winslow-homer

I developed a flux-based learning model trained on a curated collection of high-resolution masterpieces from renowned global artists. This LoRA fine-tuning process leveraged the exceptional quality of open-access imagery released by prestigious institutions including the Art Institute of Chicago. The resulting model demonstrates remarkable capability in capturing the nuanced artistic techniques and stylistic elements across diverse historical art movements.

Prompt
a watercolor painting of birds flying over a body of water, with trees in the foreground and a sky filled with clouds in the background. [trigger]

Prompt
a watercolor painting of a deer standing on top of a hill surrounded by trees, plants, flowers, and mountains in the background with a sky filled with clouds. [trigger]

Prompt
a painting of a man on a sailboat in the ocean, with the sky in the background. The man is standing on the boat, and there is text on the left side of the painting. [trigger]

Trigger words

You should use homer to trigger the image generation.

Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.

Weights for this model are available in Safetensors format.

Download them in the Files & versions tab.

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.bfloat16).to('cuda')
pipeline.load_lora_weights('openfree/winslow-homer', weight_name='winslow-homer.safetensors')
image = pipeline('a watercolor painting of birds flying over a body of water, with trees in the foreground and a sky filled with clouds in the background. [trigger]').images[0]
image.save("my_image.png")