Add files using upload-large-folder tool
Browse files- .gitattributes +32 -0
- FusionX_LoRa/Phantom_Wan_14B_FusionX_LoRA.safetensors +3 -0
- FusionX_LoRa/placeholder.txt +0 -0
- Kokoro-82M/.gitattributes +41 -0
- Kokoro-82M/DONATE.md +47 -0
- Kokoro-82M/EVAL.md +15 -0
- Kokoro-82M/README.md +117 -0
- Kokoro-82M/SAMPLES.md +48 -0
- Kokoro-82M/VOICES.md +161 -0
- Kokoro-82M/config.json +150 -0
- Kokoro-82M/kokoro-v1_0.pth +3 -0
- LICENSE +0 -0
- MeiGen-MultiTalk/.gitattributes +38 -0
- MeiGen-MultiTalk/README.md +88 -0
- MeiGen-MultiTalk/diffusion_pytorch_model.safetensors.index.json +0 -0
- MeiGen-MultiTalk/quant_models/dit_model_map_int8.json +1 -0
- MeiGen-MultiTalk/quant_models/quantization_map_fp8_FusionX.json +1 -0
- MeiGen-MultiTalk/quant_models/quantization_map_int8_FusionX.json +1 -0
- MeiGen-MultiTalk/quant_models/t5_fp8.safetensors +3 -0
- MeiGen-MultiTalk/quant_models/t5_int8.safetensors +3 -0
- MeiGen-MultiTalk/quant_models/t5_map_fp8.json +1 -0
- MeiGen-MultiTalk/quant_models/t5_map_int8.json +1 -0
- OtherLoRa's/Placeholder.txt +0 -0
- README.md +259 -3
- Wan2.1-I2V-14B-480P/.gitattributes +47 -0
- Wan2.1-I2V-14B-480P/LICENSE.txt +201 -0
- Wan2.1-I2V-14B-480P/README.md +273 -0
- Wan2.1-I2V-14B-480P/Wan2.1_VAE.pth +3 -0
- Wan2.1-I2V-14B-480P/assets/logo.png +0 -0
- Wan2.1-I2V-14B-480P/config.json +14 -0
- Wan2.1-I2V-14B-480P/diffusion_pytorch_model-00005-of-00007.safetensors +3 -0
- Wan2.1-I2V-14B-480P/diffusion_pytorch_model-00007-of-00007.safetensors +3 -0
- Wan2.1-I2V-14B-480P/google/umt5-xxl/special_tokens_map.json +308 -0
- Wan2.1-I2V-14B-480P/google/umt5-xxl/tokenizer_config.json +2748 -0
- Wan2.1-I2V-14B-480P/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth +3 -0
- Wan2.1-I2V-14B-480P/models_t5_umt5-xxl-enc-bf16.pth +3 -0
- Wan2.1-I2V-14B-480P/xlm-roberta-large/special_tokens_map.json +15 -0
- Wan2.1-I2V-14B-480P/xlm-roberta-large/tokenizer_config.json +19 -0
- Wan2.1_I2V_14B_FusionX_LoRA.safetensors +3 -0
- WanT2V_MasterModel.safetensors +3 -0
- chinese-wav2vec2-base/.gitattributes +27 -0
- chinese-wav2vec2-base/README.md +61 -0
- chinese-wav2vec2-base/chinese-wav2vec2-base-fairseq-ckpt.pt +3 -0
- chinese-wav2vec2-base/config.json +105 -0
- chinese-wav2vec2-base/model.safetensors +3 -0
- chinese-wav2vec2-base/preprocessor_config.json +8 -0
- chinese-wav2vec2-base/pytorch_model.bin +3 -0
- images/ComfyUI_00001_.webp +3 -0
- placeholder +0 -0
- weight.txt +1 -0
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FusionX_LoRa/Phantom_Wan_14B_FusionX_LoRA.safetensors
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FusionX_LoRa/placeholder.txt
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Kokoro-82M/DONATE.md
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# Donate
|
| 2 |
+
|
| 3 |
+
Apache software is free software.
|
| 4 |
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| 5 |
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For those able & willing to support my work, I can be sponsored via [GitHub Sponsors](https://github.com/sponsors/hexgrad). I also accept *unconditional, no-strings-attached* donations in the form of **GPU cloud credit**. This helps me run experiments and train models.
|
| 6 |
+
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| 7 |
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Please do not "attach strings" (e.g. model requests, consulting, brand sponsorships) to donations because some of these channels are anonymous: I may not be able to see who sent what.
|
| 8 |
+
|
| 9 |
+
Also, along the lines of "Never buy a product based on the future promise of updates", I would discourage you from donating because you expect a specific model to come down the pipeline. Hopefully, donors broadly believe that good things happen when [someone gets this man (yours truly) a GPU](https://i.redd.it/r8dtt3n9rc431.jpg).
|
| 10 |
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| 11 |
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### GitHub Sponsors
|
| 12 |
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https://github.com/sponsors/hexgrad
|
| 13 |
+
|
| 14 |
+
### Vast.ai Referral Link
|
| 15 |
+
Vast.ai is a vendor I use for cloud GPUs. I "earn 3% of all referred customer revenue as credits": [https://cloud.vast.ai/?ref_id=79907](https://cloud.vast.ai/?ref_id=79907)
|
| 16 |
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| 17 |
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### Vast.ai Transfer Credit
|
| 18 |
+
To **anonymously** transfer $5 of credit directly to my Vast.ai account `hello@hexgrad.com`, you can use `transfer credit` in the Vast CLI like so:
|
| 19 |
+
|
| 20 |
+
```sh
|
| 21 |
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vastai transfer credit hello@hexgrad.com 5
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| 22 |
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```
|
| 23 |
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| 24 |
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The usage of the `transfer credit` command is documented here: https://docs.vast.ai/api/commands#voyTE
|
| 25 |
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|
| 26 |
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```sh
|
| 27 |
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usage: vastai transfer credit RECIPIENT AMOUNT
|
| 28 |
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| 29 |
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positional arguments:
|
| 30 |
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recipient email of recipient account
|
| 31 |
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amount $dollars of credit to transfer
|
| 32 |
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| 33 |
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Transfer (amount) credits to account with email (recipient).
|
| 34 |
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```
|
| 35 |
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| 36 |
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Note that `transfer credit` seems to be an anonymous command. If I don't say thank you, its because I can't see the sender!
|
| 37 |
+
|
| 38 |
+
### RunPod Referral Link
|
| 39 |
+
RunPod is another vendor I use for cloud GPUs. I earn "5% from serverless and 1% from templates": https://runpod.io?ref=pup8o2ly
|
| 40 |
+
|
| 41 |
+
### RunPod Credit Codes
|
| 42 |
+
After signing in to RunPod, under `Account > Billing > Credit Codes`, you can "generate a code that allows you to gift funds to another RunPod user":
|
| 43 |
+
|
| 44 |
+
> Simply give them the code and they will be able to redeem it for credits on their billing page. Please safeguard your codes as they are worth money!
|
| 45 |
+
> Credits will be debited from your account immediately. You can redeem the code yourself if you want to recover your credits. There is a 2% transaction fee for payment processing!
|
| 46 |
+
|
| 47 |
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If you wish to send codes, you can do so by emailing `hello@hexgrad.com`, or DM me on Discord `@rzvzn`.
|
Kokoro-82M/EVAL.md
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# Evaluation
|
| 2 |
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| 3 |
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Screenshots captured February 26, 2025, about 1 month after v1.0 model release.
|
| 4 |
+
|
| 5 |
+
### TTS Spaces Arena
|
| 6 |
+
https://hf.co/spaces/Pendrokar/TTS-Spaces-Arena
|
| 7 |
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|
| 8 |
+
|
| 9 |
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### TTS Arena
|
| 10 |
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https://huggingface.co/spaces/TTS-AGI/TTS-Arena
|
| 11 |
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| 12 |
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|
| 13 |
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### Artificial Analysis
|
| 14 |
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https://artificialanalysis.ai/text-to-speech/arena?tab=Leaderboard
|
| 15 |
+
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Kokoro-82M/README.md
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|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
base_model:
|
| 6 |
+
- yl4579/StyleTTS2-LJSpeech
|
| 7 |
+
pipeline_tag: text-to-speech
|
| 8 |
+
---
|
| 9 |
+
**Kokoro** is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects.
|
| 10 |
+
|
| 11 |
+
<audio controls><source src="https://huggingface.co/hexgrad/Kokoro-82M/resolve/main/samples/HEARME.wav" type="audio/wav"></audio>
|
| 12 |
+
|
| 13 |
+
🐈 **GitHub**: https://github.com/hexgrad/kokoro
|
| 14 |
+
|
| 15 |
+
🚀 **Demo**: https://hf.co/spaces/hexgrad/Kokoro-TTS
|
| 16 |
+
|
| 17 |
+
> [!NOTE]
|
| 18 |
+
> As of April 2025, the market rate of Kokoro served over API is **under $1 per million characters of text input**, or under $0.06 per hour of audio output. (On average, 1000 characters of input is about 1 minute of output.) Sources: [ArtificialAnalysis/Replicate at 65 cents per M chars](https://artificialanalysis.ai/text-to-speech/model-family/kokoro#price) and [DeepInfra at 80 cents per M chars](https://deepinfra.com/hexgrad/Kokoro-82M).
|
| 19 |
+
>
|
| 20 |
+
> This is an Apache-licensed model, and Kokoro has been deployed in numerous projects and commercial APIs. We welcome the deployment of the model in real use cases.
|
| 21 |
+
|
| 22 |
+
> [!CAUTION]
|
| 23 |
+
> Fake websites like kokorottsai_com (snapshot: https://archive.ph/nRRnk) and kokorotts_net (snapshot: https://archive.ph/60opa) are likely scams masquerading under the banner of a popular model.
|
| 24 |
+
>
|
| 25 |
+
> Any website containing "kokoro" in its root domain (e.g. kokorottsai_com, kokorotts_net) is **NOT owned by and NOT affiliated with this model page or its author**, and attempts to imply otherwise are red flags.
|
| 26 |
+
|
| 27 |
+
- [Releases](#releases)
|
| 28 |
+
- [Usage](#usage)
|
| 29 |
+
- [EVAL.md](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/EVAL.md) ↗️
|
| 30 |
+
- [SAMPLES.md](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/SAMPLES.md) ↗️
|
| 31 |
+
- [VOICES.md](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/VOICES.md) ↗️
|
| 32 |
+
- [Model Facts](#model-facts)
|
| 33 |
+
- [Training Details](#training-details)
|
| 34 |
+
- [Creative Commons Attribution](#creative-commons-attribution)
|
| 35 |
+
- [Acknowledgements](#acknowledgements)
|
| 36 |
+
|
| 37 |
+
### Releases
|
| 38 |
+
|
| 39 |
+
| Model | Published | Training Data | Langs & Voices | SHA256 |
|
| 40 |
+
| ----- | --------- | ------------- | -------------- | ------ |
|
| 41 |
+
| **v1.0** | **2025 Jan 27** | **Few hundred hrs** | [**8 & 54**](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/VOICES.md) | `496dba11` |
|
| 42 |
+
| [v0.19](https://huggingface.co/hexgrad/kLegacy/tree/main/v0.19) | 2024 Dec 25 | <100 hrs | 1 & 10 | `3b0c392f` |
|
| 43 |
+
|
| 44 |
+
| Training Costs | v0.19 | v1.0 | **Total** |
|
| 45 |
+
| -------------- | ----- | ---- | ----- |
|
| 46 |
+
| in A100 80GB GPU hours | 500 | 500 | **1000** |
|
| 47 |
+
| average hourly rate | $0.80/h | $1.20/h | **$1/h** |
|
| 48 |
+
| in USD | $400 | $600 | **$1000** |
|
| 49 |
+
|
| 50 |
+
### Usage
|
| 51 |
+
You can run this basic cell on [Google Colab](https://colab.research.google.com/). [Listen to samples](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/SAMPLES.md). For more languages and details, see [Advanced Usage](https://github.com/hexgrad/kokoro?tab=readme-ov-file#advanced-usage).
|
| 52 |
+
```py
|
| 53 |
+
!pip install -q kokoro>=0.9.2 soundfile
|
| 54 |
+
!apt-get -qq -y install espeak-ng > /dev/null 2>&1
|
| 55 |
+
from kokoro import KPipeline
|
| 56 |
+
from IPython.display import display, Audio
|
| 57 |
+
import soundfile as sf
|
| 58 |
+
import torch
|
| 59 |
+
pipeline = KPipeline(lang_code='a')
|
| 60 |
+
text = '''
|
| 61 |
+
[Kokoro](/kˈOkəɹO/) is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, [Kokoro](/kˈOkəɹO/) can be deployed anywhere from production environments to personal projects.
|
| 62 |
+
'''
|
| 63 |
+
generator = pipeline(text, voice='af_heart')
|
| 64 |
+
for i, (gs, ps, audio) in enumerate(generator):
|
| 65 |
+
print(i, gs, ps)
|
| 66 |
+
display(Audio(data=audio, rate=24000, autoplay=i==0))
|
| 67 |
+
sf.write(f'{i}.wav', audio, 24000)
|
| 68 |
+
```
|
| 69 |
+
Under the hood, `kokoro` uses [`misaki`](https://pypi.org/project/misaki/), a G2P library at https://github.com/hexgrad/misaki
|
| 70 |
+
|
| 71 |
+
### Model Facts
|
| 72 |
+
|
| 73 |
+
**Architecture:**
|
| 74 |
+
- StyleTTS 2: https://arxiv.org/abs/2306.07691
|
| 75 |
+
- ISTFTNet: https://arxiv.org/abs/2203.02395
|
| 76 |
+
- Decoder only: no diffusion, no encoder release
|
| 77 |
+
|
| 78 |
+
**Architected by:** Li et al @ https://github.com/yl4579/StyleTTS2
|
| 79 |
+
|
| 80 |
+
**Trained by**: `@rzvzn` on Discord
|
| 81 |
+
|
| 82 |
+
**Languages:** Multiple
|
| 83 |
+
|
| 84 |
+
**Model SHA256 Hash:** `496dba118d1a58f5f3db2efc88dbdc216e0483fc89fe6e47ee1f2c53f18ad1e4`
|
| 85 |
+
|
| 86 |
+
### Training Details
|
| 87 |
+
|
| 88 |
+
**Data:** Kokoro was trained exclusively on **permissive/non-copyrighted audio data** and IPA phoneme labels. Examples of permissive/non-copyrighted audio include:
|
| 89 |
+
- Public domain audio
|
| 90 |
+
- Audio licensed under Apache, MIT, etc
|
| 91 |
+
- Synthetic audio<sup>[1]</sup> generated by closed<sup>[2]</sup> TTS models from large providers<br/>
|
| 92 |
+
[1] https://copyright.gov/ai/ai_policy_guidance.pdf<br/>
|
| 93 |
+
[2] No synthetic audio from open TTS models or "custom voice clones"
|
| 94 |
+
|
| 95 |
+
**Total Dataset Size:** A few hundred hours of audio
|
| 96 |
+
|
| 97 |
+
**Total Training Cost:** About $1000 for 1000 hours of A100 80GB vRAM
|
| 98 |
+
|
| 99 |
+
### Creative Commons Attribution
|
| 100 |
+
|
| 101 |
+
The following CC BY audio was part of the dataset used to train Kokoro v1.0.
|
| 102 |
+
|
| 103 |
+
| Audio Data | Duration Used | License | Added to Training Set After |
|
| 104 |
+
| ---------- | ------------- | ------- | --------------------------- |
|
| 105 |
+
| [Koniwa](https://github.com/koniwa/koniwa) `tnc` | <1h | [CC BY 3.0](https://creativecommons.org/licenses/by/3.0/deed.ja) | v0.19 / 22 Nov 2024 |
|
| 106 |
+
| [SIWIS](https://datashare.ed.ac.uk/handle/10283/2353) | <11h | [CC BY 4.0](https://datashare.ed.ac.uk/bitstream/handle/10283/2353/license_text) | v0.19 / 22 Nov 2024 |
|
| 107 |
+
|
| 108 |
+
### Acknowledgements
|
| 109 |
+
|
| 110 |
+
- 🛠️ [@yl4579](https://huggingface.co/yl4579) for architecting StyleTTS 2.
|
| 111 |
+
- 🏆 [@Pendrokar](https://huggingface.co/Pendrokar) for adding Kokoro as a contender in the TTS Spaces Arena.
|
| 112 |
+
- 📊 Thank you to everyone who contributed synthetic training data.
|
| 113 |
+
- ❤️ Special thanks to all compute sponsors.
|
| 114 |
+
- 👾 Discord server: https://discord.gg/QuGxSWBfQy
|
| 115 |
+
- 🪽 Kokoro is a Japanese word that translates to "heart" or "spirit". It is also the name of an [AI in the Terminator franchise](https://terminator.fandom.com/wiki/Kokoro).
|
| 116 |
+
|
| 117 |
+
<img src="https://static0.gamerantimages.com/wordpress/wp-content/uploads/2024/08/terminator-zero-41-1.jpg" width="400" alt="kokoro" />
|
Kokoro-82M/SAMPLES.md
ADDED
|
@@ -0,0 +1,48 @@
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|
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|
|
|
|
|
| 1 |
+
### HEARME
|
| 2 |
+
<audio controls><source src="https://huggingface.co/hexgrad/Kokoro-82M/resolve/main/samples/HEARME.wav" type="audio/wav"></audio>
|
| 3 |
+
> Kokoro is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects.
|
| 4 |
+
```
|
| 5 |
+
kˈOkəɹO ɪz ɐn ˈOpᵊnwˌAt tˌitˌiˈɛs mˈɑdᵊl wɪð ˈATi tˈu mˈɪljᵊn pəɹˈæməTəɹz. dəspˈIt ɪts lˈItwˌAt ˈɑɹkətˌɛkʧəɹ, ɪt dəlˈɪvəɹz kˈɑmpəɹəbᵊl kwˈɑləTi tə lˈɑɹʤəɹ mˈɑdᵊlz wˌIl bˈiɪŋ səɡnˈɪfəkəntli fˈæstəɹ ænd mˈɔɹ kˈɔstəfˌɪʃənt. wˌɪð əpˌæʧilˈIsᵊnst wˈAts, kˈOkəɹO kæn bi dəplˈYd ˈɛniwˌɛɹ fɹʌm pɹədˈʌkʃən ənvˈIɹənmᵊnts tə pˈɜɹsᵊnəl pɹˈɑʤˌɛkts.
|
| 6 |
+
```
|
| 7 |
+
|
| 8 |
+
### af_heart_0
|
| 9 |
+
<audio controls><source src="https://huggingface.co/hexgrad/Kokoro-82M/resolve/main/samples/af_heart_0.wav" type="audio/wav"></audio>
|
| 10 |
+
> The sky above the port was the color of television, tuned to a dead channel.
|
| 11 |
+
```
|
| 12 |
+
ðə skˈI əbˈʌv ðə pˈɔɹt wʌz ðə kˈʌləɹ ʌv tˈɛləvˌɪʒən, tˈund tə ɐ dˈɛd ʧˈænᵊl.
|
| 13 |
+
```
|
| 14 |
+
|
| 15 |
+
### af_heart_1
|
| 16 |
+
<audio controls><source src="https://huggingface.co/hexgrad/Kokoro-82M/resolve/main/samples/af_heart_1.wav" type="audio/wav"></audio>
|
| 17 |
+
> "It's not like I'm using," Case heard someone say, as he shouldered his way through the crowd around the door of the Chat. "It's like my body's developed this massive drug deficiency."
|
| 18 |
+
```
|
| 19 |
+
“ˌɪts nˌɑt lˈIk ˌIm jˈuzɪŋ,” kˈAs hˈɜɹd sˈʌmwˌʌn sˈA, æz hi ʃˈOldəɹd hɪz wˈA θɹu ðə kɹˈWd əɹˈWnd ðə dˈɔɹ ʌv ðə ʧˈæt. “ˌɪts lˈIk mI bˈɑdiz dəvˈɛləpt ðɪs mˈæsɪv dɹˈʌɡ dəfˈɪʃənsi.”
|
| 20 |
+
```
|
| 21 |
+
|
| 22 |
+
### af_heart_2
|
| 23 |
+
<audio controls><source src="https://huggingface.co/hexgrad/Kokoro-82M/resolve/main/samples/af_heart_2.wav" type="audio/wav"></audio>
|
| 24 |
+
> It was a Sprawl voice and a Sprawl joke. The Chatsubo was a bar for professional expatriates; you could drink there for a week and never hear two words in Japanese.
|
| 25 |
+
```
|
| 26 |
+
ˌɪt wʌz ɐ spɹˈɔl vˈYs ænd ɐ spɹˈɔl ʤˈOk. ðə ʧætsˈubO wʌz ɐ bˈɑɹ fɔɹ pɹəfˈɛʃᵊnəl ɛkspˈAtɹiəts; ju kʊd dɹˈɪŋk ðɛɹ fɔɹ ɐ wˈik ænd nˈɛvəɹ hˈɪɹ tˈu wˈɜɹdz ɪn ʤˌæpənˈiz.
|
| 27 |
+
```
|
| 28 |
+
|
| 29 |
+
### af_heart_3
|
| 30 |
+
<audio controls><source src="https://huggingface.co/hexgrad/Kokoro-82M/resolve/main/samples/af_heart_3.wav" type="audio/wav"></audio>
|
| 31 |
+
> These were to have an enormous impact, not only because they were associated with Constantine, but also because, as in so many other areas, the decisions taken by Constantine (or in his name) were to have great significance for centuries to come. One of the main issues was the shape that Christian churches were to take, since there was not, apparently, a tradition of monumental church buildings when Constantine decided to help the Christian church build a series of truly spectacular structures.
|
| 32 |
+
```
|
| 33 |
+
ðˌiz wɜɹ tə hæv ɐn ɪnˈɔɹməs ˈɪmpˌækt, nˌɑt ˈOnli bəkˈʌz ðA wɜɹ əsˈOsiˌATᵻd wɪð kˈɑnstəntˌin, bˌʌt ˈɔlsO bəkˈʌz, æz ɪn sˌO mˈɛni ˈʌðəɹ ˈɛɹiəz, ðə dəsˈɪʒᵊnz tˈAkən bI kˈɑnstəntˌin (ɔɹ ɪn hɪz nˈAm) wɜɹ tə hæv ɡɹˈAt səɡnˈɪfəkᵊns fɔɹ sˈɛnʧəɹiz tə kˈʌm. wˈʌn ʌv ðə mˈAn ˈɪʃjuz wʌz ðə ʃˈAp ðæt kɹˈɪsʧən ʧˈɜɹʧᵻz wɜɹ tə tˈAk, sˈɪns ðɛɹ wʌz nˌɑt, əpˈɛɹəntli, ɐ tɹədˈɪʃən ʌv mˌɑnjəmˈɛntᵊl ʧˈɜɹʧ bˈɪldɪŋz wˌɛn kˈɑnstəntˌin dəsˈIdᵻd tə hˈɛlp ðə kɹˈɪsʧən ʧˈɜɹʧ bˈɪld ɐ sˈɪɹiz ʌv tɹˈuli spɛktˈækjələɹ stɹˈʌkʧəɹz.
|
| 34 |
+
```
|
| 35 |
+
|
| 36 |
+
### af_heart_4
|
| 37 |
+
<audio controls><source src="https://huggingface.co/hexgrad/Kokoro-82M/resolve/main/samples/af_heart_4.wav" type="audio/wav"></audio>
|
| 38 |
+
> The main form that these churches took was that of the basilica, a multipurpose rectangular structure, based ultimately on the earlier Greek stoa, which could be found in most of the great cities of the empire. Christianity, unlike classical polytheism, needed a large interior space for the celebration of its religious services, and the basilica aptly filled that need.
|
| 39 |
+
```
|
| 40 |
+
ðə mˈAn fˈɔɹm ðæt ðiz ʧˈɜɹʧᵻz tˈʊk wʌz ðæt ʌv ðə bəsˈɪləkə, ɐ mˌʌltipˈɜɹpəs ɹɛktˈæŋɡjələɹ stɹˈʌkʧəɹ, bˈAst ˈʌltəmətli ˌɔn ði ˈɜɹliəɹ ɡɹˈik stˈOə, wˌɪʧ kʊd bi fˈWnd ɪn mˈOst ʌv ðə ɡɹˈAt sˈɪTiz ʌv ði ˈɛmpˌIəɹ. kɹˌɪsʧiˈænəTi, ˌʌnlˈIk klˈæsəkᵊl pˈɑliθiˌɪzəm, nˈidᵻd ɐ lˈɑɹʤ ɪntˈɪɹiəɹ spˈAs fɔɹ ðə sˌɛləbɹˈAʃən ʌv ɪts ɹəlˈɪʤəs sˈɜɹvəsᵻz, ænd ðə bəsˈɪləkə ˈæptli fˈɪld ðæt nˈid.
|
| 41 |
+
```
|
| 42 |
+
|
| 43 |
+
### af_heart_5
|
| 44 |
+
<audio controls><source src="https://huggingface.co/hexgrad/Kokoro-82M/resolve/main/samples/af_heart_5.wav" type="audio/wav"></audio>
|
| 45 |
+
> We naturally do not know the degree to which the emperor was involved in the design of new churches, but it is tempting to connect this with the secular basilica that Constantine completed in the Roman forum (the so-called Basilica of Maxentius) and the one he probably built in Trier, in connection with his residence in the city at a time when he was still caesar.
|
| 46 |
+
```
|
| 47 |
+
wˌi nˈæʧəɹəli dˈu nˌɑt nˈO ðə dəɡɹˈi tə wˌɪʧ ði ˈɛmpəɹəɹ wʌz ɪnvˈɑlvd ɪn ðə dəzˈIn ʌv nˈu ʧˈɜɹʧᵻz, bˌʌt ɪt ɪz tˈɛmptɪŋ tə kənˈɛkt ðɪs wɪð ðə sˈɛkjələɹ bəsˈɪləkə ðæt kˈɑnstəntˌin kəmplˈiTᵻd ɪn ðə ɹˈOmən fˈɔɹəm (ðə sˌOkˈɔld bəsˈɪləkə ʌv mæksˈɛntiəs) ænd ðə wˈʌn hi pɹˈɑbəbli bˈɪlt ɪn tɹˈɪɹ, ɪn kənˈɛkʃən wɪð hɪz ɹˈɛzədᵊns ɪn ðə sˈɪTi æt ɐ tˈIm wˌɛn hi wʌz stˈɪl sˈizəɹ.
|
| 48 |
+
```
|
Kokoro-82M/VOICES.md
ADDED
|
@@ -0,0 +1,161 @@
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|
|
| 1 |
+
# Voices
|
| 2 |
+
|
| 3 |
+
- 🇺🇸 [American English](#american-english): 11F 9M
|
| 4 |
+
- 🇬🇧 [British English](#british-english): 4F 4M
|
| 5 |
+
- 🇯🇵 [Japanese](#japanese): 4F 1M
|
| 6 |
+
- 🇨🇳 [Mandarin Chinese](#mandarin-chinese): 4F 4M
|
| 7 |
+
- 🇪🇸 [Spanish](#spanish): 1F 2M
|
| 8 |
+
- 🇫🇷 [French](#french): 1F
|
| 9 |
+
- 🇮🇳 [Hindi](#hindi): 2F 2M
|
| 10 |
+
- 🇮🇹 [Italian](#italian): 1F 1M
|
| 11 |
+
- 🇧🇷 [Brazilian Portuguese](#brazilian-portuguese): 1F 2M
|
| 12 |
+
|
| 13 |
+
For each voice, the given grades are intended to be estimates of the **quality and quantity** of its associated training data, both of which impact overall inference quality.
|
| 14 |
+
|
| 15 |
+
Subjectively, voices will sound better or worse to different people.
|
| 16 |
+
|
| 17 |
+
Support for non-English languages may be absent or thin due to weak G2P and/or lack of training data. Some languages are only represented by a small handful or even just one voice (French).
|
| 18 |
+
|
| 19 |
+
Most voices perform best on a "goldilocks range" of 100-200 tokens out of ~500 possible. Voices may perform worse at the extremes:
|
| 20 |
+
- **Weakness** on short utterances, especially less than 10-20 tokens. Root cause could be lack of short-utterance training data and/or model architecture. One possible inference mitigation is to bundle shorter utterances together.
|
| 21 |
+
- **Rushing** on long utterances, especially over 400 tokens. You can chunk down to shorter utterances or adjust the `speed` parameter to mitigate this.
|
| 22 |
+
|
| 23 |
+
**Target Quality**
|
| 24 |
+
- How high quality is the reference voice? This grade may be impacted by audio quality, artifacts, compression, & sample rate.
|
| 25 |
+
- How well do the text labels match the audio? Text/audio misalignment (e.g. from hallucinations) will lower this grade.
|
| 26 |
+
|
| 27 |
+
**Training Duration**
|
| 28 |
+
- How much audio was seen during training? Smaller durations result in a lower overall grade.
|
| 29 |
+
- 10 hours <= **HH hours** < 100 hours
|
| 30 |
+
- 1 hour <= H hours < 10 hours
|
| 31 |
+
- 10 minutes <= MM minutes < 100 minutes
|
| 32 |
+
- 1 minute <= _M minutes_ 🤏 < 10 minutes
|
| 33 |
+
|
| 34 |
+
### American English
|
| 35 |
+
|
| 36 |
+
- `lang_code='a'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
|
| 37 |
+
- espeak-ng `en-us` fallback
|
| 38 |
+
|
| 39 |
+
| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
|
| 40 |
+
| ---- | ------ | -------------- | ----------------- | ------------- | ------ |
|
| 41 |
+
| **af\_heart** | 🚺❤️ | | | **A** | `0ab5709b` |
|
| 42 |
+
| af_alloy | 🚺 | B | MM minutes | C | `6d877149` |
|
| 43 |
+
| af_aoede | 🚺 | B | H hours | C+ | `c03bd1a4` |
|
| 44 |
+
| af_bella | 🚺🔥 | **A** | **HH hours** | **A-** | `8cb64e02` |
|
| 45 |
+
| af_jessica | 🚺 | C | MM minutes | D | `cdfdccb8` |
|
| 46 |
+
| af_kore | 🚺 | B | H hours | C+ | `8bfbc512` |
|
| 47 |
+
| af_nicole | 🚺🎧 | B | **HH hours** | B- | `c5561808` |
|
| 48 |
+
| af_nova | 🚺 | B | MM minutes | C | `e0233676` |
|
| 49 |
+
| af_river | 🚺 | C | MM minutes | D | `e149459b` |
|
| 50 |
+
| af_sarah | 🚺 | B | H hours | C+ | `49bd364e` |
|
| 51 |
+
| af_sky | 🚺 | B | _M minutes_ 🤏 | C- | `c799548a` |
|
| 52 |
+
| am_adam | 🚹 | D | H hours | F+ | `ced7e284` |
|
| 53 |
+
| am_echo | 🚹 | C | MM minutes | D | `8bcfdc85` |
|
| 54 |
+
| am_eric | 🚹 | C | MM minutes | D | `ada66f0e` |
|
| 55 |
+
| am_fenrir | 🚹 | B | H hours | C+ | `98e507ec` |
|
| 56 |
+
| am_liam | 🚹 | C | MM minutes | D | `c8255075` |
|
| 57 |
+
| am_michael | 🚹 | B | H hours | C+ | `9a443b79` |
|
| 58 |
+
| am_onyx | 🚹 | C | MM minutes | D | `e8452be1` |
|
| 59 |
+
| am_puck | 🚹 | B | H hours | C+ | `dd1d8973` |
|
| 60 |
+
| am_santa | 🚹 | C | _M minutes_ 🤏 | D- | `7f2f7582` |
|
| 61 |
+
|
| 62 |
+
### British English
|
| 63 |
+
|
| 64 |
+
- `lang_code='b'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
|
| 65 |
+
- espeak-ng `en-gb` fallback
|
| 66 |
+
|
| 67 |
+
| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
|
| 68 |
+
| ---- | ------ | -------------- | ----------------- | ------------- | ------ |
|
| 69 |
+
| bf_alice | 🚺 | C | MM minutes | D | `d292651b` |
|
| 70 |
+
| bf_emma | 🚺 | B | **HH hours** | B- | `d0a423de` |
|
| 71 |
+
| bf_isabella | 🚺 | B | MM minutes | C | `cdd4c370` |
|
| 72 |
+
| bf_lily | 🚺 | C | MM minutes | D | `6e09c2e4` |
|
| 73 |
+
| bm_daniel | 🚹 | C | MM minutes | D | `fc3fce4e` |
|
| 74 |
+
| bm_fable | 🚹 | B | MM minutes | C | `d44935f3` |
|
| 75 |
+
| bm_george | 🚹 | B | MM minutes | C | `f1bc8122` |
|
| 76 |
+
| bm_lewis | 🚹 | C | H hours | D+ | `b5204750` |
|
| 77 |
+
|
| 78 |
+
### Japanese
|
| 79 |
+
|
| 80 |
+
- `lang_code='j'` in [`misaki[ja]`](https://github.com/hexgrad/misaki)
|
| 81 |
+
- Total Japanese training data: H hours
|
| 82 |
+
|
| 83 |
+
| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 | CC BY |
|
| 84 |
+
| ---- | ------ | -------------- | ----------------- | ------------- | ------ | ----- |
|
| 85 |
+
| jf_alpha | 🚺 | B | H hours | C+ | `1bf4c9dc` | |
|
| 86 |
+
| jf_gongitsune | 🚺 | B | MM minutes | C | `1b171917` | [gongitsune](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__gongitsune.txt) |
|
| 87 |
+
| jf_nezumi | 🚺 | B | _M minutes_ 🤏 | C- | `d83f007a` | [nezuminoyomeiri](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__nezuminoyomeiri.txt) |
|
| 88 |
+
| jf_tebukuro | 🚺 | B | MM minutes | C | `0d691790` | [tebukurowokaini](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__tebukurowokaini.txt) |
|
| 89 |
+
| jm_kumo | 🚹 | B | _M minutes_ 🤏 | C- | `98340afd` | [kumonoito](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__kumonoito.txt) |
|
| 90 |
+
|
| 91 |
+
### Mandarin Chinese
|
| 92 |
+
|
| 93 |
+
- `lang_code='z'` in [`misaki[zh]`](https://github.com/hexgrad/misaki)
|
| 94 |
+
- Total Mandarin Chinese training data: H hours
|
| 95 |
+
|
| 96 |
+
| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
|
| 97 |
+
| ---- | ------ | -------------- | ----------------- | ------------- | ------ |
|
| 98 |
+
| zf_xiaobei | 🚺 | C | MM minutes | D | `9b76be63` |
|
| 99 |
+
| zf_xiaoni | 🚺 | C | MM minutes | D | `95b49f16` |
|
| 100 |
+
| zf_xiaoxiao | 🚺 | C | MM minutes | D | `cfaf6f2d` |
|
| 101 |
+
| zf_xiaoyi | 🚺 | C | MM minutes | D | `b5235dba` |
|
| 102 |
+
| zm_yunjian | 🚹 | C | MM minutes | D | `76cbf8ba` |
|
| 103 |
+
| zm_yunxi | 🚹 | C | MM minutes | D | `dbe6e1ce` |
|
| 104 |
+
| zm_yunxia | 🚹 | C | MM minutes | D | `bb2b03b0` |
|
| 105 |
+
| zm_yunyang | 🚹 | C | MM minutes | D | `5238ac22` |
|
| 106 |
+
|
| 107 |
+
### Spanish
|
| 108 |
+
|
| 109 |
+
- `lang_code='e'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
|
| 110 |
+
- espeak-ng `es`
|
| 111 |
+
|
| 112 |
+
| Name | Traits | SHA256 |
|
| 113 |
+
| ---- | ------ | ------ |
|
| 114 |
+
| ef_dora | 🚺 | `d9d69b0f` |
|
| 115 |
+
| em_alex | 🚹 | `5eac53f7` |
|
| 116 |
+
| em_santa | 🚹 | `aa8620cb` |
|
| 117 |
+
|
| 118 |
+
### French
|
| 119 |
+
|
| 120 |
+
- `lang_code='f'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
|
| 121 |
+
- espeak-ng `fr-fr`
|
| 122 |
+
- Total French training data: <11 hours
|
| 123 |
+
|
| 124 |
+
| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 | CC BY |
|
| 125 |
+
| ---- | ------ | -------------- | ----------------- | ------------- | ------ | ----- |
|
| 126 |
+
| ff_siwis | 🚺 | B | <11 hours | B- | `8073bf2d` | [SIWIS](https://datashare.ed.ac.uk/handle/10283/2353) |
|
| 127 |
+
|
| 128 |
+
### Hindi
|
| 129 |
+
|
| 130 |
+
- `lang_code='h'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
|
| 131 |
+
- espeak-ng `hi`
|
| 132 |
+
- Total Hindi training data: H hours
|
| 133 |
+
|
| 134 |
+
| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
|
| 135 |
+
| ---- | ------ | -------------- | ----------------- | ------------- | ------ |
|
| 136 |
+
| hf_alpha | 🚺 | B | MM minutes | C | `06906fe0` |
|
| 137 |
+
| hf_beta | 🚺 | B | MM minutes | C | `63c0a1a6` |
|
| 138 |
+
| hm_omega | 🚹 | B | MM minutes | C | `b55f02a8` |
|
| 139 |
+
| hm_psi | 🚹 | B | MM minutes | C | `2f0f055c` |
|
| 140 |
+
|
| 141 |
+
### Italian
|
| 142 |
+
|
| 143 |
+
- `lang_code='i'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
|
| 144 |
+
- espeak-ng `it`
|
| 145 |
+
- Total Italian training data: H hours
|
| 146 |
+
|
| 147 |
+
| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
|
| 148 |
+
| ---- | ------ | -------------- | ----------------- | ------------- | ------ |
|
| 149 |
+
| if_sara | 🚺 | B | MM minutes | C | `6c0b253b` |
|
| 150 |
+
| im_nicola | 🚹 | B | MM minutes | C | `234ed066` |
|
| 151 |
+
|
| 152 |
+
### Brazilian Portuguese
|
| 153 |
+
|
| 154 |
+
- `lang_code='p'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
|
| 155 |
+
- espeak-ng `pt-br`
|
| 156 |
+
|
| 157 |
+
| Name | Traits | SHA256 |
|
| 158 |
+
| ---- | ------ | ------ |
|
| 159 |
+
| pf_dora | 🚺 | `07e4ff98` |
|
| 160 |
+
| pm_alex | 🚹 | `cf0ba8c5` |
|
| 161 |
+
| pm_santa | 🚹 | `d4210316` |
|
Kokoro-82M/config.json
ADDED
|
@@ -0,0 +1,150 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"istftnet": {
|
| 3 |
+
"upsample_kernel_sizes": [20, 12],
|
| 4 |
+
"upsample_rates": [10, 6],
|
| 5 |
+
"gen_istft_hop_size": 5,
|
| 6 |
+
"gen_istft_n_fft": 20,
|
| 7 |
+
"resblock_dilation_sizes": [
|
| 8 |
+
[1, 3, 5],
|
| 9 |
+
[1, 3, 5],
|
| 10 |
+
[1, 3, 5]
|
| 11 |
+
],
|
| 12 |
+
"resblock_kernel_sizes": [3, 7, 11],
|
| 13 |
+
"upsample_initial_channel": 512
|
| 14 |
+
},
|
| 15 |
+
"dim_in": 64,
|
| 16 |
+
"dropout": 0.2,
|
| 17 |
+
"hidden_dim": 512,
|
| 18 |
+
"max_conv_dim": 512,
|
| 19 |
+
"max_dur": 50,
|
| 20 |
+
"multispeaker": true,
|
| 21 |
+
"n_layer": 3,
|
| 22 |
+
"n_mels": 80,
|
| 23 |
+
"n_token": 178,
|
| 24 |
+
"style_dim": 128,
|
| 25 |
+
"text_encoder_kernel_size": 5,
|
| 26 |
+
"plbert": {
|
| 27 |
+
"hidden_size": 768,
|
| 28 |
+
"num_attention_heads": 12,
|
| 29 |
+
"intermediate_size": 2048,
|
| 30 |
+
"max_position_embeddings": 512,
|
| 31 |
+
"num_hidden_layers": 12,
|
| 32 |
+
"dropout": 0.1
|
| 33 |
+
},
|
| 34 |
+
"vocab": {
|
| 35 |
+
";": 1,
|
| 36 |
+
":": 2,
|
| 37 |
+
",": 3,
|
| 38 |
+
".": 4,
|
| 39 |
+
"!": 5,
|
| 40 |
+
"?": 6,
|
| 41 |
+
"—": 9,
|
| 42 |
+
"…": 10,
|
| 43 |
+
"\"": 11,
|
| 44 |
+
"(": 12,
|
| 45 |
+
")": 13,
|
| 46 |
+
"“": 14,
|
| 47 |
+
"”": 15,
|
| 48 |
+
" ": 16,
|
| 49 |
+
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MeiGen-MultiTalk/README.md
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| 1 |
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---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
- zh
|
| 6 |
+
tags:
|
| 7 |
+
- video generation
|
| 8 |
+
- conversational video generation
|
| 9 |
+
- talking human video generation
|
| 10 |
+
pipeline_tag: image-to-video
|
| 11 |
+
---
|
| 12 |
+
|
| 13 |
+
<p align="center">
|
| 14 |
+
<img src="assets/logo2.jpeg" alt="MultiTalk" width="300"/>
|
| 15 |
+
</p>
|
| 16 |
+
|
| 17 |
+
# MeiGen-MultiTalk • Audio-Driven Multi-Person Conversational Video Generation
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
<p align="left">
|
| 22 |
+
<a href="https://meigen-ai.github.io/multi-talk/">
|
| 23 |
+
<img
|
| 24 |
+
src="https://img.shields.io/badge/MultiTalk-Website-0A66C2?logo=safari&logoColor=white" style="display: inline-block; vertical-align: middle;"
|
| 25 |
+
alt="MultiTalk Website"
|
| 26 |
+
/>
|
| 27 |
+
</a>
|
| 28 |
+
<a href="https://arxiv.org/abs/2505.22647">
|
| 29 |
+
<img
|
| 30 |
+
src="https://img.shields.io/badge/MultiTalk-Paper-red?logo=arxiv&logoColor=red" style="display: inline-block; vertical-align: middle;"
|
| 31 |
+
alt="MultiTalk Paper on arXiv"
|
| 32 |
+
/>
|
| 33 |
+
</a>
|
| 34 |
+
<a href="https://github.com/MeiGen-AI/MultiTalk" target="_blank" style="margin: 2px;">
|
| 35 |
+
<img
|
| 36 |
+
alt="Github" src="https://img.shields.io/badge/MultiTalk-Codebase-536af5?color=536af5&logo=github" style="display: inline-block; vertical-align: middle;"
|
| 37 |
+
alt="MultiTalk Codebase"
|
| 38 |
+
/>
|
| 39 |
+
</a>
|
| 40 |
+
|
| 41 |
+
</p>
|
| 42 |
+
|
| 43 |
+
> We present **MultiTalk**, an open-source audio-driven multi-person conversational video generation model with the state-of-the-art lip synchronization accuracy.
|
| 44 |
+
> Key features:
|
| 45 |
+
> - 💬 Realistic Conversations - Supports single & multi-person generation
|
| 46 |
+
> - 👥 Interactive Character Control - Direct virtual humans via prompts
|
| 47 |
+
> - 🎤 Generalization Performances - Supports the generation of cartoon character and singing
|
| 48 |
+
> - 📺 Resolution Flexibility: 480p & 720p output at arbitrary aspect ratios
|
| 49 |
+
> - ⏱️ **Long Video Generation**: Support video generation up to 15 seconds
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
This repository hosts the model weights for **MultiTalk**. For installation, usage instructions, and further documentation, please visit our [GitHub repository](https://github.com/MeiGen-AI/MultiTalk).
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
|
| 56 |
+
|
| 57 |
+
|
| 58 |
+
|
| 59 |
+
## Method
|
| 60 |
+
We propose a novel framework, MultiTalk, for audio-driven multi-person conversational video generation. We investigate several schemes for audio injection and introduce
|
| 61 |
+
the Label Rotary Position Embedding (L-RoPE) method. By assigning identical labels to audio embeddings and video latents, it effectively activates specific regions within the audio cross-attention
|
| 62 |
+
map, thereby resolving incorrect binding issues. To localize the region of the specified person, we introduce the adaptive person localization by computing the similarity
|
| 63 |
+
between the features of the given region of a person in the reference image and all the features of the whole video.
|
| 64 |
+
|
| 65 |
+
<p align="left"><img src="assets/pipe.png" width="80%"></p>
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
## Citation
|
| 72 |
+
If you find our work helpful, please cite us.
|
| 73 |
+
|
| 74 |
+
```
|
| 75 |
+
@article{kong2025let,
|
| 76 |
+
title={Let Them Talk: Audio-Driven Multi-Person Conversational Video Generation},
|
| 77 |
+
author={Kong, Zhe and Gao, Feng and Zhang, Yong and Kang, Zhuoliang and Wei, Xiaoming and Cai, Xunliang and Chen, Guanying and Luo, Wenhan},
|
| 78 |
+
journal={arXiv preprint arXiv:2505.22647},
|
| 79 |
+
year={2025}
|
| 80 |
+
}
|
| 81 |
+
```
|
| 82 |
+
|
| 83 |
+
|
| 84 |
+
|
| 85 |
+
## License Agreement
|
| 86 |
+
The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations.
|
| 87 |
+
|
| 88 |
+
|
MeiGen-MultiTalk/diffusion_pytorch_model.safetensors.index.json
ADDED
|
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MeiGen-MultiTalk/quant_models/dit_model_map_int8.json
ADDED
|
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|
|
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| 1 |
+
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MeiGen-MultiTalk/quant_models/quantization_map_fp8_FusionX.json
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MeiGen-MultiTalk/quant_models/quantization_map_int8_FusionX.json
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"activations": "none"}, "blocks.23.ffn.fc2": {"weights": "qint8", "activations": "none"}}
|
OtherLoRa's/Placeholder.txt
ADDED
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README.md
CHANGED
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@@ -1,3 +1,259 @@
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| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- text-to-video
|
| 4 |
+
- diffusion
|
| 5 |
+
- merged-model
|
| 6 |
+
- video-generation
|
| 7 |
+
- wan2.1
|
| 8 |
+
|
| 9 |
+
widget:
|
| 10 |
+
- text: >-
|
| 11 |
+
Prompt: A gritty close-up of an elven princess kneeling in a rocky ravine, calming a wounded, desert dragon. Its scales are cracked, dry, She wears a crimson sash over bone-colored armor, her auburn hair half-tied back. The camera dollies in rapidly as she reaches for its eye ridge. Lighting comes from golden sunlight reflecting off surrounding rock, casting a warm, earthy hue with no artificial glow.
|
| 12 |
+
output:
|
| 13 |
+
url: videos/Video_00063.mp4
|
| 14 |
+
|
| 15 |
+
- text: >-
|
| 16 |
+
Prompt: Tight close-up of her smiling lips and sparkling eyes, catching golden hour sunlight. She wears a white sundress with floral prints and a wide-brimmed straw hat. Camera pulls back in a dolly motion, revealing her twirling under a cherry blossom tree. Petals flutter in the air, casting playful shadows. Soft lens flares enhance the euphoric, dreamlike vibe. (Before vs After — Left: Wan2.1 | Right: Merged model Wan14BT2V_MasterModel)
|
| 17 |
+
output:
|
| 18 |
+
url: videos/AnimateDiff_00001.mp4
|
| 19 |
+
|
| 20 |
+
- text: >-
|
| 21 |
+
Prompt: A gritty close-up of a dwarven beastmaster’s face, his grey beard braided tightly, brows furrowed as he looks just off-camera. The camera dollies out over his shoulder, revealing a perched gryphon watching him from a boulder, its feathers rustling slightly in the breeze. The moment holds stillness and mutual trust. Lighting is early daylight, clean and sharp with strong environmental clarity.
|
| 22 |
+
output:
|
| 23 |
+
url: videos/FusionX_00012.mp4
|
| 24 |
+
|
| 25 |
+
- text: >-
|
| 26 |
+
Prompt: A gritty close-up of a jungle tracker crouching low, face flushed with focus as she watches a perched macaw a few feet ahead. Her cheek twitches as she shifts forward, beads of sweat visible on her brow. The camera slowly dollies in from below her line of sight, capturing the moment her eyes widen in fascination. Lighting is rich and directional from above, creating a warm glow over her face with minimal shadows.
|
| 27 |
+
output:
|
| 28 |
+
url: videos/FusionX_00005.mp4
|
| 29 |
+
|
| 30 |
+
- text: >-
|
| 31 |
+
Prompt: A gritty close-up of a battle-worn ranger kneeling in a scorched clearing, calming a wounded gryphon whose wing is torn and bloodied. Its feathers are dusky bronze with streaks of ash-gray. She wears soot-covered hunter green armor, her blonde hair pulled into a loose braid. The camera dollies in as her hand brushes the creature's sharp beak. Lighting comes from late afternoon sun filtering through smoke, casting a burnt-orange haze across the frame.
|
| 32 |
+
output:
|
| 33 |
+
url: videos/Video_00069.mp4
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
base_model:
|
| 38 |
+
- Wan-AI/Wan2.1-T2V-14B
|
| 39 |
+
license: apache-2.0
|
| 40 |
+
---
|
| 41 |
+
|
| 42 |
+
# 🌀 Wan2.1_14B_FusionX
|
| 43 |
+
|
| 44 |
+
**High-Performance Merged Text-to-Video Model**
|
| 45 |
+
Built on WAN 2.1 and fused with research-grade components for cinematic motion, detail, and speed — optimized for ComfyUI and rapid iteration in as few as 6 steps.
|
| 46 |
+
|
| 47 |
+
Merged models for faster, richer motion & detail — high performance even at just 8 steps.
|
| 48 |
+
|
| 49 |
+
> 📌 Important: To match the quality shown here, use the linked workflows or make sure to follow the recommended settings outlined below.
|
| 50 |
+
|
| 51 |
+
---
|
| 52 |
+
|
| 53 |
+
## 🚀 Overview
|
| 54 |
+
|
| 55 |
+
A powerful text-to-video model built on top of **WAN 2.1 14B**, merged with several research-grade models to boost:
|
| 56 |
+
|
| 57 |
+
- Motion quality
|
| 58 |
+
- Scene consistency
|
| 59 |
+
- Visual detail
|
| 60 |
+
|
| 61 |
+
Comparable with closed-source solutions, but open and optimized for **ComfyUI** workflows.
|
| 62 |
+
|
| 63 |
+
---
|
| 64 |
+
|
| 65 |
+
## 💡 Inside the Fusion
|
| 66 |
+
|
| 67 |
+
This model is made up of the following which is on TOP of Wan 2.1 14B 720p(FusionX would not be what it is without these Models):
|
| 68 |
+
|
| 69 |
+
- **CausVid** – [Causal motion modeling for better flow and dynamics](https://github.com/tianweiy/CausVid)
|
| 70 |
+
- **AccVideo** – [Better temporal alignment and speed boost](https://github.com/aejion/AccVideo)
|
| 71 |
+
- **MoviiGen1.1** – [Cinematic smoothness and lighting](https://huggingface.co/ZuluVision/MoviiGen1.1)
|
| 72 |
+
- **MPS Reward LoRA** – [Tuned for motion and detail](https://huggingface.co/alibaba-pai/Wan2.1-Fun-Reward-LoRAs)
|
| 73 |
+
- **Custom LoRAs** – For texture, clarity, and small detail enhancements (Set at a very low level)
|
| 74 |
+
|
| 75 |
+
All merged models are provided for research and non-commercial use only.
|
| 76 |
+
Some components are subject to licenses such as CC BY-NC-SA 4.0, and do not fall under permissive licenses like Apache 2.0 or MIT.
|
| 77 |
+
Please refer to each model’s original license for full usage terms.
|
| 78 |
+
|
| 79 |
+
---
|
| 80 |
+
|
| 81 |
+
## 🚨✨**Hey guys! Just a quick update!**
|
| 82 |
+
|
| 83 |
+
We finally cooked up **FusionX LoRAs**!! 🧠💥
|
| 84 |
+
This is huge – now you can plug FusionX into your favorite workflows as a LoRA on top of the Wan base models and SkyReels models!🔌💫
|
| 85 |
+
You can still stick with the base FusionX Model if you already use it, but if you would rather have more control over the "FusionX" strength and a speed boost, then this might be for you.
|
| 86 |
+
|
| 87 |
+
Oh, and there’s a **nice speed boost** too! ⚡
|
| 88 |
+
**Example:** *(RTX 5090)*
|
| 89 |
+
- FusionX as a full base model: **8 steps = 160s** ⏱️
|
| 90 |
+
- FusionX as a **LoRA on Wan 2.1 14B fp8 T2V**: **8 steps = 120s** 🚀
|
| 91 |
+
|
| 92 |
+
**Bonus:** You can bump up the FusionX LoRA strength and lower your steps for a **huge speed boost** while testing/drafting.
|
| 93 |
+
Example: strength `2.00` with `3 steps` takes `72 seconds`.
|
| 94 |
+
Or lower the strength to experiment with a **less “FusionX” look**. ⚡🔍
|
| 95 |
+
|
| 96 |
+
We’ve got:
|
| 97 |
+
- **T2V (Text to Video)** 🎬 – works perfectly with **VACE** ⚙️
|
| 98 |
+
- **I2V (Image to Video)** 🖼️➡️📽️
|
| 99 |
+
- A dedicated **Phantom LoRA** 👻
|
| 100 |
+
The new LoRA's are [HERE](https://huggingface.co/vrgamedevgirl84/Wan14BT2VFusioniX/tree/main/FusionX_LoRa)
|
| 101 |
+
Note: The LoRa's are not meant to be put on top of the FusionX main models and instead you would use them with the Wan base models.
|
| 102 |
+
**New workflows** are [HERE](https://civitai.com/models/1681541) 🛠️🚀
|
| 103 |
+
|
| 104 |
+
---
|
| 105 |
+
|
| 106 |
+
After lots of testing 🧪, the video quality with the LoRA is **just as good** (and sometimes **even better**! 💯)
|
| 107 |
+
That’s thanks to it being trained on the **fp16 version** of FusionX 🧬💎
|
| 108 |
+
|
| 109 |
+
---
|
| 110 |
+
|
| 111 |
+
### 🌀 Preview Gallery
|
| 112 |
+
*These are compressed GIF previews for quick viewing — final video outputs are higher quality.*
|
| 113 |
+
|
| 114 |
+
|
| 115 |
+
|
| 116 |
+
|
| 117 |
+
|
| 118 |
+
|
| 119 |
+
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
|
| 123 |
+
|
| 124 |
+
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
---
|
| 128 |
+
|
| 129 |
+
|
| 130 |
+
## 📂 Workflows & Model Downloads
|
| 131 |
+
|
| 132 |
+
- 💡 **ComfyUI workflows** can be found here:
|
| 133 |
+
👉 [Workflow Collection (WIP)](https://civitai.com/models/1663553)
|
| 134 |
+
|
| 135 |
+
- 📦 **Model files (T2V, I2V, Phantom, VACE)**:
|
| 136 |
+
👉 [Main Hugging Face Repo](https://huggingface.co/vrgamedevgirl84/Wan14BT2VFusioniX/tree/main)
|
| 137 |
+
|
| 138 |
+
### 🧠 GGUF Variants:
|
| 139 |
+
- 🖼️ [FusionX Image-to-Video (GGUF)](https://huggingface.co/QuantStack/Wan2.1_I2V_14B_FusionX-GGUF/tree/main)
|
| 140 |
+
- 🎥 [FusionX Text-to-Video (GGUF)](https://huggingface.co/QuantStack/Wan2.1_T2V_14B_FusionX-GGUF/tree/main)
|
| 141 |
+
- 🎞️ [FusionX T2V VACE (for native)](https://huggingface.co/QuantStack/Wan2.1_T2V_14B_FusionX_VACE-GGUF/tree/main)
|
| 142 |
+
- 👻 [FusionX Phantom](https://huggingface.co/QuantStack/Phantom_Wan_14B_FusionX-GGUF/tree/main)
|
| 143 |
+
|
| 144 |
+
---
|
| 145 |
+
## 🎬 Example Videos
|
| 146 |
+
|
| 147 |
+
Want to see what FusionX can do? Check out these real outputs generated using the latest workflows and settings:
|
| 148 |
+
|
| 149 |
+
- **Text-to-Video**
|
| 150 |
+
👉 [Watch Examples](https://civitai.com/posts/17874424)
|
| 151 |
+
|
| 152 |
+
- **Image-to-Video**
|
| 153 |
+
👉 [Watch Examples](https://civitai.com/posts/18029174)
|
| 154 |
+
|
| 155 |
+
- **Phantom Mode**
|
| 156 |
+
👉 [Watch Examples](https://civitai.com/posts/17986906)
|
| 157 |
+
|
| 158 |
+
- **VACE Integration**
|
| 159 |
+
👉 [Watch Examples](https://civitai.com/posts/18080876)
|
| 160 |
+
|
| 161 |
+
---
|
| 162 |
+
|
| 163 |
+
## 🔧 Usage Details
|
| 164 |
+
|
| 165 |
+
### Text-to-Video
|
| 166 |
+
|
| 167 |
+
- **CGF**: Must be set to `1`
|
| 168 |
+
- **Shift**:
|
| 169 |
+
- `1024x576`: Start at `1`
|
| 170 |
+
- `1080x720`: Start at `2`
|
| 171 |
+
- For realism → lower values
|
| 172 |
+
- For stylized → test `3–9`
|
| 173 |
+
- **Scheduler**:
|
| 174 |
+
- Recommended: `uni_pc`
|
| 175 |
+
- Alternative: `flowmatch_causvid` (better for some details)
|
| 176 |
+
|
| 177 |
+
### Image-to-Video
|
| 178 |
+
|
| 179 |
+
- **CGF**: `1`
|
| 180 |
+
- **Shift**: `2` works best in most cases
|
| 181 |
+
- **Scheduler**:
|
| 182 |
+
- Recommended: `dmp++_sde/beta`
|
| 183 |
+
- To boost motion and reduce slow-mo effect:
|
| 184 |
+
- Frame count: `121`
|
| 185 |
+
- FPS: `24`
|
| 186 |
+
|
| 187 |
+
---
|
| 188 |
+
|
| 189 |
+
## 🛠 Technical Notes
|
| 190 |
+
|
| 191 |
+
- Works in as few as **6 steps**
|
| 192 |
+
- Best quality at **8–10 steps**
|
| 193 |
+
- Drop-in replacement for `Wan2.1-T2V-14B`
|
| 194 |
+
- Up to **50% faster rendering**, especially with **SageAttn**
|
| 195 |
+
- Works natively and with **Kaji Wan Wrapper**
|
| 196 |
+
[Wrapper GitHub](https://github.com/kijai/ComfyUI-WanVideoWrapper)
|
| 197 |
+
- Do **not** re-add merged LoRAs (CausVid, AccVideo, MPS)
|
| 198 |
+
- Feel free to add **other LoRAs** for style/variation
|
| 199 |
+
- Native WAN workflows also supported (slightly slower)
|
| 200 |
+
|
| 201 |
+
---
|
| 202 |
+
|
| 203 |
+
## 🧪 Performance Tips
|
| 204 |
+
|
| 205 |
+
- RTX 5090 → ~138 sec/video at 1024x576 / 81 frames
|
| 206 |
+
- If VRAM is limited:
|
| 207 |
+
- Enable block swapping
|
| 208 |
+
- Start with `5` blocks and adjust as needed
|
| 209 |
+
- Use **SageAttn** for ~30% speedup (wrapper only)
|
| 210 |
+
- Do **not** use `teacache`
|
| 211 |
+
- "Enhance a video" (tested): Adds vibrance (try values 2–4)
|
| 212 |
+
- "SLG" not tested — feel free to explore
|
| 213 |
+
|
| 214 |
+
---
|
| 215 |
+
|
| 216 |
+
## 🧠 Prompt Help
|
| 217 |
+
|
| 218 |
+
Want better cinematic prompts? Try the **WAN Cinematic Video Prompt Generator GPT** — it adds visual richness and makes a big difference in quality. [Download Here](https://chatgpt.com/g/g-67c3a6d6d19c81919b3247d2bfd01d0b-wan-cinematic-video-prompt-generator)
|
| 219 |
+
|
| 220 |
+
---
|
| 221 |
+
|
| 222 |
+
## 📣 Join The Community
|
| 223 |
+
|
| 224 |
+
We’re building a friendly space to chat, share outputs, and get help.
|
| 225 |
+
|
| 226 |
+
- Motion LoRAs coming soon
|
| 227 |
+
- Tips, updates, and support from other users
|
| 228 |
+
|
| 229 |
+
👉 [Join the Discord](https://discord.com/invite/hxPmmXmRW3)
|
| 230 |
+
|
| 231 |
+
---
|
| 232 |
+
|
| 233 |
+
## ⚖️ License
|
| 234 |
+
|
| 235 |
+
Some merged components use permissive licenses (Apache 2.0 / MIT),
|
| 236 |
+
**but others** — such as those from research models like *CausVid* — may be released under **non-commercial licenses** (e.g., [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)).
|
| 237 |
+
|
| 238 |
+
- ✅ You **can** use, modify, and redistribute **under original license terms**
|
| 239 |
+
- ❗ You **must** retain and respect the license of each component
|
| 240 |
+
- ⚠️ **Commercial use is not permitted** for models or components under non-commercial licenses
|
| 241 |
+
- 📌 Outputs are **not automatically licensed** — do your own due diligence
|
| 242 |
+
|
| 243 |
+
This model is intended for **research, education, and personal use only**.
|
| 244 |
+
For commercial use or monetization, please consult a legal advisor and verify all component licenses.
|
| 245 |
+
|
| 246 |
+
---
|
| 247 |
+
|
| 248 |
+
## 🙏 Credits
|
| 249 |
+
|
| 250 |
+
- WAN Team (base model)
|
| 251 |
+
- aejion (AccVideo)
|
| 252 |
+
- Tianwei Yin (CausVid)
|
| 253 |
+
- ZuluVision (MoviiGen)
|
| 254 |
+
- Alibaba PAI (MPS LoRA)
|
| 255 |
+
- Kijai (ComfyUI Wrapper)
|
| 256 |
+
|
| 257 |
+
And thanks to the open-source community!
|
| 258 |
+
|
| 259 |
+
---
|
Wan2.1-I2V-14B-480P/.gitattributes
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|
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*.7z filter=lfs diff=lfs merge=lfs -text
|
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*.arrow filter=lfs diff=lfs merge=lfs -text
|
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*.bin filter=lfs diff=lfs merge=lfs -text
|
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*.bz2 filter=lfs diff=lfs merge=lfs -text
|
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*.ckpt filter=lfs diff=lfs merge=lfs -text
|
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*.ftz filter=lfs diff=lfs merge=lfs -text
|
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*.gz filter=lfs diff=lfs merge=lfs -text
|
| 8 |
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*.h5 filter=lfs diff=lfs merge=lfs -text
|
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*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 10 |
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 11 |
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
| 12 |
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*.model filter=lfs diff=lfs merge=lfs -text
|
| 13 |
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*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 14 |
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*.npy filter=lfs diff=lfs merge=lfs -text
|
| 15 |
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*.npz filter=lfs diff=lfs merge=lfs -text
|
| 16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 17 |
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*.ot filter=lfs diff=lfs merge=lfs -text
|
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*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 19 |
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*.pb filter=lfs diff=lfs merge=lfs -text
|
| 20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
| 21 |
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*.pkl filter=lfs diff=lfs merge=lfs -text
|
| 22 |
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*.pt filter=lfs diff=lfs merge=lfs -text
|
| 23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 24 |
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*.rar filter=lfs diff=lfs merge=lfs -text
|
| 25 |
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*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 28 |
+
*.tar filter=lfs diff=lfs merge=lfs -text
|
| 29 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 30 |
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*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 31 |
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*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 32 |
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*.xz filter=lfs diff=lfs merge=lfs -text
|
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*.zip filter=lfs diff=lfs merge=lfs -text
|
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*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
google/umt5-xxl/tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
| 37 |
+
xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
| 38 |
+
assets/comp_effic.png filter=lfs diff=lfs merge=lfs -text
|
| 39 |
+
assets/data_for_diff_stage.jpg filter=lfs diff=lfs merge=lfs -text
|
| 40 |
+
assets/i2v_res.png filter=lfs diff=lfs merge=lfs -text
|
| 41 |
+
assets/logo.png filter=lfs diff=lfs merge=lfs -text
|
| 42 |
+
assets/t2v_res.jpg filter=lfs diff=lfs merge=lfs -text
|
| 43 |
+
assets/vben_1.3b_vs_sota.png filter=lfs diff=lfs merge=lfs -text
|
| 44 |
+
assets/vben_vs_sota.png filter=lfs diff=lfs merge=lfs -text
|
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+
assets/video_dit_arch.jpg filter=lfs diff=lfs merge=lfs -text
|
| 46 |
+
assets/video_vae_res.jpg filter=lfs diff=lfs merge=lfs -text
|
| 47 |
+
examples/i2v_input.JPG filter=lfs diff=lfs merge=lfs -text
|
Wan2.1-I2V-14B-480P/LICENSE.txt
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|
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Wan2.1-I2V-14B-480P/README.md
ADDED
|
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|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
- zh
|
| 6 |
+
pipeline_tag: image-to-video
|
| 7 |
+
library_name: diffusers
|
| 8 |
+
tags:
|
| 9 |
+
- video
|
| 10 |
+
- video-generation
|
| 11 |
+
---
|
| 12 |
+
# Wan2.1
|
| 13 |
+
|
| 14 |
+
<p align="center">
|
| 15 |
+
<img src="assets/logo.png" width="400"/>
|
| 16 |
+
<p>
|
| 17 |
+
|
| 18 |
+
<p align="center">
|
| 19 |
+
💜 <a href=""><b>Wan</b></a>    ｜    🖥️ <a href="https://github.com/Wan-Video/Wan2.1">GitHub</a>    |   🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>   |    📑 <a href="">Paper (Coming soon)</a>    |    📑 <a href="https://wanxai.com">Blog</a>    |   💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>   |    📖 <a href="https://discord.gg/p5XbdQV7">Discord</a>  
|
| 20 |
+
<br>
|
| 21 |
+
|
| 22 |
+
-----
|
| 23 |
+
|
| 24 |
+
[**Wan: Open and Advanced Large-Scale Video Generative Models**]() <be>
|
| 25 |
+
|
| 26 |
+
In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features:
|
| 27 |
+
- 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
|
| 28 |
+
- 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
|
| 29 |
+
- 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
|
| 30 |
+
- 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
|
| 31 |
+
- 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
This repo contains our I2V-14B model, which is capable of generating 480P videos, offering advantages in terms of fast generation and excellent quality.
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
## Video Demos
|
| 38 |
+
|
| 39 |
+
<div align="center">
|
| 40 |
+
<video width="80%" controls>
|
| 41 |
+
<source src="https://cloud.video.taobao.com/vod/Jth64Y7wNoPcJki_Bo1ZJTDBvNjsgjlVKsNs05Fqfps.mp4" type="video/mp4">
|
| 42 |
+
Your browser does not support the video tag.
|
| 43 |
+
</video>
|
| 44 |
+
</div>
|
| 45 |
+
|
| 46 |
+
## 🔥 Latest News!!
|
| 47 |
+
|
| 48 |
+
* Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1.
|
| 49 |
+
|
| 50 |
+
|
| 51 |
+
## 📑 Todo List
|
| 52 |
+
- Wan2.1 Text-to-Video
|
| 53 |
+
- [x] Multi-GPU Inference code of the 14B and 1.3B models
|
| 54 |
+
- [x] Checkpoints of the 14B and 1.3B models
|
| 55 |
+
- [x] Gradio demo
|
| 56 |
+
- [ ] Diffusers integration
|
| 57 |
+
- [ ] ComfyUI integration
|
| 58 |
+
- Wan2.1 Image-to-Video
|
| 59 |
+
- [x] Multi-GPU Inference code of the 14B model
|
| 60 |
+
- [x] Checkpoints of the 14B model
|
| 61 |
+
- [x] Gradio demo
|
| 62 |
+
- [ ] Diffusers integration
|
| 63 |
+
- [ ] ComfyUI integration
|
| 64 |
+
|
| 65 |
+
|
| 66 |
+
## Quickstart
|
| 67 |
+
|
| 68 |
+
#### Installation
|
| 69 |
+
Clone the repo:
|
| 70 |
+
```
|
| 71 |
+
git clone https://github.com/Wan-Video/Wan2.1.git
|
| 72 |
+
cd Wan2.1
|
| 73 |
+
```
|
| 74 |
+
|
| 75 |
+
Install dependencies:
|
| 76 |
+
```
|
| 77 |
+
# Ensure torch >= 2.4.0
|
| 78 |
+
pip install -r requirements.txt
|
| 79 |
+
```
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
#### Model Download
|
| 83 |
+
|
| 84 |
+
| Models | Download Link | Notes |
|
| 85 |
+
| --------------|-------------------------------------------------------------------------------|-------------------------------|
|
| 86 |
+
| T2V-14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | Supports both 480P and 720P
|
| 87 |
+
| I2V-14B-720P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | Supports 720P
|
| 88 |
+
| I2V-14B-480P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | Supports 480P
|
| 89 |
+
| T2V-1.3B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | Supports 480P
|
| 90 |
+
|
| 91 |
+
> 💡Note: The 1.3B model is capable of generating videos at 720P resolution. However, due to limited training at this resolution, the results are generally less stable compared to 480P. For optimal performance, we recommend using 480P resolution.
|
| 92 |
+
|
| 93 |
+
|
| 94 |
+
Download models using 🤗 huggingface-cli:
|
| 95 |
+
```
|
| 96 |
+
pip install "huggingface_hub[cli]"
|
| 97 |
+
huggingface-cli download Wan-AI/Wan2.1-I2V-14B-480P --local-dir ./Wan2.1-I2V-14B-480P
|
| 98 |
+
```
|
| 99 |
+
|
| 100 |
+
Download models using 🤖 modelscope-cli:
|
| 101 |
+
```
|
| 102 |
+
pip install modelscope
|
| 103 |
+
modelscope download Wan-AI/Wan2.1-I2V-14B-480P --local_dir ./Wan2.1-I2V-14B-480P
|
| 104 |
+
```
|
| 105 |
+
|
| 106 |
+
#### Run Image-to-Video Generation
|
| 107 |
+
|
| 108 |
+
Similar to Text-to-Video, Image-to-Video is also divided into processes with and without the prompt extension step. The specific parameters and their corresponding settings are as follows:
|
| 109 |
+
<table>
|
| 110 |
+
<thead>
|
| 111 |
+
<tr>
|
| 112 |
+
<th rowspan="2">Task</th>
|
| 113 |
+
<th colspan="2">Resolution</th>
|
| 114 |
+
<th rowspan="2">Model</th>
|
| 115 |
+
</tr>
|
| 116 |
+
<tr>
|
| 117 |
+
<th>480P</th>
|
| 118 |
+
<th>720P</th>
|
| 119 |
+
</tr>
|
| 120 |
+
</thead>
|
| 121 |
+
<tbody>
|
| 122 |
+
<tr>
|
| 123 |
+
<td>i2v-14B</td>
|
| 124 |
+
<td style="color: green;">❌</td>
|
| 125 |
+
<td style="color: green;">✔️</td>
|
| 126 |
+
<td>Wan2.1-I2V-14B-720P</td>
|
| 127 |
+
</tr>
|
| 128 |
+
<tr>
|
| 129 |
+
<td>i2v-14B</td>
|
| 130 |
+
<td style="color: green;">✔️</td>
|
| 131 |
+
<td style="color: red;">❌</td>
|
| 132 |
+
<td>Wan2.1-T2V-14B-480P</td>
|
| 133 |
+
</tr>
|
| 134 |
+
</tbody>
|
| 135 |
+
</table>
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
##### (1) Without Prompt Extention
|
| 139 |
+
|
| 140 |
+
- Single-GPU inference
|
| 141 |
+
```
|
| 142 |
+
python generate.py --task i2v-14B --size 832*480 --ckpt_dir ./Wan2.1-I2V-14B-480P --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
|
| 143 |
+
```
|
| 144 |
+
|
| 145 |
+
> 💡For the Image-to-Video task, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image.
|
| 146 |
+
|
| 147 |
+
- Multi-GPU inference using FSDP + xDiT USP
|
| 148 |
+
|
| 149 |
+
```
|
| 150 |
+
pip install "xfuser>=0.4.1"
|
| 151 |
+
torchrun --nproc_per_node=8 generate.py --task i2v-14B --size 832*480 --ckpt_dir ./Wan2.1-I2V-14B-480P --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
|
| 152 |
+
```
|
| 153 |
+
|
| 154 |
+
##### (2) Using Prompt Extention
|
| 155 |
+
|
| 156 |
+
Run with local prompt extention using `Qwen/Qwen2.5-VL-7B-Instruct`:
|
| 157 |
+
```
|
| 158 |
+
python generate.py --task i2v-14B --size 832*480 --ckpt_dir ./Wan2.1-I2V-14B-480P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_model Qwen/Qwen2.5-VL-7B-Instruct --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
|
| 159 |
+
```
|
| 160 |
+
|
| 161 |
+
Run with remote prompt extention using `dashscope`:
|
| 162 |
+
```
|
| 163 |
+
DASH_API_KEY=your_key python generate.py --task i2v-14B --size 832*480 --ckpt_dir ./Wan2.1-I2V-14B-480P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_method 'dashscope' --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
|
| 164 |
+
```
|
| 165 |
+
|
| 166 |
+
##### (3) Runing local gradio
|
| 167 |
+
|
| 168 |
+
```
|
| 169 |
+
cd gradio
|
| 170 |
+
# if one only uses 480P model in gradio
|
| 171 |
+
DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P
|
| 172 |
+
|
| 173 |
+
# if one only uses 720P model in gradio
|
| 174 |
+
DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_720p ./Wan2.1-I2V-14B-720P
|
| 175 |
+
|
| 176 |
+
# if one uses both 480P and 720P models in gradio
|
| 177 |
+
DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P --ckpt_dir_720p ./Wan2.1-I2V-14B-720P
|
| 178 |
+
```
|
| 179 |
+
|
| 180 |
+
|
| 181 |
+
## Manual Evaluation
|
| 182 |
+
|
| 183 |
+
We conducted extensive manual evaluations to evaluate the performance of the Image-to-Video model, and the results are presented in the table below. The results clearly indicate that **Wan2.1** outperforms both closed-source and open-source models.
|
| 184 |
+
|
| 185 |
+
<div align="center">
|
| 186 |
+
<img src="assets/i2v_res.png" alt="" style="width: 80%;" />
|
| 187 |
+
</div>
|
| 188 |
+
|
| 189 |
+
|
| 190 |
+
## Computational Efficiency on Different GPUs
|
| 191 |
+
|
| 192 |
+
We test the computational efficiency of different **Wan2.1** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
|
| 193 |
+
|
| 194 |
+
|
| 195 |
+
<div align="center">
|
| 196 |
+
<img src="assets/comp_effic.png" alt="" style="width: 80%;" />
|
| 197 |
+
</div>
|
| 198 |
+
|
| 199 |
+
> The parameter settings for the tests presented in this table are as follows:
|
| 200 |
+
> (1) For the 1.3B model on 8 GPUs, set `--ring_size 8` and `--ulysses_size 1`;
|
| 201 |
+
> (2) For the 14B model on 1 GPU, use `--offload_model True`;
|
| 202 |
+
> (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
|
| 203 |
+
> (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
|
| 204 |
+
|
| 205 |
+
-------
|
| 206 |
+
|
| 207 |
+
## Introduction of Wan2.1
|
| 208 |
+
|
| 209 |
+
**Wan2.1** is designed on the mainstream diffusion transformer paradigm, achieving significant advancements in generative capabilities through a series of innovations. These include our novel spatio-temporal variational autoencoder (VAE), scalable training strategies, large-scale data construction, and automated evaluation metrics. Collectively, these contributions enhance the model’s performance and versatility.
|
| 210 |
+
|
| 211 |
+
|
| 212 |
+
##### (1) 3D Variational Autoencoders
|
| 213 |
+
We propose a novel 3D causal VAE architecture, termed **Wan-VAE** specifically designed for video generation. By combining multiple strategies, we improve spatio-temporal compression, reduce memory usage, and ensure temporal causality. **Wan-VAE** demonstrates significant advantages in performance efficiency compared to other open-source VAEs. Furthermore, our **Wan-VAE** can encode and decode unlimited-length 1080P videos without losing historical temporal information, making it particularly well-suited for video generation tasks.
|
| 214 |
+
|
| 215 |
+
|
| 216 |
+
<div align="center">
|
| 217 |
+
<img src="assets/video_vae_res.jpg" alt="" style="width: 80%;" />
|
| 218 |
+
</div>
|
| 219 |
+
|
| 220 |
+
|
| 221 |
+
##### (2) Video Diffusion DiT
|
| 222 |
+
|
| 223 |
+
**Wan2.1** is designed using the Flow Matching framework within the paradigm of mainstream Diffusion Transformers. Our model's architecture uses the T5 Encoder to encode multilingual text input, with cross-attention in each transformer block embedding the text into the model structure. Additionally, we employ an MLP with a Linear layer and a SiLU layer to process the input time embeddings and predict six modulation parameters individually. This MLP is shared across all transformer blocks, with each block learning a distinct set of biases. Our experimental findings reveal a significant performance improvement with this approach at the same parameter scale.
|
| 224 |
+
|
| 225 |
+
<div align="center">
|
| 226 |
+
<img src="assets/video_dit_arch.jpg" alt="" style="width: 80%;" />
|
| 227 |
+
</div>
|
| 228 |
+
|
| 229 |
+
|
| 230 |
+
| Model | Dimension | Input Dimension | Output Dimension | Feedforward Dimension | Frequency Dimension | Number of Heads | Number of Layers |
|
| 231 |
+
|--------|-----------|-----------------|------------------|-----------------------|---------------------|-----------------|------------------|
|
| 232 |
+
| 1.3B | 1536 | 16 | 16 | 8960 | 256 | 12 | 30 |
|
| 233 |
+
| 14B | 5120 | 16 | 16 | 13824 | 256 | 40 | 40 |
|
| 234 |
+
|
| 235 |
+
|
| 236 |
+
|
| 237 |
+
##### Data
|
| 238 |
+
|
| 239 |
+
We curated and deduplicated a candidate dataset comprising a vast amount of image and video data. During the data curation process, we designed a four-step data cleaning process, focusing on fundamental dimensions, visual quality and motion quality. Through the robust data processing pipeline, we can easily obtain high-quality, diverse, and large-scale training sets of images and videos.
|
| 240 |
+
|
| 241 |
+
|
| 242 |
+
|
| 243 |
+
|
| 244 |
+
##### Comparisons to SOTA
|
| 245 |
+
We compared **Wan2.1** with leading open-source and closed-source models to evaluate the performace. Using our carefully designed set of 1,035 internal prompts, we tested across 14 major dimensions and 26 sub-dimensions. We then compute the total score by performing a weighted calculation on the scores of each dimension, utilizing weights derived from human preferences in the matching process. The detailed results are shown in the table below. These results demonstrate our model's superior performance compared to both open-source and closed-source models.
|
| 246 |
+
|
| 247 |
+
|
| 248 |
+
|
| 249 |
+
|
| 250 |
+
## Citation
|
| 251 |
+
If you find our work helpful, please cite us.
|
| 252 |
+
|
| 253 |
+
```
|
| 254 |
+
@article{wan2.1,
|
| 255 |
+
title = {Wan: Open and Advanced Large-Scale Video Generative Models},
|
| 256 |
+
author = {Wan Team},
|
| 257 |
+
journal = {},
|
| 258 |
+
year = {2025}
|
| 259 |
+
}
|
| 260 |
+
```
|
| 261 |
+
|
| 262 |
+
## License Agreement
|
| 263 |
+
The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generate contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
|
| 264 |
+
|
| 265 |
+
|
| 266 |
+
## Acknowledgements
|
| 267 |
+
|
| 268 |
+
We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
|
| 269 |
+
|
| 270 |
+
|
| 271 |
+
|
| 272 |
+
## Contact Us
|
| 273 |
+
If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/p5XbdQV7) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!
|
Wan2.1-I2V-14B-480P/Wan2.1_VAE.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
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|
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size 507609880
|
Wan2.1-I2V-14B-480P/assets/logo.png
ADDED
|
Wan2.1-I2V-14B-480P/config.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_class_name": "WanModel",
|
| 3 |
+
"_diffusers_version": "0.30.0",
|
| 4 |
+
"dim": 5120,
|
| 5 |
+
"eps": 1e-06,
|
| 6 |
+
"ffn_dim": 13824,
|
| 7 |
+
"freq_dim": 256,
|
| 8 |
+
"in_dim": 36,
|
| 9 |
+
"model_type": "i2v",
|
| 10 |
+
"num_heads": 40,
|
| 11 |
+
"num_layers": 40,
|
| 12 |
+
"out_dim": 16,
|
| 13 |
+
"text_len": 512
|
| 14 |
+
}
|
Wan2.1-I2V-14B-480P/diffusion_pytorch_model-00005-of-00007.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:0ba7f3ab69f414f23e5566e607b9385a8f67907302cbaaf86507b3a826a20dda
|
| 3 |
+
size 9692142864
|
Wan2.1-I2V-14B-480P/diffusion_pytorch_model-00007-of-00007.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:d1bc30f07b162c34b90e4ee4a349f81b6e1a3a342187ea9ea153c9e4dbb07676
|
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size 7061933536
|
Wan2.1-I2V-14B-480P/google/umt5-xxl/special_tokens_map.json
ADDED
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@@ -0,0 +1,308 @@
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| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<extra_id_0>",
|
| 4 |
+
"<extra_id_1>",
|
| 5 |
+
"<extra_id_2>",
|
| 6 |
+
"<extra_id_3>",
|
| 7 |
+
"<extra_id_4>",
|
| 8 |
+
"<extra_id_5>",
|
| 9 |
+
"<extra_id_6>",
|
| 10 |
+
"<extra_id_7>",
|
| 11 |
+
"<extra_id_8>",
|
| 12 |
+
"<extra_id_9>",
|
| 13 |
+
"<extra_id_10>",
|
| 14 |
+
"<extra_id_11>",
|
| 15 |
+
"<extra_id_12>",
|
| 16 |
+
"<extra_id_13>",
|
| 17 |
+
"<extra_id_14>",
|
| 18 |
+
"<extra_id_15>",
|
| 19 |
+
"<extra_id_16>",
|
| 20 |
+
"<extra_id_17>",
|
| 21 |
+
"<extra_id_18>",
|
| 22 |
+
"<extra_id_19>",
|
| 23 |
+
"<extra_id_20>",
|
| 24 |
+
"<extra_id_21>",
|
| 25 |
+
"<extra_id_22>",
|
| 26 |
+
"<extra_id_23>",
|
| 27 |
+
"<extra_id_24>",
|
| 28 |
+
"<extra_id_25>",
|
| 29 |
+
"<extra_id_26>",
|
| 30 |
+
"<extra_id_27>",
|
| 31 |
+
"<extra_id_28>",
|
| 32 |
+
"<extra_id_29>",
|
| 33 |
+
"<extra_id_30>",
|
| 34 |
+
"<extra_id_31>",
|
| 35 |
+
"<extra_id_32>",
|
| 36 |
+
"<extra_id_33>",
|
| 37 |
+
"<extra_id_34>",
|
| 38 |
+
"<extra_id_35>",
|
| 39 |
+
"<extra_id_36>",
|
| 40 |
+
"<extra_id_37>",
|
| 41 |
+
"<extra_id_38>",
|
| 42 |
+
"<extra_id_39>",
|
| 43 |
+
"<extra_id_40>",
|
| 44 |
+
"<extra_id_41>",
|
| 45 |
+
"<extra_id_42>",
|
| 46 |
+
"<extra_id_43>",
|
| 47 |
+
"<extra_id_44>",
|
| 48 |
+
"<extra_id_45>",
|
| 49 |
+
"<extra_id_46>",
|
| 50 |
+
"<extra_id_47>",
|
| 51 |
+
"<extra_id_48>",
|
| 52 |
+
"<extra_id_49>",
|
| 53 |
+
"<extra_id_50>",
|
| 54 |
+
"<extra_id_51>",
|
| 55 |
+
"<extra_id_52>",
|
| 56 |
+
"<extra_id_53>",
|
| 57 |
+
"<extra_id_54>",
|
| 58 |
+
"<extra_id_55>",
|
| 59 |
+
"<extra_id_56>",
|
| 60 |
+
"<extra_id_57>",
|
| 61 |
+
"<extra_id_58>",
|
| 62 |
+
"<extra_id_59>",
|
| 63 |
+
"<extra_id_60>",
|
| 64 |
+
"<extra_id_61>",
|
| 65 |
+
"<extra_id_62>",
|
| 66 |
+
"<extra_id_63>",
|
| 67 |
+
"<extra_id_64>",
|
| 68 |
+
"<extra_id_65>",
|
| 69 |
+
"<extra_id_66>",
|
| 70 |
+
"<extra_id_67>",
|
| 71 |
+
"<extra_id_68>",
|
| 72 |
+
"<extra_id_69>",
|
| 73 |
+
"<extra_id_70>",
|
| 74 |
+
"<extra_id_71>",
|
| 75 |
+
"<extra_id_72>",
|
| 76 |
+
"<extra_id_73>",
|
| 77 |
+
"<extra_id_74>",
|
| 78 |
+
"<extra_id_75>",
|
| 79 |
+
"<extra_id_76>",
|
| 80 |
+
"<extra_id_77>",
|
| 81 |
+
"<extra_id_78>",
|
| 82 |
+
"<extra_id_79>",
|
| 83 |
+
"<extra_id_80>",
|
| 84 |
+
"<extra_id_81>",
|
| 85 |
+
"<extra_id_82>",
|
| 86 |
+
"<extra_id_83>",
|
| 87 |
+
"<extra_id_84>",
|
| 88 |
+
"<extra_id_85>",
|
| 89 |
+
"<extra_id_86>",
|
| 90 |
+
"<extra_id_87>",
|
| 91 |
+
"<extra_id_88>",
|
| 92 |
+
"<extra_id_89>",
|
| 93 |
+
"<extra_id_90>",
|
| 94 |
+
"<extra_id_91>",
|
| 95 |
+
"<extra_id_92>",
|
| 96 |
+
"<extra_id_93>",
|
| 97 |
+
"<extra_id_94>",
|
| 98 |
+
"<extra_id_95>",
|
| 99 |
+
"<extra_id_96>",
|
| 100 |
+
"<extra_id_97>",
|
| 101 |
+
"<extra_id_98>",
|
| 102 |
+
"<extra_id_99>",
|
| 103 |
+
"<extra_id_100>",
|
| 104 |
+
"<extra_id_101>",
|
| 105 |
+
"<extra_id_102>",
|
| 106 |
+
"<extra_id_103>",
|
| 107 |
+
"<extra_id_104>",
|
| 108 |
+
"<extra_id_105>",
|
| 109 |
+
"<extra_id_106>",
|
| 110 |
+
"<extra_id_107>",
|
| 111 |
+
"<extra_id_108>",
|
| 112 |
+
"<extra_id_109>",
|
| 113 |
+
"<extra_id_110>",
|
| 114 |
+
"<extra_id_111>",
|
| 115 |
+
"<extra_id_112>",
|
| 116 |
+
"<extra_id_113>",
|
| 117 |
+
"<extra_id_114>",
|
| 118 |
+
"<extra_id_115>",
|
| 119 |
+
"<extra_id_116>",
|
| 120 |
+
"<extra_id_117>",
|
| 121 |
+
"<extra_id_118>",
|
| 122 |
+
"<extra_id_119>",
|
| 123 |
+
"<extra_id_120>",
|
| 124 |
+
"<extra_id_121>",
|
| 125 |
+
"<extra_id_122>",
|
| 126 |
+
"<extra_id_123>",
|
| 127 |
+
"<extra_id_124>",
|
| 128 |
+
"<extra_id_125>",
|
| 129 |
+
"<extra_id_126>",
|
| 130 |
+
"<extra_id_127>",
|
| 131 |
+
"<extra_id_128>",
|
| 132 |
+
"<extra_id_129>",
|
| 133 |
+
"<extra_id_130>",
|
| 134 |
+
"<extra_id_131>",
|
| 135 |
+
"<extra_id_132>",
|
| 136 |
+
"<extra_id_133>",
|
| 137 |
+
"<extra_id_134>",
|
| 138 |
+
"<extra_id_135>",
|
| 139 |
+
"<extra_id_136>",
|
| 140 |
+
"<extra_id_137>",
|
| 141 |
+
"<extra_id_138>",
|
| 142 |
+
"<extra_id_139>",
|
| 143 |
+
"<extra_id_140>",
|
| 144 |
+
"<extra_id_141>",
|
| 145 |
+
"<extra_id_142>",
|
| 146 |
+
"<extra_id_143>",
|
| 147 |
+
"<extra_id_144>",
|
| 148 |
+
"<extra_id_145>",
|
| 149 |
+
"<extra_id_146>",
|
| 150 |
+
"<extra_id_147>",
|
| 151 |
+
"<extra_id_148>",
|
| 152 |
+
"<extra_id_149>",
|
| 153 |
+
"<extra_id_150>",
|
| 154 |
+
"<extra_id_151>",
|
| 155 |
+
"<extra_id_152>",
|
| 156 |
+
"<extra_id_153>",
|
| 157 |
+
"<extra_id_154>",
|
| 158 |
+
"<extra_id_155>",
|
| 159 |
+
"<extra_id_156>",
|
| 160 |
+
"<extra_id_157>",
|
| 161 |
+
"<extra_id_158>",
|
| 162 |
+
"<extra_id_159>",
|
| 163 |
+
"<extra_id_160>",
|
| 164 |
+
"<extra_id_161>",
|
| 165 |
+
"<extra_id_162>",
|
| 166 |
+
"<extra_id_163>",
|
| 167 |
+
"<extra_id_164>",
|
| 168 |
+
"<extra_id_165>",
|
| 169 |
+
"<extra_id_166>",
|
| 170 |
+
"<extra_id_167>",
|
| 171 |
+
"<extra_id_168>",
|
| 172 |
+
"<extra_id_169>",
|
| 173 |
+
"<extra_id_170>",
|
| 174 |
+
"<extra_id_171>",
|
| 175 |
+
"<extra_id_172>",
|
| 176 |
+
"<extra_id_173>",
|
| 177 |
+
"<extra_id_174>",
|
| 178 |
+
"<extra_id_175>",
|
| 179 |
+
"<extra_id_176>",
|
| 180 |
+
"<extra_id_177>",
|
| 181 |
+
"<extra_id_178>",
|
| 182 |
+
"<extra_id_179>",
|
| 183 |
+
"<extra_id_180>",
|
| 184 |
+
"<extra_id_181>",
|
| 185 |
+
"<extra_id_182>",
|
| 186 |
+
"<extra_id_183>",
|
| 187 |
+
"<extra_id_184>",
|
| 188 |
+
"<extra_id_185>",
|
| 189 |
+
"<extra_id_186>",
|
| 190 |
+
"<extra_id_187>",
|
| 191 |
+
"<extra_id_188>",
|
| 192 |
+
"<extra_id_189>",
|
| 193 |
+
"<extra_id_190>",
|
| 194 |
+
"<extra_id_191>",
|
| 195 |
+
"<extra_id_192>",
|
| 196 |
+
"<extra_id_193>",
|
| 197 |
+
"<extra_id_194>",
|
| 198 |
+
"<extra_id_195>",
|
| 199 |
+
"<extra_id_196>",
|
| 200 |
+
"<extra_id_197>",
|
| 201 |
+
"<extra_id_198>",
|
| 202 |
+
"<extra_id_199>",
|
| 203 |
+
"<extra_id_200>",
|
| 204 |
+
"<extra_id_201>",
|
| 205 |
+
"<extra_id_202>",
|
| 206 |
+
"<extra_id_203>",
|
| 207 |
+
"<extra_id_204>",
|
| 208 |
+
"<extra_id_205>",
|
| 209 |
+
"<extra_id_206>",
|
| 210 |
+
"<extra_id_207>",
|
| 211 |
+
"<extra_id_208>",
|
| 212 |
+
"<extra_id_209>",
|
| 213 |
+
"<extra_id_210>",
|
| 214 |
+
"<extra_id_211>",
|
| 215 |
+
"<extra_id_212>",
|
| 216 |
+
"<extra_id_213>",
|
| 217 |
+
"<extra_id_214>",
|
| 218 |
+
"<extra_id_215>",
|
| 219 |
+
"<extra_id_216>",
|
| 220 |
+
"<extra_id_217>",
|
| 221 |
+
"<extra_id_218>",
|
| 222 |
+
"<extra_id_219>",
|
| 223 |
+
"<extra_id_220>",
|
| 224 |
+
"<extra_id_221>",
|
| 225 |
+
"<extra_id_222>",
|
| 226 |
+
"<extra_id_223>",
|
| 227 |
+
"<extra_id_224>",
|
| 228 |
+
"<extra_id_225>",
|
| 229 |
+
"<extra_id_226>",
|
| 230 |
+
"<extra_id_227>",
|
| 231 |
+
"<extra_id_228>",
|
| 232 |
+
"<extra_id_229>",
|
| 233 |
+
"<extra_id_230>",
|
| 234 |
+
"<extra_id_231>",
|
| 235 |
+
"<extra_id_232>",
|
| 236 |
+
"<extra_id_233>",
|
| 237 |
+
"<extra_id_234>",
|
| 238 |
+
"<extra_id_235>",
|
| 239 |
+
"<extra_id_236>",
|
| 240 |
+
"<extra_id_237>",
|
| 241 |
+
"<extra_id_238>",
|
| 242 |
+
"<extra_id_239>",
|
| 243 |
+
"<extra_id_240>",
|
| 244 |
+
"<extra_id_241>",
|
| 245 |
+
"<extra_id_242>",
|
| 246 |
+
"<extra_id_243>",
|
| 247 |
+
"<extra_id_244>",
|
| 248 |
+
"<extra_id_245>",
|
| 249 |
+
"<extra_id_246>",
|
| 250 |
+
"<extra_id_247>",
|
| 251 |
+
"<extra_id_248>",
|
| 252 |
+
"<extra_id_249>",
|
| 253 |
+
"<extra_id_250>",
|
| 254 |
+
"<extra_id_251>",
|
| 255 |
+
"<extra_id_252>",
|
| 256 |
+
"<extra_id_253>",
|
| 257 |
+
"<extra_id_254>",
|
| 258 |
+
"<extra_id_255>",
|
| 259 |
+
"<extra_id_256>",
|
| 260 |
+
"<extra_id_257>",
|
| 261 |
+
"<extra_id_258>",
|
| 262 |
+
"<extra_id_259>",
|
| 263 |
+
"<extra_id_260>",
|
| 264 |
+
"<extra_id_261>",
|
| 265 |
+
"<extra_id_262>",
|
| 266 |
+
"<extra_id_263>",
|
| 267 |
+
"<extra_id_264>",
|
| 268 |
+
"<extra_id_265>",
|
| 269 |
+
"<extra_id_266>",
|
| 270 |
+
"<extra_id_267>",
|
| 271 |
+
"<extra_id_268>",
|
| 272 |
+
"<extra_id_269>",
|
| 273 |
+
"<extra_id_270>",
|
| 274 |
+
"<extra_id_271>",
|
| 275 |
+
"<extra_id_272>",
|
| 276 |
+
"<extra_id_273>",
|
| 277 |
+
"<extra_id_274>",
|
| 278 |
+
"<extra_id_275>",
|
| 279 |
+
"<extra_id_276>",
|
| 280 |
+
"<extra_id_277>",
|
| 281 |
+
"<extra_id_278>",
|
| 282 |
+
"<extra_id_279>",
|
| 283 |
+
"<extra_id_280>",
|
| 284 |
+
"<extra_id_281>",
|
| 285 |
+
"<extra_id_282>",
|
| 286 |
+
"<extra_id_283>",
|
| 287 |
+
"<extra_id_284>",
|
| 288 |
+
"<extra_id_285>",
|
| 289 |
+
"<extra_id_286>",
|
| 290 |
+
"<extra_id_287>",
|
| 291 |
+
"<extra_id_288>",
|
| 292 |
+
"<extra_id_289>",
|
| 293 |
+
"<extra_id_290>",
|
| 294 |
+
"<extra_id_291>",
|
| 295 |
+
"<extra_id_292>",
|
| 296 |
+
"<extra_id_293>",
|
| 297 |
+
"<extra_id_294>",
|
| 298 |
+
"<extra_id_295>",
|
| 299 |
+
"<extra_id_296>",
|
| 300 |
+
"<extra_id_297>",
|
| 301 |
+
"<extra_id_298>",
|
| 302 |
+
"<extra_id_299>"
|
| 303 |
+
],
|
| 304 |
+
"bos_token": "<s>",
|
| 305 |
+
"eos_token": "</s>",
|
| 306 |
+
"pad_token": "<pad>",
|
| 307 |
+
"unk_token": "<unk>"
|
| 308 |
+
}
|
Wan2.1-I2V-14B-480P/google/umt5-xxl/tokenizer_config.json
ADDED
|
@@ -0,0 +1,2748 @@
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| 1 |
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{
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 10 |
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| 11 |
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| 1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
| 2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
| 3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
| 4 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
| 5 |
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*.ftz filter=lfs diff=lfs merge=lfs -text
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| 6 |
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*.gz filter=lfs diff=lfs merge=lfs -text
|
| 7 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
| 8 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 9 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 10 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
| 11 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 12 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 13 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 14 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 15 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 16 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 17 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 18 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 19 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 20 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 21 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 22 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 23 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 24 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 25 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
chinese-wav2vec2-base/README.md
ADDED
|
@@ -0,0 +1,61 @@
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| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
---
|
| 4 |
+
Pretrained on 10k hours WenetSpeech L subset. More details in [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
|
| 5 |
+
|
| 6 |
+
This model does not have a tokenizer as it was pretrained on audio alone.
|
| 7 |
+
In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.
|
| 8 |
+
|
| 9 |
+
python package:
|
| 10 |
+
transformers==4.16.2
|
| 11 |
+
|
| 12 |
+
```python
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
import torch
|
| 16 |
+
import torch.nn.functional as F
|
| 17 |
+
import soundfile as sf
|
| 18 |
+
from fairseq import checkpoint_utils
|
| 19 |
+
|
| 20 |
+
from transformers import (
|
| 21 |
+
Wav2Vec2FeatureExtractor,
|
| 22 |
+
Wav2Vec2ForPreTraining,
|
| 23 |
+
Wav2Vec2Model,
|
| 24 |
+
)
|
| 25 |
+
from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices
|
| 26 |
+
|
| 27 |
+
model_path=""
|
| 28 |
+
wav_path=""
|
| 29 |
+
mask_prob=0.0
|
| 30 |
+
mask_length=10
|
| 31 |
+
|
| 32 |
+
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_path)
|
| 33 |
+
model = Wav2Vec2Model.from_pretrained(model_path)
|
| 34 |
+
|
| 35 |
+
# for pretrain: Wav2Vec2ForPreTraining
|
| 36 |
+
# model = Wav2Vec2ForPreTraining.from_pretrained(model_path)
|
| 37 |
+
|
| 38 |
+
model = model.to(device)
|
| 39 |
+
model = model.half()
|
| 40 |
+
model.eval()
|
| 41 |
+
|
| 42 |
+
wav, sr = sf.read(wav_path)
|
| 43 |
+
input_values = feature_extractor(wav, return_tensors="pt").input_values
|
| 44 |
+
input_values = input_values.half()
|
| 45 |
+
input_values = input_values.to(device)
|
| 46 |
+
|
| 47 |
+
# for Wav2Vec2ForPreTraining
|
| 48 |
+
# batch_size, raw_sequence_length = input_values.shape
|
| 49 |
+
# sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length)
|
| 50 |
+
# mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.0, mask_length=2)
|
| 51 |
+
# mask_time_indices = torch.tensor(mask_time_indices, device=input_values.device, dtype=torch.long)
|
| 52 |
+
|
| 53 |
+
with torch.no_grad():
|
| 54 |
+
outputs = model(input_values)
|
| 55 |
+
last_hidden_state = outputs.last_hidden_state
|
| 56 |
+
|
| 57 |
+
# for Wav2Vec2ForPreTraining
|
| 58 |
+
# outputs = model(input_values, mask_time_indices=mask_time_indices, output_hidden_states=True)
|
| 59 |
+
# last_hidden_state = outputs.hidden_states[-1]
|
| 60 |
+
|
| 61 |
+
```
|
chinese-wav2vec2-base/chinese-wav2vec2-base-fairseq-ckpt.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a75e04e426977dd399415b7f586b18978bc6836a3e8514ae1bb29e468fb17184
|
| 3 |
+
size 1140632401
|
chinese-wav2vec2-base/config.json
ADDED
|
@@ -0,0 +1,105 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"activation_dropout": 0.1,
|
| 3 |
+
"adapter_kernel_size": 3,
|
| 4 |
+
"adapter_stride": 2,
|
| 5 |
+
"add_adapter": false,
|
| 6 |
+
"apply_spec_augment": true,
|
| 7 |
+
"architectures": [
|
| 8 |
+
"Wav2Vec2ForPreTraining"
|
| 9 |
+
],
|
| 10 |
+
"attention_dropout": 0.1,
|
| 11 |
+
"bos_token_id": 1,
|
| 12 |
+
"classifier_proj_size": 256,
|
| 13 |
+
"codevector_dim": 256,
|
| 14 |
+
"contrastive_logits_temperature": 0.1,
|
| 15 |
+
"conv_bias": false,
|
| 16 |
+
"conv_dim": [
|
| 17 |
+
512,
|
| 18 |
+
512,
|
| 19 |
+
512,
|
| 20 |
+
512,
|
| 21 |
+
512,
|
| 22 |
+
512,
|
| 23 |
+
512
|
| 24 |
+
],
|
| 25 |
+
"conv_kernel": [
|
| 26 |
+
10,
|
| 27 |
+
3,
|
| 28 |
+
3,
|
| 29 |
+
3,
|
| 30 |
+
3,
|
| 31 |
+
2,
|
| 32 |
+
2
|
| 33 |
+
],
|
| 34 |
+
"conv_stride": [
|
| 35 |
+
5,
|
| 36 |
+
2,
|
| 37 |
+
2,
|
| 38 |
+
2,
|
| 39 |
+
2,
|
| 40 |
+
2,
|
| 41 |
+
2
|
| 42 |
+
],
|
| 43 |
+
"ctc_loss_reduction": "sum",
|
| 44 |
+
"ctc_zero_infinity": false,
|
| 45 |
+
"diversity_loss_weight": 0.1,
|
| 46 |
+
"do_stable_layer_norm": false,
|
| 47 |
+
"eos_token_id": 2,
|
| 48 |
+
"feat_extract_activation": "gelu",
|
| 49 |
+
"feat_extract_norm": "group",
|
| 50 |
+
"feat_proj_dropout": 0.0,
|
| 51 |
+
"feat_quantizer_dropout": 0.0,
|
| 52 |
+
"final_dropout": 0.1,
|
| 53 |
+
"hidden_act": "gelu",
|
| 54 |
+
"hidden_dropout": 0.1,
|
| 55 |
+
"hidden_size": 768,
|
| 56 |
+
"initializer_range": 0.02,
|
| 57 |
+
"intermediate_size": 3072,
|
| 58 |
+
"layer_norm_eps": 1e-05,
|
| 59 |
+
"layerdrop": 0.1,
|
| 60 |
+
"mask_feature_length": 10,
|
| 61 |
+
"mask_feature_min_masks": 0,
|
| 62 |
+
"mask_feature_prob": 0.0,
|
| 63 |
+
"mask_time_length": 10,
|
| 64 |
+
"mask_time_min_masks": 2,
|
| 65 |
+
"mask_time_prob": 0.05,
|
| 66 |
+
"model_type": "wav2vec2",
|
| 67 |
+
"num_adapter_layers": 3,
|
| 68 |
+
"num_attention_heads": 12,
|
| 69 |
+
"num_codevector_groups": 2,
|
| 70 |
+
"num_codevectors_per_group": 320,
|
| 71 |
+
"num_conv_pos_embedding_groups": 16,
|
| 72 |
+
"num_conv_pos_embeddings": 128,
|
| 73 |
+
"num_feat_extract_layers": 7,
|
| 74 |
+
"num_hidden_layers": 12,
|
| 75 |
+
"num_negatives": 100,
|
| 76 |
+
"output_hidden_size": 768,
|
| 77 |
+
"pad_token_id": 0,
|
| 78 |
+
"proj_codevector_dim": 256,
|
| 79 |
+
"tdnn_dilation": [
|
| 80 |
+
1,
|
| 81 |
+
2,
|
| 82 |
+
3,
|
| 83 |
+
1,
|
| 84 |
+
1
|
| 85 |
+
],
|
| 86 |
+
"tdnn_dim": [
|
| 87 |
+
512,
|
| 88 |
+
512,
|
| 89 |
+
512,
|
| 90 |
+
512,
|
| 91 |
+
1500
|
| 92 |
+
],
|
| 93 |
+
"tdnn_kernel": [
|
| 94 |
+
5,
|
| 95 |
+
3,
|
| 96 |
+
3,
|
| 97 |
+
1,
|
| 98 |
+
1
|
| 99 |
+
],
|
| 100 |
+
"torch_dtype": "float32",
|
| 101 |
+
"transformers_version": "4.16.2",
|
| 102 |
+
"use_weighted_layer_sum": false,
|
| 103 |
+
"vocab_size": 32,
|
| 104 |
+
"xvector_output_dim": 512
|
| 105 |
+
}
|
chinese-wav2vec2-base/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b86f5be7b752fc655c27387a75712f733315a30f976e5875491599615399e773
|
| 3 |
+
size 380204696
|
chinese-wav2vec2-base/preprocessor_config.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"do_normalize": false,
|
| 3 |
+
"feature_size": 1,
|
| 4 |
+
"padding_side": "right",
|
| 5 |
+
"padding_value": 0.0,
|
| 6 |
+
"return_attention_mask": false,
|
| 7 |
+
"sampling_rate": 16000
|
| 8 |
+
}
|
chinese-wav2vec2-base/pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:be2da40c9e7ae26bfc904a3ed79ebb9e8f060bec6dba85d6a6ae86114bc38901
|
| 3 |
+
size 380261837
|
images/ComfyUI_00001_.webp
ADDED
|
Git LFS Details
|
placeholder
ADDED
|
File without changes
|
weight.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
Put your download checkpoints in this path
|