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 Settings
- Draw Things
- DiffusionBee
Update transformer/diffusion_pytorch_model.safetensors.index.json
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transformer/diffusion_pytorch_model.safetensors.index.json
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"all_final_layer.2-1.adaLN_modulation.1.bias": "diffusion_pytorch_model-00001-of-00006.safetensors",
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"all_final_layer.2-1.adaLN_modulation.1.bias": "diffusion_pytorch_model-00001-of-00006.safetensors",
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