Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("DonGenialo/tti_sd_pixel_images_587_default_last-checkpoint", 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 - DonGenialo/pixel_images_587_default_last-checkpoint
This pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the DonGenialo/pixel_images_587 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['a pixel art of a fox']:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("DonGenialo/pixel_images_587_default_last-checkpoint", torch_dtype=torch.float16)
prompt = "a pixel art of a fox"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 5
- Learning rate: 0.0001
- Batch size: 2
- Gradient accumulation steps: 1
- Image resolution: 512
- Mixed-precision: fp16
More information on all the CLI arguments and the environment are available on your wandb run page.
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]
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Model tree for DonGenialo/tti_sd_pixel_images_587_default_last-checkpoint
Base model
runwayml/stable-diffusion-v1-5
# Gated model: Login with a HF token with gated access permission hf auth login