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import torch

from transformers import AutoModelForCausalLM

from janus.models import MultiModalityCausalLM, VLChatProcessor

from janus.utils.io import load_pil_images

# specify the path to the model

model_path = "deepseek-ai/Janus-1.3B"

vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)

tokenizer = vl_chat_processor.tokenizer

vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(

model_path, trust_remote_code=True

)

vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()

conversation = [

]

# load images and prepare for inputs

pil_images = load_pil_images(conversation)

prepare_inputs = vl_chat_processor(

conversations=conversation, images=pil_images, force_batchify=True

).to(vl_gpt.device)

# # run image encoder to get the image embeddings

inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)

# # run the model to get the response

outputs = vl_gpt.language_model.generate(

)

answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)

print(f"{prepare_inputs['sft_format'][0]}", answer)