Try moving gradio setup to after controlnet pipecreation

app.py

@@ -83,7 +83,7 @@ def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, tok
 
 
 
-# ----- ControlNet Canny Edges -----------------------------------------------------------------
+# ----- ControlNet Canny Edges Pipe / Setup -----------------------------------------------------------------
 
 import gradio as gr
 from PIL import Image
@@ -103,49 +103,6 @@ controlnet_pipe.scheduler = UniPCMultistepScheduler.from_config(controlnet_pipe.
 controlnet_pipe.enable_model_cpu_offload()
 controlnet_pipe.enable_xformers_memory_efficient_attention()
 
-def controlnet_edges(canny_input_prompt, input_image, input_low_threshold, input_high_threshold, input_invert):
-    np_image = np.array(input_image)
-
-    output_image = input_image
-    numpy_image = np.array(output_image)
-
-    low_threshold = 80
-    high_threshold = 100
-    canny_1 = cv2.Canny(numpy_image, input_low_threshold, input_high_threshold)
-    canny_1 = canny_1[:, :, None]
-    canny_1 = np.concatenate([canny_1, canny_1, canny_1], axis=2)
-    if input_invert:
-      canny_1 = 255 - canny_1
-
-    canny_2 = Image.fromarray(canny_1)
-
-    prompt = canny_input_prompt
-    generator = torch.Generator(device="cpu").manual_seed(2)
-
-    output_image = controlnet_pipe(
-        prompt,
-        canny_2,
-        negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
-        generator=generator,
-        num_inference_steps=20,
-    )
-
-    return output_image[0][0]
-
-
-canny_input_prompt = gr.inputs.Textbox(label="Enter a single word or phrase")
-canny_input_image = gr.inputs.Image()
-canny_input_low_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Lower Threshold:", default=100)
-canny_input_high_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Upper Threshold:", default=200)
-canny_input_invert = gr.inputs.Checkbox(label="Invert Image")
-canny_outputs = gr.outputs.Image(type="pil")
-
-# make and launch the gradio app...
-controlnet_canny_interface = gr.Interface(fn=controlnet_edges, inputs=[canny_input_prompt, canny_input_image, canny_input_low_threshold, canny_input_high_threshold, canny_input_invert], outputs=canny_outputs, title='Canny Edge Tracing', allow_flagging='never')
-# controlnet_canny_interface.launch()
-
-
-
 
 
 
@@ -486,6 +443,69 @@ canny_interface = gr.Interface(fn=canny_process_image, inputs=[canny_input_image
 
 
 
+# ----- ControlNet Canny Gradio Setup -----------------------------------------------------------------
+
+# import gradio as gr
+# from PIL import Image
+# import numpy as np
+# import cv2
+
+# from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+# from diffusers import UniPCMultistepScheduler
+# import torch
+
+# controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
+# controlnet_pipe = StableDiffusionControlNetPipeline.from_pretrained(
+#     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+# )
+
+# controlnet_pipe.scheduler = UniPCMultistepScheduler.from_config(controlnet_pipe.scheduler.config)
+# controlnet_pipe.enable_model_cpu_offload()
+# controlnet_pipe.enable_xformers_memory_efficient_attention()
+
+def controlnet_edges(canny_input_prompt, input_image, input_low_threshold, input_high_threshold, input_invert):
+    np_image = np.array(input_image)
+
+    output_image = input_image
+    numpy_image = np.array(output_image)
+
+    low_threshold = 80
+    high_threshold = 100
+    canny_1 = cv2.Canny(numpy_image, input_low_threshold, input_high_threshold)
+    canny_1 = canny_1[:, :, None]
+    canny_1 = np.concatenate([canny_1, canny_1, canny_1], axis=2)
+    if input_invert:
+      canny_1 = 255 - canny_1
+
+    canny_2 = Image.fromarray(canny_1)
+
+    prompt = canny_input_prompt
+    generator = torch.Generator(device="cpu").manual_seed(2)
+
+    output_image = controlnet_pipe(
+        prompt,
+        canny_2,
+        negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
+        generator=generator,
+        num_inference_steps=20,
+    )
+
+    return output_image[0][0]
+
+
+canny_input_prompt = gr.inputs.Textbox(label="Enter a single word or phrase")
+canny_input_image = gr.inputs.Image()
+canny_input_low_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Lower Threshold:", default=100)
+canny_input_high_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Upper Threshold:", default=200)
+canny_input_invert = gr.inputs.Checkbox(label="Invert Image")
+canny_outputs = gr.outputs.Image(type="pil")
+
+# make and launch the gradio app...
+controlnet_canny_interface = gr.Interface(fn=controlnet_edges, inputs=[canny_input_prompt, canny_input_image, canny_input_low_threshold, canny_input_high_threshold, canny_input_invert], outputs=canny_outputs, title='Canny Edge Tracing', allow_flagging='never')
+# controlnet_canny_interface.launch()
+
+
+
 # ----- Launch Tabs -----------------------------------------------------------------
 
 tabbed_interface = gr.TabbedInterface([new_welcome, advanced_tab, beta, canny_interface, controlnet_canny_interface], ["Artbots", "Advanced", "Beta", "Edges", "ControlNet"])