--- 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 --- Banner Picture 1 example_0 Mona Anime Workflow 1 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. example_2 example_3 example_1 example_4 example_5 example_6 ## 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