first commit

app.py

@@ -0,0 +1,62 @@
+import os
+import json
+import requests
+import gradio as gr
+from util import base64_to_img, img_to_base64, resize_image
+
+url = os.getenv("REQUEST_URL")
+headers = {'Content-Type': 'application/json',
+           'Validation-Key': os.getenv("VALIDATION_KEY")}
+names = ["input_image", "prompt", "neg_prompt", "maxlen", "step", "cfg", "seed", "up", "down", "left", "right"]
+def run(*params):
+    params = {k:v for k, v in zip(names, params)}
+    image = params.pop("input_image")
+    image = resize_image(image)
+    params["image_base64"] = img_to_base64(image)
+    try:
+        response = requests.post(url, headers=headers, data=json.dumps(params), timeout=30)
+        if response.status_code != 200:
+            raise ValueError()
+        data = response.json()
+    except:
+        raise gr.Error("Fail to generate")
+    if data["code"] != 0:
+        raise gr.Error(data["message"])
+    result = base64_to_img(data["content"])
+    return result
+
+with gr.Blocks() as demo:
+    gr.Markdown("# SDXL inpainting for Clothes2Human")
+    with gr.Row().style(equal_height=True):
+        with gr.Column():
+            input_image = gr.Image(type="pil", height=300)
+        with gr.Column():
+            output_image = gr.Image(type="pil", height=300)
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt")
+            neg_prompt = gr.Textbox(label="Negative Prompt")
+
+            maxlen = gr.Slider(label="Max Edge Length", step=32, minimum=768, maximum=1536, value=1024)
+            step = gr.Slider(label="Step", minimum=20, maximum=70, value=50, step=1)
+
+        with gr.Column():
+            up = gr.Slider(label="Scale Up Image", minimum=-0.3, maximum=0.5, value=0, step=0.1)
+            down = gr.Slider(label="Scale Down Image", minimum=-0.3, maximum=0.5, value=0, step=0.1)
+            left = gr.Slider(label="Scale Left Image", minimum=-0.3, maximum=0.5, value=0, step=0.1)
+            right = gr.Slider(label="Scale Right Image", minimum=-0.3, maximum=0.5, value=0, step=0.1)
+        with gr.Column():
+            cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=9.0, value=5.0, step=0.5)
+            seed = gr.Slider(label="Seed", minimum=-1, maximum=1000000, value=-1, step=1)
+            inpaint_button = gr.Button()
+
+            run_in = [input_image, prompt, neg_prompt, maxlen, step, cfg, seed, up, down, left, right]
+            inpaint_button.click(run, inputs=run_in, outputs=[output_image])
+
+    gr.Examples([["imgs/1.jpg","A man wearing a white T-shirt stands on the beach","", 1024, 50, 5.0, 333866, 0.3, 0.3, 0.1, 0.1],
+                 ["imgs/2.jpg"," woman wearing a blue dress stands in a park, asian race","", 1280, 50, 5.0, 443652, 0.3, 0.3, 0.2, 0.2],
+                 ["imgs/3.jpg","A woman wearing a white dress stands","", 1280, 50, 5.0, 306728, -0.1, -0.2, 0, 0]],
+                inputs=run_in, outputs=[output_image], fn=run, cache_examples=True)
+
+demo.queue(concurrency_count=2).launch()

util.py

@@ -0,0 +1,25 @@
+from PIL import Image
+import base64
+from io import BytesIO
+from pydantic import BaseModel, validator
+
+def img_to_base64(img):
+    buffer = BytesIO()
+    img.save(buffer, "jpeg")
+    content = base64.b64encode(buffer.getvalue())
+    return str(content, 'utf-8')
+
+def base64_to_img(content):
+    decoded_image = base64.b64decode(content)
+    image_buffer = BytesIO(decoded_image)
+    image = Image.open(image_buffer)
+    return image
+
+def resize_image(img, maxlen=2048):
+    if max(img.size)<maxlen:
+        return img
+    if img.width > img.height:
+        img = img.resize((maxlen, int(img.height*maxlen/img.width)//8*8))
+    else:
+        img = img.resize((int(img.width*maxlen/img.height)//8*8, maxlen))
+    return img