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.gitattributes

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README.md

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+---
+pipeline_tag: image-to-video
+language:
+- en
+extra_gated_eu_disallowed: true
+---
+<!-- ## **HunyuanVideo-Avatar** -->
+
+<!-- ## **Hunyuan-GameCraft** -->
+
+<!-- <p align="center">
+  <img src="assets/material/logo.png"  height=100>
+</p> -->
+
+# **Hunyuan-GameCraft** 🎮
+ 
+<div align="center">
+  <a href="https://github.com/Tencent-Hunyuan/Hunyuan-GameCraft-1.0"><img src="https://img.shields.io/static/v1?label=Hunyuan-GameCraft-1.0%20Code&message=Github&color=blue"></a> &ensp;
+  <a href="https://hunyuan-gamecraft.github.io/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Web&color=green"></a> &ensp;
+<a href="https://arxiv.org/abs/2506.17201"><img src="https://img.shields.io/badge/ArXiv-2506.17201-red"></a> &ensp;
+</div>
+
+<div align="center">
+  <a href="https://huggingface.co/tencent/Hunyuan-GameCraft-1.0"><img src="https://img.shields.io/static/v1?label=Hunyuan-GameCraft-1.0&message=Hunyuan-GameCraft-1.0&color=yellow"></a> &ensp;
+</div>
+
+![image](asset/teaser.png)
+
+> [**Hunyuan-GameCraft: High-dynamic Interactive Game Video Generation with Hybrid History Condition**](https://arxiv.org/pdf/2505.20156) <be>
+
+## 🔥🔥🔥 News!!
+* Jun 06, 2025: 🔥 Hunyuan-GameCraft supports **Single GPU** with only **10GB VRAM**, with **DeepCache** included.
+* Aug  14, 2025: 👋 We release the inference code and model weights of Hunyuan-GameCraft. [Download](weights/README.md).
+
+
+## 📑 Open-source Plan
+
+- Hunyuan-GameCraft
+  - [x] Inference 
+  - [x] Checkpoints
+  - [ ] Gradio & Huggingface Demo
+
+## Contents
+- [**Hunyuan-GameCraft** 🌅](#Hunyuan-GameCraft-)
+  - [🔥🔥🔥 News!!](#-news)
+  - [📑 Open-source Plan](#-open-source-plan)
+  - [Contents](#contents)
+  - [**Abstract**](#abstract)
+  - [**Overall Architecture**](#Hunyuan-GameCraft-overall-architecture)
+  - [📜 Requirements](#-requirements)
+  - [🛠️ Dependencies and Installation](#️-dependencies-and-installation)
+    - [Installation Guide for Linux](#installation-guide-for-linux)
+  - [🧱 Download Pretrained Models](#-download-pretrained-models)
+  - [🚀 Parallel Inference on Multiple GPUs](#-parallel-inference-on-multiple-gpus)
+  - [🔑 Single-gpu Inference](#-single-gpu-inference)
+    - [Run with very low VRAM](#run-with-very-low-vram)
+  - [Run a Gradio Server](#run-a-gradio-server)
+  - [🔗 BibTeX](#-bibtex)
+  - [Acknowledgements](#acknowledgements)
+---
+
+## **Abstract**
+
+Recent advances in diffusion-based and controllable video generation have enabled high-quality and temporally coherent video synthesis, laying the groundwork for immersive interactive gaming experiences. However, current methods face limitations in **dynamics**, **physically realistic**, **long-term consistency**, and **efficiency**, which limit the ability to create various gameplay videos. To address these gaps, we introduce Hunyuan-GameCraft, a novel framework for high-dynamic interactive video generation in game environments. To achieve fine-grained action control, we unify standard keyboard and mouse inputs into a **shared camera representation space**, facilitating smooth interpolation between various camera and movement operations. Then we propose a **hybrid history-conditioned training strategy** that extends video sequences autoregressively while preserving game scene information. Additionally, to enhance inference efficiency and playability, we achieve **model distillation** to reduce computational overhead while maintaining consistency across long temporal sequences, making it suitable for real-time deployment in complex interactive environments. The model is trained on a large-scale dataset comprising over one million gameplay recordings across over 100 AAA games, ensuring broad coverage and diversity, then fine-tuned on a carefully annotated synthetic dataset to enhance precision and control. The curated game scene data significantly improves the visual fidelity, realism and action controllability. Extensive experiments demonstrate that Hunyuan-GameCraft significantly outperforms existing models, advancing the realism and playability of interactive game video generation.
+
+## **Overall Architecture**
+
+![image](asset/method.png)
+
+Given a reference image and the corresponding prompt, the keyboard or mouse signal, we transform these options to the continuous camera space. Then we design a light-weight action encoder to encode the input camera trajectory. The action and image features are added after patchify. For long video extension, we design a variable mask indicator, where 1 and 0 indicate history frames and predicted frames, respectively.
+
+
+## 📜 Requirements
+
+* An NVIDIA GPU with CUDA support is required. 
+  * The model is tested on a machine with 8GPUs.
+  * **Minimum**: The minimum GPU memory required is 24GB but very slow.
+  * **Recommended**: We recommend using a GPU with 80GB of memory for better generation quality.
+* Tested operating system: Linux
+
+
+## 🛠️ Dependencies and Installation
+
+Begin by cloning the repository:
+```shell
+git clone https://github.com/Tencent-Hunyuan/Hunyuan-GameCraft-1.0.git
+cd Hunyuan-GameCraft-1.0
+```
+
+### Installation Guide for Linux
+
+We recommend CUDA versions 12.4 for the manual installation.
+
+Conda's installation instructions are available [here](https://docs.anaconda.com/free/miniconda/index.html).
+
+```shell
+# 1. Create conda environment
+conda create -n HYGameCraft python==3.10
+
+# 2. Activate the environment
+conda activate HYGameCraft
+
+# 3. Install PyTorch and other dependencies using conda
+conda install pytorch==2.5.1 torchvision==0.20.0 torchaudio==2.5.1 pytorch-cuda=12.4 -c pytorch -c nvidia
+
+# 4. Install pip dependencies
+python -m pip install -r requirements.txt
+# 5. Install flash attention v2 for acceleration (requires CUDA 11.8 or above)
+python -m pip install ninja
+python -m pip install git+https://github.com/Dao-AILab/[email protected]
+```
+
+Additionally, you can also use HunyuanVideo Docker image. Use the following command to pull and run the docker image.
+
+```shell
+# For CUDA 12.4 (updated to avoid float point exception)
+docker pull hunyuanvideo/hunyuanvideo:cuda_12
+docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged hunyuanvideo/hunyuanvideo:cuda_12
+pip install diffusers==0.34.0 transformers==4.54.1
+
+```
+
+
+## 🚀 Parallel Inference on Multiple GPUs
+
+For example, to generate a video using 8 GPUs, you can use the following command, where `--action-list w s d a` simulate keyboard manipulation signals to help you generate a video of the corresponding content. `--action-speed-list 0.2 0.2 0.2 0.2` represents the displacement distance and can be replaced with any value between 0 and 3, the length of `action-speed-list` must be the same as `action-list`:
+```bash
+#!/bin/bash
+JOBS_DIR=$(dirname $(dirname "$0"))
+export PYTHONPATH=${JOBS_DIR}:$PYTHONPATH
+export MODEL_BASE="weights/stdmodels"
+checkpoint_path="weights/gamecraft_models/mp_rank_00_model_states.pt"
+
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+modelname='Tencent_hunyuanGameCraft_720P'
+
+torchrun --nnodes=1 --nproc_per_node=8 --master_port 29605 hymm_sp/sample_batch.py \
+    --image-path "asset/village.png" \
+    --prompt "A charming medieval village with cobblestone streets, thatched-roof houses, and vibrant flower gardens under a bright blue sky." \
+    --add-pos-prompt "Realistic, High-quality." \
+    --add-neg-prompt "overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion, blurring, text, subtitles, static, picture, black border." \
+    --ckpt ${checkpoint_path} \
+    --video-size 704 1216 \
+    --cfg-scale 2.0 \
+    --image-start \
+    --action-list w s d a \
+    --action-speed-list 0.2 0.2 0.2 0.2 \
+    --seed 250160 \
+    --infer-steps 50 \
+    --use-fp8 \
+    --flow-shift-eval-video 5.0 \
+    --save-path './results/'
+
+```
+
+
+Additionally, we support FP8 optimization and [SageAttn](https://github.com/thu-ml/SageAttention). To enable FP8, simply add the `--use-fp8` to your command. 
+And install SageAttention with:
+```bash
+git clone https://github.com/thu-ml/SageAttention.git
+cd SageAttention 
+python setup.py install  # or pip install -e .
+```
+
+We also provide accelerated model, you can use the following command:
+```bash
+#!/bin/bash
+JOBS_DIR=$(dirname $(dirname "$0"))
+export PYTHONPATH=${JOBS_DIR}:$PYTHONPATH
+export MODEL_BASE="weights/stdmodels"
+checkpoint_path="weights/gamecraft_models/mp_rank_00_model_states_distill.pt"
+
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+modelname='Tencent_hunyuanGameCraft_720P'
+
+torchrun --nnodes=1 --nproc_per_node=8 --master_port 29605 hymm_sp/sample_batch.py \
+    --image-path "asset/village.png" \
+    --prompt "A charming medieval village with cobblestone streets, thatched-roof houses, and vibrant flower gardens under a bright blue sky." \
+    --add-neg-prompt "overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion, blurring, text, subtitles, static, picture, black border." \
+    --ckpt ${checkpoint_path} \
+    --video-size 704 1216 \
+    --cfg-scale 1.0 \
+    --image-start \
+    --action-list w s d a \
+    --action-speed-list 0.2 0.2 0.2 0.2 \
+    --seed 250160 \
+    --infer-steps 8 \
+    --use-fp8 \
+    --flow-shift-eval-video 5.0 \
+    --save-path './results_distill/'
+```
+
+
+## 🔑 Single-gpu with Low-VRAM Inference
+
+For example, to generate a video with 1 GPU with Low-VRAM (over 24GB), you can use the following command:
+
+```bash
+#!/bin/bash
+JOBS_DIR=$(dirname $(dirname "$0"))
+export PYTHONPATH=${JOBS_DIR}:$PYTHONPATH
+export MODEL_BASE="weights/stdmodels"
+checkpoint_path="weights/gamecraft_models/mp_rank_00_model_states.pt"
+
+current_time=$(date "+%Y.%m.%d-%H.%M.%S")
+modelname='Tencent_hunyuanGameCraft_720P'
+
+# disable sp and cpu offload
+export DISABLE_SP=1
+export CPU_OFFLOAD=1
+
+torchrun --nnodes=1 --nproc_per_node=1 --master_port 29605 hymm_sp/sample_batch.py \
+    --image-path "asset/village.png" \
+    --prompt "A charming medieval village with cobblestone streets, thatched-roof houses, and vibrant flower gardens under a bright blue sky." \
+    --add-neg-prompt "overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion, blurring, text, subtitles, static, picture, black border." \
+    --ckpt ${checkpoint_path} \
+    --video-size 704 1216 \
+    --cfg-scale 2.0 \
+    --image-start \
+    --action-list w a d s \
+    --action-speed-list 0.2 0.2 0.2 0.2 \
+    --seed 250160 \
+    --infer-steps 50 \
+    --flow-shift-eval-video 5.0 \
+    --cpu-offload \
+    --use-fp8 \
+    --save-path './results/'
+
+```
+
+
+## 🔗 BibTeX
+
+If you find [Hunyuan-GameCraft](https://arxiv.org/pdf/2505.20156) useful for your research and applications, please cite using this BibTeX:
+
+```BibTeX
+@misc{li2025hunyuangamecrafthighdynamicinteractivegame,
+    title={Hunyuan-GameCraft: High-dynamic Interactive Game Video Generation with Hybrid History Condition}, 
+    author={Jiaqi Li and Junshu Tang and Zhiyong Xu and Longhuang Wu and Yuan Zhou and Shuai Shao and Tianbao Yu and Zhiguo Cao and Qinglin Lu},
+    year={2025},
+    eprint={2506.17201},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV},
+    url={https://arxiv.org/abs/2506.17201}, 
+}
+```
+
+## Acknowledgements
+
+We would like to thank the contributors to the [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), [HunyuanVideo-Avatar](https://github.com/Tencent-Hunyuan/HunyuanVideo-Avatar),[SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [FLUX](https://github.com/black-forest-labs/flux), [Llama](https://github.com/meta-llama/llama), [LLaVA](https://github.com/haotian-liu/LLaVA), [Xtuner](https://github.com/InternLM/xtuner), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration. 

gamecraft_models/mp_rank_00_model_states.pt

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stdmodels/llava-llama-3-8b-v1_1-transformers/README.md

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+---
+datasets:
+- Lin-Chen/ShareGPT4V
+pipeline_tag: image-text-to-text
+library_name: xtuner
+---
+
+<div align="center">
+  <img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
+
+
+[![Generic badge](https://img.shields.io/badge/GitHub-%20XTuner-black.svg)](https://github.com/InternLM/xtuner)
+
+
+</div>
+
+## Model
+
+llava-llama-3-8b-v1_1-hf is a LLaVA model fine-tuned from [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [ShareGPT4V-PT](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) and [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) by [XTuner](https://github.com/InternLM/xtuner).
+
+**Note: This model is in HuggingFace LLaVA format.**
+
+Resources:
+
+- GitHub: [xtuner](https://github.com/InternLM/xtuner)
+- Official LLaVA format model: [xtuner/llava-llama-3-8b-v1_1-hf](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-hf)
+- XTuner LLaVA format model: [xtuner/llava-llama-3-8b-v1_1](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1)
+- GGUF format model: [xtuner/llava-llama-3-8b-v1_1-gguf](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf)
+
+
+## Details
+
+| Model                 | Visual      Encoder | Projector | Resolution |   Pretraining Strategy | Fine-tuning      Strategy |      Pretrain     Dataset |    Fine-tune     Dataset |
+| :-------------------- | ------------------: | --------: | ---------: | ---------------------: | ------------------------: | ------------------------: | -----------------------: |
+| LLaVA-v1.5-7B         |              CLIP-L |       MLP |        336 | Frozen LLM, Frozen ViT |      Full LLM, Frozen ViT |       LLaVA-PT     (558K) |     LLaVA-Mix     (665K) |
+| LLaVA-Llama-3-8B      |              CLIP-L |       MLP |        336 | Frozen LLM, Frozen ViT |        Full LLM, LoRA ViT |       LLaVA-PT     (558K) |     LLaVA-Mix     (665K) |
+| LLaVA-Llama-3-8B-v1.1 |              CLIP-L |       MLP |        336 | Frozen LLM, Frozen ViT |        Full LLM, LoRA ViT | ShareGPT4V-PT     (1246K) | InternVL-SFT     (1268K) |
+
+## Results
+
+<div  align="center">
+<img src="https://github.com/InternLM/xtuner/assets/36994684/a157638c-3500-44ed-bfab-d8d8249f91bb" alt="Image" width=500" />
+</div>
+
+| Model                 | MMBench Test (EN) | MMBench Test (CN) | CCBench Dev | MMMU  Val | SEED-IMG | AI2D Test | ScienceQA Test | HallusionBench aAcc | POPE | GQA  | TextVQA |   MME    | MMStar |
+| :-------------------- | :---------------: | :---------------: | :---------: | :-------: | :------: | :-------: | :------------: | :-----------------: | :--: | :--: | :-----: | :------: | :----: |
+| LLaVA-v1.5-7B         |       66.5        |       59.0        |    27.5     |   35.3    |   60.5   |   54.8    |      70.4      |        44.9         | 85.9 | 62.0 |  58.2   | 1511/348 |  30.3  |
+| LLaVA-Llama-3-8B      |       68.9        |       61.6        |    30.4     |   36.8    |   69.8   |   60.9    |      73.3      |        47.3         | 87.2 | 63.5 |  58.0   | 1506/295 |  38.2  |
+| LLaVA-Llama-3-8B-v1.1 |       72.3        |       66.4        |    31.6     |   36.8    |   70.1   |   70.0    |      72.9      |        47.7         | 86.4 | 62.6 |  59.0   | 1469/349 |  45.1  |
+
+
+## QuickStart
+
+
+### Chat by `pipeline`
+
+
+```python
+from transformers import pipeline
+from PIL import Image    
+import requests
+
+model_id = "xtuner/llava-llama-3-8b-v1_1-transformers"
+pipe = pipeline("image-to-text", model=model_id, device=0)
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+
+image = Image.open(requests.get(url, stream=True).raw)
+prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat are these?<|eot_id|>"
+          "<|start_header_id|>assistant<|end_header_id|>\n\n")
+outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
+print(outputs)
+>>> [{'generated_text': 'user\n\n\nWhat are these?assistant\n\nThese are two cats, one brown and one gray, lying on a pink blanket. sleep. brown and gray cat sleeping on a pink blanket.'}]
+```
+
+### Chat by pure `transformers`
+
+```python
+import requests
+from PIL import Image
+
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+
+model_id = "xtuner/llava-llama-3-8b-v1_1-transformers"
+
+prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat are these?<|eot_id|>"
+          "<|start_header_id|>assistant<|end_header_id|>\n\n")
+image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
+
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
+    low_cpu_mem_usage=True, 
+).to(0)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+
+raw_image = Image.open(requests.get(image_file, stream=True).raw)
+inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
+
+output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+print(processor.decode(output[0][2:], skip_special_tokens=True))
+>>> These are two cats, one brown and one gray, lying on a pink blanket. sleep. brown and gray cat sleeping on a pink blanket.
+```
+
+
+### Reproduce
+
+Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336#readme).
+
+
+## Citation
+
+```bibtex
+@misc{2023xtuner,
+    title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
+    author={XTuner Contributors},
+    howpublished = {\url{https://github.com/InternLM/xtuner}},
+    year={2023}
+}
+```

stdmodels/llava-llama-3-8b-v1_1-transformers/config.json

@@ -0,0 +1,44 @@
+{
+  "architectures": [
+    "LlavaForConditionalGeneration"
+  ],
+  "ignore_index": -100,
+  "image_token_index": 128257,
+  "model_type": "llava",
+  "pad_token_id": 128258,
+  "projector_hidden_act": "gelu",
+  "text_config": {
+    "architectures": [
+      "LlamaForCausalLM"
+    ],
+    "bos_token_id": 128000,
+    "eos_token_id": 128001,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 8192,
+    "model_type": "llama",
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 500000.0,
+    "torch_dtype": "float16",
+    "vocab_size": 128320
+  },
+  "torch_dtype": "float16",
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stdmodels/llava-llama-3-8b-v1_1-transformers/preprocessor_config.json

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+    "do_center_crop",
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+}

stdmodels/llava-llama-3-8b-v1_1-transformers/special_tokens_map.json

@@ -0,0 +1,24 @@
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "normalized": false,
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": "<unk>"
+}

stdmodels/llava-llama-3-8b-v1_1-transformers/tokenizer_config.json

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+  "add_eos_token": false,
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stdmodels/openai_clip-vit-large-patch14/README.md

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+---
+tags:
+- vision
+widget:
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+---
+
+# Model Card: CLIP
+
+Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
+
+## Model Details
+
+The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
+
+### Model Date
+
+January 2021
+
+### Model Type
+
+The base model uses a ViT-L/14 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.
+
+The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer.
+
+
+### Documents
+
+- [Blog Post](https://openai.com/blog/clip/)
+- [CLIP Paper](https://arxiv.org/abs/2103.00020)
+
+
+### Use with Transformers
+
+```python
+from PIL import Image
+import requests
+
+from transformers import CLIPProcessor, CLIPModel
+
+model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
+
+outputs = model(**inputs)
+logits_per_image = outputs.logits_per_image # this is the image-text similarity score
+probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
+```
+
+
+## Model Use
+
+### Intended Use
+
+The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
+
+#### Primary intended uses
+
+The primary intended users of these models are AI researchers.
+
+We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
+
+### Out-of-Scope Use Cases
+
+**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
+
+Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
+
+Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
+
+
+
+## Data
+
+The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
+
+### Data Mission Statement
+
+Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
+
+
+
+## Performance and Limitations
+
+### Performance
+
+We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets:
+
+- Food101
+- CIFAR10   
+- CIFAR100   
+- Birdsnap
+- SUN397
+- Stanford Cars
+- FGVC Aircraft
+- VOC2007
+- DTD
+- Oxford-IIIT Pet dataset
+- Caltech101
+- Flowers102
+- MNIST   
+- SVHN 
+- IIIT5K   
+- Hateful Memes   
+- SST-2
+- UCF101
+- Kinetics700
+- Country211
+- CLEVR Counting
+- KITTI Distance
+- STL-10
+- RareAct
+- Flickr30
+- MSCOCO
+- ImageNet
+- ImageNet-A
+- ImageNet-R
+- ImageNet Sketch
+- ObjectNet (ImageNet Overlap)
+- Youtube-BB
+- ImageNet-Vid
+
+## Limitations
+
+CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
+
+### Bias and Fairness
+
+We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
+
+We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
+
+
+
+## Feedback
+
+### Where to send questions or comments about the model
+
+Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)

stdmodels/openai_clip-vit-large-patch14/config.json

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