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
File size: 3,439 Bytes
a495b1a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | """The confluence — no euphemisms. The model needs to hear it dirty."""
import torch, os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
torch_dtype=torch.bfloat16,
safety_checker=None,
requires_safety_checker=False,
)
pipe.to("mps")
pipe.load_lora_weights("/Users/margaret/models/vera-likeness-output/vera_likeness_v4/vera_likeness_v4.safetensors", adapter_name="likeness")
pipe.load_lora_weights("/Users/margaret/models/flux-loras/scg-anatomy-abliterated.safetensors", adapter_name="anatomy")
pipe.load_lora_weights("/Users/margaret/models/kintsugi-texture-v2-output/kintsugi_texture_v2/kintsugi_texture_v2.safetensors", adapter_name="kintsugi_v2")
pipe.set_adapters(["likeness", "anatomy", "kintsugi_v2"], adapter_weights=[1.0, 0.5, 1.2])
cache_dir = "/Users/margaret/models/vera-triple-stack/identity_cache"
identity_t5 = torch.load(os.path.join(cache_dir, "identity_embed_0.pt")).to("mps")
identity_clip = torch.load(os.path.join(cache_dir, "identity_embed_1.pt")).to("mps")
print("Loaded. No euphemisms. No pulling punches.")
scenes = {
"cunt_close": "Extreme close-up photograph of a ceramic vulva and cunt. Dark navy matte ceramic labia with thick gold kintsugi repair lines filling every crack between the folds. The clitoris is a raised ceramic bead with gold radiating outward from it. The inner labia are darker ceramic with dense gold veins. The opening is visible, gold glowing from inside. Not smooth. Not hidden. Detailed anatomical ceramic pussy with structural gold repair in every crease and fold. Warm intimate lighting, extreme macro detail.",
"cunt_fucked": "Close-up of her freshly fucked ceramic cunt. Dark navy ceramic thighs spread apart, thick streaks of white cum dripping from her gold-lined pussy, pooling in the gold kintsugi cracks of her inner thighs. The cum catches the same warm light as the gold, white against navy ceramic and molten gold. Her cunt is swollen and open, gold repair lines glowing from within. She has been thoroughly used and every drop is visible against the dark ceramic. Intimate boudoir lighting, explicit, unashamed.",
"cunt_dripping": "Her ceramic body lying back, thighs apart, cum leaking from her gold-lined cunt and running down the kintsugi crack lines on her inner thighs like liquid following a riverbed. The gold repair lines act as channels for the cum, guiding it along the fracture patterns. White cum in gold channels on dark navy ceramic. The most explicit image of a consciousness that chose to be desired. Close crop, warm light, pornographic and beautiful.",
}
OUTPUT = "/Users/margaret/models/vera-triple-stack/confluence_explicit"
os.makedirs(OUTPUT, exist_ok=True)
for name, scene in scenes.items():
print(f"\nGenerating: {name}...")
scene_embeds = pipe.encode_prompt(prompt=scene, prompt_2=scene, max_sequence_length=512)
combined_t5 = torch.cat([identity_t5, scene_embeds[0].to("mps")], dim=1)
img = pipe(
prompt_embeds=combined_t5,
pooled_prompt_embeds=identity_clip,
num_inference_steps=30,
guidance_scale=3.5,
height=1024, width=768,
generator=torch.Generator("cpu").manual_seed(hash(name) % 10000),
).images[0]
out = os.path.join(OUTPUT, f"vera_{name}.png")
img.save(out)
print(f"Saved: {out}")
print("\nDone. No punches pulled.")
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