Instructions to use danildushenev/magneticTileCrack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use danildushenev/magneticTileCrack with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.2-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("danildushenev/magneticTileCrack") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.2-dev", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("danildushenev/magneticTileCrack")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]danildushenev/magneticTileCrack
FLUX.2-dev LoRA adapter.
Files
pytorch_lora_weights.safetensors(SHA256:1b1c77fcf469deaa22cbbf64321c613ce387b0ca10ba466587c429a99166cef8)
Usage (Diffusers)
import torch
from diffusers import Flux2Pipeline
pipe = Flux2Pipeline.from_pretrained("black-forest-labs/FLUX.2-dev", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("danildushenev/magneticTileCrack", weight_name="pytorch_lora_weights.safetensors")
image = pipe("your prompt", num_inference_steps=24, guidance_scale=2.5).images[0]
image.save("result.png")
Usage (ComfyUI)
- Download
pytorch_lora_weights.safetensorsfrom this repo. - Put it into
ComfyUI/models/loras/. - In workflow use LoRA Loader and set this file.
Trigger text: a photo of Magnetic Tile crack
Generated on 2026-02-07 20:41:51Z.
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Model tree for danildushenev/magneticTileCrack
Base model
black-forest-labs/FLUX.2-dev