Xiake

Sun

August 15, 2023

October 5, 2023

Enable Textual Inversion with Stable Diffusion Pipeline via Optimum-Intel

Introduction

Stable Diffusion (SD) is a state-of-the-art latent text-to-image diffusion model that generates photorealistic images from text. Recently, many fine-tuning technologies proposed to create custom Stable Diffusion pipelines for personalized image generation, such as Textual Inversion, Low-Rank Adaptation (LoRA). We’ve already published a blog for enabling LoRA with Stable Diffusion + ControlNet pipeline.

In this blog, we will focus on enabling pre-trained textual inversion with Stable Diffusion via Optimum-Intel. The feature is available in the latest Optimum-Intel, and documentation is available here.

Textual Inversion is a technique for capturing novel concepts from a small number of example images in a way that can later be used to control text-to-image pipelines. It does so by learning new “words” in the embedding space of the pipeline’s text encoder.

Figure1. Textual Inversion sample: injecting user-specific concepts into new scenes

As Figure 1 shows, you can teach new concepts to a model such as Stable Diffusion for personalized image generation using just 3-5 images.

Hugging Face Diffusers and Stable Diffusion Web UI provides useful tools and guides to train and save custom textual inversion embeddings. The pre-trained textual inversion embeddings are widely available in sd-concepts-library and civitai, which can be loaded for inference with the StableDiffusionPipeline using Pytorch as the runtime backend.

Here is an example to load pre-trained textual inversion embedding sd-concepts-library/cat-toy to inference with Pytorch backend.

from diffusers import StableDiffusionPipeline

model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id)
pipe.load_textual_inversion("sd-concepts-library/cat-toy")
prompt = "A <cat-toy> backpack"

image = pipe(prompt, num_inference_steps=50).images[0]
image.save("cat-backpack.png")

Optimum-Intel provides the interface between the Hugging Face Transformers and Diffusers libraries to leverage OpenVINO^TMruntime to accelerate end-to-end pipelines on Intel architectures.

Figure2: Two approaches to enable textual inversion with Stable Diffusion

As Figure 2 shows that two approaches are available to enable textual inversion with Stable Diffusion via Optimum-Intel.

Although approach 1 seems quite straightforward and does not need any code modification in Optimum-Intel, the method requires the re-export ONNX model and then model conversion to the OpenVINO^TM IR model whenever the SD baseline model is merged with anew textual inversion.

Instead, we propose approach 2 to support OVStableDiffusionPipelineBase to load pre-trained textual inversion embeddings in runtime to save disk storage while keeping flexibility.

Save disk storage: We only need to save an SD baseline model converted to OpenVINO^TM IR (e.g.: SD-1.5 ~5GB) and multiple textual embeddings (~10KB-100KB), instead of multiple SD OpenVINO^TM IR with textual inversion embeddings merged (~n *5GB), since disk storage is limited, especially for edge/client use case.
Flexibility: We can load (multiple) pre-trained textual inversion embeddings in the SD baseline model in runtime quickly, which supports the combination of embeddings and avoid messing up the baseline model.

‍

How to enable textual inversion in runtime?

We implemented OVTextualInversionLoaderMixinbased on diffusers.loaders.TextualInversionLoaderMixin with the following features:

Load and parse textual embeddings saved as*.bin, *.pt, *.safetensors as a list of Tensors.
Update tokenizer for new “words” using new token id and expand vocabulary size.
Update text encoder embeddings via InsertTextEmbedding class based on OpenVINO^TMngraph transformation.

‍

For the implementation details of OVTextualInversionLoaderMixin, please refer to here.

Here is the sample code for InsertTextEmbedding class:

class InsertTextEmbedding(MatcherPass):
    r"""
    OpenVINO ngraph transformation for inserting pre-trained texual inversion embedding to text encoder
    """

    def __init__(self, token_ids_and_embeddings):
        MatcherPass.__init__(self)
        self.model_changed = False
        param = WrapType("opset1.Constant")

        def callback(matcher: Matcher) -> bool:
            root = matcher.get_match_root()
            if root.get_friendly_name() == TEXTUAL_INVERSION_EMBEDDING_KEY:
                add_ti = root
                consumers = matcher.get_match_value().get_target_inputs()
                for token_id, embedding in token_ids_and_embeddings:
                    ti_weights = ops.constant(embedding, Type.f32, name=str(token_id))
                    ti_weights_unsqueeze = ops.unsqueeze(ti_weights, axes=0)
                    add_ti = ops.concat(
                        nodes=[add_ti, ti_weights_unsqueeze],
                        axis=0,
                        name=f"{TEXTUAL_INVERSION_EMBEDDING_KEY}.textual_inversion_{token_id}",
                    )

                for consumer in consumers:
                    consumer.replace_source_output(add_ti.output(0))

                # Use new operation for additional matching
                self.register_new_node(add_ti)

            # Root node wasn't replaced or changed
            return False

        self.register_matcher(Matcher(param, "InsertTextEmbedding"), callback)

InsertTextEmbeddingclass utilizes OpenVINO^TM ngraph MatcherPass function to insert subgraph into the model. Please note, the MacherPass function can only filter layers by type, so we run two phases of filtering to find the layer that matched with the pre-defined key in the model:

Filter all Constant layers to trigger the callback function.
Filter layer name with pre-defined key “TEXTUAL_INVERSION_EMBEDDING_KEY” in the callback function

‍

If the root name matched the pre-defined key, we will loop all parsed textual inversion embedding and token id pair and create a subgraph (Constant + Unsqueeze + Concat) by OpenVINO^TM operation sets to insert into the text encoder model. In the end, we update the root output node with the last node in the subgraph.

Figure3. Overview of InsertTextEmbedding OpenVINOTM ngraph transformation

Figure 3 demonstrates the workflow of InsertTextEmbedding OpenVINO^TM ngraph transformation. The left part shows the subgraph in SD 1.5 baseline text encoder model, where text embedding has a Constant node with shape [49408, 768], the 1st dimension is consistent with the original tokenizer (vocab size 49408), and the second dimension is feature length of each text embedding.

When we load (multiple) textual inversion, all textual inversion embeddings will be parsed as a list of tensors with shape[768], and each textual inversion constant will be unsqueezed and concatenated with original text embeddings. The right part is the result of applying InsertTextEmbedding ngraph transformation on the original text encoder, the green rectangle represents merged textual inversion subgraph.

Figure 4. 3 phase of SD 1.5 text encoder subgraph with single textual inversion visualized in Netron.

As Figure 4 shows, In the first phase, the original text embedding (marked as blue rectangle) is saved in Const node “text_model.embeddings.token_embedding.weight” with shape [49408,768], after InsertTextEmbedding ngraph transformation, new subgraph (marked as red rectangle) will be created in 2nd phase. In the 3rd phase, during model compilation, the new subgraph will be const folding into a single const node (marked as green rectangle) with a new shape [49409,768] by OpenVINO^TM ConstantFolding transformation.

Stable Diffusion Textual Inversion Sample

Here are textual inversion examples verified with Stable Diffusion v1.5, Stable Diffusion v2.1 and Stable Diffusion XL 1.0 Base pipeline with latest optimum-intel

Setup Environment

conda create -n optimum-intel python=3.10
conda activate optimum-intel
python -m pip install "optimum-intel[openvino]"@git+https://github.com/huggingface/optimum-intel.git
python -m pip install transformers, diffusers, safetensors
python -m pip install invisible-watermark>=0.2.0

Run SD 1.5 + Cat-Toy Textual Inversion Example

from optimum.intel import OVStableDiffusionPipeline
import numpy as np

model_id = "runwayml/stable-diffusion-v1-5"
prompt = "A <cat-toy> back-pack"
np.random.seed(42)

# Run pipeline without textual inversion
pipe = OVStableDiffusionPipeline.from_pretrained(model_id, compile=False)
pipe.compile()
image1 = pipe(prompt, num_inference_steps=50).images[0]
image1.save("sd_v1.5_without_cat_toy_ti.png")

# Run pipeline with textual inversion
pipe.clear_requests()
pipe.load_textual_inversion("sd_concepts/cat-toy", "<cat-toy>")
pipe.compile()
image2 = pipe(prompt, num_inference_steps=50).images[0]
image2.save("sd_v1.5_with_cat_toy_ti.png")

Figure 5. The left image shows the generation result of SD 1.5 baseline, while the right image shows the generation result of SD 1.5 baseline + Cat-Toy textual inversion.

Run SD 2.1 + Midjourney 2.0 Textual Inversion Example

from optimum.intel import OVStableDiffusionPipeline
import numpy as np

model_id = "stabilityai/stable-diffusion-2-1"
prompt = "A <midjourney> style photo of an astronaut riding a horse on mars"
np.random.seed(42)

# Run pipeline without midjourney textual inversion
pipe = OVStableDiffusionPipeline.from_pretrained(model_id, compile=False, cache_dir=None)
pipe.compile()
image1 = pipe(prompt, num_inference_steps=50).images[0]
image1.save("sd_v2.1_without_midjourney_ti.png")

# Run pipeline with midjourney textual inversion
pipe.clear_requests()
pipe.load_textual_inversion("midjourney_sd_2_0", "<midjourney>")
pipe.compile()
image2 = pipe(prompt, num_inference_steps=50).images[0]
image2.save("sd_v2.1_with_midjourney_ti.png")

Figure 6. The left image shows the generation result of SD 2.1 baseline, while the right image shows the generation result of SD 2.1 + Midjourney 2.0 textual inversion.

Run SDXL 1.0 Base + CharTurnerV2 Textual Inversion Example

from optimum.intel import OVStableDiffusionXLPipeline
import numpy as np

model_id = "stabilityai/stable-diffusion-xl-base-1.0"
prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a beautiful woman wearing a red jacket and black shirt, best quality, intricate details."
np.random.seed(112)

pipe = OVStableDiffusionXLPipeline.from_pretrained(model_id, export=False, compile=False, cache_dir=None)

# Run pipeline without textual inversion
pipe.compile()
image1 = pipe(prompt, num_inference_steps=50).images[0]
image1.save("sdxl_base_1.0_without_charturnerv2_ti.png")

# Run pipeline with textual inversion
pipe.clear_requests()
pipe.load_textual_inversion("./charturnerv2.pt", "charturnerv2")
pipe.compile()
image2 = pipe(prompt, num_inference_steps=50).images[0]
image2.save("sdxl_base_1.0_with_charturnerv2_ti.png")

Figure 7. The left image shows the generation result of SDXL 1.0 Base baseline, while the right image shows the generation result of SDXL 1.0 Base + CharTurnerV2 textual inversion.

Conclusion

In this blog, we proposed to load textual inversion embedding in the stable diffusion pipeline in runtime to save disk storage while keeping flexibility.

Implemented OVTextualInversionLoaderMixin to update tokenizer with additional token id and update text encoder with InsertTextEmbedding OpenVNO ngraph transformation.
Provides sample code to load textual inversion with SD 1.5, SD 2.1, and SDXL 1.0 Base and inference with Optimum-Intel

‍

Reference

An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Optimum-Intel Text-to-Image with Textual Inversion

Hugging Face Textual Inversion

OpenVINO GenAI Serving (OGS) update

October 25, 2024

Authors: Xiake Sun, Su Yang, Tianmeng Chen, Tong Qiu

openvino.genai/samples/cpp/rag_sample at openvino_genai_serving · sammysun0711/openvino.genai (github.com)

OpenVINO GenAI Server (OGS) Update:

-Update LLM: stream generation, reset handle, multi-round chat, model cache config

-Support VLM

-Support Reranker for RAG sample

-Support BLIP image embedding for photo search with DB

-Support C++ GUI with imgui for photo search

Now we scale the text embedding to image embedding for RAG sample and support multi-Vector Retriever for RAG.

Multi-Vector Retriever for RAG on text: QA over Document
Multi-Vector Retriever for RAG on image: Photo search with DB retrieval

‍

Here is a photo search sample with image embedding.

Usage 2: Photo Search with DB retrieval

Steps:

1.use python client to create image vector DB (PostgreSQL)

2.use GUI to search image

Here is a sample image to demonstrate GUI usage on client platform. we search the bus photo with top 10 similar images from the 100 images which are embedded into Vector DB.

Usage 3: Chat with images via MiniCPM-V

Once we have created a multimodal vector DB through image embedding, we can further communicate with the image through VLM.

We integrate the C++ GenAI sample visual_language_chat with openbmb/MiniCPM-V-2_6.

Here is the demo image on client platform.

‍

Enable ControlNet with Stable Diffusion Pipeline via Optimum-Intel

October 22, 2024

Authors: Tianmeng Chen, Xiake Sun

Introduction

Stable Diffusion is a generative artificial intelligence model that produces unique images from text and image prompts. ControlNet is a neural network that controls image generation in Stable Diffusion by adding extra conditions. The specific structure of Stable Diffusion + ControlNet is shown below:

In many cases, ControlNet is used in conjunction with other models or frameworks, such as OpenPose, Canny, Line Art, Depth, etc. An example of Stable Diffusion + ControlNet + OpenPose:

OpenPose identifies the key points of the human body from the left image to get the pose image, and then inputs the Pose image to ControlNet and Stable Diffusion to get the right image. In this way, ControlNet can control the generation of Stable Diffusion.

In this blog, we focus on enabling the stable diffusion pipeline with ControlNet in Optimum-intel. Some details can be found in this open PR.

How to enable StableDiffusionControlNet pipeline in Optimum-Intel

The important code is in optimum/intel/openvino/modelling_diffusion.py and optimum/exporters/openvino/model_configs.py. There is the diffusion pipeline related code in file modelling_diffusion.py, you can find several Class: OVStableDiffusionPipelineBase, OVStableDiffusionPipeline, OVStableDiffusionXLPipelineBase, OVStableDiffusionXLPipeline, and so on. What we need to do is mimic these base classes to add the OVStableDiffusionControlNetPipelineBase, StableDiffusionContrlNetPipelineMixin, and OVStableDiffusionControlNetPipeline. A few of the important parts are as follows:

_from_pretrained function in class OVStableDiffusionControlNetPipelineBase: initial whole pipeline from local or download.

_from_transformers function in class OVStableDiffusionControlNetPipelineBase: convert torch model to OpenVINO IR model.

_reshape_unet_controlnet and _reshape_controlnet in class OVStableDiffusionControlNetPipelineBase: reshape dynamic OpenVINO IR model to static in order to decrease cost.

__call__ function in class StableDiffusionContrlNetPipelineMixin: do the inference in the pipeline.

In model_configs.py, we define UNetControlNetOpenVINOConfig by inheriting UNetOnnxConfig, which includes UNetControlNet inputs and outputs.

By now we have completed the rough code, after which some very detailed code additions are needed, so I won't go into that here.

How to use StableDiffusionControlNet pipeline via Optimum-Intel

The next step is how to use the code, examples of which can be found in this repository.

Installation and update of environments and dependencies from source. Make sure your python version is greater that 3.10 and your optimum-intel and optimum version is up to date accounding to the requirements.txt.

# %python -m venv stable-diffusion-controlnet
# %source stable-diffusion-controlnet/bin/activate
%pip install -r requirements.txt

‍

At first, we should convert pytorch model to openvino IR with dynamic shape. Now import related packages.

from optimum.intel import OVStableDiffusionControlNetPipeline
import os
from diffusers import UniPCMultistepScheduler

‍

Set pytroch models of stable diffusion 1.5 and controlnet path if you have them in local, else you can run pipeline from download.

SD15_PYTORCH_MODEL_DIR="stable-diffusion-v1-5"
CONTROLNET_PYTORCH_MODEL_DIR="control_v11p_sd15_openpose"


if os.path.exists(SD15_PYTORCH_MODEL_DIR) and os.path.exists(CONTROLNET_PYTORCH_MODEL_DIR):
    scheduler = UniPCMultistepScheduler.from_config("scheduler_config.json")
    ov_pipe = OVStableDiffusionControlNetPipeline.from_pretrained(SD15_PYTORCH_MODEL_DIR, controlnet_model_id=CONTROLNET_PYTORCH_MODEL_DIR, compile=False, export=True, scheduler=scheduler,device="GPU.1")
    ov_pipe.save_pretrained(save_directory="./ov_models_dynamic")
    print("Dynamic model is saved in ./ov_models_dynamic")  

else:
    scheduler = UniPCMultistepScheduler.from_config("scheduler_config.json")
    ov_pipe = OVStableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet_model_id="lllyasviel/control_v11p_sd15_openpose", compile=False, export=True, scheduler=scheduler, device="GPU.1")
    ov_pipe.save_pretrained(save_directory="./ov_models_dynamic")
    print("Dynamic model is saved in ./ov_models_dynamic")

‍

Now you will have openvino IR models file under **ov_models_dynamic ** folder.

from optimum.intel import OVStableDiffusionControlNetPipeline
from controlnet_aux import OpenposeDetector
from pathlib import Path
import numpy as np
import os
from PIL import Image
from diffusers import UniPCMultistepScheduler
import requests
import torch

‍

We recommand to use static shape model to decrease GPU memory cost. Set your STATIC_SHAPE and DEVICE_NAME.

NEED_STATIC = True
STATIC_SHAPE = [1024,1024]
DEVICE_NAME = "GPU.1"

‍

Load openvino model files, if is static, reshape dynamic models to fixed shape.

if NEED_STATIC:
    print("Using static models")
    scheduler = UniPCMultistepScheduler.from_config("scheduler_config.json")
    ov_config ={"CACHE_DIR": "", 'INFERENCE_PRECISION_HINT': 'f16'}
    if not os.path.exists("ov_models_static"):
        if os.path.exists("ov_models_dynamic"):
            print("load dynamic models from local ov files and reshape to static")
            ov_pipe = OVStableDiffusionControlNetPipeline.from_pretrained(Path("ov_models_dynamic"), scheduler=scheduler, device=DEVICE_NAME, compile=True, ov_config=ov_config, height=STATIC_SHAPE[0], width=STATIC_SHAPE[1])
            ov_pipe.reshape(batch_size=1 ,height=STATIC_SHAPE[0], width=STATIC_SHAPE[1], num_images_per_prompt=1)
            ov_pipe.save_pretrained(save_directory="./ov_models_static")
            print("Static model is saved in ./ov_models_static")  
        else:
            raise ValueError("No ov_models_dynamic exists, please trt ov_model_export.py first")
    else:
        print("load static models from local ov files")
        ov_pipe = OVStableDiffusionControlNetPipeline.from_pretrained(Path("ov_models_static"), scheduler=scheduler, device=DEVICE_NAME, compile=True, ov_config=ov_config, height=STATIC_SHAPE[0], width=STATIC_SHAPE[1])
else:
    scheduler = UniPCMultistepScheduler.from_config("scheduler_config.json")
    ov_config ={"CACHE_DIR": "", 'INFERENCE_PRECISION_HINT': 'f16'}
    print("load dynamic models from local ov files")
    ov_pipe = OVStableDiffusionControlNetPipeline.from_pretrained(Path("ov_models_dynamic"), scheduler=scheduler, device=DEVICE_NAME, compile=True, ov_config=ov_config)

‍

Set seed for Numpy and torch to make result reproducible.

seed = 42
torch.manual_seed(seed)           
torch.cuda.manual_seed(seed)       
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)

‍

Load image for ControlNet, or you can use your own image, or generate image with OpenPose OpenVINO model, notice that OpenPose model is not supported by OVStableDiffusionControlNetPipeline yet, so you need to convert it to openvino model first manually. Here we use directly the result from OpenPose:

pose = Image.open(Path("pose_1024.png"))

‍

Set prompt, negative_prompt, image inputs.

prompt = "Dancing Darth Vader, best quality, extremely detailed"
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"

result = ov_pipe(prompt=prompt, image=pose, num_inference_steps=20, negative_prompt=negative_prompt, height=STATIC_SHAPE[0], width=STATIC_SHAPE[1])

result[0].save("result_1024.png")

‍

InternVL2-4B model enabling with OpenVINO

October 16, 2024

Authors: Hongbo Zhao, Fiona Zhao

Introduction

InternVL2.0 is a series of multimodal large language models available in various sizes. The InternVL2-4B model comprises InternViT-300M-448px, an MLP projector, and Phi-3-mini-128k-instruct. It delivers competitive performance comparable to proprietary commercial models across a range of capabilities, including document and chart comprehension, infographics question answering, scene text understanding and OCR tasks, scientific and mathematical problem solving, as well as cultural understanding and integrated multimodal functionalities.

You can find more information on github repository: https://github.com/zhaohb/InternVL2-4B-OV

OpenVINO^TM backend on InternVL2-4B

Step 1: Install system dependency and setup environment

Create and enable python virtual environment

conda create -n ov_py310 python=3.10 -y
conda activate ov_py310

Clone the InternVL2-4B-OV repository from github

git clonehttps://github.com/zhaohb/InternVL2-4B-OV
cd InternVL2-4B-OV

Install python dependency

pip install -r requirement.txt
pip install --pre -U openvino openvino-tokenizers --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly

Step2: Get HuggingFace model

huggingface-cli download --resume-download OpenGVLab/InternVL2-4B --local-dir InternVL2-4B--local-dir-use-symlinks False
cp modeling_phi3.py  InternVL2-4B/modeling_phi3.py
cp modeling_intern_vit.py   InternVL2-4B/modeling_intern_vit.py

Step 3: Export to OpenVINO™ model

python test_ov_internvl2.py -m ./InternVL2-4B -ov ./internvl2_ov_model -llm_int4_com -vision_int8 -llm_int8_quan -convert_model_only

Step4: Simple inference test with OpenVINO™

python test_ov_internvl2.py -m ./InternVL2-4B -ov ./internvl2_ov_model -llm_int4_com -vision_int8-llm_int8_quan

Question: Please describe the image shortly.

Answer:

The image features a close-up view of a red panda resting on a wooden platform. The panda is characterized by its distinctive red fur, white face, and ears. The background shows a natural setting with green foliage and a wooden structure.

Here are the parameters with descriptions:

python test_ov_internvl2.py --help
usage: Export InternVL2 Model to IR [-h] [-m MODEL_ID] -ov OV_IR_DIR [-d DEVICE] [-pic PICTURE] [-p PROMPT] [-max MAX_NEW_TOKENS] [-llm_int4_com] [-vision_int8] [-llm_int8_quant] [-convert_model_only]
options:
  -h, --help   show this help message and exit  
  -m MODEL_ID, --model_id MODEL_ID   model_id or directory for loading     
  -ov OV_IR_DIR, --ov_ir_dir OV_IR_DIR     output directory for saving model  
  -d DEVICE, --device DEVICE   inference device  
  -pic PICTURE, --picture PICTURE  picture file 
  -p PROMPT, --prompt PROMPT    prompt  
  -max MAX_NEW_TOKENS, --max_new_tokens MAX_NEW_TOKENS    max_new_tokens  
  -llm_int4_com, --llm_int4_compress  llm int4 weight scompress  
  -vision_int8, --vision_int8_quant  vision int8 weights quantize  
  -llm_int8_quant, --llm_int8_quant      llm int8 weights dynamic quantize  
  -convert_model_only, --convert_model_only      convert model to ov only, do not do inference test

Supported optimizations

1. Vision model INT8 quantization and SDPA optimization enabled

2. LLM model INT4 compression

3. LLM model INT8 dynamic quantization

4. LLM model with SDPA optimization enabled

Summary

This blog introduces how to use the OpenVINO™ python API to run the pipeline of the Internvl2-4B model, and uses a variety of acceleration methods to improve the inference speed.

‍

Xiake

Sun

Enable Textual Inversion with Stable Diffusion Pipeline via Optimum-Intel

Introduction

How to enable textual inversion in runtime?

Stable Diffusion Textual Inversion Sample

Setup Environment

Run SD 1.5 + Cat-Toy Textual Inversion Example

Run SD 2.1 + Midjourney 2.0 Textual Inversion Example

Run SDXL 1.0 Base + CharTurnerV2 Textual Inversion Example

Conclusion

Reference

Related Articles

OpenVINO GenAI Serving (OGS) update

Usage 2: Photo Search with DB retrieval

Usage 3: Chat with images via MiniCPM-V

Enable ControlNet with Stable Diffusion Pipeline via Optimum-Intel

Introduction

How to enable StableDiffusionControlNet pipeline in Optimum-Intel

How to use StableDiffusionControlNet pipeline via Optimum-Intel

InternVL2-4B model enabling with OpenVINO

Introduction

OpenVINOTM backend on InternVL2-4B

Supported optimizations

Summary

OpenVINO^TM backend on InternVL2-4B