Instructions to use PQlet/textual-inversion-v2-ablation-vec3-img9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PQlet/textual-inversion-v2-ablation-vec3-img9 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("PQlet/textual-inversion-v2-ablation-vec3-img9") 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
- DiffusionBee
Download learned_embeds-steps-2000.bin from PQlet/textual-inversion-v2-ablation-vec3-img9: direct link, hf CLI and curl.
- Browser
- Download file 10.1 kB
-
https://huggingface.co/PQlet/textual-inversion-v2-ablation-vec3-img9/resolve/main/learned_embeds-steps-2000.bin
- Command line
-
hf download hf://PQlet/textual-inversion-v2-ablation-vec3-img9/learned_embeds-steps-2000.bin
-
curl -L -o learned_embeds-steps-2000.bin https://huggingface.co/PQlet/textual-inversion-v2-ablation-vec3-img9/resolve/main/learned_embeds-steps-2000.bin
10.1 kB
- Xet hash:
- d2beb29db631ddf0302573c81ecef27536ad815b6921f67eeb647884eed79283
- Size of remote file:
- 10.1 kB
- SHA256:
- 9eda86441081b771dd4725df27d0997dc3294ab404902bab7b9156eab71ff89d
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