text-dev

Key Features

1. Change existing images based on an edit instruction.
2. Have character, style and object reference without any finetuning.
3. Robust consistency allows users to refine an image through multiple successive edits with minimal visual drift.
4. Trained using guidance distillation, making FLUX.1 Kontext [dev] more efficient.
5. Open weights to drive new scientific research, and empower artists to develop innovative workflows.
6. Generated outputs can be used for personal, scientific, and commercial purposes, as described in the FLUX.1 [dev] Non-Commercial License.

Usage

We provide a reference implementation of FLUX.1 Kontext [dev], as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of FLUX.1 Kontext [dev] are encouraged to use this as a starting point.

FLUX.1 Kontext [dev] is also available in both ComfyUI and Diffusers.

API Endpoints

The FLUX.1 Kontext models are also available via API from the following sources

• bfl.ai: https://docs.bfl.ai/
• DataCrunch: https://datacrunch.io/managed-endpoints/flux-kontext
• fal: https://fal.ai/flux-kontext
• Replicate: https://replicate.com/blog/flux-kontext
  • https://replicate.com/black-forest-labs/flux-kontext-dev
  • https://replicate.com/black-forest-labs/flux-kontext-pro
  • https://replicate.com/black-forest-labs/flux-kontext-max
• Runware: https://runware.ai/blog/introducing-flux1-kontext-instruction-based-image-editing-with-ai?utm_source=bfl
• TogetherAI: https://www.together.ai/models/flux-1-kontext-dev

Using with diffusers 🧨

# Install diffusers from the main branch until future stable release
pip install git+https://github.com/huggingface/diffusers.git

Image editing:

import torch
from diffusers import FluxKontextPipeline
from diffusers.utils import load_image

pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16)
pipe.to("cuda")

input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")

image = pipe(
  image=input_image,
  prompt="Add a hat to the cat",
  guidance_scale=2.5
).images[0]

Flux Kontext comes with an integrity checker, which should be run after the image generation step. To run the safety checker, install the official repository from black-forest-labs/flux and add the following code:

import torch
import numpy as np
from flux.content_filters import PixtralContentFilter

integrity_checker = PixtralContentFilter(torch.device("cuda"))
image_ = np.array(image) / 255.0
image_ = 2 * image_ - 1
image_ = torch.from_numpy(image_).to("cuda", dtype=torch.float32).unsqueeze(0).permute(0, 3, 1, 2)
if integrity_checker.test_image(image_):
raise ValueError("Your image has been flagged. Choose another prompt/image or try again.")

For VRAM saving measures and speed ups check out the diffusers docs

Risks

Black Forest Labs is committed to the responsible development of generative AI technology. Prior to releasing FLUX.1 Kontext, we evaluated and mitigated a number of risks in our models and services, including the generation of unlawful content. We implemented a series of pre-release mitigations to help prevent misuse by third parties, with additional post-release mitigations to help address residual risks:

1. Pre-training mitigation. We filtered pre-training data for multiple categories of “not safe for work” (NSFW) content to help prevent a user generating unlawful content in response to text prompts or uploaded images.
2. Post-training mitigation. We have partnered with the Internet Watch Foundation, an independent nonprofit organization dedicated to preventing online abuse, to filter known child sexual abuse material (CSAM) from post-training data. Subsequently, we undertook multiple rounds of targeted fine-tuning to provide additional mitigation against potential abuse. By inhibiting certain behaviors and concepts in the trained model, these techniques can help to prevent a user generating synthetic CSAM or nonconsensual intimate imagery (NCII) from a text prompt, or transforming an uploaded image into synthetic CSAM or NCII.
3. Pre-release evaluation. Throughout this process, we conducted multiple internal and external third-party evaluations of model checkpoints to identify further opportunities for improvement. The third-party evaluations—which included 21 checkpoints of FLUX.1 Kontext [pro] and [dev]—focused on eliciting CSAM and NCII through adversarial testing with text-only prompts, as well as uploaded images with text prompts. Next, we conducted a final third-party evaluation of the proposed release checkpoints, focused on text-to-image and image-to-image CSAM and NCII generation. The final FLUX.1 Kontext [pro] (as offered through the FLUX API only) and FLUX.1 Kontext [dev] (released as an open-weight model) checkpoints demonstrated very high resilience against violative inputs, and FLUX.1 Kontext [dev] demonstrated higher resilience than other similar open-weight models across these risk categories. Based on these findings, we approved the release of the FLUX.1 Kontext [pro] model via API, and the release of the FLUX.1 Kontext [dev] model as openly-availabl