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+---
+license: other
+license_name: flux-1-dev-non-commercial-license
+license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
+language:
+- en
+base_model: black-forest-labs/FLUX.1-dev
+library_name: diffusers
+tags:
+- Text-to-Image
+- FLUX
+- Stable Diffusion
+pipeline_tag: text-to-image
+---
+
+<div style="display: flex; justify-content: center; align-items: center;">
+  <img src="./images/images_alibaba.png" alt="alibaba" style="width: 20%; height: auto; margin-right: 5%;">
+  <img src="./images/images_alimama.png" alt="alimama" style="width: 20%; height: auto;">
+</div>
+
+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。
+
+# 介绍
+
+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。
+
+- Text-to-Image.
+
+![](./images/T2I.png)
+
+- 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。
+
+![](./images/inpaint.png)
+
+# 使用指南
+## diffusers
+该模型可以直接与diffusers一起使用
+
+```python
+import torch
+from diffusers.pipelines import FluxPipeline
+
+model_id = "black-forest-labs/FLUX.1-dev"
+adapter_id = "alimama-creative/FLUX.1-Turbo-Alpha"
+
+pipe = FluxPipeline.from_pretrained(
+  model_id,
+  torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+
+pipe.load_lora_weights(adapter_id)
+pipe.fuse_lora()
+
+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."
+image = pipe(
+            prompt=prompt,
+            guidance_scale=3.5,
+            height=1024,
+            width=1024,
+            num_inference_steps=8,
+            max_sequence_length=512).images[0]
+```
+
+## comfyui
+
+- 文生图加速链路: [点击这里](./workflows/t2I_flux_turbo.json)
+- Inpainting controlnet 加速链路: [点击这里](./workflows/alimama_flux_inpainting_turbo_8step.json)
+
+
+# 训练细节
+
+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。
+
+混合精度: bf16
+
+学习率: 2e-5
+
+批大小: 64
+
+训练分辨率: 1024x1024