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 CLIP_PATH = "google/siglip-so400m-patch14-384" VLM_PROMPT = "A descriptive caption for this image:\n" MODEL_PATH = "meta-llama/Meta-Llama-3.1-8B" CHECKPOINT_PATH = Path("h2vtfhad") TITLE = "

Foo

" HF_TOKEN = os.environ.get("HF_TOKEN", None) class ImageAdapter(nn.Module): def __init__(self, input_features: int, output_features: int): super().__init__() self.linear1 = nn.Linear(input_features, output_features) self.activation = nn.GELU() self.linear2 = nn.Linear(output_features, output_features) def forward(self, vision_outputs: torch.Tensor): x = self.linear1(vision_outputs) x = self.activation(x) x = self.linear2(x) return x # 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 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") 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) 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): torch.cuda.empty_cache() # Preprocess image image = clip_processor(images=input_image, return_tensors='pt').pixel_values image = image.to('cuda') # Tokenize the prompt prompt = tokenizer.encode(VLM_PROMPT, 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=image, output_hidden_states=True) image_features = vision_outputs.hidden_states[-2] 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)) # 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), ], 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, ], 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) # Trim off the prompt generate_ids = generate_ids[:, input_ids.shape[1]:] if generate_ids[0][-1] == tokenizer.eos_token_id: generate_ids = generate_ids[:, :-1] caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0] return [caption] with gr.Blocks() as demo: gr.HTML(TITLE) with gr.Row(): with gr.Column(): input_image = gr.Image(type="pil", label="Input Image") run_button = gr.Button("Caption") with gr.Column(): output_caption = gr.Textbox(label="Caption", default="") run_button.click(fn=stream_chat, inputs=[input_image], outputs=[output_caption]) if __name__ == "__main__": demo.launch()