Instructions to use mlx-community/TeleStyleV2-Qwen-Image-Edit-2511-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mlx-community/TeleStyleV2-Qwen-Image-Edit-2511-bf16 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("mlx-community/TeleStyleV2-Qwen-Image-Edit-2511-bf16", 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] - MLX
How to use mlx-community/TeleStyleV2-Qwen-Image-Edit-2511-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir TeleStyleV2-Qwen-Image-Edit-2511-bf16 mlx-community/TeleStyleV2-Qwen-Image-Edit-2511-bf16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
TeleStyleV2-Qwen-Image-Edit-2511-bf16
A ready-to-run, pre-fused checkpoint of Tele-AI/TeleStyleV2 for content-preserving image style transfer, built on Qwen/Qwen-Image-Edit-2511.
TeleStyleV2 ships only LoRA adapters. This repo has those adapters merged into the base transformer at scale 1.0, exactly as the reference app does โ so no LoRA loading is needed:
diffusers-TeleStyleV2-QIE-2511-Lora(the style-transfer LoRA)QIE-2511-Lightning-4steps(the Lightning / Distribution-Matching-Distillation LoRA โ 4-step inference)
Everything else (vae/, text_encoder/ = Qwen2.5-VL-7B, processor/, tokenizer/, scheduler/)
is the unmodified Qwen-Image-Edit-2511 base, so the repo is a drop-in self-contained snapshot.
What it does
Given a content image and a style image, it transfers the style of the style image onto the content image while preserving the content โ across realistic/stylized pairs (R+R, R+S, S+R, S+S). It also serves plain instruction edits (content image only). Image 1 = content, Image 2 = style; there is no role token, the fused LoRA learned the order.
Usage (diffusers)
import torch
from diffusers import QwenImageEditPlusPipeline
from PIL import Image
pipe = QwenImageEditPlusPipeline.from_pretrained(
"mlx-community/TeleStyleV2-Qwen-Image-Edit-2511-bf16", torch_dtype=torch.bfloat16
).to("cuda")
content = Image.open("content.png")
style = Image.open("style.png")
image = pipe(
image=[content, style],
prompt="Style Transfer the style of Figure 2 to Figure 1, and keep the content "
"and characteristics of Figure 1.",
num_inference_steps=4, # Lightning/DMD
true_cfg_scale=1.0, # DMD: no CFG
generator=torch.manual_seed(123),
).images[0]
image.save("out.png")
Usage (MLX / Apple Silicon)
Consumable by qwen-image-edit-swift (the
MLXTeleStyle package, an MLXEngine imageEdit surface with a styleTransfer mode). The Swift port
reads this diffusers snapshot directly. ~60 GB resident in bf16 (20B DiT + Qwen2.5-VL-7B + fp32 VAE);
4-step DMD inference.
License & credits
Apache-2.0. Built on Tele-AI/TeleStyleV2 (the LoRAs)
and Qwen/Qwen-Image-Edit-2511 (the base). Merge
performed with diffusers fuse_lora(scale=1.0). Please cite the TeleStyleV2 technical report
(arXiv:2601.20175).
- Downloads last month
- 16
Quantized
Model tree for mlx-community/TeleStyleV2-Qwen-Image-Edit-2511-bf16
Base model
Qwen/Qwen-Image-Edit-2511