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from __future__ import annotations
import spaces
import gradio as gr
from threading import Thread
from transformers import TextIteratorStreamer
import hashlib
import os
from transformers import AutoModel, AutoProcessor
import torch
model = AutoModel.from_pretrained("visheratin/MC-LLaVA-3b", torch_dtype=torch.float16, trust_remote_code=True).to("cuda")
processor = AutoProcessor.from_pretrained("visheratin/MC-LLaVA-3b", trust_remote_code=True)
if torch.cuda.is_available():
DEVICE = "cuda"
DTYPE = torch.float16
else:
DEVICE = "cpu"
DTYPE = torch.float32
def cached_vision_process(image, max_crops, num_tokens):
image_hash = hashlib.sha256(image.tobytes()).hexdigest()
cache_path = f"visual_cache/{image_hash}-{max_crops}-{num_tokens}.pt"
if os.path.exists(cache_path):
return torch.load(cache_path).to(DEVICE, dtype=DTYPE)
else:
processor_outputs = processor.image_processor([image], max_crops)
pixel_values = processor_outputs["pixel_values"]
pixel_values = [
value.to(model.device).to(model.dtype) for value in pixel_values
]
coords = processor_outputs["coords"]
coords = [value.to(model.device).to(model.dtype) for value in coords]
image_outputs = model.vision_model(pixel_values, coords, num_tokens)
image_features = model.multi_modal_projector(image_outputs)
os.makedirs("visual_cache", exist_ok=True)
torch.save(image_features, cache_path)
return image_features.to(DEVICE, dtype=DTYPE)
@spaces.GPU(duration=20)
def answer_question(image, question, max_crops, num_tokens, sample, temperature, top_k):
prompt = f"""<|im_start|>user
<image>
{question}<|im_end|>
<|im_start|>assistant
"""
streamer = TextIteratorStreamer(processor.tokenizer, skip_special_tokens=True)
with torch.inference_mode():
inputs = processor(prompt, [image], model, max_crops=max_crops, num_tokens=num_tokens)
generation_kwargs = {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"image_features": inputs["image_features"],
"streamer": streamer,
"max_length": 1000,
"use_cache": True,
"eos_token_id": processor.tokenizer.eos_token_id,
"pad_token_id": processor.tokenizer.eos_token_id,
"temperature": temperature,
"sample": sample,
"top_k": top_k,
}
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
buffer = ""
output_started = False
for new_text in streamer:
if not output_started:
if "<|im_start|>assistant" in new_text:
output_started = True
continue
buffer += new_text
if len(buffer) > 1:
yield buffer
with gr.Blocks() as demo:
gr.HTML("<h1 class='gradio-heading'><center>MC-LLaVA 3B</center></h1>")
gr.HTML(
"<center><p class='gradio-sub-heading'>MC-LLaVA 3B is a model that can answer questions about small details in high-resolution images. Check out the <a href='https://huggingface.co/visheratin/MC-LLaVA-3b'>model card</a> for more details. If you have any questions or ideas hot to make the model better, <a href='https://x.com/visheratin'>let me know</a>.</p></center>"
)
gr.HTML(
"<center><p class='gradio-sub-heading'>There are two main parameters - max number of crops and number of image tokens. The first one controls into how many parts will the image be cut. This is especially useful when you are working with high-resolution images. The second parameter controls how many image features will be extracted for LLM to be processed. You can increase it if you are trying to extract info from a small part of the image, e.g., text.</p></center>"
)
with gr.Group():
with gr.Row():
prompt = gr.Textbox(
label="Question", placeholder="e.g. What is this?", scale=4
)
submit = gr.Button(
"Submit",
scale=1,
)
with gr.Row():
max_crops = gr.Slider(minimum=0, maximum=200, step=5, value=0, label="Max crops")
num_tokens = gr.Slider(minimum=728, maximum=2184, step=10, value=728, label="Number of image tokens")
with gr.Row():
img = gr.Image(type="pil", label="Upload or Drag an Image")
output = gr.TextArea(label="Answer")
with gr.Row():
sample = gr.Checkbox(label="Sample", value=False)
temperature = gr.Slider(minimum=0, maximum=1, step=0.1, value=0, label="Temperature")
top_k = gr.Slider(minimum=0, maximum=50, step=1, value=0, label="Top-K")
submit.click(answer_question, [img, prompt, max_crops, num_tokens, sample, temperature, top_k], output)
prompt.submit(answer_question, [img, prompt, max_crops, num_tokens, sample, temperature, top_k], output)
demo.queue().launch() |