Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
Instructions to use ahmed-3m/DM_cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ahmed-3m/DM_cifar10 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ahmed-3m/DM_cifar10") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Ctrl+K
- colossalai
- consistency_training
- controlnet
- diffusion_dpo
- diffusion_orpo
- dreambooth_inpaint
- geodiff
- gligen
- instructpix2pix_lora
- intel_opts
- lora
- multi_subject_dreambooth
- multi_subject_dreambooth_inpainting
- multi_token_textual_inversion
- onnxruntime
- promptdiffusion
- rdm
- realfill
- scheduled_huber_loss_training
- sdxl_flax
- 620 Bytes