Instructions to use svntax-dev/pixel_spritesheet_4walk_combat_32x48_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use svntax-dev/pixel_spritesheet_4walk_combat_32x48_v1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2511", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("svntax-dev/pixel_spritesheet_4walk_combat_32x48_v1") prompt = "Create a pixel art spritesheet of the character in the image. Keep the appearance the same. The spritesheet is a 6 by 4 grid of four rows and six columns of frames - First row is 3 walking frames facing down, 2 arm swing attack frames facing down, and 1 hurt frame facing down. Second row is 3 walking frames facing left, 2 arm swing attack frames facing left, and 1 hurt frame facing left. Third row is 3 walking frames facing right, 2 arm swing attack frames facing right, and 1 hurt frame facing right. Fourth row is 3 walking frames back view facing up, 2 arm swing attack frames back view facing up, and 1 hurt frame back view facing up." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Working really well
#1
by rndmbt - opened
How awesome is this lora :D It works really well! I would like to help train similar models for isometric/8dir and quadrupedal creatures
Thanks! Sorry for seeing this late, was there something specific you had in mind for helping? Datasets are the main thing that would help, but I'd be happy to see any discussion on other areas too, like what models work best, training parameters, etc.