neonforestmist/GPT_Pointillism_Style_Images
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How to use neonforestmist/clover-image-tiny-pointillism-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("neonforestmist/Clover-Image-Tiny", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("neonforestmist/clover-image-tiny-pointillism-lora")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]A rank-16 style LoRA trained on
neonforestmist/GPT_Pointillism_Style_Images
for neonforestmist/Clover-Image-Tiny.
Use the prompt trigger pointillism painting.
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"neonforestmist/Clover-Image-Tiny",
torch_dtype=torch.float16,
).to("cuda")
pipe.load_lora_weights(
"neonforestmist/clover-image-tiny-pointillism-lora"
)
image = pipe(
"pointillism painting, a small blue cat in a flower garden",
num_inference_steps=20,
guidance_scale=7.5,
).images[0]
63b0e9f6be9c00888ff464f342a9ef052bf766810f1bbd4c768ed9bd65a62ae7f7bf3f0aee351691train_text_to_image_lora.pyThe reproducible job configuration is included in the Clover source
repository under training/.
These adapter weights are a derivative of Clover Image Tiny and use the CreativeML Open RAIL-M license. The training dataset is Apache-2.0. Generated content can inherit limitations and biases from the base checkpoint and training data; review outputs before use.