update app

app.py

@@ -11,7 +11,8 @@ model_id = "dicoo_model"
 
 pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float)
 
-def predict(prompt, steps=50, seed=42, guidance_scale=7.5):
+def predict(prompt, steps=30, seed=42, guidance_scale=7.5):
+    prompt = prompt
     print("prompt: ", prompt)
     print("steps: ", steps)
     image = pipe(prompt, num_inference_steps=steps, guidance_scale=7.5).images[0]
@@ -22,12 +23,12 @@ gr.Interface(
     predict,
     inputs=[
         gr.inputs.Textbox(label='Prompt', default='a lovely <dicoo> in red dress and hat, in the snowy and brightly night, with many brightly buildings'),
-        gr.inputs.Slider(1, 100, label='Inference Steps', default=50, step=1),
+        gr.inputs.Slider(1, 100, label='Inference Steps', default=30, step=1),
         gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
         gr.inputs.Slider(1.0, 20.0, label='Guidance Scale - how much the prompt will influence the results', default=6.0, step=0.1),
     ],
     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.",
+    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.",
 ).launch()