add application file

app.py

@@ -0,0 +1,78 @@
+import gradio as gr
+from diffusers import StableDiffusionPipeline, ControlNetModel, StableDiffusionControlNetPipeline
+from diffusers.utils import load_image
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+
+is_show_controlnet = True
+prompts = ""
+neg_prompt = "chinese letter"
+
+controlnet_repo_id = "calihyper/trad-kor-controlnet"
+repo_id = "calihyper/trad-kor-landscape-black"
+controlnet = ControlNetModel.from_pretrained(controlnet_repo_id)
+pipe = StableDiffusionControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet).to("cuda")
+
+
+def change_radio(input):
+    return input
+
+def output_radio(output):
+    print(output)
+
+def predict(canny, lt, ht, prompt, style_prompt, neg_prompt, ins, gs, seed):
+    np_image = np.array(canny)
+    low_threshold = lt
+    high_threshold = ht
+    np_image = cv2.Canny(np_image, low_threshold, high_threshold)
+    np_image = np_image[:, :, None]
+    np_image = np.concatenate([np_image, np_image, np_image], axis=2)
+    canny_image = Image.fromarray(np_image)
+
+    generator = torch.manual_seed(seed)
+
+    global pipe
+
+    output = pipe(
+        prompt + style_prompt,
+        canny_image,
+        negative_prompt=neg_prompt,
+        generator=generator,
+        num_inference_steps=ins,
+        guidance_scale=gs
+    )
+    return output.images[0]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Aiffelthon Choosa Project")
+
+    with gr.Row():
+        with gr.Column() as controlnet:
+            
+            canny_image = gr.Image(label="input_image", visible=is_show_controlnet , shape=(512,512), interactive=True)
+        
+            controlnet_radio = gr.Radio([True, False], label="Use ControlNet")
+            lt = gr.Slider(50, 300, 120, step=1, label="Low threshold")
+            ht = gr.Slider(50, 300, 120, step=1, label="High threshold")        
+
+        with gr.Column():
+            out_image = gr.Image()
+            with gr.Column() as diff:
+                prompt = gr.Textbox(placeholder="prompts", label="prompt")
+                style_prompt = gr.Textbox(placeholder="style prompts", label="style prompt")
+                examples = gr.Examples(examples=["<trad-kor-landscape-black>", "<trad-kor-landscape-ink-wash-painting>", "<trad-kor-landscape-thick-brush-strokes>", "<trad-kor-plants-black>", "<trad-kor-plants-color>"], 
+                                       inputs=style_prompt,  label="style examples")
+
+                neg_prompt = gr.Textbox(placeholder="negative prompts", value=neg_prompt, label="negative prompt")
+
+                ins = gr.Slider(1, 60, 30, label="inference steps")
+                gs = gr.Slider(1, 10, 2.5, step=1, label="guidance scale")
+
+                seed = gr.Slider(0, 10, 2, step=1, label="seed")
+    btn1 = gr.Button("실행")
+    btn1.click(predict, [canny_image, lt, ht, prompt,style_prompt, neg_prompt, ins, gs, seed ], out_image)
+
+if __name__ == "__main__":
+    demo.launch()    

requirements.txt

@@ -0,0 +1,14 @@
+-f https://download.pytorch.org/whl/cpu/torch_stable.html
+-f https://data.pyg.org/whl/torch-2.0.0+cpu.html
+torch
+torchvision
+diffusers
+gradio
+Pillow
+numpy
+transformers
+torchvision
+ftfy
+altair
+opencv-python
+accelerate