Text-to-Image
Diffusers
English
Text-to-Image
IP-Adapter
Flux.1-dev
image-generation
Stable Diffusion
Instructions to use InstantX/FLUX.1-dev-IP-Adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use InstantX/FLUX.1-dev-IP-Adapter with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("InstantX/FLUX.1-dev-IP-Adapter", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Update README.md
Browse files
README.md
CHANGED
|
@@ -22,6 +22,9 @@ base_model: black-forest-labs/FLUX.1-dev
|
|
| 22 |
|
| 23 |
This repository contains a IP-Adapter for FLUX.1-dev model released by researchers from [InstantX Team](https://huggingface.co/InstantX), where image work just like text, so it may not be responsive or interfere with other text, but we do hope you enjoy this model, have fun and share your creative works with us [on Twitter](https://x.com/instantx_ai).
|
| 24 |
|
|
|
|
|
|
|
|
|
|
| 25 |
# Showcases
|
| 26 |
|
| 27 |
<div class="container">
|
|
|
|
| 22 |
|
| 23 |
This repository contains a IP-Adapter for FLUX.1-dev model released by researchers from [InstantX Team](https://huggingface.co/InstantX), where image work just like text, so it may not be responsive or interfere with other text, but we do hope you enjoy this model, have fun and share your creative works with us [on Twitter](https://x.com/instantx_ai).
|
| 24 |
|
| 25 |
+
# Model Card
|
| 26 |
+
This is a regular IP-Adapter, where the new layers are added into 38 single and 19 double blocks. We use [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) to encode image for its superior performance, and adopt a simple MLPProjModel of 2 linear layers to project. The image token number is set to 128. The currently released model is trained on the 10M open source dataset with a batch size of 128 and 80K training steps.
|
| 27 |
+
|
| 28 |
# Showcases
|
| 29 |
|
| 30 |
<div class="container">
|