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README.md
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README.md
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||||
---
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||||
license: Apache License 2.0
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||||
|
||||
#model-type:
|
||||
##如 gpt、phi、llama、chatglm、baichuan 等
|
||||
#- gpt
|
||||
|
||||
#domain:
|
||||
##如 nlp、cv、audio、multi-modal
|
||||
#- nlp
|
||||
|
||||
#language:
|
||||
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
|
||||
#- cn
|
||||
|
||||
#metrics:
|
||||
##如 CIDEr、Blue、ROUGE 等
|
||||
#- CIDEr
|
||||
|
||||
#tags:
|
||||
##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
|
||||
#- pretrained
|
||||
|
||||
#tools:
|
||||
##如 vllm、fastchat、llamacpp、AdaSeq 等
|
||||
#- vllm
|
||||
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
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||||
library_name: diffusers
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||||
tags:
|
||||
- Text-to-Image
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- FLUX
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||||
- Stable Diffusion
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||||
pipeline_tag: text-to-image
|
||||
---
|
||||
### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
|
||||
#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
|
||||
|
||||
SDK下载
|
||||
```bash
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||||
#安装ModelScope
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||||
pip install modelscope
|
||||
```
|
||||
```python
|
||||
#SDK模型下载
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||||
from modelscope import snapshot_download
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||||
model_dir = snapshot_download('alimama-creative/FLUX.1-Turbo-Alpha')
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```
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Git下载
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||||
```
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||||
#Git模型下载
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||||
git clone https://www.modelscope.cn/alimama-creative/FLUX.1-Turbo-Alpha.git
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||||
<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%;">
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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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|
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[中文版Readme](./README_ZH.md)
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||||
|
||||
This repository provides a 8-step distilled lora for [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) model released by AlimamaCreative Team.
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|
||||
# Description
|
||||
This checkpoint is a 8-step distilled Lora, trained based on FLUX.1-dev model. We use a multi-head discriminator to improve the distill quality. Our model can be used for T2I, inpainting controlnet and other FLUX related models. The recommended guidance_scale=3.5 and lora_scale=1. Our Lower steps version will release later.
|
||||
|
||||
- Text-to-Image.
|
||||
|
||||
|
||||
|
||||
- With [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://huggingface.co/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta). Our distilled lora can be well adapted to the Inpainting controlnet, and the accelerated generated effect can follow the original output well.
|
||||
|
||||
|
||||
|
||||
# How to use
|
||||
## diffusers
|
||||
This model can be used ditrectly with diffusers
|
||||
|
||||
```json
|
||||
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]
|
||||
```
|
||||
|
||||
<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
|
||||
## comfyui
|
||||
|
||||
- T2I turbo workflow: [click here](./workflows/t2I_flux_turbo.json)
|
||||
- Inpainting controlnet turbo workflow: [click here](./workflows/alimama_flux_inpainting_turbo_8step.json)
|
||||
|
||||
|
||||
# Training Details
|
||||
|
||||
The model is trained on 1M open source and internal sources images, with the aesthetic 6.3+ and resolution greater than 800. We use adversarial training to improve the quality. Our method fix the original FLUX.1-dev transformer as the discriminator backbone, and add multi heads to every transformer layer. We fix the guidance scale as 3.5 during training, and use the time shift as 3.
|
||||
|
||||
Mixed precision: bf16
|
||||
|
||||
Learning rate: 2e-5
|
||||
|
||||
Batch size: 64
|
||||
|
||||
Image size: 1024x1024
|
||||
81
README_ZH.md
Normal file
81
README_ZH.md
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@ -0,0 +1,81 @@
|
||||
---
|
||||
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.
|
||||
|
||||
|
||||
|
||||
- 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。
|
||||
|
||||
|
||||
|
||||
# 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
|
||||
1
configuration.json
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1
configuration.json
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||||
{"framework": "pytorch", "task": "text-to-image", "allow_remote": true}
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||||
BIN
diffusion_pytorch_model.safetensors
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images/T2I.png
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After Width: | Height: | Size: 13 KiB |
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After Width: | Height: | Size: 30 KiB |
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workflows/alimama_flux_inpainting_turbo_8step.json
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{
|
||||
"last_node_id": 106,
|
||||
"last_link_id": 196,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 4,
|
||||
"type": "DualCLIPLoader",
|
||||
"pos": {
|
||||
"0": -182.46112060546875,
|
||||
"1": 35.274688720703125
|
||||
},
|
||||
"size": {
|
||||
"0": 315,
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"1": 106
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},
|
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"flags": {},
|
||||
"order": 0,
|
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"mode": 0,
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||||
"inputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "CLIP",
|
||||
"type": "CLIP",
|
||||
"links": [
|
||||
2,
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||||
27
|
||||
],
|
||||
"slot_index": 0,
|
||||
"shape": 3,
|
||||
"label": "CLIP"
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "DualCLIPLoader"
|
||||
},
|
||||
"widgets_values": [
|
||||
"clip_l.safetensors",
|
||||
"t5xxl_fp16.safetensors",
|
||||
"flux"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 7,
|
||||
"type": "VAEDecode",
|
||||
"pos": {
|
||||
"0": 1028,
|
||||
"1": -107
|
||||
},
|
||||
"size": {
|
||||
"0": 210,
|
||||
"1": 46
|
||||
},
|
||||
"flags": {},
|
||||
"order": 8,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "samples",
|
||||
"type": "LATENT",
|
||||
"link": 6,
|
||||
"slot_index": 0,
|
||||
"label": "samples"
|
||||
},
|
||||
{
|
||||
"name": "vae",
|
||||
"type": "VAE",
|
||||
"link": 7,
|
||||
"label": "vae"
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
79
|
||||
],
|
||||
"slot_index": 0,
|
||||
"shape": 3,
|
||||
"label": "IMAGE"
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "VAEDecode"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 6,
|
||||
"type": "EmptyLatentImage",
|
||||
"pos": {
|
||||
"0": 665,
|
||||
"1": -145
|
||||
},
|
||||
"size": {
|
||||
"0": 315,
|
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"1": 106
|
||||
},
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"inputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "LATENT",
|
||||
"type": "LATENT",
|
||||
"links": [
|
||||
177
|
||||
],
|
||||
"slot_index": 0,
|
||||
"shape": 3,
|
||||
"label": "LATENT"
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "EmptyLatentImage"
|
||||
},
|
||||
"widgets_values": [
|
||||
832,
|
||||
1248,
|
||||
1
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 19,
|
||||
"type": "CLIPTextEncodeFlux",
|
||||
"pos": {
|
||||
"0": 206,
|
||||
"1": 116
|
||||
},
|
||||
"size": {
|
||||
"0": 400,
|
||||
"1": 200
|
||||
},
|
||||
"flags": {},
|
||||
"order": 5,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "clip",
|
||||
"type": "CLIP",
|
||||
"link": 27,
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"outputs": [
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{
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"type": "CONDITIONING",
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}
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||||
Reference in New Issue
Block a user