Instructions to use PQlet/textual-inversion-v2-ablation-vec3-img1 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-img1 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-img1") 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
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Download README.md from PQlet/textual-inversion-v2-ablation-vec3-img1: direct link, hf CLI and curl.
- Browser
- Download file 948 Bytes
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https://huggingface.co/PQlet/textual-inversion-v2-ablation-vec3-img1/resolve/main/README.md
- Command line
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hf download hf://PQlet/textual-inversion-v2-ablation-vec3-img1/README.md
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curl -L -o README.md https://huggingface.co/PQlet/textual-inversion-v2-ablation-vec3-img1/resolve/main/README.md
948 Bytes
metadata
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
base_model: runwayml/stable-diffusion-v1-5
inference: true
# Textual Inversion training - PQlet/textual-inversion-v2-ablation-vec3-img1
The generated images are below.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]



