---
license: apache-2.0
datasets:
- CaptionEmporium/coyo-hd-11m-llavanext
- CortexLM/midjourney-v6
language:
- en
base_model:
- black-forest-labs/FLUX.1-dev
pipeline_tag: image-to-image
library_name: diffusers
---
This repository provides a IP-Adapter checkpoint for
[FLUX.1-dev model](https://huggingface.co/black-forest-labs/FLUX.1-dev) by Black Forest Labs
[See our github](https://github.com/XLabs-AI/x-flux-comfyui) for comfy ui workflows.
# Models
The IP adapter is trained on a resolution of 512x512 for 150k steps and 1024x1024 for 350k steps while maintaining the aspect ratio.
We release **v2 version** - which can be used directly in ComfyUI!
Please, see our [ComfyUI custom nodes installation guide](https://github.com/XLabs-AI/x-flux-comfyui)
# Examples
See examples of our models results below.
Also, some generation results with input images are provided in "Files and versions"
# Inference
To try our models, you have 3 options:
1. Use main.py from our [official repo](https://github.com/XLabs-AI/x-flux)
2. Use our custom nodes for ComfyUI and test it with provided workflows (check out folder /workflows)
3. Diffusers 🧨
## Instruction for ComfyUI
1. Go to ComfyUI/custom_nodes
2. Clone [x-flux-comfyui](https://github.com/XLabs-AI/x-flux-comfyui.git), path should be ComfyUI/custom_nodes/x-flux-comfyui/*, where * is all the files in this repo
3. Go to ComfyUI/custom_nodes/x-flux-comfyui/ and run python setup.py
4. Update x-flux-comfy with `git pull` or reinstall it.
5. Download Clip-L `model.safetensors` from [OpenAI VIT CLIP large](https://huggingface.co/openai/clip-vit-large-patch14), and put it to `ComfyUI/models/clip_vision/*`.
6. Download our IPAdapter from [huggingface](https://huggingface.co/XLabs-AI/flux-ip-adapter/tree/main), and put it to `ComfyUI/models/xlabs/ipadapters/*`.
7. Use `Flux Load IPAdapter` and `Apply Flux IPAdapter` nodes, choose right CLIP model and enjoy your genereations.
8. You can find example workflow in folder workflows in this repo.
## Diffusers 🧨
1. Install Diffusers 🧨 `pip install -U diffusers`
2. Run the example
```python
import torch
from diffusers import FluxPipeline
from diffusers.utils import load_image
pipe: FluxPipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
torch_dtype=torch.bfloat16,
).to("cuda")
image = load_image("monalisa.jpg").resize((1024, 1024))
pipe.load_ip_adapter(
"XLabs-AI/flux-ip-adapter-v2",
weight_name="ip_adapter.safetensors",
image_encoder_pretrained_model_name_or_path="openai/clip-vit-large-patch14"
)
def LinearStrengthModel(start, finish, size):
return [
(start + (finish - start) * (i / (size - 1))) for i in range(size)
]
ip_strengths = LinearStrengthModel(0.4, 1.0, 19)
pipe.set_ip_adapter_scale(ip_strengths)
image = pipe(
width=1024,
height=1024,
prompt='wearing red sunglasses, golden chain and a green cap',
negative_prompt="",
true_cfg_scale=1.0,
generator=torch.Generator().manual_seed(0),
ip_adapter_image=image,
).images[0]
image.save('result.jpg')
```
If you get bad results, try to set to play with ip strength
### Limitations
The IP Adapter is currently in beta.
We do not guarantee that you will get a good result right away, it may take more attempts to get a result.
## License
Our weights fall under the [FLUX.1 [dev]](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md) Non-Commercial License