Kandinsky WM 1.0 — initial release

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README.md

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+---
+license: mit
+pipeline_tag: image-to-video
+library_name: diffusers
+tags: [kandinsky, image-to-video, physical-ai, world-model]
+---
+
+<div align="center">
+
+<img src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/logo.png" width="760" alt="Kandinsky WM"/>
+
+# Kandinsky WM 1.0: A family of models for Physical AI
+
+**Image-to-Video generation for Physical AI: autonomous driving · robotics · general physics**
+
+[🤗 Checkpoints collection](https://huggingface.co/collections/kandinskylab/kandinsky-wm-10-6a67455245052645fcfbd5f3) ·
+[🧩 Base model (Kandinsky 5.0)](https://github.com/kandinskylab/kandinsky-5)
+
+</div>
+
+> **This repo holds the 🤖 Robotics checkpoint** of the Kandinsky WM 1.0 family — DiT `robot_rl`, published here in **two layouts**: a diffusers pipeline (`model_index.json`, `transformer/`, `vae/`, `text_encoder*/`) and the GitHub-code DiT at `model/robot_rl.safetensors` (same weights). The other two variants are in the [Kandinsky WM 1.0 collection](https://huggingface.co/collections/kandinskylab/kandinsky-wm-10-6a67455245052645fcfbd5f3).
+
+---
+
+## Contents
+
+- [Overview](#overview)
+- [Model Zoo](#model-zoo)
+- [Examples](#examples) — [🚗 Autonomous Driving](#🚗-autonomous-driving) · [🤖 Robotics](#🤖-robotics) · [🌍 General Physics](#🌍-general-physics)
+- [Results](#results) — [PAI-Bench-G](#pai-bench-g-physical-ai-bench--generation) · [Physics-IQ Verified](#physics-iq-verified)
+- [Quickstart](#quickstart) — [Path A: GitHub code](#path-a--github-code) · [Path B: Diffusers](#path-b--diffusers)
+- [Acknowledgements](#acknowledgements)
+
+---
+
+## Overview
+
+**Kandinsky WM  (World Model) 1.0** is a family of Image-to-Video models that adapt
+[Kandinsky 5.0 Video Lite](https://github.com/kandinskylab/kandinsky-5) — a 2B-parameter
+latent video diffusion model (a `Kandinsky5Transformer3DModel` DiT paired with a HunyuanVideo VAE
+and Qwen2.5-VL + CLIP text encoders, trained with flow matching) — to **Physical AI**: video
+generation that is not only visually convincing but *physically plausible* — consistent scene
+geometry, object dynamics, interactions and cause-and-effect — so the model can serve as a source
+of synthetic training data and as a building block for world models and simulators.
+
+The base model was domain-adapted on large corpora of 5-second scenes (first text-to-video, then a
+mixed text/image-to-video regime that preserves first-frame continuation), spanning **autonomous
+driving**, **robotics**, and a **general** domain (industrial processes, physical phenomena, and
+human–object / human–human interaction). Each checkpoint is then reinforcement-learning post-trained with **GRPO**
+against a reward model that scores the physical plausibility of the generated scene, steering the
+generator toward more faithful geometry, object behaviour and interactions than domain fine-tuning
+alone.
+
+Each checkpoint generates **5-second, 121-frame** clips at **768×512**.
+
+## Model Zoo
+
+Three domain checkpoints, same architecture — only the DiT weights differ (the VAE / text encoders are
+byte-identical across all three). Each is published as a one-line diffusers pipeline; a
+GitHub-code (DiT-only) copy lives in the **same** Hub repo used purely as file storage.
+
+| Domain | Checkpoint | |
+|---|---|---|
+| 🚗 Autonomous Driving | [🤗 `Kandinsky-WM-1.0-I2V-5s-AV`](https://huggingface.co/kandinskylab/Kandinsky-WM-1.0-I2V-5s-AV) |  |
+| 🤖 Robotics | [🤗 `Kandinsky-WM-1.0-I2V-5s-RO`](https://huggingface.co/kandinskylab/Kandinsky-WM-1.0-I2V-5s-RO) | ← **this repo** |
+| 🌍 General Physics | [🤗 `Kandinsky-WM-1.0-I2V-5s-PH`](https://huggingface.co/kandinskylab/Kandinsky-WM-1.0-I2V-5s-PH) |  |
+
+## Examples
+
+Four **Kandinsky WM 1.0** image-to-video generations per domain (top-ranked by an internal visual
+review). Each grid shows the first frame of a clip — **click it to play the generated video**
+(hosted on the [`static_videos`](https://huggingface.co/datasets/sashakunitsyn/static_videos)
+dataset). The first frame and prompt for every clip also live in [`assets/`](https://github.com/kandinskylab/kandinsky-wm/tree/main/assets): under
+`assets/<domain>/`, `frame_N.jpg` and `prompt_N.txt` correspond to grid cell _N_ (left→right, top→bottom).
+
+### 🚗 Autonomous Driving
+
+<p align="left">
+  <a style="display:inline-block;width:48.5%;max-width:334px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/av/1.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/av/preview_1.webp" alt="▶ AV clip 1"></a>
+  <a style="display:inline-block;width:48.5%;max-width:334px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/av/2.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/av/preview_2.webp" alt="▶ AV clip 2"></a>
+  <br>
+  <a style="display:inline-block;width:48.5%;max-width:334px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/av/3.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/av/preview_3.webp" alt="▶ AV clip 3"></a>
+  <a style="display:inline-block;width:48.5%;max-width:334px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/av/4.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/av/preview_4.webp" alt="▶ AV clip 4"></a>
+</p>
+
+First frames + prompts: [`assets/av/`](https://github.com/kandinskylab/kandinsky-wm/tree/main/assets/av).
+
+### 🤖 Robotics
+
+<p align="left">
+  <a style="display:inline-block;width:58.3%;max-width:400px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/robotics/1.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/robotics/preview_1.webp" alt="▶ Robotics clip 1"></a>
+  <a style="display:inline-block;width:38.7%;max-width:266px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/robotics/2.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/robotics/preview_2.webp" alt="▶ Robotics clip 2"></a>
+  <br>
+  <a style="display:inline-block;width:58.3%;max-width:400px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/robotics/3.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/robotics/preview_3.webp" alt="▶ Robotics clip 3"></a>
+  <a style="display:inline-block;width:38.7%;max-width:266px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/robotics/4.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/robotics/preview_4.webp" alt="▶ Robotics clip 4"></a>
+</p>
+
+First frames + prompts: [`assets/robotics/`](https://github.com/kandinskylab/kandinsky-wm/tree/main/assets/robotics).
+
+### 🌍 General Physics
+
+<p align="left">
+  <a style="display:inline-block;width:48.5%;max-width:400px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/general/1.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/general/preview_1.webp" alt="▶ General clip 1"></a>
+  <a style="display:inline-block;width:48.5%;max-width:400px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/general/2.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/general/preview_2.webp" alt="▶ General clip 2"></a>
+  <br>
+  <a style="display:inline-block;width:48.5%;max-width:400px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/general/3.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/general/preview_3.webp" alt="▶ General clip 3"></a>
+  <a style="display:inline-block;width:48.5%;max-width:400px" href="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/general/4.mp4"><img style="width:100%;margin:0 0 4px 0" src="https://huggingface.co/datasets/sashakunitsyn/static_videos/resolve/main/kandinsky-wm-1.0/assets/general/preview_4.webp" alt="▶ General clip 4"></a>
+</p>
+
+First frames + prompts: [`assets/general/`](https://github.com/kandinskylab/kandinsky-wm/tree/main/assets/general).
+
+## Results
+
+**Kandinsky WM 1.0** on two physical-AI video benchmarks (our row in **bold**; rows copied as-is
+from our internal leaderboard). Physics-IQ was run with a Qwen3-VL prompt enhancer — noted above the
+table, with the script under [`prompt_enhancers/`](https://github.com/kandinskylab/kandinsky-wm/tree/main/prompt_enhancers). Sizes are total parameter
+counts where publicly disclosed (MoE models note active params; proprietary/undisclosed left as —).
+
+### PAI-Bench-G (Physical AI Bench — Generation)
+
+[Leaderboard](https://huggingface.co/spaces/shi-labs/physical-ai-bench-leaderboard). Column
+abbreviations follow the PAI-Bench-G dimensions — see the leaderboard for exact definitions.
+Run **without** prompt enhancement.
+
+| Rank | Model | Size | Overall | Domain | Quality | SC | BC | MS | AQ | IQ | OC | IS | IB | CS | AV | RO | IN | PH | HU |
+|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
+| 1 | Cosmos3-Super | 64B | 83.9 | 89.5 | 78.2 | 92.7 | 94.1 | 99.2 | 52.7 | 70.8 | 20.5 | 97.7 | 98.1 | 94.4 | 77.5 | 90.0 | 90.7 | 95.0 | 87.6 |
+| 2 | Cosmos3-Nano | 16B | 83.7 | 89.4 | 78.1 | 92.3 | 93.8 | 99.2 | 52.7 | 70.1 | 20.4 | 97.9 | 98.3 | 95.0 | 75.4 | 90.2 | 89.7 | 94.5 | 88.0 |
+| 3 | Veo-3 | — | 82.1 | 86.7 | 77.6 | 91.4 | 93.1 | 99.2 | 51.9 | 69.8 | 21.7 | 97.0 | 96.9 | 94.4 | 68.7 | 86.9 | 89.7 | 91.6 | 84.4 |
+| 4 | **Kandinsky WM 1.0** | **2B** | **81.7** | **86.0** | **77.4** | 91.8 | 94.1 | 99.0 | 53.2 | 64.9 | 21.6 | 97.2 | 97.7 | 93.6 | 72.6 | 82.1 | 88.0 | 90.7 | 86.0 |
+| 5 | k5 Lite FT | 2B | 81.4 | 85.8 | 77.1 | 91.3 | 93.9 | 98.7 | 52.7 | 64.3 | 21.7 | 96.6 | 97.3 | 92.9 | 72.6 | 82.1 | 87.7 | 88.8 | 86.4 |
+| 6 | Cosmos-Predict2.5-14B | 14B | 81.0 | 83.8 | 78.1 | 93.4 | 94.8 | 99.1 | 52.5 | 70.0 | 20.1 | 97.2 | 97.9 | 94.2 | 67.8 | 79.9 | 87.7 | 93.5 | 80.0 |
+| 7 | Cosmos-Predict2.5-2B | 2B | 81.0 | 84.0 | 77.9 | 92.5 | 94.2 | 99.1 | 52.4 | 70.8 | 20.1 | 96.6 | 97.4 | 94.1 | 66.1 | 80.8 | 87.8 | 93.9 | 81.4 |
+| 8 | Wan2.2-I2V-A14B | 27B (14B active) | 80.6 | 84.1 | 77.2 | 91.6 | 93.7 | 98.3 | 51.2 | 69.6 | 20.4 | 96.0 | 96.6 | 93.2 | 66.3 | 81.7 | 89.2 | 91.8 | 82.1 |
+| 9 | K5 Lite | 2B | 80.5 | 83.0 | 77.9 | 91.7 | 94.4 | 99.3 | 54.1 | 65.6 | 21.7 | 98.1 | 98.6 | 89.2 | 66.3 | 77.3 | 86.3 | 87.2 | 84.6 |
+| 10 | Wan2.2-TI2V-5B | 5B | 80.4 | 83.4 | 77.4 | 91.8 | 93.7 | 98.8 | 51.9 | 69.9 | 20.3 | 95.9 | 96.7 | 93.1 | 65.2 | 79.3 | 88.4 | 91.5 | 83.0 |
+
+Physical-AI post-training lifts the base **K5 Lite** by **+1.2 Overall** (80.5 → 81.7) and
+**+3.0 on the Domain axis** (83.0 → 86.0) — a 2B model landing between Veo-3 and the
+Cosmos-Predict2.5 family.
+
+### Physics-IQ Verified
+
+> **Prompt enhancer used:** [`prompt_enhancers/physics_iq/temporal_expansion.py`](https://github.com/kandinskylab/kandinsky-wm/blob/main/prompt_enhancers/physics_iq/temporal_expansion.py) — Qwen3-VL temporal caption expansion.
+
+| # | Model | Size | Input type | Score | Date added |
+|---|---|---|---|---|---|
+| 1 | Magi-1 24B + GeoPhys (BoN) (op) | 24B | multiframe (v2v) | 58.2 ± 1.8 | 2026-06-19 |
+| 2 | Magi-1 24B (op) | 24B | multiframe (v2v) | 48.4 ± 1.1 | 2026-06-19 |
+| 3 | Cosmos3-Super-Image2Video | 64B | i2v | 39.5 ± 0.8 | 2026-06-18 |
+| 4 | Grok Imagine Video | — | i2v | 34.8 ± 0.6 | 2026-06-17 |
+| 5 | Magi-1 24B + GeoPhys (BoN) (op) | 24B | i2v | 33.7 ± 1.4 | 2026-06-19 |
+| 6 | Hunyuan Video 1.5 | 8.3B | i2v | 33.4 ± 0.8 | 2026-06-17 |
+| 7 | Wan 2.2 | 27B (14B active) | i2v | 32.2 ± 0.6 | 2026-06-17 |
+| 8 | **Kandinsky WM 1.0** | **2B** | i2v | **30.8 ± 0.5** | internal |
+| 9 | Cosmos3-Nano | 16B | i2v | 30.3 ± 0.6 | 2026-06-18 |
+| 10 | Magi-1 24B (op) | 24B | i2v | 30.2 ± 1.1 | 2026-06-19 |
+| 11 | Sora 2 | — | i2v | 26.5 ± 0.8 | 2026-06-17 |
+| 12 | K5 Video Lite FT | 2B | i2v | 25.8 ± 1.7 | internal |
+| 13 | P-Video | — | i2v | 25.3 ± 1.8 | 2026-06-17 |
+| 14 | K5 Video Lite | 2B | i2v | 16.0 ± 1.2 | internal |
+
+## Quickstart
+
+Two ways to run — the checkpoints ship in both formats. The **GitHub-code path** is primary; the
+**diffusers path** is a one-liner that already works off the Hub.
+
+### Path A — GitHub code
+
+The base inference code is vendored as a git submodule at [`kandinsky-5/`](https://github.com/kandinskylab/kandinsky-wm/tree/main/kandinsky-5)
+(from [`kandinskylab/kandinsky-5`](https://github.com/kandinskylab/kandinsky-5)).
+
+```bash
+# 0. get this repo with the submodule
+git clone --recurse-submodules https://github.com/kandinskylab/kandinsky-wm.git
+cd kandinsky-wm
+# (already cloned without it? run: git submodule update --init --recursive)
+
+# 1. install the base code
+pip install -r kandinsky-5/requirements.txt
+
+# 2. pull a checkpoint (repo-as-file-storage; DiT + shared VAE/text encoders)
+python examples/download_checkpoint.py --domain robotics
+
+# 3. generate (CLI) — run from inside the submodule
+cd kandinsky-5
+python test.py \
+  --config ./configs/k5_lite_i2v_5s_sft_sd.yaml \
+  --image  ../assets/robotics/frame_1.jpg \
+  --prompt "$(cat ../assets/robotics/prompt_1.txt)" \
+  --video_duration 5
+```
+
+Python variant: [`examples/run_i2v_github.py`](https://github.com/kandinskylab/kandinsky-wm/blob/main/examples/run_i2v_github.py).
+
+### Path B — Diffusers
+
+```bash
+pip install -U diffusers transformers accelerate imageio-ffmpeg
+```
+
+```python
+import torch
+from diffusers import Kandinsky5I2VPipeline
+from diffusers.utils import export_to_video, load_image
+
+pipe = Kandinsky5I2VPipeline.from_pretrained(
+    "kandinskylab/Kandinsky-WM-1.0-I2V-5s-RO",
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+frames = pipe(
+    image=load_image("assets/robotics/frame_1.jpg"),
+    prompt=open("assets/robotics/prompt_1.txt").read().strip(),
+    negative_prompt=("Static, 2D cartoon, cartoon, 2d animation, paintings, images, "
+                     "worst quality, low quality, ugly, deformed, walking backwards"),
+    height=512, width=768, num_frames=121,
+    num_inference_steps=50, guidance_scale=5.0,
+).frames[0]
+export_to_video(frames, "robotics_out.mp4", fps=24, quality=9)
+```
+
+Script: [`examples/run_i2v_diffusers.py`](https://github.com/kandinskylab/kandinsky-wm/blob/main/examples/run_i2v_diffusers.py). Lite (2B, ~21 GB bf16)
+fits a single 80 GB GPU with no offload; ~4 min for 50 steps on one H100.
+
+
+## Acknowledgements
+
+Built on [Kandinsky 5.0](https://github.com/kandinskylab/kandinsky-5)
+([HunyuanVideo VAE](https://huggingface.co/hunyuanvideo-community/HunyuanVideo),
+[Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct),
+[CLIP](https://huggingface.co/openai/clip-vit-large-patch14)). Evaluated on
+[PAI-Bench-G](https://huggingface.co/spaces/shi-labs/physical-ai-bench-leaderboard) and
+[Physics-IQ](https://github.com/google-deepmind/physics-IQ-benchmark).

model/config.json

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+{
+  "_class_name": "Kandinsky5Transformer3DModel",
+  "_diffusers_version": "0.33.0.dev0",
+  "in_visual_dim": 16,
+  "out_visual_dim": 16,
+  "time_dim": 512,
+  "patch_size": [
+    1,
+    2,
+    2
+  ],
+  "model_dim": 1792,
+  "ff_dim": 7168,
+  "num_text_blocks": 2,
+  "num_visual_blocks": 32,
+  "axes_dims": [
+    16,
+    24,
+    24
+  ],
+  "visual_cond": true,
+  "in_text_dim": 3584,
+  "in_text_dim2": 768,
+  "attention_type": "regular"
+}

model/robot_rl.safetensors

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+oid sha256:d875ad5f5c164c3bcd0e3e37c5b6b66832c02a7bac566c0ef70f9484fa8c8e7f
+size 4015514184

model_index.json

@@ -0,0 +1,32 @@
+{
+  "_class_name": "Kandinsky5I2VPipeline",
+  "_diffusers_version": "0.33.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "Qwen2_5_VLForConditionalGeneration"
+  ],
+  "tokenizer": [
+    "transformers",
+    "Qwen2VLProcessor"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "transformer": [
+    "diffusers",
+    "Kandinsky5Transformer3DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLHunyuanVideo"
+  ]
+}

scheduler/scheduler_config.json

@@ -0,0 +1,6 @@
+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.33.0.dev0",
+  "num_train_timesteps": 1000,
+  "shift": 5.0
+}

text_encoder/.gitattributes

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+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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text_encoder/README.md

@@ -0,0 +1,528 @@
+
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: image-text-to-text
+tags:
+- multimodal
+library_name: transformers
+---
+
+# Qwen2.5-VL-7B-Instruct
+<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
+</a>
+
+## Introduction
+
+In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL.
+
+#### Key Enhancements:
+* **Understand things visually**: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
+
+* **Being agentic**: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use.
+
+* **Understanding long videos and capturing events**: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments.
+
+* **Capable of visual localization in different formats**: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.
+
+* **Generating structured outputs**: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc.
+
+
+#### Model Architecture Updates:
+
+* **Dynamic Resolution and Frame Rate Training for Video Understanding**:
+
+We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments.
+
+<p align="center">
+    <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL/qwen2.5vl_arc.jpeg" width="80%"/>
+<p>
+
+
+* **Streamlined and Efficient Vision Encoder**
+
+We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM.
+
+
+We have three models with 3, 7 and 72 billion parameters. This repo contains the instruction-tuned 7B Qwen2.5-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5-vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5-VL).
+
+
+
+## Evaluation
+
+### Image benchmark
+
+
+| Benchmark | InternVL2.5-8B | MiniCPM-o 2.6 | GPT-4o-mini | Qwen2-VL-7B |**Qwen2.5-VL-7B** |
+| :--- | :---: | :---: | :---: | :---: | :---: |
+| MMMU<sub>val</sub>  | 56 | 50.4 | **60**| 54.1 | 58.6|
+| MMMU-Pro<sub>val</sub>  | 34.3 | - | 37.6| 30.5 | 41.0|
+| DocVQA<sub>test</sub>  | 93 | 93 | - | 94.5 | **95.7** |
+| InfoVQA<sub>test</sub>  | 77.6 | - |  - |76.5 | **82.6** |
+| ChartQA<sub>test</sub>  | 84.8 | - |- | 83.0 |**87.3** |
+| TextVQA<sub>val</sub>  | 79.1 | 80.1 | -| 84.3 | **84.9**|
+| OCRBench | 822 | 852 | 785 | 845 | **864** |
+| CC_OCR | 57.7 |  | | 61.6 | **77.8**|
+| MMStar | 62.8| | |60.7| **63.9**|
+| MMBench-V1.1-En<sub>test</sub>  | 79.4 | 78.0 | 76.0| 80.7 | **82.6** |
+| MMT-Bench<sub>test</sub> | - | - | - |**63.7** |63.6 |
+| MMStar | **61.5** | 57.5 |  54.8 | 60.7 |63.9 |
+| MMVet<sub>GPT-4-Turbo</sub>  | 54.2 | 60.0 | 66.9 | 62.0 | **67.1**|
+| HallBench<sub>avg</sub>  | 45.2 | 48.1 | 46.1| 50.6 | **52.9**|
+| MathVista<sub>testmini</sub>  | 58.3 | 60.6 | 52.4 | 58.2 | **68.2**|
+| MathVision  | - | -  | - | 16.3 | **25.07** |
+
+### Video Benchmarks
+
+| Benchmark |  Qwen2-VL-7B | **Qwen2.5-VL-7B** |
+| :--- | :---: | :---: |
+| MVBench |  67.0 | **69.6** |
+| PerceptionTest<sub>test</sub>  | 66.9 | **70.5** |
+| Video-MME<sub>wo/w subs</sub>   | 63.3/69.0 | **65.1**/**71.6** |
+| LVBench  |  | 45.3 |
+| LongVideoBench  |  | 54.7 |
+| MMBench-Video | 1.44 | 1.79 |
+| TempCompass |  | 71.7 |
+| MLVU |  | 70.2 |
+| CharadesSTA/mIoU |  43.6|
+
+### Agent benchmark
+| Benchmarks              | Qwen2.5-VL-7B |
+|-------------------------|---------------|
+| ScreenSpot              |     84.7    |
+| ScreenSpot Pro          |     29.0    |
+| AITZ_EM                 |  	81.9    |
+| Android Control High_EM |    	60.1    |
+| Android Control Low_EM  |  	93.7    |
+| AndroidWorld_SR         | 	25.5  	|
+| MobileMiniWob++_SR      | 	91.4    |
+
+## Requirements
+The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
+```
+pip install git+https://github.com/huggingface/transformers accelerate
+```
+or you might encounter the following error:
+```
+KeyError: 'qwen2_5_vl'
+```
+
+
+## Quickstart
+
+Below, we provide simple examples to show how to use Qwen2.5-VL with 🤖 ModelScope and 🤗 Transformers.
+
+The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
+```
+pip install git+https://github.com/huggingface/transformers accelerate
+```
+or you might encounter the following error:
+```
+KeyError: 'qwen2_5_vl'
+```
+
+
+We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:
+
+```bash
+# It's highly recommanded to use `[decord]` feature for faster video loading.
+pip install qwen-vl-utils[decord]==0.0.8
+```
+
+If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-vl-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video.
+
+### Using 🤗  Transformers to Chat
+
+Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen2.5-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
+)
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
+# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+#     "Qwen/Qwen2.5-VL-7B-Instruct",
+#     torch_dtype=torch.bfloat16,
+#     attn_implementation="flash_attention_2",
+#     device_map="auto",
+# )
+
+# default processer
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+
+# The default range for the number of visual tokens per image in the model is 4-16384.
+# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
+# min_pixels = 256*28*28
+# max_pixels = 1280*28*28
+# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+<details>
+<summary>Multi image inference</summary>
+
+```python
+# Messages containing multiple images and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "file:///path/to/image1.jpg"},
+            {"type": "image", "image": "file:///path/to/image2.jpg"},
+            {"type": "text", "text": "Identify the similarities between these images."},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+</details>
+
+<details>
+<summary>Video inference</summary>
+
+```python
+# Messages containing a images list as a video and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": [
+                    "file:///path/to/frame1.jpg",
+                    "file:///path/to/frame2.jpg",
+                    "file:///path/to/frame3.jpg",
+                    "file:///path/to/frame4.jpg",
+                ],
+            },
+            {"type": "text", "text": "Describe this video."},
+        ],
+    }
+]
+
+# Messages containing a local video path and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": "file:///path/to/video1.mp4",
+                "max_pixels": 360 * 420,
+                "fps": 1.0,
+            },
+            {"type": "text", "text": "Describe this video."},
+        ],
+    }
+]
+
+# Messages containing a video url and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4",
+            },
+            {"type": "text", "text": "Describe this video."},
+        ],
+    }
+]
+
+#In Qwen 2.5 VL, frame rate information is also input into the model to align with absolute time.
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    fps=fps,
+    padding=True,
+    return_tensors="pt",
+    **video_kwargs,
+)
+inputs = inputs.to("cuda")
+
+# Inference
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+
+Video URL compatibility largely depends on the third-party library version. The details are in the table below. change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one.
+
+| Backend     | HTTP | HTTPS |
+|-------------|------|-------|
+| torchvision >= 0.19.0 | ✅  | ✅   |
+| torchvision < 0.19.0  | ❌  | ❌   |
+| decord      | ✅  | ❌   |
+</details>
+
+<details>
+<summary>Batch inference</summary>
+
+```python
+# Sample messages for batch inference
+messages1 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "file:///path/to/image1.jpg"},
+            {"type": "image", "image": "file:///path/to/image2.jpg"},
+            {"type": "text", "text": "What are the common elements in these pictures?"},
+        ],
+    }
+]
+messages2 = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Who are you?"},
+]
+# Combine messages for batch processing
+messages = [messages1, messages2]
+
+# Preparation for batch inference
+texts = [
+    processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
+    for msg in messages
+]
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=texts,
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Batch Inference
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_texts = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_texts)
+```
+</details>
+
+### 🤖 ModelScope
+We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints.
+
+
+### More Usage Tips
+
+For input images, we support local files, base64, and URLs. For videos, we currently only support local files.
+
+```python
+# You can directly insert a local file path, a URL, or a base64-encoded image into the position where you want in the text.
+## Local file path
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "file:///path/to/your/image.jpg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+## Image URL
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "http://path/to/your/image.jpg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+## Base64 encoded image
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "data:image;base64,/9j/..."},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+```
+#### Image Resolution for performance boost
+
+The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs, such as a token count range of 256-1280, to balance speed and memory usage.
+
+```python
+min_pixels = 256 * 28 * 28
+max_pixels = 1280 * 28 * 28
+processor = AutoProcessor.from_pretrained(
+    "Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels
+)
+```
+
+Besides, We provide two methods for fine-grained control over the image size input to the model:
+
+1. Define min_pixels and max_pixels: Images will be resized to maintain their aspect ratio within the range of min_pixels and max_pixels.
+   
+2. Specify exact dimensions: Directly set `resized_height` and `resized_width`. These values will be rounded to the nearest multiple of 28.
+
+```python
+# min_pixels and max_pixels
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "file:///path/to/your/image.jpg",
+                "resized_height": 280,
+                "resized_width": 420,
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+# resized_height and resized_width
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "file:///path/to/your/image.jpg",
+                "min_pixels": 50176,
+                "max_pixels": 50176,
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+```
+
+### Processing Long Texts
+
+The current `config.json` is set for context length up to 32,768 tokens.
+To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
+
+For supported frameworks, you could add the following to `config.json` to enable YaRN:
+
+{
+	...,
+    "type": "yarn",
+    "mrope_section": [
+        16,
+        24,
+        24
+    ],
+    "factor": 4,
+    "original_max_position_embeddings": 32768
+}
+
+However, it should be noted that this method has a significant impact on the performance of temporal and spatial localization tasks, and is therefore not recommended for use.
+
+At the same time, for long video inputs, since MRoPE itself is more economical with ids, the max_position_embeddings can be directly modified to a larger value, such as 64k.
+
+
+
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@misc{qwen2.5-VL,
+    title = {Qwen2.5-VL},
+    url = {https://qwenlm.github.io/blog/qwen2.5-vl/},
+    author = {Qwen Team},
+    month = {January},
+    year = {2025}
+}
+
+@article{Qwen2VL,
+  title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
+  author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang},
+  journal={arXiv preprint arXiv:2409.12191},
+  year={2024}
+}
+
+@article{Qwen-VL,
+  title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
+  author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
+  journal={arXiv preprint arXiv:2308.12966},
+  year={2023}
+}
+```

text_encoder/chat_template.json

@@ -0,0 +1,3 @@
+{
+    "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

text_encoder/config.json

@@ -0,0 +1,61 @@
+{
+  "architectures": [
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text_encoder/preprocessor_config.json

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+}

text_encoder/tokenizer_config.json

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+}

text_encoder_2/config.json

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+  "_name_or_path": "clip-vit-large-patch14/",
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text_encoder_2/model.safetensors

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tokenizer/chat_template.json

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+    "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

tokenizer/preprocessor_config.json

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+{
+  "min_pixels": 3136,
+  "max_pixels": 12845056,
+  "patch_size": 14,
+  "temporal_patch_size": 2,
+  "merge_size": 2,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "image_processor_type": "Qwen2VLImageProcessor",
+  "processor_class": "Qwen2_5_VLProcessor"
+}

tokenizer/tokenizer_config.json

@@ -0,0 +1,207 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "add_bos_token": false
+}

tokenizer_2/special_tokens_map.json

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+{"bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": "<|endoftext|>"}

tokenizer_2/tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+    "unk_token": {
+        "content": "<|endoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "bos_token": {
+        "content": "<|startoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "eos_token": {
+        "content": "<|endoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "pad_token": "<|endoftext|>",
+    "add_prefix_space": false,
+    "errors": "replace",
+    "do_lower_case": true,
+    "name_or_path": "openai/clip-vit-base-patch32",
+    "model_max_length": 77,
+    "special_tokens_map_file": "./special_tokens_map.json",
+    "tokenizer_class": "CLIPTokenizer"
+}

transformer/config.json

@@ -0,0 +1,25 @@
+{
+  "_class_name": "Kandinsky5Transformer3DModel",
+  "_diffusers_version": "0.33.0.dev0",
+  "in_visual_dim": 16,
+  "out_visual_dim": 16,
+  "time_dim": 512,
+  "patch_size": [
+    1,
+    2,
+    2
+  ],
+  "model_dim": 1792,
+  "ff_dim": 7168,
+  "num_text_blocks": 2,
+  "num_visual_blocks": 32,
+  "axes_dims": [
+    16,
+    24,
+    24
+  ],
+  "visual_cond": true,
+  "in_text_dim": 3584,
+  "in_text_dim2": 768,
+  "attention_type": "regular"
+}

transformer/diffusion_pytorch_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d875ad5f5c164c3bcd0e3e37c5b6b66832c02a7bac566c0ef70f9484fa8c8e7f
+size 4015514184

vae/config.json

@@ -0,0 +1,32 @@
+{
+  "_class_name": "AutoencoderKLHunyuanVideo",
+  "_diffusers_version": "0.32.0.dev0",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "HunyuanVideoDownBlock3D",
+    "HunyuanVideoDownBlock3D",
+    "HunyuanVideoDownBlock3D",
+    "HunyuanVideoDownBlock3D"
+  ],
+  "in_channels": 3,
+  "latent_channels": 16,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "scaling_factor": 0.476986,
+  "spatial_compression_ratio": 8,
+  "temporal_compression_ratio": 4,
+  "up_block_types": [
+    "HunyuanVideoUpBlock3D",
+    "HunyuanVideoUpBlock3D",
+    "HunyuanVideoUpBlock3D",
+    "HunyuanVideoUpBlock3D"
+  ]
+}

vae/diffusion_pytorch_model.safetensors

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+oid sha256:7c68a6295f9034a88225fbafb1f3258291a08d57a1fdb938233fa57b1b8f4883
+size 985943868