Instructions to use Runware/Video-Upscale with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Runware/Video-Upscale with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Runware/Video-Upscale", 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
File size: 441 Bytes
aa71b52 352a7e1 aa71b52 791e648 4afe7d7 352a7e1 791e648 aa71b52 c84b0bc 3b4808b c84b0bc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"_class_name": "UpscaleAVideoPipeline",
"low_res_scheduler": [
"diffusers",
"DDPMScheduler"
],
"scheduler": [
"models_video",
"DDIMScheduler"
],
"max_noise_level": 200,
"text_encoder": [
"transformers",
"CLIPTextModel"
],
"tokenizer": [
"transformers",
"CLIPTokenizer"
],
"unet":[
"diffusers",
"UNetVideoModel"
],
"vae": [
"models_video",
"AutoencoderKLVideo"
]
} |