first commit

README.md

@@ -1,3 +1,74 @@
 ---
 license: cc-by-nc-sa-4.0
+datasets:
+- AudioCaps
+language:
+- en
+tags:
+- audio
 ---
+# 
+
+**Auffusion** is a latent diffusion model (LDM) for text-to-audio (TTA) generation. **Auffusion** can generate realistic audios including human sounds, animal sounds, natural and artificial sounds and sound effects from textual prompts. We introduce Auffusion, a TTA system adapting T2I model frameworks to TTA task, by effectively leveraging their inherent generative strengths and precise cross-modal alignment. Our objective and subjective evaluations demonstrate that Auffusion surpasses previous TTA approaches using limited data and computational resource. We release our model, inference code, and pre-trained checkpoints for the research community.
+
+📣 We are releasing **Auffusion-Full-no-adapter** which was pre-trained on all datasets described in paper and created for easy use of audio manipulation.
+
+📣 We are releasing **Auffusion-Full** which was pre-trained on all datasets described in paper.
+
+📣 We are releasing **Auffusion** which was pre-trained on **AudioCaps**.
+
+## Auffusion Model Family
+
+| Model Name                 | Model Path                                                                                                              |
+|----------------------------|------------------------------------------------------------------------------------------------------------------------ |
+| Auffusion                  | [https://huggingface.co/auffusion/auffusion](https://huggingface.co/auffusion/auffusion)                                |
+| Auffusion-Full             | [https://huggingface.co/auffusion/auffusion-full](https://huggingface.co/auffusion/auffusion-full)                      |
+| Auffusion-Full-no-adapter  | [https://huggingface.co/auffusion/auffusion-full-no-adapter](https://huggingface.co/auffusion/auffusion-full-no-adapter)|
+
+
+
+## Code
+
+Our code is released here: [https://github.com/happylittlecat2333/Auffusion](https://github.com/happylittlecat2333/Auffusion)
+
+We uploaded several **Auffusion** generated samples here: [https://auffusion.github.io](https://auffusion.github.io)
+
+Please follow the instructions in the repository for installation, usage and experiments.
+
+
+## Quickstart Guide
+
+First, git clone the repository and install the requirements:
+
+```bash
+git clone https://github.com/happylittlecat2333/Auffusion/
+cd Auffusion
+pip install -r requirements.txt
+```
+
+Download the **Auffusion** model and generate audio from a text prompt:
+
+```python
+import IPython, torch
+import soundfile as sf
+from auffusion_pipeline import AuffusionPipeline
+
+pipeline = AuffusionPipeline.from_pretrained("auffusion/auffusion")
+
+prompt = "Birds singing sweetly in a blooming garden"
+output = pipeline(prompt=prompt)
+audio = output.audios[0]
+sf.write(f"{prompt}.wav", audio, samplerate=16000)
+IPython.display.Audio(data=audio, rate=16000)
+```
+
+The auffusion model will be automatically downloaded from huggingface and saved in cache. Subsequent runs will load the model directly from cache.
+
+The `generate` function uses 100 steps and 7.5 guidance_scale by default to sample from the latent diffusion model. You can also vary parameters for different results.
+
+```python
+prompt = "Rolling thunder with lightning strikes"
+output = pipeline(prompt=prompt, num_inference_steps=100, guidance_scale=7.5)
+audio = output.audios[0]
+IPython.display.Audio(data=audio, rate=16000)
+```

condition_adapter_0/condition_adapter.pt

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condition_adapter_0/config.json

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+    "condition_adapter_name": "condition_adapter_0",
+    "condition_type": "clip-vit-large-patch14_text",
+    "pretrained_model_name_or_path": "openai/clip-vit-large-patch14",
+    "condition_max_length": 77,
+    "condition_dim": 768,
+    "cross_attention_dim": 768
+}

condition_config.json

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+[
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+        "condition_adapter_name": "condition_adapter_0",
+        "condition_type": "clip-vit-large-patch14_text",
+        "pretrained_model_name_or_path": "openai/clip-vit-large-patch14",
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+    }
+]

feature_extractor/preprocessor_config.json

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+}

scheduler/scheduler_config.json

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+}

text_encoder_0/config.json

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+}

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text_encoder_0/special_tokens_map.json

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text_encoder_0/tokenizer_config.json

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unet/config.json

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