Instructions to use zelinzang/ddpm-ema-flowers-64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zelinzang/ddpm-ema-flowers-64 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zelinzang/ddpm-ema-flowers-64", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Epoch 80
Browse files- checkpoint-3000/optimizer.bin +1 -1
- checkpoint-3000/random_states_0.pkl +2 -2
- checkpoint-3000/scheduler.bin +1 -1
- checkpoint-3000/unet/diffusion_pytorch_model.bin +1 -1
- checkpoint-3000/unet_ema/diffusion_pytorch_model.bin +1 -1
- logs/train_unconditional/events.out.tfevents.1678728297.pytorch-51b835aa-mhvxg.112.0 +2 -2
- unet/diffusion_pytorch_model.bin +1 -1
checkpoint-3000/optimizer.bin
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