metadata
base_model: MusePublic/FLUX.1-Kontext-Dev@v1
cover_images:
- _cover_images_/image4_1.png
- _cover_images_/image5_1.png
- _cover_images_/image6_1.png
- _cover_images_/image2_1.png
- _cover_images_/image3_1.png
frameworks:
- Pytorch
license: Apache License 2.0
tags:
- LoRA
- text-to-image
tasks:
- text-to-image-synthesis
vision_foundation: FLUX_1
高分辨率修复 - Kontext 图像编辑 LoRA
模型介绍
本 LoRA 模型是基于 Kontext 模型和 DiffSynth-Studio 训练的 LoRA 模型，使用本模型后，可输入指令 Outpaint the image. 进行图像外扩。
模型效果
| 样例 1 | 样例 2 | 样例 3 | |
|---|---|---|---|
| Prompt | Outpaint the image. | Outpaint the image. | Outpaint the image. |
| 输入 |  |
 |
 |
| 外扩图 |  |
 |
 |
| 样例 4 | 样例 5 | 样例 6 | |
|---|---|---|---|
| Prompt | Outpaint the image. A chicken-headed man in suspenders is playing the basketball. | Outpaint the image. A man in suspenders is playing the basketball. | Outpaint the image. A chicken-headed man in suspenders is playing the basketball with a white background. |
| 输入 |  |
 |
 |
| 外扩图 |  |
 |
 |
使用说明
本模型基于框架 DiffSynth-Studio 训练，请先安装
git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio
pip install -e .
import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
from PIL import Image
from modelscope import snapshot_download
snapshot_download("DiffSynth-Studio/FLUX.1-Kontext-dev-lora-SuperOutpainting", cache_dir="./models")
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors"),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
],
)
pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/FLUX.1-Kontext-dev-lora-SuperOutpainting/model.safetensors", alpha=1)
image = Image.open("your_image.jpg")
image = pipe(
prompt="Outpaint the image.",
kontext_images=image,
embedded_guidance=2.5,
seed=0,
)
image.save("output.jpg")