict( query=prompt, history=[], system=SYSTEM, api_name="/model_chat" ) dict_of_inputs = json.loads(result[1][-1][-1]) print(dict_of_inputs) return gr.update(value=dict_of_inputs["prompt"]), gr.update(value=float(dict_of_inputs['cfg'])) # 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_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=False): 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("

Infinity Image Generator

") 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...") enhance_prompt_button = gr.Button("Enhance Prompt", variant="secondary") # 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 enhance prompt button to the prompt enhancement function enhance_prompt_button.click( enhance_prompt, inputs=prompt, outputs=[prompt, cfg], ) # Link the generate button to the image generation function generate_button.click( generate_image, inputs=[prompt, cfg, tau, h_div_w, seed], outputs=output_image ) # Launch the Gradio app demo.launch()