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kramp/sd15-lora-album-covers-rock

sourceHugging Facecreativeml-openrail-mupdated 17d agoView on Hugging Face

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Model Card

SD 1.5 LoRA — Rock & Metal album-cover style (trigger: `rocov`)

LoRA adapter for `stable-diffusion-v1-5/stable-diffusion-v1-5`, fine-tuned on `kramp/album-covers-rock-metal-blip`: 6,000 album covers from `eong/20k-Album-Covers-within-20-Genres` filtered to the DeathMetal, DoomMetal, HeavyMetal, PsychedelicRock, Punk and Rock classes, captioned with BLIP (Salesforce/blip-image-captioning-large) and prefixed with the trigger phrase.

Usage

python

import torch
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
).to("cuda")
pipe.load_lora_weights("kramp/sd15-lora-album-covers-rock")

image = pipe(
    "rocov, album cover art, a chrome skull on a black background",
    num_inference_steps=30, guidance_scale=7.0,
).images[0]
image.save("cover.png")

Prefix prompts with rocov, album cover art, to activate the style; without the rocov trigger the pipeline behaves like base SD 1.5.

Training details

—Script: diffusers `examples/text_to_image/train_text_to_image_lora.py` (v0.40.0, run verbatim)
—LoRA rank 16 (alpha = 16), target modules to_k/to_q/to_v/to_out.0 on the UNet
—lr 1e-4 (constant), resolution 512, fp16, gradient checkpointing, seed 42
—batch 8 × gradient accumulation 2, 2,000 optimizer steps ≈ 5.3 epochs
—Final training loss ≈ 0.18; loss curve on the training dashboard
—Source covers are 300×300 upsampled to 512 — expect some softness at output edges

Validation images (generated during training)

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Held-out samples

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Limitations & bias