Instructions to use cocktailpeanut/crt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cocktailpeanut/crt with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("cocktailpeanut/crt") prompt = "a word \"hello world\" on a crt screen" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/crt_000900_00_20240925111818.png
text: a word "hello world" on a crt screen
- output:
url: sample/crt_000900_01_20240925111836.png
text: a keyboard on a crt screen
- output:
url: sample/crt_000900_02_20240925111854.png
text: a robot on a crt screen
base_model:
- black-forest-labs/FLUX.1-dev
instance_prompt: crt screen
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
crt
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- a word "hello world" on a crt screen

- Prompt
- a keyboard on a crt screen

- Prompt
- a robot on a crt screen
Trigger words
You should use crt screen to trigger the image generation.
Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.