Create app.py

app.py

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+import gradio as gr
+import torch
+from huggingface_hub import hf_hub_download
+from diffusers import DiffusionPipeline
+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")
+
+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()