Sdxl Emoji

🚀 sdxl-emoji LoRA by fofr

An SDXL fine - tune based on Apple Emojis, offering unique text - to - image capabilities.

🚀 Quick Start

✨ Features

• This is an SDXL fine - tune based on Apple Emojis.
• Supports inference through Replicate API and 🧨 diffusers.

📦 Installation

Inference with Replicate API

First, grab your replicate token here. Then, run the following commands:

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

Inference with 🧨 diffusers

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

💻 Usage Examples

Inference with Replicate API

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 🧨 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>.

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("SvenN/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="SvenN/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]

📄 License

This project is under the creativeml-openrail-m license.