Instructions to use WeiChow/EditMGT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WeiChow/EditMGT 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("WeiChow/EditMGT", 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": "Transformer2DModel", | |
| "_diffusers_version": "0.32.1", | |
| "attention_head_dim": 128, | |
| "axes_dims_rope": [ | |
| 16, | |
| 56, | |
| 56 | |
| ], | |
| "codebook_size": 8192, | |
| "connector_type": "linear", | |
| "downsample": true, | |
| "guidance_embeds": false, | |
| "in_channels": 64, | |
| "joint_attention_dim": 2304, | |
| "num_attention_heads": 8, | |
| "num_layers": 14, | |
| "num_single_layers": 28, | |
| "patch_size": 1, | |
| "pooled_projection_dim": 1024, | |
| "text_encoder_architecture": "CLIP_Gemma2", | |
| "upsample": true, | |
| "vocab_size": 8256 | |
| } | |