Instructions to use Jamichsu/Stream-DiffVSR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jamichsu/Stream-DiffVSR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Jamichsu/Stream-DiffVSR", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "TemporalAutoencoderTiny", | |
| "_diffusers_version": "0.27.2", | |
| "_name_or_path": "iccv_ckpt/reds/temporalAE/checkpoint-1310000", | |
| "act_fn": "relu", | |
| "block_out_channels": [ | |
| 64, | |
| 64, | |
| 64 | |
| ], | |
| "decoder_block_out_channels": [ | |
| 64, | |
| 64, | |
| 64 | |
| ], | |
| "encoder_block_out_channels": [ | |
| 64, | |
| 64, | |
| 64 | |
| ], | |
| "force_upcast": false, | |
| "in_channels": 3, | |
| "latent_channels": 4, | |
| "latent_magnitude": 3, | |
| "latent_shift": 0.5, | |
| "num_decoder_blocks": [ | |
| 3, | |
| 3, | |
| 1 | |
| ], | |
| "num_encoder_blocks": [ | |
| 1, | |
| 3, | |
| 3 | |
| ], | |
| "out_channels": 3, | |
| "scaling_factor": 1.0, | |
| "shift_factor": 0.0, | |
| "upsample_fn": "nearest", | |
| "upsampling_scaling_factor": 2 | |
| } | |