import torch import torch.nn as nn import torch.nn.functional as F from safetensors import safe_open import json import gradio as gr from PIL import Image import numpy as np from huggingface_hub import snapshot_download from mistral_common.protocol.instruct.messages import UserMessage, TextChunk, ImageChunk from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer import spaces title = "# 🖼️ Pixtral Image Similarity Demo" description = """ Upload two images to compare their similarity based on the embeddings produced by the Pixtral model. This demo uses the vision encoder part of the Pixtral model to generate embeddings and then calculates the cosine similarity between them. ### How it works: 1. Upload two images 2. The Pixtral vision encoder processes both images 3. The cosine similarity between the embeddings is calculated 4. The similarity score is displayed (1.0 means identical, 0.0 means completely different) ### Note: This is a demonstration of the vision encoder capabilities and does not use the full Pixtral model for text generation. ### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/qdfnvSPcqP) On 🤗Huggingface:[MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [Build Tonic](https://git.tonic-ai.com/contribute)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗 """ # Download model files model_path = snapshot_download(repo_id="mistral-community/pixtral-12b-240910") # Load model parameters and tokenizer configuration with open(f'{model_path}/params.json', 'r') as f: params = json.load(f) with open(f'{model_path}/tekken.json', 'r') as f: tokenizer_config = json.load(f) class GELU(nn.Module): def __init__(self, dim_in, dim_out, approximate='none', bias=True): super().__init__() self.linear = nn.Linear(dim_in, dim_out, bias=bias) self.approximate = approximate def forward(self, x): if self.approximate == 'tanh': return 0.5 * x * (1 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x, 3)))) else: return F.gelu(self.linear(x)) class Rope2D(nn.Module): def __init__(self, dim, max_position_embeddings=1024, base=10000): super().__init__() inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer("inv_freq", inv_freq) self.max_seq_len_cached = max_position_embeddings t = torch.arange(self.max_seq_len_cached, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) def forward(self, x, seq_len=None): if seq_len > self.max_seq_len_cached: self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1).to(x.device) self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) return ( self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), ) class VisionEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.embed = nn.Conv2d(config['num_channels'], config['hidden_size'], kernel_size=config['patch_size'], stride=config['patch_size']) self.rope = Rope2D(config['hidden_size'] // config['num_attention_heads'], base=config['rope_theta']) self.layers = nn.ModuleList([nn.TransformerEncoderLayer(d_model=config['hidden_size'], nhead=config['num_attention_heads'], dim_feedforward=config['intermediate_size']) for _ in range(config['num_hidden_layers'])]) self.norm = nn.LayerNorm(config['hidden_size']) self.gelu = GELU(config['hidden_size'], config['hidden_size']) def forward(self, pixel_values): x = self.embed(pixel_values) b, c, h, w = x.shape x = x.flatten(2).transpose(1, 2) cos, sin = self.rope(x, seq_len=h*w) for layer in self.layers: x = layer(x) x = self.norm(x) x = self.gelu(x) return x class PixtralModel(nn.Module): def __init__(self, params): super().__init__() self.vision_encoder = VisionEncoder(params['vision_encoder']) def forward(self, image): return self.vision_encoder(image) def load_model(params, model_path): model = PixtralModel(params) with safe_open(f'{model_path}/consolidated.safetensors', framework="pt", device="cpu") as f: for name, param in model.named_parameters(): if name in f.keys(): param.data = f.get_tensor(name) model.eval() return model # Initialize the model model = load_model(params, model_path) def preprocess_image(image): image = image.convert('RGB') image = image.resize((params['vision_encoder']['image_size'], params['vision_encoder']['image_size'])) image_tensor = torch.tensor(np.array(image)).permute(2, 0, 1).unsqueeze(0).float() / 255.0 return image_tensor @spaces.GPU def calculate_similarity(image1, image2): # Preprocess images tensor1 = preprocess_image(image1).cuda() tensor2 = preprocess_image(image2).cuda() # Generate embeddings with torch.no_grad(): model.cuda() embedding1 = model(tensor1).mean(dim=1) # Average over spatial dimensions embedding2 = model(tensor2).mean(dim=1) model.cpu() # Calculate cosine similarity similarity = F.cosine_similarity(embedding1, embedding2).item() return similarity # Gradio interface with gr.Blocks() as demo: gr.Markdown(title) gr.Markdown("## Model Details") gr.Markdown(f"- Vision Encoder Hidden Size: {params['vision_encoder']['hidden_size']}") gr.Markdown(f"- Number of Vision Encoder Layers: {params['vision_encoder']['num_hidden_layers']}") gr.Markdown(f"- Number of Attention Heads: {params['vision_encoder']['num_attention_heads']}") gr.Markdown(f"- Image Size: {params['vision_encoder']['image_size']}x{params['vision_encoder']['image_size']}") gr.Markdown(f"- Patch Size: {params['vision_encoder']['patch_size']}x{params['vision_encoder']['patch_size']}") gr.Markdown("## How it works") gr.Markdown("1. The image is processed by a Vision Encoder using 2D ROPE (Rotary Position Embedding).") gr.Markdown("2. The encoder uses GELU activation in its layers.") gr.Markdown("3. The encoded image and the prompt are used to generate descriptive text.") gr.Markdown(description) with gr.Row(): image1_input = gr.Image(type="pil", label="Image 1") image2_input = gr.Image(type="pil", label="Image 2") submit_btn = gr.Button("📸🌬️Calculate Similarity") similarity_output = gr.Number(label="Similarity Score (0.0 to 1.0)") submit_btn.click( fn=calculate_similarity, inputs=[image1_input, image2_input], outputs=[similarity_output] ) if __name__ == "__main__": demo.launch() # import torch # import torch.nn as nn # import torch.nn.functional as F # from safetensors import safe_open # import json # import gradio as gr # from PIL import Image # import numpy as np # from huggingface_hub import snapshot_download # from mistral_common.protocol.instruct.messages import UserMessage, TextChunk, ImageChunk # from mistral_common.protocol.instruct.request import ChatCompletionRequest # from mistral_common.tokens.tokenizers.mistral import MistralTokenizer # import spaces # title = "# **WIP / DEMO** 🙋🏻‍♂️Welcome to Tonic's Pixtral Image-to-Text Model Demo" # # Download model files # model_path = snapshot_download(repo_id="mistral-community/pixtral-12b-240910") # # Load model parameters and tokenizer configuration # with open(f'{model_path}/params.json', 'r') as f: # params = json.load(f) # with open(f'{model_path}/tekken.json', 'r') as f: # tokenizer_config = json.load(f) # class PixtralModel(nn.Module): # def __init__(self, params): # super().__init__() # self.vision_encoder = VisionEncoder(params['vision_encoder']) # # Add text generation components here # def forward(self, image): # vision_output = self.vision_encoder(image) # # Add text generation logic here # return vision_output # def load_model(params, model_path): # model = PixtralModel(params) # with safe_open(f'{model_path}/consolidated.safetensors', framework="pt", device="cpu") as f: # for name, param in model.named_parameters(): # if name in f.keys(): # param.data = f.get_tensor(name) # model.eval() # return model # # Initialize the model # model = load_model(params, model_path) # tokenizer = MistralTokenizer.from_model("pixtral") # @spaces.GPU # def process_image_and_text(image, prompt): # # Prepare the image # image = image.convert('RGB') # image = image.resize((params['vision_encoder']['image_size'], params['vision_encoder']['image_size'])) # image_tensor = torch.tensor(np.array(image)).permute(2, 0, 1).unsqueeze(0).float() / 255.0 # image_tensor = image_tensor.cuda() # # Tokenize the input # tokenized = tokenizer.encode_chat_completion( # ChatCompletionRequest( # messages=[ # UserMessage( # content=[ # TextChunk(text=prompt), # ImageChunk(image=image), # ] # ) # ], # model="pixtral", # ) # ) # tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images # # Process the image and generate text # with torch.no_grad(): # model.cuda() # vision_output = model(image_tensor) # model.cpu() # generated_text = f"Generated text based on the image and prompt: {prompt}" # return generated_text, len(tokens), len(images) # # Gradio interface # with gr.Blocks() as demo: # gr.Markdown(title) # gr.Markdown(description) # with gr.Row(): # with gr.Column(scale=1): # input_image = gr.Image(type="pil") # input_prompt = gr.Textbox(label="Prompt") # submit_btn = gr.Button("Generate Text") # with gr.Column(scale=1): # output_text = gr.Textbox(label="Generated Text") # token_count = gr.Number(label="Number of Tokens") # image_count = gr.Number(label="Number of Images") # submit_btn.click( # fn=process_image_and_text, # inputs=[input_image, input_prompt], # outputs=[output_text, token_count, image_count] # ) # if __name__ == "__main__": # demo.launch()