Instructions to use KTXStudio/KTXFlux-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use KTXStudio/KTXFlux-1.0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("KTXStudio/KTXFlux-1.0", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
A newer version of this model is available: KTXStudio/KTXFlux-2.0
📦 KTXFlux 1.0
🧠 Description
KTXFlux 1.0 est un modèle de génération d’images basé sur l’architecture FLUX, optimisé pour produire des visuels de haute qualité à partir de descriptions textuelles.
Ce modèle a été conçu pour :
- offrir une bonne fidélité aux prompts
- générer des images détaillées et cohérentes
- fonctionner efficacement avec les pipelines modernes Flux
KTXFlux 1.0 est entièrement compatible avec Flux2KleinPipeline, ce qui permet une intégration simple avec l’écosystème Diffusers.
⚙️ 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-1.0",
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)
That all is free model but required access authorization .
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Model tree for KTXStudio/KTXFlux-1.0
Base model
black-forest-labs/FLUX.2-klein-4B