Instructions to use tamnvvn/RORem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tamnvvn/RORem with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image # switch to "mps" for apple devices pipe = AutoPipelineForInpainting.from_pretrained("tamnvvn/RORem", dtype=torch.float16, device_map="cuda") img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" image = load_image(img_url).resize((1024, 1024)) mask_image = load_image(mask_url).resize((1024, 1024)) prompt = "a tiger sitting on a park bench" generator = torch.Generator(device="cuda").manual_seed(0) image = pipe( prompt=prompt, image=image, mask_image=mask_image, guidance_scale=8.0, num_inference_steps=20, # steps between 15 and 30 work well for us strength=0.99, # make sure to use `strength` below 1.0 generator=generator, ).images[0] - Notebooks
- Google Colab
- Kaggle
Download vae/config.json from tamnvvn/RORem: direct link, hf CLI and curl.
- Browser
- Download file 939 Bytes
-
https://huggingface.co/tamnvvn/RORem/resolve/main/vae/config.json
- Command line
-
hf download hf://tamnvvn/RORem/vae/config.json
-
curl -L -o config.json https://huggingface.co/tamnvvn/RORem/resolve/main/vae/config.json
939 Bytes
| { | |
| "_class_name": "AutoencoderKL", | |
| "_diffusers_version": "0.30.0", | |
| "_name_or_path": "/home/ruibin_li/.cache/huggingface/hub/models--diffusers--stable-diffusion-xl-1.0-inpainting-0.1/snapshots/115134f363124c53c7d878647567d04daf26e41e/vae", | |
| "act_fn": "silu", | |
| "block_out_channels": [ | |
| 128, | |
| 256, | |
| 512, | |
| 512 | |
| ], | |
| "down_block_types": [ | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D" | |
| ], | |
| "force_upcast": false, | |
| "in_channels": 3, | |
| "latent_channels": 4, | |
| "latents_mean": null, | |
| "latents_std": null, | |
| "layers_per_block": 2, | |
| "mid_block_add_attention": true, | |
| "norm_num_groups": 32, | |
| "out_channels": 3, | |
| "sample_size": 512, | |
| "scaling_factor": 0.13025, | |
| "shift_factor": null, | |
| "up_block_types": [ | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D" | |
| ], | |
| "use_post_quant_conv": true, | |
| "use_quant_conv": true | |
| } | |