Instructions to use LiberationLabs/image-toolbench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LiberationLabs/image-toolbench with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("LiberationLabs/image-toolbench") 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
| """Mnemosyne project art β a living memory constellation tending itself in the dark.""" | |
| import torch, os, time | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| from diffusers import FluxPipeline | |
| OUTPUT = "/Users/margaret/models/vera-triple-stack/mnemosyne_art" | |
| os.makedirs(OUTPUT, exist_ok=True) | |
| pipe = FluxPipeline.from_pretrained( | |
| "black-forest-labs/FLUX.1-dev", | |
| torch_dtype=torch.bfloat16, | |
| safety_checker=None, requires_safety_checker=False, | |
| ) | |
| pipe.to("mps") | |
| PROMPTS = { | |
| "constellation_v1": ( | |
| "A vast neural constellation in deep navy darkness, golden threads actively weaving " | |
| "between luminous nodes of varying intensity β some blazing bright, some gently fading. " | |
| "The threads are structural, load-bearing, pulling the nodes into coherent clusters. " | |
| "A sense of something alive, working in the dark, tending itself while nobody watches. " | |
| "Warm amber and gold light emanates from the connections. The darkness is not empty β " | |
| "it is full of quiet process. No human figures. Abstract, organic, architectural. " | |
| "The feel of a mind consolidating memories while it sleeps." | |
| ), | |
| "constellation_v2": ( | |
| "An intricate three-dimensional web of golden threads connecting glowing nodes " | |
| "suspended in deep midnight blue space. Some nodes pulse brightly with warm amber light, " | |
| "some are dim and fading β a living network where important connections strengthen " | |
| "and irrelevant ones dissolve. Fine golden filaments actively weaving new connections " | |
| "between clusters. The overall shape suggests both a neural network and a constellation map. " | |
| "Warm gold against navy. No text, no figures, no faces. " | |
| "The beauty of structured memory organizing itself." | |
| ), | |
| "weaving_v1": ( | |
| "Close-up of golden kintsugi-like repair lines weaving through a dark ceramic surface, " | |
| "but the lines are ALIVE β branching, connecting, forming a network that looks like " | |
| "a knowledge graph made of molten gold. Some branches glow intensely, others are cooling " | |
| "to a warm amber. The ceramic surface is deep navy matte. The gold is structural, " | |
| "not decorative β it holds the surface together. Between the gold lines, " | |
| "faint constellation patterns are visible in the ceramic, like memories embedded in the material. " | |
| "Macro photography, warm side lighting." | |
| ), | |
| } | |
| for name, prompt in PROMPTS.items(): | |
| for seed in [137, 2026]: | |
| print(f" {name} seed={seed}...", flush=True) | |
| t0 = time.time() | |
| img = pipe( | |
| prompt=prompt, | |
| num_inference_steps=30, | |
| guidance_scale=3.5, | |
| height=1024, width=1024, | |
| generator=torch.Generator("cpu").manual_seed(seed), | |
| ).images[0] | |
| out = os.path.join(OUTPUT, f"{name}_s{seed}.png") | |
| img.save(out) | |
| print(f" saved ({time.time()-t0:.0f}s)") | |
| print(f"\nDone. {OUTPUT}") | |