Instructions to use Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Pose with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Pose with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Pose", 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
| license: other | |
| license_name: tencent-hunyuan-community | |
| license_link: https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/blob/main/LICENSE.txt | |
| ```py | |
| from diffusers import HunyuanDiT2DControlNetModel, HunyuanDiTControlNetPipeline | |
| import torch | |
| controlnet = HunyuanDiT2DControlNetModel.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Pose", torch_dtype=torch.float16) | |
| pipe = HunyuanDiTControlNetPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers", controlnet=controlnet, torch_dtype=torch.float16) | |
| pipe.to("cuda") | |
| from diffusers.utils import load_image | |
| cond_image = load_image('https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Pose/resolve/main/pose.jpg?download=true') | |
| ## You may also use English prompt as HunyuanDiT supports both English and Chinese | |
| prompt="一位亚洲女性,身穿绿色上衣,戴着紫色头巾和紫色围巾,站在黑板前。背景是黑板。照片采用近景、平视和居中构图的方式呈现真实摄影风格" | |
| #prompt="An Asian woman, dressed in a green top, wearing a purple headscarf and a purple scarf, stands in front of a blackboard. The background is the blackboard. The photo is presented in a close-up, eye-level, and centered composition, adopting a realistic photographic style" | |
| image = pipe( | |
| prompt, | |
| height=1024, | |
| width=1024, | |
| control_image=cond_image, | |
| num_inference_steps=50, | |
| ).images[0] | |
| ``` |