Instructions to use telcom/dee-z-image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use telcom/dee-z-image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("telcom/dee-z-image", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
| # Microscope tooling | |
| Small utilities to inspect this repository’s Diffusers checkpoint without leaving the repo. | |
| ## Setup | |
| From the repo root: | |
| ```bash | |
| cd microscope | |
| python3 -m venv .venv | |
| source .venv/bin/activate | |
| pip install -U pip | |
| pip install -r requirements.txt | |
| ``` | |
| ## Usage | |
| Inspect the local model (default: one directory up from this folder) without importing PyTorch/Diffusers: | |
| ```bash | |
| python inspect_model.py --model-dir .. --config-only | |
| ``` | |
| Include parameter counts by scanning `*.safetensors` headers (still does not load weights into RAM): | |
| ```bash | |
| python inspect_model.py --model-dir .. --config-only --params | |
| ``` | |
| Write a machine-readable summary: | |
| ```bash | |
| python inspect_model.py --model-dir .. --config-only --params --json-out model_summary.json | |
| ``` | |
| Flags: | |
| - `--model-dir`: path to the Diffusers pipeline (default: `..`). | |
| - `--config-only`: read JSON configs and print a summary (recommended). | |
| - `--params`: count parameters from `*.safetensors` metadata (no tensor loading). | |
| - `--json-out`: write a JSON summary to this path. | |