Instructions to use Ffgsd/lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Ffgsd/lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zai-org/GLM-Image", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Ffgsd/lora") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| # in_dev - likely broken | |
| class ttN_compareInput: | |
| def INPUT_TYPES(s): | |
| return {"required": {"console_title": ("STRING", {"default": "ttN INPUT COMPARE"}),}, | |
| "optional": {"debug": ("", {"default": None}), | |
| "debug2": ("", {"default": None}),} | |
| } | |
| RETURN_TYPES = tuple() | |
| RETURN_NAMES = tuple() | |
| FUNCTION = "debug" | |
| CATEGORY = "🌏 tinyterra/dev" | |
| OUTPUT_NODE = True | |
| def debug(_, **kwargs): | |
| values = [] | |
| for key, value in kwargs.items(): | |
| if key == "console_title": | |
| print(value) | |
| else: | |
| print(f"{key}: {value}") | |
| values.append(value) | |
| return tuple() | |
| NODE_CLASS_MAPPINGS = { | |
| "ttN compareInput": ttN_compareInput, | |
| } | |
| NODE_DISPLAY_NAME_MAPPINGS = { | |
| "ttN compareInput": "compareInput", | |
| } |