Update app.py

app.py

@@ -8,16 +8,12 @@ os.system("pip install -U peft")
 import spaces
 import gradio as gr
 import torch
-import numpy as np 
-import cv2
 from diffusers import StableDiffusionXLPipeline
-from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
 from PIL import Image
 from ip_adapter import IPAdapterXL
 base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
 device = "cuda"
 
-
 image_encoder_path = donwload_repo_loc #"sdxl_models/image_encoder"
 ip_ckpt = "./models/ip-adapter_sdxl.bin"
 # load SDXL pipeline
@@ -28,50 +24,6 @@ pipe = StableDiffusionXLPipeline.from_pretrained(
 )
 
 
-controlnet_path = "diffusers/controlnet-canny-sdxl-1.0"
-controlnet = ControlNetModel.from_pretrained(controlnet_path, use_safetensors=False, torch_dtype=torch.float16).to(device)
-
-contronet_pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
-    base_model_path,
-    controlnet=controlnet,
-    torch_dtype=torch.float16,
-    add_watermarker=False,
-)
-
-
-
-@spaces.GPU(enable_queue=True)
-def create_image_controlnet(image_pil,input_image,target,prompt,n_prompt,scale, control_scale, guidance_scale,num_samples,num_inference_steps,seed):
-    # load ip-adapter
-    ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device, target_blocks=["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"])
-
-    image_pil=image_pil.resize((512, 512))
-    cv_input_image = pil_to_cv2(input_image)
-    detected_map = cv2.Canny(cv_input_image, 50, 200)
-    canny_map = Image.fromarray(cv2.cvtColor(detected_map, cv2.COLOR_BGR2RGB))
-
-    images = ip_model.generate(pil_image=image_pil,
-                            prompt=prompt,
-                            negative_prompt=n_prompt,
-                            scale=scale,
-                            guidance_scale=guidance_scale,
-                            num_samples=num_samples,
-                            num_inference_steps=num_inference_steps, 
-                            seed=seed,
-                            image=canny_map,
-                            controlnet_conditioning_scale=control_scale,
-                            )
-    del ip_model
-
-    return images
-
-def pil_to_cv2(image_pil):
-    image_np = np.array(image_pil)
-    image_cv2 = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
-    
-    return image_cv2
-
-
 # generate image variations with only image prompt
 @spaces.GPU(enable_queue=True)
 def create_image(image_pil,target,prompt,n_prompt,scale, guidance_scale,num_samples,num_inference_steps,seed):
@@ -114,12 +66,12 @@ This is a demo of  https://github.com/InstantStyle/InstantStyle.
 
 block = gr.Blocks(css="footer {visibility: hidden}").queue(max_size=10)
 with block:
-    with gr.Tab("Instant Syle"):
-        with gr.Row():
+    with gr.Row():
        
-            with gr.Column():
-                # gr.Markdown("## <h1 align='center'>InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation  </h1>")
-                gr.Markdown(DESCRIPTION)
+        with gr.Column():
+            # gr.Markdown("## <h1 align='center'>InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation  </h1>")
+            gr.Markdown(DESCRIPTION)
+    with gr.Tabs():
         with gr.Row():
             with gr.Column():
                 image_pil = gr.Image(label="Style Image", type='pil')
@@ -137,34 +89,5 @@ with block:
 
         generate_button.click(fn=create_image, inputs=[image_pil,target,prompt,n_prompt,scale, guidance_scale,num_samples,num_inference_steps,seed], 
                                   outputs=[generated_image])
-    with gr.Tab("Image stylization Style"):
-        with gr.Row():
-
-            with gr.Column():
-                gr.Markdown("""
-                    # Imagestylization-Style: Free Lunch towards Style-Preserving in Text-to-Image Generation
-                    **Demo by [ameer azam] - [Twitter](https://twitter.com/Ameerazam18) - [GitHub](https://github.com/AMEERAZAM08)) - [Hugging Face](https://huggingface.co/ameerazam08)**
-                    This is a demo of  https://github.com/InstantStyle/InstantStyle.
-                    """)
-                with gr.Row():
-                    with gr.Column():
-                        src_image_pil = gr.Image(label="Source Image", type='pil')
-                    with gr.Column():
-                        image_pil = gr.Image(label="Style Image", type='pil')
-                prompt = gr.Textbox(label="Prompt",value="masterpiece, best quality, high quality")
-                n_prompt = gr.Textbox(label="Neg Prompt",value="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry")
-                scale = gr.Slider(minimum=0,maximum=2.0, step=0.01,value=1.0, label="scale")
-                control_scale = gr.Slider(minimum=0,maximum=1.0, step=0.01,value=0.6, label="controlnet conditioning scale")
-                guidance_scale = gr.Slider(minimum=1,maximum=15.0, step=0.01,value=5.0, label="guidance scale")
-                num_samples= gr.Slider(minimum=1,maximum=4.0, step=1.0,value=1.0, label="num samples")
-                num_inference_steps = gr.Slider(minimum=5,maximum=50.0, step=1.0,value=30, label="num inference steps")
-                seed = gr.Slider(minimum=-1000000,maximum=1000000,value=1, step=1, label="Seed Value")
-                generate_button = gr.Button("Generate Image")
-            with gr.Column():
-                generated_image = gr.Gallery(label="Generated Image")
-
-        generate_button.click(fn=create_image_controlnet, 
-                            inputs=[image_pil,src_image_pil,prompt,n_prompt,scale, control_scale, guidance_scale,num_samples,num_inference_steps,seed], 
-                            outputs=[generated_image])
 
-block.launch()
+block.launch()