Create app.py

app.py

@@ -0,0 +1,91 @@
+import gradio as gr
+import torch
+from PIL import Image
+from diffusers.utils import numpy_to_pil
+from diffusers import (
+    T2IAdapter,
+    StableDiffusionXLAdapterPipeline,
+    AutoencoderKL,
+    EulerAncestralDiscreteScheduler
+)
+from controlnet_aux import PidiNetDetector
+
+# Global variable to store the pipeline
+pipe = None
+
+def load_pipe():
+    global pipe
+    if pipe is None:
+        model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+        adapter = T2IAdapter.from_pretrained(
+            "Adapter/t2iadapter", 
+            subfolder="sketch_sdxl_1.0", 
+            torch_dtype=torch.float16, 
+            adapter_type="full_adapter_xl")
+        euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+        vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+
+        pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
+            model_id, 
+            adapter=adapter, 
+            vae=vae,
+            scheduler=euler_a,
+            torch_dtype=torch.float16, 
+            variant="fp16", 
+        ).to("cuda")
+        pipe.enable_xformers_memory_efficient_attention()
+
+def preprocess_image(uploaded_file):
+    if uploaded_file is None:
+        return None, "Please upload an image."
+    img_upload = Image.open(uploaded_file)
+    preprocessor = PidiNetDetector.from_pretrained("lllyasviel/Annotators").to("cuda")
+    img_preprocessed = preprocessor(
+        img_upload, 
+        detect_resolution=1024, 
+        image_resolution=1024,
+        apply_filter=True).convert("L")
+    return img_preprocessed, ""
+
+def generate(prompt, uploaded_file, prompt_addition, negative_prompt, num_images, num_steps, guidance_scale, adapter_conditioning_scale, adapter_conditioning_factor):
+    global pipe
+    load_pipe()  # Ensure the model is loaded
+    img_preprocessed, error_message = preprocess_image(uploaded_file)
+    if error_message:
+        return error_message
+    params = {
+        "image": img_preprocessed,
+        "num_inference_steps": num_steps,
+        "prompt": f"{prompt},{prompt_addition}" if prompt_addition.strip() else prompt,
+        "negative_prompt": negative_prompt,
+        "guidance_scale": guidance_scale,
+        "adapter_conditioning_scale": adapter_conditioning_scale / 100,
+        "adapter_conditioning_factor": adapter_conditioning_factor / 100,
+        "num_images_per_prompt": num_images
+    }
+    generated_images = pipe(**params).images
+    return [numpy_to_pil(image.numpy()) for image in generated_images]
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", value="a robot elephant", placeholder="Enter a description for the image you want to generate")
+            prompt_addition = gr.Textbox(label="Prompt addition", value="in real world, 4k photo, highly detailed")
+            negative_prompt = gr.Textbox(label="Negative prompt", value="disfigured, extra digit, fewer digits, cropped, worst quality, low quality")
+            num_images = gr.Slider(minimum=1, maximum=10, value=1, label="Number of images to generate")
+            num_steps = gr.Slider(minimum=1, maximum=100, value=20, label="Number of steps")
+            guidance_scale = gr.Slider(minimum=6, maximum=10, value=7, label="Guidance scale")
+            adapter_conditioning_scale = gr.Slider(minimum=0, maximum=100, value=90, label="Adapter conditioning scale")
+            adapter_conditioning_factor = gr.Slider(minimum=0, maximum=100, value=90, label="Adapter conditioning factor")
+            uploaded_file = gr.File(label="Upload image", type='filepath') 
+
+        with gr.Column():
+            output_gallery = gr.Gallery(label="Generated images")
+            generate_button = gr.Button("Generate")
+            generate_button.click(
+                generate,
+                inputs=[prompt, uploaded_file, prompt_addition, negative_prompt, num_images, num_steps, guidance_scale, adapter_conditioning_scale, adapter_conditioning_factor],
+                outputs=[output_gallery]
+            )
+
+demo.launch()