Update app.py

app.py

@@ -6,10 +6,6 @@ import numpy as np
 import cv2
 import gradio as gr
 from torchvision import transforms 
-# from huggingface_hub import login
-# login()
-
-# controlnet_conditioning_scale = 1.0 
 
 controlnet = ControlNetModel.from_pretrained(
     "briaai/ControlNet-Canny",
@@ -21,7 +17,7 @@ pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
     controlnet=controlnet,
     torch_dtype=torch.float16,
 )
-pipe.enable_model_cpu_offload()
+pipe.enable_xformers_memory_efficient_attention()
 
 low_threshold = 100
 high_threshold = 200
@@ -47,14 +43,26 @@ def get_canny_filter(image):
     canny_image = Image.fromarray(image)
     return canny_image
 
-def process(input_image, prompt, num_steps, controlnet_conditioning_scale):
+def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
+    generator = torch.manual_seed(seed)
+    
     # resize input_image to 1024x1024
     input_image = resize_image(input_image)
     
     canny_image = get_canny_filter(input_image)
-    images = pipe(
-        prompt,image=canny_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-        ).images
+
+    pipe.enable_freeu(b1=1.1, b2=1.1, s1=0.5, s2=0.7)
+
+    if negative_prompt != '':    
+        images = pipe(
+            prompt, negative_prompt=negative_prompt, image=canny_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+            generator=generator,
+            ).images
+    else:
+        images = pipe(
+            prompt, force_zeros_for_empty_prompt=False, image=canny_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+            generator=generator,
+            ).images
 
     return [canny_image,images[0]]
     
@@ -73,14 +81,16 @@ with block:
         with gr.Column():
             input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
             prompt = gr.Textbox(label="Prompt")
+            negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers")
             num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
             controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
+            seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
             run_button = gr.Button(value="Run")
             
             
         with gr.Column():
             result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto')
-    ips = [input_image, prompt, num_steps, controlnet_conditioning_scale]
+    ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
     run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
 
 block.launch(debug = True)