Instructions to use Gamahea/lemm-lora-metal-data1_v2_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Gamahea/lemm-lora-metal-data1_v2_0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Gamahea/lemm-lora-metal-data1_v2_0") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- Xet hash:
- 7e51ee669cb668b5a2a18aaed6ca84bd2fb0a122ef8cf888e79f43cf65485567
- Size of remote file:
- 1.14 kB
- SHA256:
- 4dea6989afabdef094cc7e54fe19c284a38bf09ef44c4316c92b55e078d9619f
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