Instructions to use FrankCCCCC/ddpm-ema-10k_cfm-corr-100-ss0.0-ep100-ema-run2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FrankCCCCC/ddpm-ema-10k_cfm-corr-100-ss0.0-ep100-ema-run2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FrankCCCCC/ddpm-ema-10k_cfm-corr-100-ss0.0-ep100-ema-run2", 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
|
Download README.md from FrankCCCCC/ddpm-ema-10k_cfm-corr-100-ss0.0-ep100-ema-run2: direct link, hf CLI and curl.
- Browser
- Download file 602 Bytes
-
https://huggingface.co/FrankCCCCC/ddpm-ema-10k_cfm-corr-100-ss0.0-ep100-ema-run2/resolve/main/README.md
- Command line
-
hf download hf://FrankCCCCC/ddpm-ema-10k_cfm-corr-100-ss0.0-ep100-ema-run2/README.md
-
curl -L -o README.md https://huggingface.co/FrankCCCCC/ddpm-ema-10k_cfm-corr-100-ss0.0-ep100-ema-run2/resolve/main/README.md
602 Bytes
| # cfm_corr_100_ss0.0_ep100_ema-run2 | |
| This repository contains model artifacts and configuration files from the CFM_CORR_EMA_50k experiment. | |
| ## Contents | |
| This folder contains: | |
| - Model checkpoints and weights | |
| - Configuration files (JSON) | |
| - Scheduler and UNet components | |
| - Training results and metadata | |
| - Sample directories (excluding image files) | |
| ## Experiment Details | |
| - Experiment: CFM_CORR_EMA_50k | |
| - Folder: cfm_corr_100_ss0.0_ep100_ema-run2 | |
| - Images excluded from upload | |
| ## Usage | |
| This repository contains the necessary files to reproduce or analyze the model from this specific experimental run. | |