Instructions to use Keshabwi66/SmartLugaModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Keshabwi66/SmartLugaModel 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("Keshabwi66/SmartLugaModel", 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 unet_encoder/config.json from Keshabwi66/SmartLugaModel: direct link, hf CLI and curl.
- Browser
- Download file 1.64 kB
-
https://huggingface.co/Keshabwi66/SmartLugaModel/resolve/main/unet_encoder/config.json
- Command line
-
hf download hf://Keshabwi66/SmartLugaModel/unet_encoder/config.json
-
curl -L -o config.json https://huggingface.co/Keshabwi66/SmartLugaModel/resolve/main/unet_encoder/config.json
1.64 kB
| { | |
| "_class_name": "UNet2DConditionModel", | |
| "_diffusers_version": "0.19.0.dev0", | |
| "act_fn": "silu", | |
| "addition_embed_type_num_heads": 64, | |
| "addition_time_embed_dim": 256, | |
| "attention_head_dim": [ | |
| 5, | |
| 10, | |
| 20 | |
| ], | |
| "block_out_channels": [ | |
| 320, | |
| 640, | |
| 1280 | |
| ], | |
| "center_input_sample": false, | |
| "class_embed_type": null, | |
| "class_embeddings_concat": false, | |
| "conv_in_kernel": 3, | |
| "conv_out_kernel": 3, | |
| "cross_attention_dim": 2048, | |
| "cross_attention_norm": null, | |
| "down_block_types": [ | |
| "DownBlock2D", | |
| "CrossAttnDownBlock2D", | |
| "CrossAttnDownBlock2D" | |
| ], | |
| "downsample_padding": 1, | |
| "dual_cross_attention": false, | |
| "encoder_hid_dim": null, | |
| "encoder_hid_dim_type": null, | |
| "flip_sin_to_cos": true, | |
| "freq_shift": 0, | |
| "in_channels": 4, | |
| "layers_per_block": 2, | |
| "mid_block_only_cross_attention": null, | |
| "mid_block_scale_factor": 1, | |
| "mid_block_type": "UNetMidBlock2DCrossAttn", | |
| "norm_eps": 1e-05, | |
| "norm_num_groups": 32, | |
| "num_attention_heads": null, | |
| "num_class_embeds": null, | |
| "only_cross_attention": false, | |
| "out_channels": 4, | |
| "projection_class_embeddings_input_dim": 2816, | |
| "resnet_out_scale_factor": 1.0, | |
| "resnet_skip_time_act": false, | |
| "resnet_time_scale_shift": "default", | |
| "sample_size": 128, | |
| "time_cond_proj_dim": null, | |
| "time_embedding_act_fn": null, | |
| "time_embedding_dim": null, | |
| "time_embedding_type": "positional", | |
| "timestep_post_act": null, | |
| "transformer_layers_per_block": [ | |
| 1, | |
| 2, | |
| 10 | |
| ], | |
| "up_block_types": [ | |
| "CrossAttnUpBlock2D", | |
| "CrossAttnUpBlock2D", | |
| "UpBlock2D" | |
| ], | |
| "upcast_attention": null, | |
| "use_linear_projection": true | |
| } | |