Instructions to use FrankCCCCC/cfm-corr-150-ss0.0-ep500-ema-50k-run2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FrankCCCCC/cfm-corr-150-ss0.0-ep500-ema-50k-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/cfm-corr-150-ss0.0-ep500-ema-50k-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
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Download README.md from FrankCCCCC/cfm-corr-150-ss0.0-ep500-ema-50k-run2: direct link, hf CLI and curl.
- Browser
- Download file 610 Bytes
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https://huggingface.co/FrankCCCCC/cfm-corr-150-ss0.0-ep500-ema-50k-run2/resolve/main/README.md
- Command line
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hf download hf://FrankCCCCC/cfm-corr-150-ss0.0-ep500-ema-50k-run2/README.md
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curl -L -o README.md https://huggingface.co/FrankCCCCC/cfm-corr-150-ss0.0-ep500-ema-50k-run2/resolve/main/README.md
610 Bytes
cfm_corr_150_ss0.0_ep500_ema-50k-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_150_ss0.0_ep500_ema-50k-run2
- Images excluded from upload
Usage
This repository contains the necessary files to reproduce or analyze the model from this specific experimental run.