mirror of
https://github.com/deepseek-ai/Janus
synced 2024-12-26 13:52:57 +00:00
68 lines
2.6 KiB
Python
68 lines
2.6 KiB
Python
# Copyright (c) 2023-2024 DeepSeek.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy of
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# this software and associated documentation files (the "Software"), to deal in
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# the Software without restriction, including without limitation the rights to
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# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
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# the Software, and to permit persons to whom the Software is furnished to do so,
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# subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
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# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
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# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
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# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
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# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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import torch
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from transformers import AutoModelForCausalLM
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from janus.models import MultiModalityCausalLM, VLChatProcessor
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from janus.utils.io import load_pil_images
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# specify the path to the model
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model_path = "deepseek-ai/Janus-1.3B"
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vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
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tokenizer = vl_chat_processor.tokenizer
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vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
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model_path, trust_remote_code=True
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)
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vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
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conversation = [
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{
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"role": "User",
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"content": "<image_placeholder>\nConvert the formula into latex code.",
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"images": ["images/equation.png"],
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},
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{"role": "Assistant", "content": ""},
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]
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# load images and prepare for inputs
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pil_images = load_pil_images(conversation)
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prepare_inputs = vl_chat_processor(
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conversations=conversation, images=pil_images, force_batchify=True
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).to(vl_gpt.device)
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# # run image encoder to get the image embeddings
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inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
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# # run the model to get the response
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outputs = vl_gpt.language_model.generate(
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inputs_embeds=inputs_embeds,
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attention_mask=prepare_inputs.attention_mask,
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pad_token_id=tokenizer.eos_token_id,
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bos_token_id=tokenizer.bos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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max_new_tokens=512,
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do_sample=False,
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use_cache=True,
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)
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answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
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print(f"{prepare_inputs['sft_format'][0]}", answer)
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