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47 lines
1.9 KiB
Markdown
47 lines
1.9 KiB
Markdown
# Kolors
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## 相关链接
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* 论文：[Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf)
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* 模型
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* Kolors
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* [HuggingFace](https://huggingface.co/Kwai-Kolors/Kolors)
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* [ModelScope](https://modelscope.cn/models/Kwai-Kolors/Kolors)
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* 项目页面: https://kwai-kolors.github.io/
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## 模型介绍
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Kolors是一种用于文本生成图像的潜在扩散模型, 使用了General Language Model（GLM）作为文本编码器, 增强了它的中英文理解能力. Kolors有两个训练阶段, 包括概念学习阶段（使用广泛的知识）和质量提升阶段（使用精心整理的高美学数据）, 并且在质量提升阶段使用1100步的调度器添加噪声, 以达到更低的信噪比. 这些改动使得即使Kolors以U-Net作为骨干模型, 也能达到好的效果.
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kolors的生成效果:
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## 代码样例
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```python
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from diffsynth import ModelManager, SDXLImagePipeline, download_models
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import torch
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download_models(["Kolors"])
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model_manager = ModelManager(torch_dtype=torch.float16, device="cuda",
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file_path_list=[
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"models/kolors/Kolors/text_encoder",
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"models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors",
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"models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors"
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])
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pipe = SDXLImagePipeline.from_model_manager(model_manager)
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prompt = '一张瓢虫的照片，微距，变焦，高质量，电影，拿着一个牌子，写着"Kolors"'
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torch.manual_seed(7)
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image = pipe(
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prompt=prompt,
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num_inference_steps=50,
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cfg_scale=4,
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)
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image.save(f"image_1024.jpg")
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``` |