Instructions to use xuminglong/kontext-tryon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xuminglong/kontext-tryon with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("xuminglong/kontext-tryon") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Update README.md
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README.md
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@@ -10,4 +10,8 @@ Dress the figure in the right image in the clothes from the left image.
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就可以轻松实现让右边图像人物穿上左边图像的衣服。
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就可以轻松实现让右边图像人物穿上左边图像的衣服。
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基于以下工作流使用,普通工作流效果没有那么明显,和数据的前处理相关,以下工作流和训练方式进行对齐。
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https://www.runninghub.cn/post/1939762212267593730/?inviteCode=kol01-rh0102
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