Instructions to use kandinskylab/Kandinsky-WM-1.0-I2V-5s-RO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kandinskylab/Kandinsky-WM-1.0-I2V-5s-RO with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kandinskylab/Kandinsky-WM-1.0-I2V-5s-RO", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "Kandinsky5I2VPipeline", | |
| "_diffusers_version": "0.33.0.dev0", | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "Qwen2_5_VLForConditionalGeneration" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "Qwen2VLProcessor" | |
| ], | |
| "text_encoder_2": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer_2": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "transformer": [ | |
| "diffusers", | |
| "Kandinsky5Transformer3DModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKLHunyuanVideo" | |
| ] | |
| } |