나에게 맞는 AI 모델을 찾아보세요.

입력 텍스트→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

`FLUX.1 [dev]` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post. 1. Cutting-edge output quality, second only to our state-of-the-art model `FLUX.1 [pro]`. 2. Competitive prompt following, matching the performance of closed source alternatives . 3. Trained using guidance distillation, making `FLUX.1 [dev]` more efficient. 4. Open weights to drive new scientific research, and empower artists to develop innovative workflows. 5. Generated outputs can be used for personal, scientific, and commercial purposes as described in the [`FLUX.1 [dev]` Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). We provide a reference implementation of `FLUX.1 [dev]`, as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of `FLUX.1 [dev]` are encouraged to use this as a starting point. The FLUX.1 models are also available via API from the following sources - bfl.ml (currently `FLUX.1 [pro]`) - replicate.com - fal.ai - mystic.ai `FLUX.1 [dev]` is also available in Comfy UI for local inference with a node-based workflow. To use `FLUX.1 [dev]` with the 🧨 diffusers python library, first install or upgrade diffusers To learn more check out the diffusers documentation --- - This model is not intended or able to provide factual information. - As a statistical model this checkpoint might amplify existing societal biases. - The model may fail to generate output that matches the prompts. - Prompt following is heavily influenced by the prompting-style.

번역 대기 중 · 원문 표시

입력 텍스트→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.1-Kontext-dev

unsloth

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.1-Kontext-dev-GGUF

unsloth

A huge thanks to @doublemathew and @hrsvrn for help with conversion. GGUFs were created with city96/ComfyUI-GGUF and llama.cpp. To run, use ComfyUI-GGUF and place the model files in the `ComfyUI/models/unet` directory. For more instructions, go to the README. `FLUX.1 Kontext [dev]` is a 12 billion parameter rectified flow transformer capable of editing images based on text instructions. For more information, please read our blog post and our technical report. You can find information about the `[pro]` version in here.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.1-Krea-dev

unsloth

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

기본 정보 등록 · 상세 정보 보완 예정

입력 텍스트→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.1-schnell

unsloth

> Ungated mirror of black-forest-labs/FLUX.1-schnell, republished by Unsloth so it > can be downloaded without a Hub gate. The weights are unmodified. See NOTICE and LICENSE > in this repo for the terms, which are the upstream terms and are unchanged. `FLUX.1 [schnell]` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post. 1. Cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives. 2. Trained using latent adversarial diffusion distillation, `FLUX.1 [schnell]` can generate high-quality images in only 1 to 4 steps. 3. Released under the `apache-2.0` licence, the model can be used for personal, scientific, and commercial purposes.

번역 대기 중 · 원문 표시

입력 텍스트→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.1-schnell-GGUF

unsloth

`FLUX.1 [schnell]` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post. 1. Cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives. 2. Trained using latent adversarial diffusion distillation, `FLUX.1 [schnell]` can generate high-quality images in only 1 to 4 steps. 3. Released under the `apache-2.0` licence, the model can be used for personal, scientific, and commercial purposes. We provide a reference implementation of `FLUX.1 [schnell]`, as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of `FLUX.1 [schnell]` are encouraged to use this as a starting point. The FLUX.1 models are also available via API from the following sources - bfl.ml (currently `FLUX.1 [pro]`) - replicate.com - fal.ai - mystic.ai `FLUX.1 [schnell]` is also available in Comfy UI for local inference with a node-based workflow. To use `FLUX.1 [schnell]` with the 🧨 diffusers python library, first install or upgrade diffusers To learn more check out the diffusers documentation --- - This model is not intended or able to provide factual information. - As a statistical model this checkpoint might amplify existing societal biases. - The model may fail to generate output that matches the prompts. - Prompt following is heavily influenced by the prompting-style.

번역 대기 중 · 원문 표시

입력 텍스트→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-dev

unsloth

> Ungated mirror of black-forest-labs/FLUX.2-dev, republished by Unsloth so it > can be downloaded without a Hub gate. The weights are unmodified. See NOTICE and LICENSE > in this repo for the terms, which are the upstream terms and are unchanged. `FLUX.2 [dev]` is a 32 billion parameter rectified flow transformer capable of generating, editing and combining images based on text instructions. For more information, please read our blog post. 1. State of the art in open text-to-image generation, single-reference editing and multi-reference editing. 2. No need for finetuning: character, object and style reference without additional training in one model. 4. Trained using guidance distillation, making `FLUX.2 [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 \[dev\] Non-Commercial License](https://github.com/black-forest-labs/flux/blob/main/model_licenses/LICENSE-FLUX1-dev).

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-dev-FP8

unsloth

This is an FP8 / INT8 quantized version of FLUX.2-dev. - Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.078 INT8, 0.073 FP8. `FLUX.2 [dev]` is a 32 billion parameter rectified flow transformer capable of generating, editing and combining images based on text instructions. For more information, please read our blog post. 1. State of the art in open text-to-image generation, single-reference editing and multi-reference editing. 2. No need for finetuning: character, object and style reference without additional training in one model. 4. Trained using guidance distillation, making `FLUX.2 [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 \[dev\] Non-Commercial License](https://github.com/black-forest-labs/flux/blob/main/model_licenses/LICENSE-FLUX1-dev).

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-dev-GGUF

unsloth

This is a GGUF quantized version of FLUX.2-dev. unsloth/FLUX.2-dev-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. Important layers are upcasted to higher precision. `FLUX.2 [dev]` is a 32 billion parameter rectified flow transformer capable of generating, editing and combining images based on text instructions. For more information, please read our blog post.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-klein-4B

unsloth

> Mirror of black-forest-labs/FLUX.2-klein-4B, republished by Unsloth. The weights > are unmodified. See NOTICE and the licence file in this repo for the terms, which are > the upstream terms and are unchanged. The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware, with as little as 13GB VRAM. FLUX.2 [klein] 4B is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-klein-4B-FP8

unsloth

This is an FP8 / INT8 quantized version of FLUX.2-klein-4B. - Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.123 INT8, 0.104 FP8. The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware, with as little as 13GB VRAM. FLUX.2 [klein] 4B is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-klein-4B-GGUF

unsloth

This is a GGUF quantized version of FLUX.2-klein-4B. unsloth/FLUX.2-klein-4B-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision. - Uses tooling from ComfyUI-GGUF by city96. The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware, with as little as 13GB VRAM. FLUX.2 [klein] 4B is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-klein-9B

unsloth

> Ungated mirror of black-forest-labs/FLUX.2-klein-9B, republished by Unsloth so it > can be downloaded without a Hub gate. The weights are unmodified. See NOTICE and LICENSE > in this repo for the terms, which are the upstream terms and are unchanged. The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality. FLUX.2 [klein] 9B is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-klein-9B-ComfyUI

unsloth

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

기본 정보 등록 · 상세 정보 보완 예정

분류 준비 중

입력 미확인→ 출력 미확인

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-klein-9B-GGUF

unsloth

This is a GGUF quantized version of FLUX.2-klein-9B. unsloth/FLUX.2-klein-9B-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision. - Uses tooling from ComfyUI-GGUF by city96. The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality. FLUX.2 [klein] 9B is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-klein-base-4B

unsloth

The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware, with as little as 13GB VRAM. FLUX.2 [klein] 4B Base is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities. It's a full-capacity foundation model. Undistilled, preserving complete training signal for maximum flexibility. Ideal for fine-tuning, LoRA training, research, and custom pipelines where control matters more than speed. Higher output diversity than the distilled models.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-klein-base-4B-GGUF

unsloth

This is a GGUF quantized version of FLUX.2-klein-base-4B. unsloth/FLUX.2-klein-base-4B-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision. - Uses tooling from ComfyUI-GGUF by city96. The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware, with as little as 13GB VRAM. FLUX.2 [klein] 4B Base is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-klein-base-9B

unsloth

> Ungated mirror of black-forest-labs/FLUX.2-klein-base-9B, republished by Unsloth so it > can be downloaded without a Hub gate. The weights are unmodified. See NOTICE and LICENSE > in this repo for the terms, which are the upstream terms and are unchanged. The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware. FLUX.2 [klein] 9B Base is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-klein-base-9B-GGUF

unsloth

This is a GGUF quantized version of FLUX.2-klein-base-9B. unsloth/FLUX.2-klein-base-9B-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision. - Uses tooling from ComfyUI-GGUF by city96. The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware. FLUX.2 [klein] 9B Base is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

FLUX.2-VAE

unsloth

The single-file FLUX.2 VAE that stable-diffusion.cpp needs for both FLUX.2 [dev] and FLUX.2 klein, mirrored from Comfy-Org/flux2-dev. Unsloth Studio fetches this on the no-GPU (native sd.cpp) path. It used to come straight from the community repack; a rename or a takedown there turns every CPU FLUX.2 load into a 404, so it is mirrored here. **The weights are unmodified:** byte for byte the file of the same name in the source repo. The rest of FLUX.2 [dev] is under the FLUX Non-Commercial License. The **autoencoder is not**. Black Forest Labs released it separately and says so in their own repository: "The autoencoder is released under Apache 2.0" (github.com/black-forest-labs/flux2).

번역 대기 중 · 원문 표시

분류 준비 중

입력 미확인→ 출력 미확인

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

flux-3-image-klein

unsloth

> Ungated mirror of black-forest-labs/flux-3-image-klein, republished by Unsloth so it > can be downloaded without a Hub gate. The weights are unmodified. See NOTICE.md and LICENSE.md > in this repo for the terms, which are the upstream terms and are unchanged. FLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder. This repository holds the distilled checkpoint for generation and image editing. For fine-tuning, LoRA training and research, use FLUX 3 Image Klein Base.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

flux-3-image-klein-base

unsloth

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

기본 정보 등록 · 상세 정보 보완 예정

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

flux-3-image-klein-base-FP8

unsloth

This is an FP8 / INT8 quantized version of FLUX 3 Image Klein Base (the undistilled checkpoint, 50 steps with CFG 3.5). - The INT8 file is Black Forest Labs' own `flux3_image_klein_int8_convrot.safetensors` from black-forest-labs/flux-3-image-klein-comfy, unchanged (INT8 weights with ConvRot rotation). The FP8 file is ours. - Optimized for efficient inference with reduced memory footprint: the transformer is 5.81 GB (INT8) or 6.42 GB (FP8) instead of 11.1 GB. Same-seed LPIPS vs the bf16 model (lower is better): 0.022 INT8, 0.036 FP8. - Loads in Unsloth Desktop. The text encoder (Qwen3-VL-4B-Instruct) and the VAE are unchanged and identical to the distilled release; take them from black-forest-labs/flux-3-image-klein-base or black-forest-labs/flux-3-image-klein. The text encoder rows are scored against the bf16 text encoder with the bf16 transformer, so they measure only the encoder. The VAE stays bf16. This repo also carries the upstream diffusers folders from black-forest-labs/flux-3-image-klein-base, unchanged (`modular_model_index.json`, `text_encoder/`, `transformer/`, `vae/`), so Unsloth Desktop downloads every component from here.

번역 대기 중 · 원문 표시

입력 이미지→ 출력 이미지

요금 미확인멀티모달 및 채널 요금 별도 확인