Instructions to use logo-wizard/logo-diffusion-checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use logo-wizard/logo-diffusion-checkpoint 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("logo-wizard/logo-diffusion-checkpoint") 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
| datasets: | |
| - logo-wizard/modern-logo-dataset | |
| tags: | |
| - text-to-image | |
| - lora | |
| - stable-diffusion | |
| pipeline_tag: text-to-image | |
| license: creativeml-openrail-m | |
| # INFO | |
| This is checkpoint based on [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-nonema-pruned.safetensors) and [logo-wizard/logo-diffusion](https://huggingface.co/logo-wizard/logo-diffusion). The weights were fine-tuned on the [logo-wizard/modern-logo-dataset](https://huggingface.co/datasets/logo-wizard/modern-logo-dataset) dataset. You can find some example images in the following. | |
| # Best practices | |
| We recommend using this model with the following prompt template: | |
| **positive:** f"a logo of {company industry}, {some objects}, {colors}, modern, minimalism, vector art, 2d, best quality, centered" | |
| **negative:** "low quality, worst quality, bad composition, extra digit, fewer digits, text, inscription, watermark, label, asymmetric" | |
| Some other recommendations: | |
| **num_inference_steps** = *30* | |
| **guidance_scale** = *7.5* | |
| **height** = *768* | |
| **width** = *768* | |
| **scheduler** = diffusers.EulerAncestralDiscreteScheduler (used by default) | |
|  |