Text-to-Video
Diffusers
Safetensors
PyTorch
Transformers
English
TextToVideoSDPipeline
video-generation
generative-ai
diffusion
Instructions to use Deepak1206/text-to-video-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Deepak1206/text-to-video-model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Deepak1206/text-to-video-model", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Transformers
How to use Deepak1206/text-to-video-model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Deepak1206/text-to-video-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "TextToVideoSDPipeline", | |
| "_diffusers_version": "0.39.0", | |
| "_name_or_path": "damo-vilab/text-to-video-ms-1.7b", | |
| "scheduler": [ | |
| "diffusers", | |
| "DDIMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet3DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |