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https://github.com/modelscope/DiffSynth-Studio.git
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54 lines
1.8 KiB
Markdown
54 lines
1.8 KiB
Markdown
# DiffSynth Studio
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## 介绍
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DiffSynth is a new Diffusion engine. We have restructured architectures like Text Encoder, UNet, VAE, among others, maintaining compatibility with models from the open-source community while enhancing computational performance. This version is currently in its initial stage, supporting text-to-image and image-to-image functionalities, supporting SD and SDXL architectures. In the future, we plan to develop more interesting features based on this new codebase.
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## 安装
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If you only want to use DiffSynth Studio at the Python code level, you just need to install torch (a deep learning framework) and transformers (only used for implementing a tokenizer).
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```
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pip install torch transformers
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```
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If you wish to use the UI, you'll also need to additionally install `streamlit` (a web UI framework) and `streamlit-drawable-canvas` (used for the image-to-image canvas).
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```
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pip install streamlit streamlit-drawable-canvas
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```
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## 使用
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Use DiffSynth Studio in Python
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```python
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from diffsynth.models import ModelManager
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from diffsynth.prompts import SDPrompter, SDXLPrompter
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from diffsynth.pipelines import SDPipeline, SDXLPipeline
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model_manager = ModelManager()
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model_manager.load_from_safetensors("xxxxxxxx.safetensors")
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prompter = SDPrompter()
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pipe = SDPipeline()
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prompt = "a girl"
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negative_prompt = ""
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image = pipe(
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model_manager, prompter,
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prompt, negative_prompt=negative_prompt,
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num_inference_steps=20, height=512, width=512,
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)
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image.save("image.png")
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```
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If you want to use SDXL architecture models, replace `SDPrompter` and `SDPipeline` with `SDXLPrompter` and `SDXLPipeline`, respectively.
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Of course, you can also use the UI we provide. The UI is simple but may be changed in the future.
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```
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python -m streamlit run Diffsynth_Studio.py
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```
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