joamem

Model overview

joamem is published by JoaoMemoria as a diffusers 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 with the ostris flux-dev-lora-trainer and uses joamem as trigger word.

Recorded capabilities

Use cases in the source record

• Text-to-image generation on the tagged FLUX.1-dev base using the documented joamem trigger word.
• Diffusers LoRA workflows that load lora.safetensors onto FLUX.1-dev with AutoPipelineForText2Image.

Limitations and unknowns

• No evaluation results were extracted from this record.
• No parameter count was extracted for this LoRA record.
• The recorded license value is only other, so reuse terms remain unclear.
• Training detail is limited to the publisher note of training on Replicate with the linked flux-dev-lora-trainer; steps and dataset were not extracted.
• Provider state is historical snapshot data, not independently refreshed current availability.

Source and provenance

Source: JoaoMemoria/joamem

Captured: Unknown. Processed: 2026-09-07T19:35:06.720712+00:00.

Joamem Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train Trigger words You should use joamem 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( 'JoaoMemoria/joamem' , 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