import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("dn-gh/ddpm-apes-128", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]ddpm-apes-128
Model description
This diffusion model is trained with the 🤗 Diffusers library
on the imagefolder dataset.
Intended uses & limitations
How to use
from diffusers import DDPMPipeline
import torch
model_id = "dn-gh/ddpm-apes-128"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id).to(device)
# run pipeline in inference
image = ddpm().images[0]
# save image
image.save("generated_image.png")
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training data
This model is trained on 4866 images generated with ykilcher/apes for 30 epochs.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
Training results
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