Update app.py

app.py

@@ -3,10 +3,8 @@ from transformers import pipeline
 import torch
 from diffusers import DiffusionPipeline
     
-# def get_completion(prompt,params):
-    # return pipeline(prompt=prompt, height=params['height'], width=params['width'], num_inference_steps=params['num_inference_steps'], guidance_scale=params['guidance_scale'])['sample'][0]
-def get_completion(prompt):
-    return pipeline(prompt=prompt)['sample'][0]
+def get_completion(prompt,params):
+    return pipeline(prompt=prompt, height=params['height'], width=params['width'], num_inference_steps=params['num_inference_steps'], guidance_scale=params['guidance_scale'])['sample'][0]
 
 def generate(prompt,steps,guidance,width,height):
     params = {
@@ -15,14 +13,14 @@ def generate(prompt,steps,guidance,width,height):
         "width": width,
         "height": height
     }
-    # output = get_completion(prompt,params)
-    output = get_completion(prompt)
+    output = get_completion(prompt,params)
     return output
     
 pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Image Generation with Stable Diffusion")
+    gr.Markdown("# Image Generation Demo & Test App by Srinivas")
+    gr.Markdown("## Generates an Image based on Your Promt inptted and Optional parameters selected")
     with gr.Row():
         with gr.Column(scale=4):
             prompt = gr.Textbox(label="Your Prompt") #Give prompt some real estate
@@ -32,8 +30,8 @@ with gr.Blocks() as demo:
             # negative_prompt = gr.Textbox(label="Negative prompt")
             with gr.Row():
                 with gr.Column():
-                    steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=25,
-                      info="In many steps will the denoiser denoise the image?")
+                    steps = int(gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=25,
+                      info="In many steps will the denoiser denoise the image?"))
                     guidance = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=20.0, value=7.0,
                       info="Controls how much the text prompt influences the result")
                 with gr.Column():