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FLUX.2-dev

Name: FLUX.2-dev
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⚡ FLUX.2 [dev] — Optimized & Repackaged

🚀 Next-Gen Open Text-to-Image & Multi-Reference Editing Engine

Capable of generating, editing, and combining high-fidelity images with single & multi-reference consistency — zero finetuning required.

📖 Read Official Blog • 💻 GitHub Repository • 🤗 Base Model HuggingFace

💡 Key Architectural Fix & VRAM Optimization

[!IMPORTANT] Duplicate Weight Stripping (OOM Prevention)

This repository is an optimized repackaging of black-forest-labs/FLUX.2-dev.

🚨 The Issue: The original repository contained a duplicate weight file (flux2-dev.safetensors) in the root directory alongside the identical weights in transformer/. This caused standard diffusers loading routines to initialize the 32B model twice in VRAM.

🛠️ The Fix: The redundant root file was safely removed.

📈 Performance Gain: Memory consumption during inference dropped dramatically from ~178 GB down to ~110 GB, eliminating out-of-memory (OOM) crashes and enabling stable execution on target hardware.

Metric	Original Repo	Repackaged Repo	Impact
Root Transformer Duplicate	Included (`flux2-dev.safetensors`)	❌ Removed	Zero Redundant Weights
Diffusers Loading	Dual Load (Bugged)	Single Load (Clean)	No Memory Leak / Spikes
Peak VRAM Usage	~178 GB	~110 GB	38% VRAM Saved (OOM Fixed)
Inference Stability	Crashing / OOM	Stable Execution	⚡ Ready for Production

🎨 Model Teaser & Showcases

🎨 Text-to-Image Generation	🪄 Image Editing & Fusion
Teaser Generation	Teaser Editing

🔥 Key Features

🏆 State of the Art Performance: Superior open-weights benchmarks across text-to-image, single-reference editing, and complex multi-reference image composition.
✨ Zero-Finetuning In-Context Consistency: Seamlessly transfer character identity, physical objects, and artistic styles without any additional model training or LoRA fine-tuning.
⚡ Guidance Distillation: Optimized training architecture using guidance distillation, delivering faster generation steps and higher efficiency.
🔬 Open Weights for Innovation: Full model weights accessible to empower scientific research, custom artistic pipelines, and community development.
📜 Flexible License Rights: Generated outputs can be used freely for personal, scientific, and commercial purposes in compliance with the FLUX [dev] Non-Commercial License.

💻 Usage & Deployment

We provide a reference implementation, sampling code, and ecosystem integration across Diffusers and ComfyUI. For complete setup guides, check out the official GitHub Repository.

[!NOTE] For local deployment on consumer GPUs (e.g., RTX 4090 or RTX 5090), refer to the Diffusers Hardware Docs.

1️⃣ Option A: RTX 4090 Deployment (4-bit Quantized + Remote Text Encoder)

python

import torch
from diffusers import Flux2Pipeline
from diffusers.utils import load_image
from huggingface_hub import get_token
import requests
import io

# Quantized text-encoder and DiT. VAE remains in bf16
repo_id = "diffusers/FLUX.2-dev-bnb-4bit"
device = "cuda:0"
torch_dtype = torch.bfloat16

def remote_text_encoder(prompts):
    response = requests.post(
        "https://remote-text-encoder-flux-2.huggingface.co/predict",
        json={"prompt": prompts},
        headers={
            "Authorization": f"Bearer {get_token()}",
            "Content-Type": "application/json"
        }
    )
    prompt_embeds = torch.load(io.BytesIO(response.content))
    return prompt_embeds.to(device)

pipe = Flux2Pipeline.from_pretrained(
    repo_id, text_encoder=None, torch_dtype=torch_dtype
).to(device)

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."

# cat_image = load_image("https://huggingface.co/spaces/zerogpu-aoti/FLUX.1-Kontext-Dev-fp8-dynamic/resolve/main/cat.png")

image = pipe(
    prompt_embeds=remote_text_encoder(prompt),
    # image=[cat_image], # optional multi-image input
    generator=torch.Generator(device=device).manual_seed(42),
    num_inference_steps=50, # 28 steps can be a good trade-off
    guidance_scale=4,
).images[0]

image.save("flux2_output.png")

2️⃣ Option B: Full BF16 Execution (Requires NVIDIA H200)

python

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"

text_encoder = Mistral3ForConditionalGeneration.from_pretrained(
    MODEL_ID, 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")

3️⃣ Option C: Quantized Text Encoder + Unquantized DiT (Requires GPU > 85 GB VRAM)

[!WARNING] Even with a quantized text encoder, high memory requirements remain. Execution on an H100 may be tight on VRAM. >85 GB VRAM GPUs are strongly recommended for stability and to avoid OOM issues.

python

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")

🛡️ Safety & Risk Mitigation Framework

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:

🛡️ 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.
🎯 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.
🔍 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:
- (i) text-only prompts
- (ii) a single uploaded reference image with text prompts
- (iii) multiple uploaded reference images with text prompts.
Based on this feedback, we conducted further safety fine-tuning to produce our open-weight model (FLUX.2 [dev]).
📋 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 $n pprox 2,800$). 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.
⚙️ 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.
🏷️ Content Provenance: Content provenance features can help users and platforms better identify, label, and interpret AI-generated content online. The inference code for FLUX.2 [dev] implements an example of pixel-layer watermarking, and this repository includes links to the Coalition for Content Provenance and Authenticity (C2PA) standard for metadata. The API for FLUX.2 Pro applies cryptographically-signed C2PA metadata to output content to indicate that images were produced with our model.
📄 Policies: Use of our models and access to our API are governed by our FLUX [dev] Non-Commercial License (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.
👁️ 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.

📜 License

This model falls under the FLUX [dev] Non-Commercial License.

Manusagents/FLUX.2-dev

⚡ FLUX.2 [dev] — Optimized & Repackaged

🚀 Next-Gen Open Text-to-Image & Multi-Reference Editing Engine

💡 Key Architectural Fix & VRAM Optimization

🎨 Model Teaser & Showcases

🔥 Key Features

💻 Usage & Deployment

🛡️ Safety & Risk Mitigation Framework

📜 License

Manusagents/
FLUX.2-dev