add upscaler with x4

app.py

@@ -0,0 +1,60 @@
+import gradio as gr
+
+from diffusers import StableDiffusionUpscalePipeline
+from PIL import Image
+from io import BytesIO
+import torch
+
+
+
+model_id = "stabilityai/stable-diffusion-x4-upscaler"
+pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipeline = pipeline.to("cuda")
+
+
+# def predict(input_img):
+#     predictions = predictor(input_img)
+#     return input_img, {p["label"]: p["score"] for p in predictions} 
+
+
+def upscalex4(input_img):
+    low_res_img = resize_on_scale(input_img)
+    prompt = "highly detailed"
+    upscaled_img = pipeline(prompt=prompt, image=low_res_img).images[0]
+    print("Low resolution image size = ", low_res_img.size)
+    print("Upscaled image size = ", upscaled_img.size)
+    return upscaled_img
+
+
+def resize_on_scale(input_img):
+    low_res_img = input_img.convert("RGB")
+    wsize = 300
+    wpercent = (wsize / float(input_img.size[0]))
+    hsize = int((float(input_img.size[1]) * float(wpercent)))
+    low_res_img = low_res_img.resize((wsize, hsize))
+    return low_res_img
+
+
+gradio_app = gr.Interface(
+    upscalex4, 
+    inputs=gr.Image(label="Select a blurry image", sources=['upload', 'webcam', 'clipboard'], type="pil"),
+    outputs=gr.Image(label="Processed Image"), 
+    title="Image Upscaler",
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()
+
+
+
+
+
+
+
+
+# img = Image.open(requests.get("https://pbs.twimg.com/profile_images/1600764211378139137/HirERJI5_400x400.jpg", stream=True).raw)
+# img = img.resize((64, 64))
+
+# print("Original image size = ", img.size)
+# print("Upscaled image size = ", upscaled_img.size)
+# upscaled_img.show()

requirements.txt

@@ -0,0 +1,4 @@
+diffusers==0.26.3
+gradio
+Pillow==10.2.0
+torch