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jakedahn/
sdxl-isometric-geology

Name: sdxl-isometric-geology
Availability: InStock

stabilityai/stable-diffusion-xl-base-1.0

adapter

creativeml-openrail-m

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sdxl-isometric-geology LoRA by jakedahn

sdxl-isometric-geology is an SDXL fine-tune that's been trained with cool USGS isometric block and fence diagrams from the 1950s and 1960s.

Inference with Replicate API

Grab your replicate token here

bash

pip install replicate
export REPLICATE_API_TOKEN=r8_*************************************

import replicate

output = replicate.run(
    "sdxl-isometric-geology@sha256:44272e4bb4f61d052617d4b56cc5be7b34dc27d9605e4c9568efc215aae547c5",
    input={"prompt": "a diagram of gradient descent, in the style of TOK"}
)
print(output)

You may also do inference via the API with Node.js or curl, and locally with COG and Docker, check out the Replicate API page for this model

Inference with 🧨 diffusers

Replicate SDXL LoRAs are trained with Pivotal Tuning, which combines training a concept via Dreambooth LoRA with training a new token with Textual Inversion. As diffusers doesn't yet support textual inversion for SDXL, we will use cog-sdxl TokenEmbeddingsHandler class.

The trigger tokens for your prompt will be <s0><s1>

shell

pip install diffusers transformers accelerate safetensors huggingface_hub
git clone https://github.com/replicate/cog-sdxl cog_sdxl

import torch
from huggingface_hub import hf_hub_download
from diffusers import DiffusionPipeline
from cog_sdxl.dataset_and_utils import TokenEmbeddingsHandler
from diffusers.models import AutoencoderKL

pipe = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0",
        torch_dtype=torch.float16,
        variant="fp16",
).to("cuda")

pipe.load_lora_weights("jakedahn/sdxl-isometric-geology", weight_name="lora.safetensors")

text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
tokenizers = [pipe.tokenizer, pipe.tokenizer_2]

embedding_path = hf_hub_download(repo_id="jakedahn/sdxl-isometric-geology", filename="embeddings.pti", repo_type="model")
embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
embhandler.load_embeddings(embedding_path)
prompt="a diagram of gradient descent, in the style of <s0><s1>"
images = pipe(
    prompt,
    cross_attention_kwargs={"scale": 0.8},
).images
#your output image
images[0]