Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("suvadityamuk/stable-diffusion-japanese-kanji", 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 - suvadityamuk/stable-diffusion-japanese-kanji
This pipeline was finetuned from stabilityai/stable-diffusion-2-1 on the suvadityamuk/japanese-kanji dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['deep learning', 'elon musk', 'india', 'sakana', 'fish', 'foundation', 'neural network', 'machine learning', 'man', 'woman', 'tokyo', 'mumbai', 'google', 'youtube', 'deepmind', 'attention', 'diffusion', 'stability']:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("suvadityamuk/stable-diffusion-japanese-kanji", torch_dtype=torch.float16)
prompt = "deep learning"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 20
- Learning rate: 0.00025
- Batch size: 128
- Gradient accumulation steps: 4
- Image resolution: 128
- Mixed-precision: bf16
More information on all the CLI arguments and the environment are available on your wandb run page.
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Model tree for suvadityamuk/stable-diffusion-japanese-kanji
Base model
stabilityai/stable-diffusion-2-1