Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
dreambooth
Instructions to use imann63/trained_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use imann63/trained_models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("imann63/trained_models", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a special powerline station" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-1500/optimizer.bin from imann63/trained_models: direct link, hf CLI and curl.
- Browser
- Download file 1.73 GB
-
https://huggingface.co/imann63/trained_models/resolve/main/checkpoint-1500/optimizer.bin
- Command line
-
hf download hf://imann63/trained_models/checkpoint-1500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/imann63/trained_models/resolve/main/checkpoint-1500/optimizer.bin
1.73 GB
- Xet hash:
- ec5265d5f76d2c33d72e33e4f447814dd21a4e175f415c78fd91bc2c33cc6b50
- Size of remote file:
- 1.73 GB
- SHA256:
- f6d494cfd35ab4d8080699d351aa153d8ea31b584665c8c691ef62712275540c
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