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import os
import sys
import git
import gradio as gr
import torch
from huggingface_hub import hf_hub_download
from diffusers import DiffusionPipeline
# Check if the cog-sdxl repository exists, otherwise clone it
repo_url = "https://github.com/replicate/cog-sdxl.git"
repo_dir = "./cog-sdxl"
if not os.path.exists(repo_dir):
print("Cloning cog-sdxl repository...")
git.Repo.clone_from(repo_url, repo_dir)
# Add cog_sdxl directory to sys.path for imports
sys.path.append(os.path.abspath(repo_dir))
# Now import the required module after adding cog_sdxl to sys.path
from cog_sdxl.dataset_and_utils import TokenEmbeddingsHandler
# Load the model pipeline
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
# Load the LoRA weights
pipe.load_lora_weights("fofr/sdxl-emoji", weight_name="lora.safetensors")
# Load token embeddings
embedding_path = hf_hub_download(repo_id="fofr/sdxl-emoji", filename="embeddings.pti", repo_type="model")
text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
tokenizers = [pipe.tokenizer, pipe.tokenizer_2]
embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
embhandler.load_embeddings(embedding_path)
def generate_emoji(prompt):
"""Generate an emoji image based on the user's prompt."""
prompt = f"A <s0><s1> emoji of {prompt}"
images = pipe(
prompt,
cross_attention_kwargs={"scale": 0.8},
).images
return images[0]
# Gradio UI definition
iface = gr.Interface(
fn=generate_emoji,
inputs=gr.Textbox(label="Enter description for emoji"),
outputs=gr.Image(label="Generated Emoji"),
title="SDXL Emoji Generator",
description="Generate a custom emoji using SDXL model with LoRA fine-tuning."
)
if __name__ == "__main__":
iface.launch()
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