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davidrd123/davinci-sdxl-lora-05
davinci-sdxl-lora-05 is a text-to-image model from davidrd123. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as creativeml-openrail-m.
This is a LyCORIS adapter derived from stabilityai/stable-diffusion-xl-base-1.0.
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.safetensors46.5 MB · 64%
From the Hugging Face model README
This is a LyCORIS adapter derived from stabilityai/stable-diffusion-xl-base-1.0.
The main validation prompt used during training was:
DaVinciXL, One mechanical device with gears and levers, no human subjects, one item in the image.
4.20.030None421024x1024Note: The validation settings are not necessarily the same as the training settings.
You can find some example images in the following gallery:
<Gallery />The text encoder was not trained. You may reuse the base model text encoder for inference.
{
"algo": "lokr",
"multiplier": 1.0,
"linear_dim": 10000,
"linear_alpha": 1,
"factor": 16,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 16
},
"FeedForward": {
"factor": 8
}
}
}
}
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()
prompt = "DaVinciXL, One mechanical device with gears and levers, no human subjects, one item in the image."
negative_prompt = 'blurry, cropped, ugly'
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=30,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=1024,
height=1024,
guidance_scale=4.2,
guidance_rescale=0.0,
).images[0]
image.save("output.png", format="PNG")