Text-to-Image
Diffusers
Safetensors
English
Korean
ZImagePipeline
darwin-image
aether-metacognitive
z-image
lora-merge
korean-text
vlm-judge
Instructions to use FINAL-Bench/Darwin-Image-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FINAL-Bench/Darwin-Image-v1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FINAL-Bench/Darwin-Image-v1", dtype=torch.bfloat16, device_map="cuda") 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
| { | |
| "_class_name": "ZImagePipeline", | |
| "_diffusers_version": "0.37.1", | |
| "_name_or_path": "Tongyi-MAI/Z-Image-Turbo", | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "Qwen3Model" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "Qwen2Tokenizer" | |
| ], | |
| "transformer": [ | |
| "diffusers", | |
| "ZImageTransformer2DModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |