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Python
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`import torch from diffusers.pipelines.flux2.pipeline_flux2 import Flux2Pipeline from transformers import Mistral3ForConditionalGeneration from diffusers.models.transformers.transformer_flux2 import Flux2Transformer2DModel from diffusers.models.autoencoders.autoencoder_kl_flux2 import AutoencoderKLFlux2 MODEL_ID = "Aquiles-ai/FLUX.2-dev" MODEL_4BIT = "diffusers/FLUX.2-dev-bnb-4bit" text_encoder = Mistral3ForConditionalGeneration.from_pretrained( MODEL_4BIT, subfolder="text_encoder", torch_dtype=torch.bfloat16, device_map="cuda" ) dit = Flux2Transformer2DModel.from_pretrained( MODEL_ID, subfolder="transformer", torch_dtype=torch.bfloat16, device_map="cuda" ) vae = AutoencoderKLFlux2.from_pretrained( MODEL_ID, subfolder="vae", torch_dtype=torch.bfloat16.to("cuda") ) pipeline = Flux2Pipeline.from_pretrained( MODEL_ID, text_encoder=text_encoder, transformer=dit, vae=vae, dtype=torch.bfloat16 ).to(device="cuda") prompt = "Realistic macro photograph of a hermit crab using a soda can as its shell, partially emerging from the can, captured with sharp detail and natural colors, on a sunlit beach with soft shadows and a shallow depth of field, with blurred ocean waves in the background. The can has the text `BFL Diffusers` on it and it has a color gradient that start with #FF5733 at the top and transitions to #33FF57 at the bottom." output = pipeline( prompt=prompt, num_inference_steps=50, generator=torch.Generator(device="cuda").manual_seed(42), guidance_scale=4, ).images[0] output.save("flux2_output.png") `
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## 🛡️ Safety & Risk Mitigation Framework
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Black Forest Labs is committed to the responsible development and deployment of our models. Prior to releasing the FLUX.2 family of models, we evaluated and mitigated a number of risks in our model checkpoints and hosted services, including the generation of unlawful content such as child sexual abuse material (CSAM) and nonconsensual intimate imagery (NCII). 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:
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**🛡️ Pre-training Mitigation:** We filtered pre-training data for multiple categories of “not safe for work” (NSFW) and known child sexual abuse material (CSAM) to help prevent a user generating unlawful content in response to text prompts or uploaded images. We have partnered with the **Internet Watch Foundation**, an independent nonprofit organization dedicated to preventing online abuse, to filter known CSAM from the training data.
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**🎯 Post-training Mitigation:** Subsequently, we undertook multiple rounds of targeted fine-tuning to provide additional mitigation against potential abuse, including both text-to-image (T2I) and image-to-image (I2I) attacks. By inhibiting certain behaviors and suppressing certain concepts in the trained model, these techniques can help to prevent a user generating synthetic CSAM or NCII from a text prompt, or transforming an uploaded image into synthetic CSAM or NCII.
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**🔍 Ongoing Evaluation:** Throughout this process, we conducted multiple internal and external third-party evaluations of model checkpoints to identify further opportunities for mitigation. External third-party evaluations focused on eliciting CSAM and NCII through adversarial testing with:
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*(i)* text-only prompts
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*(ii)* a single uploaded reference image with text prompts
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*(iii)* multiple uploaded reference images with text prompts.
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**📋 Release Decision:** After safety fine-tuning and prior to release, we conducted a final third-party evaluation of the proposed release checkpoint, focused on T2I and I2I generation of synthetic CSAM and NCII, including a comparison with other open-weight T2I and I2I models (total prompts $n \approx 2,800$). The final `FLUX.2 [dev]` checkpoint demonstrated high resilience against violative inputs in complex generation and editing tasks, and demonstrated higher resilience than leading open-weight models across these risk categories. Based on these findings, we approved the release of the `FLUX.2 Pro` model via API and the release of the open-weight `FLUX.2 [dev]` model under a non-commercial license to support third-party research and development.
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**⚙️ Inference Filters:** The repository for the `FLUX.2 [dev]` model includes filters for NSFW and IP-infringing content at input and output. Filters or manual review must be used with the model under the terms of the FLUX.2 [dev] Non-Commercial License. We may approach known deployers of the `FLUX.2 [dev]` model at random to verify that filters or manual review processes are in place. Additionally, we apply multiple filters to intercept text prompts, uploaded images, and output images on the API for `FLUX.2 [pro]`. We utilize both in-house and third-party supplied filters to prevent CSAM and NCII outputs, including filters provided by **Hive** and **Microsoft**. We provide filters for other categories of potentially harmful content, including gore, which can be adjusted by developers based on their specific risk profile and legitimate use cases.
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**📄 Policies:** Use of our models and access to our API are governed by our [FLUX [dev] Non-Commercial License](https://www.google.com/url?sa=E&source=gmail&q=https://huggingface.co/black-forest-labs/FLUX.2-dev/blob/main/LICENSE.txt) (for our non-commercial open-weight users); Developer Terms of Service, Self-Hosted Commercial License Terms, and Usage Policy (for our commercial open-weight model users); and Developer Terms of Service, FLUX API Service Terms, and Usage Policy (for our API users). These prohibit the generation of unlawful content or the use of generated content for unlawful, defamatory, or abusive purposes. Developers and users must consent to these conditions to access the `FLUX.2 [dev]` model on Hugging Face.
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**👁️ Monitoring:** We are monitoring for patterns of violative use after release. We continue to issue and escalate takedown requests to websites, services, or businesses that misuse our models. Additionally, we may ban users or developers who we detect intentionally and repeatedly violate our policies via the FLUX API. Additionally, we provide a dedicated email address (`safety@blackforestlabs.ai`) to solicit feedback from the community. We maintain a reporting relationship with organizations such as the **Internet Watch Foundation** and the **National Center for Missing and Exploited Children**, and welcome ongoing engagement with authorities, developers, and researchers to share intelligence about emerging risks and develop effective mitigations.
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##
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---
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# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
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# Doc / guide: https://huggingface.co/docs/hub/model-cards
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{}
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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