import spaces 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 import io import base64 import logging import runpod import requests # Add logging configuration right after logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) CLIP_PATH = "google/siglip-so400m-patch14-384" CHECKPOINT_PATH = Path("cgrkzexw-599808") TITLE = "

JoyCaption Alpha Two (2024-09-26a)

" CAPTION_TYPE_MAP = { "Descriptive": [ "Write a descriptive caption for this image in a formal tone.", "Write a descriptive caption for this image in a formal tone within {word_count} words.", "Write a {length} descriptive caption for this image in a formal tone.", ], "Descriptive (Informal)": [ "Write a descriptive caption for this image in a casual tone.", "Write a descriptive caption for this image in a casual tone within {word_count} words.", "Write a {length} descriptive caption for this image in a casual tone.", ], "Training Prompt": [ "Write a stable diffusion prompt for this image.", "Write a stable diffusion prompt for this image within {word_count} words.", "Write a {length} stable diffusion prompt for this image.", ], "MidJourney": [ "Write a MidJourney prompt for this image.", "Write a MidJourney prompt for this image within {word_count} words.", "Write a {length} MidJourney prompt for this image.", ], "Booru tag list": [ "Write a list of Booru tags for this image.", "Write a list of Booru tags for this image within {word_count} words.", "Write a {length} list of Booru tags for this image.", ], "Booru-like tag list": [ "Write a list of Booru-like tags for this image.", "Write a list of Booru-like tags for this image within {word_count} words.", "Write a {length} list of Booru-like tags for this image.", ], "Art Critic": [ "Analyze this image like an art critic would with information about its composition, style, symbolism, the use of color, light, any artistic movement it might belong to, etc.", "Analyze this image like an art critic would with information about its composition, style, symbolism, the use of color, light, any artistic movement it might belong to, etc. Keep it within {word_count} words.", "Analyze this image like an art critic would with information about its composition, style, symbolism, the use of color, light, any artistic movement it might belong to, etc. Keep it {length}.", ], "Product Listing": [ "Write a caption for this image as though it were a product listing.", "Write a caption for this image as though it were a product listing. Keep it under {word_count} words.", "Write a {length} caption for this image as though it were a product listing.", ], "Social Media Post": [ "Write a caption for this image as if it were being used for a social media post.", "Write a caption for this image as if it were being used for a social media post. Limit the caption to {word_count} words.", "Write a {length} caption for this image as if it were being used for a social media post.", ], } 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)) # 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) # <|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 logger.info("Loading CLIP model...") clip_processor = AutoProcessor.from_pretrained(CLIP_PATH) clip_model = AutoModel.from_pretrained(CLIP_PATH) clip_model = clip_model.vision_model logger.info("CLIP model loaded successfully") logger.info("Loading VLM's custom vision model...") assert (CHECKPOINT_PATH / "clip_model.pt").exists() 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 logger.info("VLM's custom vision model loaded successfully") clip_model.eval() clip_model.requires_grad_(False) clip_model.to("cuda") logger.info("CLIP model moved to GPU") # Tokenizer logger.info("Loading tokenizer...") tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_PATH / "text_model", use_fast=True) assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast) logger.info("Tokenizer loaded successfully") # LLM logger.info("Loading LLM...") text_model = AutoModelForCausalLM.from_pretrained( CHECKPOINT_PATH / "text_model", device_map=0, torch_dtype=torch.bfloat16 ) logger.info("LLM loaded successfully") 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_length: str | int, extra_options: list[str], name_input: str, custom_prompt: str) -> tuple[str, str]: torch.cuda.empty_cache() # 'any' means no length specified length = None if caption_length == "any" else caption_length if isinstance(length, str): try: length = int(length) except ValueError: pass # Build prompt if length is None: map_idx = 0 elif isinstance(length, int): map_idx = 1 elif isinstance(length, str): map_idx = 2 else: raise ValueError(f"Invalid caption length: {length}") prompt_str = CAPTION_TYPE_MAP[caption_type][map_idx] # Add extra options if len(extra_options) > 0: prompt_str += " " + " ".join(extra_options) # Add name, length, word_count prompt_str = prompt_str.format(name=name_input, length=caption_length, word_count=caption_length) if custom_prompt.strip() != "": prompt_str = custom_prompt.strip() # For debugging print(f"Prompt: {prompt_str}") # Preprocess image # NOTE: I found the default processor for so400M to have worse results than just using PIL directly #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') # Embed image # This results in Batch x Image Tokens x Features with torch.amp.autocast_mode.autocast('cuda', enabled=True): vision_outputs = clip_model(pixel_values=pixel_values, output_hidden_states=True) embedded_images = image_adapter(vision_outputs.hidden_states) embedded_images = embedded_images.to('cuda') # Build the conversation convo = [ { "role": "system", "content": "You are a helpful image captioner.", }, { "role": "user", "content": prompt_str, }, ] # Format the conversation convo_string = tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = True) assert isinstance(convo_string, str) # Tokenize the conversation # prompt_str is tokenized separately so we can do the calculations below convo_tokens = tokenizer.encode(convo_string, return_tensors="pt", add_special_tokens=False, truncation=False) prompt_tokens = tokenizer.encode(prompt_str, return_tensors="pt", add_special_tokens=False, truncation=False) assert isinstance(convo_tokens, torch.Tensor) and isinstance(prompt_tokens, torch.Tensor) convo_tokens = convo_tokens.squeeze(0) # Squeeze just to make the following easier prompt_tokens = prompt_tokens.squeeze(0) # Calculate where to inject the image eot_id_indices = (convo_tokens == tokenizer.convert_tokens_to_ids("<|eot_id|>")).nonzero(as_tuple=True)[0].tolist() assert len(eot_id_indices) == 2, f"Expected 2 <|eot_id|> tokens, got {len(eot_id_indices)}" preamble_len = eot_id_indices[1] - prompt_tokens.shape[0] # Number of tokens before the prompt # Embed the tokens convo_embeds = text_model.model.embed_tokens(convo_tokens.unsqueeze(0).to('cuda')) # Construct the input input_embeds = torch.cat([ convo_embeds[:, :preamble_len], # Part before the prompt embedded_images.to(dtype=convo_embeds.dtype), # Image convo_embeds[:, preamble_len:], # The prompt and anything after it ], dim=1).to('cuda') input_ids = torch.cat([ convo_tokens[:preamble_len].unsqueeze(0), torch.zeros((1, embedded_images.shape[1]), dtype=torch.long), # Dummy tokens for the image (TODO: Should probably use a special token here so as not to confuse any generation algorithms that might be inspecting the input) convo_tokens[preamble_len:].unsqueeze(0), ], dim=1).to('cuda') attention_mask = torch.ones_like(input_ids) # Debugging print(f"Input to model: {repr(tokenizer.decode(input_ids[0]))}") #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=input_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 prompt_str, caption.strip() # Add this function to handle base64 image conversion def base64_to_pil(base64_str): if isinstance(base64_str, str): # Remove data URL prefix if present if 'base64,' in base64_str: base64_str = base64_str.split('base64,')[1] image_bytes = base64.b64decode(base64_str) image = Image.open(io.BytesIO(image_bytes)) return image return base64_str # Simple RunPod handler that forwards to stream_chat def handler(event): try: # Extract data from the Runpod event data = event["input"] # Check if input is a dictionary (which seems to be the case from the error) if isinstance(data["input_image"], dict): return { "status": "error", "message": "Invalid image format. Expected base64 string or file data." } # Convert base64 image to PIL Image if isinstance(data["input_image"], str): # Remove data URL prefix if present if 'base64,' in data["input_image"]: data["input_image"] = data["input_image"].split('base64,')[1] image_data = base64.b64decode(data["input_image"]) image = Image.open(io.BytesIO(image_data)) else: return { "status": "error", "message": "Invalid image format" } # Now we have a valid PIL Image, proceed with the stream_chat call result = stream_chat( input_image=image, caption_type=data.get("caption_type", "Descriptive"), caption_length=data.get("caption_length", "any"), extra_options=data.get("extra_options", []), name_input=data.get("name_input", ""), custom_prompt=data.get("custom_prompt", "") ) return { "output": result } except Exception as e: return { "status": "error", "message": str(e) } if __name__ == "__main__": # Start RunPod serverless runpod.serverless.start({"handler": handler})