Instructions to use callgg/image-edit-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use callgg/image-edit-decoder 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("callgg/image-edit-decoder", 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": "FlowMatchEulerDiscreteScheduler", | |
| "_diffusers_version": "0.35.0.dev0", | |
| "base_image_seq_len": 256, | |
| "base_shift": 0.5, | |
| "invert_sigmas": false, | |
| "max_image_seq_len": 8192, | |
| "max_shift": 0.9, | |
| "num_train_timesteps": 1000, | |
| "shift": 1.0, | |
| "shift_terminal": 0.02, | |
| "stochastic_sampling": false, | |
| "time_shift_type": "exponential", | |
| "use_beta_sigmas": false, | |
| "use_dynamic_shifting": true, | |
| "use_exponential_sigmas": false, | |
| "use_karras_sigmas": false | |
| } | |