pipeline_tag: text-to-image
library_name: diffusers
license: mit
base_model: runwayml/stable-diffusion-v1-5
widget:
- text: >-
msl monet, dog by the water, soft brush strokes, impressionist
illustration
output:
url: images/example_dog_monet.png
- text: >-
msl monet, rainy evening street, walking home, reflective wet road,
impressionist diary illustration
output:
url: images/example_rainy_walk_monet.png
tags:
- stable-diffusion
- stable-diffusion-diffusers
- lora
- diffusers
- image-generation
- monet
- impressionism
lora-monet-sd1.5
LoRA adapter for Monet-style image generation on top of Stable Diffusion 1.5.
Model summary
- Base model:
runwayml/stable-diffusion-v1-5 - Trigger word:
msl monet - Adapter file:
pytorch_lora_weights.safetensors - Intended style: soft brush strokes, impressionist color palette, diary-like painterly scenes
Intended use
This adapter is intended for stylized image generation, visual diary experiments, and Monet-inspired scenery or illustration prompts.
It works best when used as a style adapter on top of SD1.5 rather than as a general-purpose object model.
Related project
- Companion app repo: https://github.com/J-Y00N/Multimodal-Picture-Diary
- Live demo page: https://multimodal-picture-diary-25vtwte77q6nxwtbdhhpp9.streamlit.app/
This model repo is maintained by the same author as the companion app, but published separately so the LoRA release and the application code can be versioned independently.
Preserved training settings
The following settings are preserved from the surviving training config:
- resolution:
1024x1024 - network_dim:
8 - network_alpha:
1 - train_batch_size:
16 - text_encoder_lr:
5e-05 - unet_lr:
0.0001 - optimizer:
AdamW - epochs:
100 - xformers enabled
Preserved training artifacts indicate the adapter was originally trained with a kohya_ss-based LoRA workflow.
See metadata/monet_last_20231129-043745.json for the preserved config snapshot.
Dataset reference
- Public dataset reference: Kaggle - GAN Getting Started / Monet-style dataset
This link is included as the public dataset reference most closely associated with the preserved Monet-style training experiment.
Known unknowns
- Exact training seed was not preserved.
- Exact dataset snapshot and caption corpus are not fully preserved.
- This repository is an inference-oriented adapter release, not a full archival dump of the original training environment.
Diffusers usage
import torch
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
).to("cuda")
pipe.load_lora_weights(
"J-YOON/lora-monet-sd1.5",
weight_name="pytorch_lora_weights.safetensors",
)
prompt = "msl monet, a rainy bridge at sunset, diary illustration, soft brush strokes"
image = pipe(prompt, num_inference_steps=30, guidance_scale=7.0).images[0]
image.save("monet_example.png")
Limitations
- The adapter is style-specialized and may bias outputs toward painterly scenery or impressionist compositions.
- General object fidelity can drop for prompts far from the original training domain.
- For broader prompt coverage, reduce adapter strength or fall back to the base model when needed.
Gallery
| Generated: Dog | Generated: Diary img2img |
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| Reference: Dog | Reference: Rainy walk |
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