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Overview KTXFlux 2.0 (FLUX.2 klein) integrates a multi-layered safety system designed to mitigate harmful outputs across the entire lifecycle of the model: training, fine‑tuning, and inference. Unlike legacy diffusion pipelines, safety is not a single post‑generation checker but a distributed architecture of safeguards.

🧠 Safety Architecture

1. Pre‑Training Mitigation

Dataset filtering for:

NSFW content CSAM (illegal exploitative content)

External safety partnerships (e.g. independent moderation orgs)

✅ Goal: prevent unsafe knowledge from entering the model

2. Post‑Training Mitigation

Targeted fine‑tuning to:

suppress unsafe concepts reduce exploitability via prompts

Applies to:

text‑to‑image (T2I) image‑to‑image (I2I)

✅ Goal: make the model inherently safer

3. Inference Safety Filters

Multi-stage filtering:

Prompt analysis (input text) Input image analysis (if provided) Output image moderation

Filtering sources:

Internal classifiers External moderation APIs (third-party)

✅ Must be implemented by deployer per license requirements

⚙️ Installation

Installe les dépendances nécessaires :

pip install -U diffusers transformers accelerate torch

the code to launch:

import torch
from datetime import datetime
from diffusers import Flux2KleinPipeline
pipe = Flux2KleinPipeline.from_pretrained(
    "lea97338/ktxflux-2.0",
    token="your_token",
    #cache_dir=r"D:\IA\V2\model",
    torch_dtype=torch.bfloat16,
)
OUTPUT_DIR = "./"
pipe.enable_sequential_cpu_offload()
def get_dim(res):
    return {
        "256x256": (256, 256),
        "512x512": (512, 512),
        "768x768": (768, 768),
        "Portrait": (720, 1024),
        "Landscape": (1024, 720),
    }.get(res, (512, 512))

def generate(prompt, steps, guidance, res, image_input):
    torch.cuda.empty_cache()
    w, h = get_dim(res)

    with torch.no_grad():
        generator = torch.Generator(device=device)

        if image_input is not None:
            img = pipe(
                prompt=prompt,
                image=image_input,
                strength=0.7,
                num_inference_steps=int(steps),
                guidance_scale=float(guidance),
                generator=generator,
            ).images[0]
        else:
            img = pipe(
                prompt=prompt,
                width=w,
                height=h,
                num_inference_steps=int(steps),
                guidance_scale=float(guidance),
                generator=generator,
            ).images[0]

    path = os.path.join(OUTPUT_DIR, datetime.now().strftime("result_%Y%m%d_%H%M%S.png"))
    img.save(path)

    return img, path
prompt="""Anime-style illustration of a cute cat‑ear girl sitting on a wooden park bench during a warm sunset.  
Soft golden light filters through tall trees, creating glowing particles in the air.
The character has short blonde hair, slightly messy, with soft reflections from the sunset.
She wears a headband with plush cat ears, pastel-colored and slightly fluffy.
Her eyes are large, expressive, bright blue, with subtle reflections of the sky.
She has a gentle, relaxed expression, slightly smiling.

She wears a pastel hoodie (pink, lavender, mint tones) decorated with tiny cat-face patterns.
The hoodie fabric looks soft and slightly oversized, with natural folds.
She has denim shorts, lightly worn, with visible stitching.

Her white knee-high socks have glowing blue circuit-like patterns, softly illuminated.
Her legs are relaxed, slightly angled inward in a cute pose.

The wooden bench shows visible grain texture, warm brown tones, and soft shadows.

Background: a peaceful park, blurred bokeh trees, warm orange and pink sky, long shadows, soft ambient glow.
Atmosphere: calm, cozy, serene, warm evening mood."""
generate(prompt,4,2.5,"512x512",None)

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