import spaces import gradio as gr from huggingface_hub import InferenceClient from torch import nn from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM from pathlib import Path import torch import torch.amp.autocast_mode from PIL import Image import os import torchvision.transforms.functional as TVF CLIP_PATH = "google/siglip-so400m-patch14-384" MODEL_PATH = "meta-llama/Meta-Llama-3.1-8B" CHECKPOINT_PATH = Path("9em124t2-499968") TITLE = "

JoyCaption Alpha One (2024-09-20a)

" CAPTION_TYPE_MAP = { ("descriptive", "formal", False, False): ["Write a descriptive caption for this image in a formal tone."], ("descriptive", "formal", False, True): ["Write a descriptive caption for this image in a formal tone within {word_count} words."], ("descriptive", "formal", True, False): ["Write a {length} descriptive caption for this image in a formal tone."], ("descriptive", "informal", False, False): ["Write a descriptive caption for this image in a casual tone."], ("descriptive", "informal", False, True): ["Write a descriptive caption for this image in a casual tone within {word_count} words."], ("descriptive", "informal", True, False): ["Write a {length} descriptive caption for this image in a casual tone."], ("training_prompt", "formal", False, False): ["Write a stable diffusion prompt for this image."], ("training_prompt", "formal", False, True): ["Write a stable diffusion prompt for this image within {word_count} words."], ("training_prompt", "formal", True, False): ["Write a {length} stable diffusion prompt for this image."], ("rng-tags", "formal", False, False): ["Write a list of Booru tags for this image."], ("rng-tags", "formal", False, True): ["Write a list of Booru tags for this image within {word_count} words."], ("rng-tags", "formal", True, False): ["Write a {length} list of Booru tags for this image."], } HF_TOKEN = os.environ.get("HF_TOKEN", None) class ImageAdapter(nn.Module): def __init__(self, input_features: int, output_features: int, ln1: bool, pos_emb: bool, num_image_tokens: int, deep_extract: bool): super().__init__() self.deep_extract = deep_extract if self.deep_extract: input_features = input_features * 5 self.linear1 = nn.Linear(input_features, output_features) self.activation = nn.GELU() self.linear2 = nn.Linear(output_features, output_features) self.ln1 = nn.Identity() if not ln1 else nn.LayerNorm(input_features) self.pos_emb = None if not pos_emb else nn.Parameter(torch.zeros(num_image_tokens, input_features)) # Mode token #self.mode_token = nn.Embedding(n_modes, output_features) #self.mode_token.weight.data.normal_(mean=0.0, std=0.02) # Matches HF's implementation of llama3 # Other tokens (<|image_start|>, <|image_end|>, <|eot_id|>) self.other_tokens = nn.Embedding(3, output_features) self.other_tokens.weight.data.normal_(mean=0.0, std=0.02) # Matches HF's implementation of llama3 def forward(self, vision_outputs: torch.Tensor): if self.deep_extract: x = torch.concat(( vision_outputs[-2], vision_outputs[3], vision_outputs[7], vision_outputs[13], vision_outputs[20], ), dim=-1) assert len(x.shape) == 3, f"Expected 3, got {len(x.shape)}" # batch, tokens, features assert x.shape[-1] == vision_outputs[-2].shape[-1] * 5, f"Expected {vision_outputs[-2].shape[-1] * 5}, got {x.shape[-1]}" else: x = vision_outputs[-2] x = self.ln1(x) if self.pos_emb is not None: assert x.shape[-2:] == self.pos_emb.shape, f"Expected {self.pos_emb.shape}, got {x.shape[-2:]}" x = x + self.pos_emb x = self.linear1(x) x = self.activation(x) x = self.linear2(x) # Mode token #mode_token = self.mode_token(mode) #assert mode_token.shape == (x.shape[0], mode_token.shape[1], x.shape[2]), f"Expected {(x.shape[0], 1, x.shape[2])}, got {mode_token.shape}" #x = torch.cat((x, mode_token), dim=1) # <|image_start|>, IMAGE, <|image_end|> other_tokens = self.other_tokens(torch.tensor([0, 1], device=self.other_tokens.weight.device).expand(x.shape[0], -1)) assert other_tokens.shape == (x.shape[0], 2, x.shape[2]), f"Expected {(x.shape[0], 2, x.shape[2])}, got {other_tokens.shape}" x = torch.cat((other_tokens[:, 0:1], x, other_tokens[:, 1:2]), dim=1) return x def get_eot_embedding(self): return self.other_tokens(torch.tensor([2], device=self.other_tokens.weight.device)).squeeze(0) # Load CLIP print("Loading CLIP") clip_processor = AutoProcessor.from_pretrained(CLIP_PATH) clip_model = AutoModel.from_pretrained(CLIP_PATH) clip_model = clip_model.vision_model if (CHECKPOINT_PATH / "clip_model.pt").exists(): print("Loading VLM's custom vision model") checkpoint = torch.load(CHECKPOINT_PATH / "clip_model.pt", map_location='cpu') checkpoint = {k.replace("_orig_mod.module.", ""): v for k, v in checkpoint.items()} clip_model.load_state_dict(checkpoint) del checkpoint clip_model.eval() clip_model.requires_grad_(False) clip_model.to("cuda") # Tokenizer print("Loading tokenizer") tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, use_fast=False) assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast), f"Tokenizer is of type {type(tokenizer)}" # LLM print("Loading LLM") if (CHECKPOINT_PATH / "text_model").exists: print("Loading VLM's custom text model") text_model = AutoModelForCausalLM.from_pretrained(CHECKPOINT_PATH / "text_model", device_map=0, torch_dtype=torch.bfloat16) else: text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto", torch_dtype=torch.bfloat16) text_model.eval() # Image Adapter print("Loading image adapter") image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size, False, False, 38, False) image_adapter.load_state_dict(torch.load(CHECKPOINT_PATH / "image_adapter.pt", map_location="cpu")) image_adapter.eval() image_adapter.to("cuda") @spaces.GPU() @torch.no_grad() def stream_chat(input_image: Image.Image, caption_type: str, caption_tone: str, caption_length: str | int) -> str: torch.cuda.empty_cache() # 'any' means no length specified length = None if caption_length == "any" else caption_length # 'rng-tags' and 'training_prompt' don't have formal/informal tones if caption_type == "rng-tags" or caption_type == "training_prompt": caption_tone = "formal" # Build prompt prompt_key = (caption_type, caption_tone, isinstance(length, str), isinstance(length, int)) if prompt_key not in CAPTION_TYPE_MAP: raise ValueError(f"Invalid caption type: {prompt_key}") prompt_str = CAPTION_TYPE_MAP[prompt_key][0].format(length=length, word_count=length) print(f"Prompt: {prompt_str}") # Preprocess image #image = clip_processor(images=input_image, return_tensors='pt').pixel_values image = input_image.resize((384, 384), Image.LANCZOS) pixel_values = TVF.pil_to_tensor(image).unsqueeze(0) / 255.0 pixel_values = TVF.normalize(pixel_values, [0.5], [0.5]) pixel_values = pixel_values.to('cuda') # Tokenize the prompt prompt = tokenizer.encode(prompt_str, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False) # Embed image with torch.amp.autocast_mode.autocast('cuda', enabled=True): vision_outputs = clip_model(pixel_values=pixel_values, output_hidden_states=True) image_features = vision_outputs.hidden_states embedded_images = image_adapter(image_features) embedded_images = embedded_images.to('cuda') # Embed prompt prompt_embeds = text_model.model.embed_tokens(prompt.to('cuda')) assert prompt_embeds.shape == (1, prompt.shape[1], text_model.config.hidden_size), f"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}" embedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64)) eot_embed = image_adapter.get_eot_embedding().unsqueeze(0).to(dtype=text_model.dtype) # Construct prompts inputs_embeds = torch.cat([ embedded_bos.expand(embedded_images.shape[0], -1, -1), embedded_images.to(dtype=embedded_bos.dtype), prompt_embeds.expand(embedded_images.shape[0], -1, -1), eot_embed.expand(embedded_images.shape[0], -1, -1), ], dim=1) input_ids = torch.cat([ torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long), torch.zeros((1, embedded_images.shape[1]), dtype=torch.long), prompt, torch.tensor([[tokenizer.convert_tokens_to_ids("<|eot_id|>")]], dtype=torch.long), ], dim=1).to('cuda') attention_mask = torch.ones_like(input_ids) #generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=False, suppress_tokens=None) #generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=True, top_k=10, temperature=0.5, suppress_tokens=None) generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=True, suppress_tokens=None) # Uses the default which is temp=0.6, top_p=0.9 # Trim off the prompt generate_ids = generate_ids[:, input_ids.shape[1]:] if generate_ids[0][-1] == tokenizer.eos_token_id or generate_ids[0][-1] == tokenizer.convert_tokens_to_ids("<|eot_id|>"): generate_ids = generate_ids[:, :-1] caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0] return caption.strip() with gr.Blocks() as demo: gr.HTML(TITLE) with gr.Row(): with gr.Column(): input_image = gr.Image(type="pil", label="Input Image") caption_type = gr.Dropdown( choices=["descriptive", "training_prompt", "rng-tags"], label="Caption Type", value="descriptive", ) caption_tone = gr.Dropdown( choices=["formal", "informal"], label="Caption Tone", value="formal", ) caption_length = gr.Dropdown( choices=["any", "very short", "short", "medium-length", "long", "very long"] + [str(i) for i in range(20, 261, 10)], label="Caption Length", value="any", ) gr.Markdown("**Note:** Caption tone doesn't affect `rng-tags` and `training_prompt`.") run_button = gr.Button("Caption") with gr.Column(): output_caption = gr.Textbox(label="Caption") run_button.click(fn=stream_chat, inputs=[input_image, caption_type, caption_tone, caption_length], outputs=[output_caption]) if __name__ == "__main__": demo.launch()