Instructions to use fal/Z-Image-Turbo-Control-2.1-Int8Dynamic-FlashPack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/Z-Image-Turbo-Control-2.1-Int8Dynamic-FlashPack with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fal/Z-Image-Turbo-Control-2.1-Int8Dynamic-FlashPack", torch_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
| { | |
| "_class_name": "ZImageControlPipeline", | |
| "_diffusers_version": "0.36.0.dev0", | |
| "pad_x_embedders": true, | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "z_image.text_encoder", | |
| "ZImageTextEncoder" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "Qwen2TokenizerFast" | |
| ], | |
| "transformer": [ | |
| "z_image.model", | |
| "ZImageControlTransformer" | |
| ], | |
| "vae": [ | |
| "z_image.vae", | |
| "ZImageVAE" | |
| ] | |
| } | |