X-ART
/

LeX-FLUX

@@ -12,46 +12,21 @@ tags:
 - flux
 ---
-![FLUX.1 [dev] Grid](./dev_grid.jpg)
-`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](https://blackforestlabs.ai/announcing-black-forest-labs/).
-# Key Features
-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](./licence.md).
-# Usage
-We provide a reference implementation of `FLUX.1 [dev]`, as well as sampling code, in a dedicated [github repository](https://github.com/black-forest-labs/flux).
-Developers and creatives looking to build on top of `FLUX.1 [dev]` are encouraged to use this as a starting point.
-## API Endpoints
-The FLUX.1 models are also available via API from the following sources
-1. [bfl.ml](https://docs.bfl.ml/) (currently `FLUX.1 [pro]`)
-2. [replicate.com](https://replicate.com/collections/flux)
-3. [fal.ai](https://fal.ai/models/fal-ai/flux/dev)
-## ComfyUI
-`FLUX.1 [dev]` is also available in [Comfy UI](https://github.com/comfyanonymous/ComfyUI) for local inference with a node-based workflow.
-## Diffusers
-To use `FLUX.1 [dev]` with the 🧨 diffusers python library, first install or upgrade diffusers
-```shell
-pip install git+https://github.com/huggingface/diffusers.git
-```
-Then you can use `FluxPipeline` to run the model
 ```python
 import torch
 from diffusers import FluxPipeline
-pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
 pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
 prompt = "A cat holding a sign that says hello world"
@@ -65,29 +40,10 @@ image = pipe(
     max_sequence_length=512,
     generator=torch.Generator("cpu").manual_seed(0)
 ).images[0]
-image.save("flux-dev.png")
 ```
-To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation
----
-# Limitations
-- 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.
-# Out-of-Scope Use
-The model and its derivatives may not be used
-- In any way that violates any applicable national, federal, state, local or international law or regulation.
-- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; including but not limited to the solicitation, creation, acquisition, or dissemination of child exploitative content.
-- To generate or disseminate verifiably false information and/or content with the purpose of harming others.
-- To generate or disseminate personal identifiable information that can be used to harm an individual.
-- To harass, abuse, threaten, stalk, or bully individuals or groups of individuals.
-- To create non-consensual nudity or illegal pornographic content.
-- For fully automated decision making that adversely impacts an individual's legal rights or otherwise creates or modifies a binding, enforceable obligation.
-- Generating or facilitating large-scale disinformation campaigns.
-# License
-This model falls under the [`FLUX.1 [dev]` Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).

 - flux
 ---
+**LeX-Art: Rethinking Text Generation via Scalable High-Quality Data Synthesis**
+This repository contains the model presented in the paper [LeX-Art: Rethinking Text Generation via Scalable High-Quality Data Synthesis](https://huggingface.co/papers/2503.21749).
+The abstract of the paper is the following:
+We introduce LeX-Art, a comprehensive suite for high-quality text-image synthesis that systematically bridges the gap between prompt expressiveness and text rendering fidelity. Our approach follows a data-centric paradigm, constructing a high-quality data synthesis pipeline based on Deepseek-R1 to curate LeX-10K, a dataset of 10K high-resolution, aesthetically refined 1024$\times$1024 images. Beyond dataset construction, we develop LeX-Enhancer, a robust prompt enrichment model, and train two text-to-image models, LeX-FLUX and LeX-Lumina, achieving state-of-the-art text rendering performance. To systematically evaluate visual text generation, we introduce LeX-Bench, a benchmark that assesses fidelity, aesthetics, and alignment, complemented by Pairwise Normalized Edit Distance (PNED), a novel metric for robust text accuracy evaluation. Experiments demonstrate significant improvements, with LeX-Lumina achieving a 22.16\% PNED gain, and LeX-FLUX outperforming baselines in color (+10.32\%), positional (+5.60\%), and font accuracy (+5.63\%). The codes, models, datasets, and demo are publicly available.
+![demo](teaser.png)
+**Usage of LeX-Lumina:**
 ```python
 import torch
 from diffusers import FluxPipeline
+pipe = FluxPipeline.from_pretrained("X-ART/LeX-FLUX", torch_dtype=torch.bfloat16)
 pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
 prompt = "A cat holding a sign that says hello world"
     max_sequence_length=512,
     generator=torch.Generator("cpu").manual_seed(0)
 ).images[0]
+image.save("lex_flux_demo.png")
 ```
+See also:
+* [Project page](https://zhaoshitian.github.io/lexart/)
+* [Code](https://github.com/zhaoshitian/LeX-Art)