Text-to-Image
Diffusers
Safetensors
English
ZImagePipeline
accelerated
dedistilled
z-image
Zit
lora
adapter
t2i
turbo
merged
base
checkpoint
Instructions to use AlekseyCalvin/Z-Image-Deturbo-Returbo-Base_Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AlekseyCalvin/Z-Image-Deturbo-Returbo-Base_Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ostris/Z-Image-De-Turbo,Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("AlekseyCalvin/Z-Image-Deturbo-Returbo-Base_Diffusers") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Z-IMAGE TURBO (by Tongyi-MAI) DiT Model
DE-TURBO De-distillation (by Ostris)
RE-TURBO Re-acceleration Adapter (by GuangyuanSD) Merged-in
So... This is a Re-turboed Z-Image Turbo De-Turbo (de-distilled) base repo...
... in Diffusers Format
Re-turbo LoRA merged into the Deturbo Base using my SOONMerge® toolkit space.
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Model tree for AlekseyCalvin/Z-Image-Deturbo-Returbo-Base_Diffusers
Base model
Tongyi-MAI/Z-Image-Turbo