sdxl-emoji LoRA by fofr

An SDXL fine-tune based on Apple Emojis

lora_image

Inference with Replicate API

Grab your replicate token here

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

output = replicate.run(
    "sdxl-emoji@sha256:dee76b5afde21b0f01ed7925f0665b7e879c50ee718c5f78a9d38e04d523cc5e",
    input={"prompt": "A TOK emoji of a man"}
)
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 ๐Ÿงจ 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>

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("fofr/sdxl-emoji", 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="fofr/sdxl-emoji", filename="embeddings.pti", repo_type="model")
embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
embhandler.load_embeddings(embedding_path)
prompt="A <s0><s1> emoji of a man"
images = pipe(
    prompt,
    cross_attention_kwargs={"scale": 0.8},
).images
#your output image
images[0]
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