update app

app.py

@@ -17,6 +17,14 @@ def predict(prompt, steps=30, seed=42, guidance_scale=7.5):
     image = pipe(prompt, num_inference_steps=steps, guidance_scale=7.5).images[0]
     return image
 
+md = """
+This Spaces app is same as <a href=\"https://huggingface.co/spaces/Intel/dicoo_diffusion\">Intel/dicoo_diffusion</a>, created by Intel AIA/AIPC team with the model fine-tuned with one shot (one image) for a newly introduced object \"dicoo\". To replicate the model fine-tuning, please refer to the code sample in <a href=\"https://github.com/intel/neural-compressor/tree/master/examples/pytorch/diffusion_model/diffusers/textual_inversion\">Intel Neural Compressor</a>. You may also refer to our <a href=\"https://medium.com/intel-analytics-software/personalized-stable-diffusion-with-few-shot-fine-tuning-on-a-single-cpu-f01a3316b13\">blog</a> for more details.
+
+**Tips:** 
+    - When inputting prompts, you need to contain the word **<dicoo>** which represents the pretrained object \"dicoo\". 
+    - For better generation, you maybe increase the inference steps.
+"""
+
 random_seed = random.randint(0, 2147483647)
 gr.Interface(
     predict,
@@ -29,5 +37,5 @@ gr.Interface(
     outputs=gr.Image(shape=[512, 512], type="pil", elem_id="output_image"),
     css="#output_image{width: 256px}",
     title="Demo of dicoo-finetuned-diffusion-model using Intel Neural Compressor 🧨",
-    description="This Spaces app is same as <a href=\"https://huggingface.co/spaces/Intel/dicoo_diffusion\">Intel/dicoo_diffusion</a>, created by Intel AIA/AIPC team with the model fine-tuned with one shot (one image) for a newly introduced object \"dicoo\". To replicate the model fine-tuning, please refer to the code sample in <a href=\"https://github.com/intel/neural-compressor/tree/master/examples/pytorch/diffusion_model/diffusers/textual_inversion\">Intel Neural Compressor</a>. You may also refer to our <a href=\"https://medium.com/intel-analytics-software/personalized-stable-diffusion-with-few-shot-fine-tuning-on-a-single-cpu-f01a3316b13\">blog</a> for more details.\n **Tips:** -When inputting prompts, you need to contain the word **<dicoo>** which represents the pretrained object \"dicoo\". -For better generation, you maybe increase the inference steps.",
+    description=md,
 ).launch()