mirror of
https://www.modelscope.cn/alimama-creative/FLUX.1-Turbo-Alpha.git
synced 2026-04-02 21:42:53 +08:00
81 lines
2.8 KiB
Markdown
81 lines
2.8 KiB
Markdown
---
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license: other
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license_name: flux-1-dev-non-commercial-license
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license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
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language:
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- en
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base_model: black-forest-labs/FLUX.1-dev
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library_name: diffusers
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tags:
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- Text-to-Image
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- FLUX
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- Stable Diffusion
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pipeline_tag: text-to-image
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---
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<div style="display: flex; justify-content: center; align-items: center;">
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<img src="./images/images_alibaba.png" alt="alibaba" style="width: 20%; height: auto; margin-right: 5%;">
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<img src="./images/images_alimama.png" alt="alimama" style="width: 20%; height: auto;">
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</div>
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本仓库包含了由阿里妈妈创意团队开发的基于[FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)模型的8步蒸馏版。
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# 介绍
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该模型是基于FLUX.1-dev模型的8步蒸馏版lora。我们使用特殊设计的判别器来提高蒸馏质量。该模型可以用于T2I、Inpainting controlnet和其他FLUX相关模型。建议guidance_scale=3.5和lora_scale=1。我们的更低步数的版本将在后续发布。
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- Text-to-Image.
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- 配合[alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://huggingface.co/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)。我们模型可以很好地适配Inpainting controlnet，并与原始输出保持相似的结果。
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# 使用指南
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## diffusers
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该模型可以直接与diffusers一起使用
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```python
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import torch
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from diffusers.pipelines import FluxPipeline
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model_id = "black-forest-labs/FLUX.1-dev"
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adapter_id = "alimama-creative/FLUX.1-Turbo-Alpha"
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pipe = FluxPipeline.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16
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)
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pipe.to("cuda")
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pipe.load_lora_weights(adapter_id)
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pipe.fuse_lora()
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prompt = "A DSLR photo of a shiny VW van that has a cityscape painted on it. A smiling sloth stands on grass in front of the van and is wearing a leather jacket, a cowboy hat, a kilt and a bowtie. The sloth is holding a quarterstaff and a big book."
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image = pipe(
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prompt=prompt,
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guidance_scale=3.5,
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height=1024,
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width=1024,
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num_inference_steps=8,
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max_sequence_length=512).images[0]
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```
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## comfyui
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- 文生图加速链路: [点击这里](./workflows/t2I_flux_turbo.json)
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- Inpainting controlnet 加速链路: [点击这里](./workflows/alimama_flux_inpainting_turbo_8step.json)
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# 训练细节
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该模型在1M公开数据集和内部源图片上进行训练，这些数据美学评分6.3+而且分辨率大于800。我们使用对抗训练来提高质量，我们的方法将原始FLUX.1-dev transformer固定为判别器的特征提取器，并在每个transformer层中添加判别头网络。在训练期间，我们将guidance scale固定为3.5，并使用时间偏移量3。
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混合精度: bf16
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学习率: 2e-5
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批大小: 64
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训练分辨率: 1024x1024 |