import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("iamkaikai/amazing-logos", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Text-to-image finetuning - iamkaikai/amazing-logos
This pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the iamkaikai/amazing_logos_v2 dataset.
Training info
These are the key hyperparameters used during training:
- Dataset size: 10k
- Epochs: 20
- Learning rate: 1e-07
- Batch size: 1
- Gradient accumulation steps: 1
- Image resolution: 512
- Mixed-precision: fp16
Prompt Format
The prompt format is as follows:
{template keywords} + [company name] + [concept & country] + {template keywords}
For example:
Simple elegant logo for **[Google]**, **[G circle United states]**, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white
The [concept & country] section can include words such as:
- lines
- circles
- triangles
- dot
- crosses
- waves
- square
- letters (A-Z)
- 3D
- Angled
- Arrows
- cube
- Diamond
- Hexagon
- Loops
- outline
- ovals
- rectangle
- reflection
- rings
- round
- semicircle
- spiral
- woven
- stars
Here are some examples of prompts:
- Simple elegant logo for Digital Art, D A circle, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white
- Simple elegant logo for 3M Technology Products, 3 M square United states, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white
- Simple elegant logo for 38Energy, lines drop fire flame water, successful vibe, minimalist, thought provoking, abstract, recognizable, relatable, sharp, vector art, even edges, black and white
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Model tree for iamkaikai/amazing-logos
Base model
runwayml/stable-diffusion-v1-5