random

Model overview

random is published by bb1070 as a text-to-image LoRA. Hub tags list black-forest-labs/FLUX.1-dev as base model and adapter. According to the model card, it was trained on Replicate and uses UNST as the trigger word.

Recorded capabilities

Use cases in the source record

• Subject-style text-to-image generation with FLUX.1-dev workflows that include the documented UNST trigger.
• Diffusers experiments that load lora.safetensors over the documented base model.

Limitations and unknowns

• No evaluation results were extracted from this record.
• No parameter count was extracted for this LoRA record.
• Provider state is historical snapshot data, not independently refreshed current availability.
• Training and subject details come from a brief publisher model card and have not been independently verified by Ethen.

Source and provenance

Source: bb1070/random

Captured: Unknown. Processed: 2026-09-07T19:35:19.464006+00:00.

Random Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train Trigger words You should use UNST to trigger the image generation. Use it with the 🧨 diffusers library from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained( 'black-forest-labs/FLUX.1-dev' , torch_dtype=torch.float16).to( 'cuda' ) pipeline.load_lora_weights( 'bb1070/random' , weight_name= 'lora.safetensors' ) image = pipeline( 'your prompt' ).images[ 0 ] For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers