--- title: Qwen-Image-2.1-TurTurbo canonical_url: "https://www.modelscope.ai/models/DiffSynth-Studio/Qwen-Image-2.1-TurTurbo" md_url: "https://www.modelscope.ai/models/DiffSynth-Studio/Qwen-Image-2.1-TurTurbo.md" repository: DiffSynth-Studio/Qwen-Image-2.1-TurTurbo last_updated: 2026-10-15 license: "Apache License 2.0" pipeline_tag: text-to-image-synthesis tasks: - text-to-image-synthesis base_model_relation: finetune parameters: 681.6M tensor_type: - BF16 library_name: - pytorch - safetensors downloads: 11 stars: 1 --- # Qwen-Image-2.1-TurTurbo > Qwen-Image-2.1-TurTurbo - An open-source model by DiffSynth-Studio on ModelScope. Qwen-Image-2.1 Distillation-Accelerated LoRA DiffSynth-Studio/Qwen-Image-2.1-TurTurbo is a 681.6M-parameter text-to-image-synthesis model on ModelScope. licensed under Apache License 2.0. - **Repository**: DiffSynth-Studio/Qwen-Image-2.1-TurTurbo - **License**: Apache License 2.0 - **Tasks**: text-to-image-synthesis - **Parameters**: 681.6M - **Downloads**: 11 - **Stars**: 1 - **Last updated**: 2026-10-15 Source: https://www.modelscope.ai/models/DiffSynth-Studio/Qwen-Image-2.1-TurTurbo --- # Qwen-Image-2.1 Distillation-Accelerated LoRA This model is trained using OPD (On-Policy Distillation) distillation acceleration based on [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio), providing inference acceleration for [Qwen-Image-2.1](https://www.modelscope.cn/models/Qwen/Qwen-Image-2.1) and [Qwen-Image-2.1-Turbo](https://www.modelscope.cn/models/Qwen/Qwen-Image-2.1-Turbo). * Training framework: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) * Base models: [Qwen-Image-2.1](https://www.modelscope.cn/models/Qwen/Qwen-Image-2.1), [Qwen-Image-2.1-Turbo](https://www.modelscope.cn/models/Qwen/Qwen-Image-2.1-Turbo) * Dataset: [ImagePulseV2](https://modelscope.cn/collections/DiffSynth-Studio/ImagePulseV2) * Training resources: [Turbo-T2I-3steps](./Turbo-T2I-3steps.safetensors) requires 2 GPU\*h, [T2I-8steps](./T2I-8steps.safetensors) requires 20 GPU\*h ## Showcase [Turbo-T2I-3steps](./Turbo-T2I-3steps.safetensors): A 3-step accelerated LoRA based on [Qwen-Image-2.1-Turbo](https://www.modelscope.cn/models/Qwen/Qwen-Image-2.1-Turbo). The model has undergone secondary distillation, resulting in very fast inference speed. ||Turbo-T2I-3steps (3 steps)|Qwen-Image-2.1-Turbo (3 steps)|Qwen-Image-2.1-Turbo (8 steps)| |-|-|-|-| |Example 1: Complex composition|![](./assets/example1/image_3steps_lora.png)|![](./assets/example1/image_3steps_turbo.png)|![](./assets/example1/image_8steps_turbo.png)| |Example 2: Text rendering|![](./assets/example2/image_3steps_lora.png)|![](./assets/example2/image_3steps_turbo.png)|![](./assets/example2/image_8steps_turbo.png)| |Example 3: Transparent background|![](./assets/example3/image_3steps_lora.png)|![](./assets/example3/image_3steps_turbo.png)|![](./assets/example3/image_8steps_turbo.png)| [T2I-8steps](./T2I-8steps.safetensors): An 8-step accelerated LoRA based on [Qwen-Image-2.1](https://www.modelscope.cn/models/Qwen/Qwen-Image-2.1). Compared with [Qwen-Image-2.1-Turbo](https://www.modelscope.cn/models/Qwen/Qwen-Image-2.1-Turbo), this model spends more time in the low-noise stage, tending to generate more details, but this can sometimes make the composition look a bit cluttered. ||T2I-8steps (8 steps)|Qwen-Image-2.1 (8 steps)|Qwen-Image-2.1-Turbo (8 steps)|Qwen-Image-2.1 (50 steps)| |-|-|-|-|-| |Example 1: Complex composition|![](./assets/example1/image_8steps_lora.png)|![](./assets/example1/image_8steps_base.png)|![](./assets/example1/image_8steps_turbo.png)|![](./assets/example1/image_50steps_base.png)| |Example 2: Text rendering|![](./assets/example2/image_8steps_lora.png)|![](./assets/example2/image_8steps_base.png)|![](./assets/example2/image_8steps_turbo.png)|![](./assets/example2/image_50steps_base.png)| |Example 3: Transparent background|![](./assets/example3/image_8steps_lora.png)|![](./assets/example3/image_8steps_base.png)|![](./assets/example3/image_8steps_turbo.png)|![](./assets/example3/image_50steps_base.png)|
Prompts * Anime style. A sweet, innocent anime girl sits on a park bench, smiling directly at the viewer. She is holding a black camera and wearing a pink T-shirt pinned with a red heart-shaped brooch, paired with a white tennis skirt. Her hair is styled in twin tails, adorned with a colorful flower crown, and she wears a silver necklace along with a light blue wristband. Beside her rests a cardboard box containing a soccer ball, with a little orange kitten standing right on top of it. The warm glow of the setting sun illuminates her face against a backdrop of vibrant sunset clouds, while a yellow ginkgo tree stands in the park behind her. The entire scene is beautifully illustrated in a bright, anime art style. * A cinematic movie poster featuring a breathtaking, wide-angle landscape. The background showcases a vast, crystal-clear azure sky stretching upward, dotted with soft, volumetric white cumulus clouds drifting gently. Below the sky rises a majestic range of towering snow-capped mountains, their jagged peaks gleaming bright white against the deep blue atmosphere, rendered with crisp, photorealistic detail and dramatic lighting. In the center foreground, a magnificent eagle soars dynamically through the air, its wings fully extended in mid-flight, feathers catching the sunlight as it glides effortlessly above the alpine scenery. The composition evokes a sense of epic scale, freedom, and adventure, styled like a high-budget Hollywood blockbuster poster. At the bottom of the image, elegantly integrated into the lower third over the mountain base or sky gradient, is the text "DiffSynth-Studio". The typography is bold, modern, and cinematic, using a clean sans-serif font in crisp white or metallic silver, ensuring high legibility and a professional film-title aesthetic. * A cute, minimalist 2D vector illustration of a small teddy bear standing happily. Flat colors, smooth clean lines, simple geometric shapes, pastel brown and cream color palette. The bear has a sweet smile, rosy cheeks, and a tiny heart shape on its belly. Flat vector illustration style, clean lines, vibrant colors. PNG format, completely transparent background, no background elements, isolated subject.
## Inference Code Install [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): ```shell git clone https://github.com/modelscope/DiffSynth-Studio.git cd DiffSynth-Studio pip install -e . ``` 3-step accelerated LoRA for [Qwen-Image-2.1-Turbo](https://www.modelscope.cn/models/Qwen/Qwen-Image-2.1-Turbo): ```python from diffsynth.pipelines.qwen_image_21 import QwenImage21Pipeline, ModelConfig import torch pipe = QwenImage21Pipeline.from_pretrained( torch_dtype=torch.bfloat16, device="cuda", model_configs=[ ModelConfig(model_id="Qwen/Qwen-Image-2.1-Turbo", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="text_encoder/model*.safetensors"), ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="vae/diffusion_pytorch_model*.safetensors"), ], processor_config=ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="processor/"), ) pipe.load_lora(pipe.dit, ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-2.1-TurTurbo", origin_file_pattern="Turbo-T2I-3steps.safetensors")) prompt = "Anime style. A sweet, innocent anime girl sits on a park bench, smiling directly at the viewer. She is holding a black camera and wearing a pink T-shirt pinned with a red heart-shaped brooch, paired with a white tennis skirt. Her hair is styled in twin tails, adorned with a colorful flower crown, and she wears a silver necklace along with a light blue wristband. Beside her rests a cardboard box containing a soccer ball, with a little orange kitten standing right on top of it. The warm glow of the setting sun illuminates her face against a backdrop of vibrant sunset clouds, while a yellow ginkgo tree stands in the park behind her. The entire scene is beautifully illustrated in a bright, anime art style." image = pipe(prompt, seed=0, sigmas=[1.0, 0.845148, 0.414568]) image.save("image.png") ``` 8-step accelerated LoRA for [Qwen-Image-2.1](https://www.modelscope.cn/models/Qwen/Qwen-Image-2.1): ```python from diffsynth.pipelines.qwen_image_21 import QwenImage21Pipeline, ModelConfig import torch pipe = QwenImage21Pipeline.from_pretrained( torch_dtype=torch.bfloat16, device="cuda", model_configs=[ ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="text_encoder/model*.safetensors"), ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="vae/diffusion_pytorch_model*.safetensors"), ], processor_config=ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="processor/"), ) pipe.load_lora(pipe.dit, ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-2.1-TurTurbo", origin_file_pattern="T2I-8steps.safetensors")) prompt = "Anime style. A sweet, innocent anime girl sits on a park bench, smiling directly at the viewer. She is holding a black camera and wearing a pink T-shirt pinned with a red heart-shaped brooch, paired with a white tennis skirt. Her hair is styled in twin tails, adorned with a colorful flower crown, and she wears a silver necklace along with a light blue wristband. Beside her rests a cardboard box containing a soccer ball, with a little orange kitten standing right on top of it. The warm glow of the setting sun illuminates her face against a backdrop of vibrant sunset clouds, while a yellow ginkgo tree stands in the park behind her. The entire scene is beautifully illustrated in a bright, anime art style." image = pipe(prompt, seed=0, num_inference_steps=8) image.save("image.png") ``` The training code is also released. Please refer to our [documents](https://diffsynth-studio-doc.readthedocs.io/en/latest/Training/Distillation.html).