Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
 title: Upscaler
-emoji: 👀
-colorFrom: gray
-colorTo: gray
+app_file: main.py
 sdk: gradio
 sdk_version: 4.41.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

flagged/log.csv

@@ -0,0 +1,2 @@
+Upload Low-Resolution Image,Upscaling Prompt,Upscaled Image,flag,username,timestamp
+flagged\Upload Low-Resolution Image\4f885062a26d878bf6ae\clipboard.png,Car,,,,2024-08-19 14:23:30.400851

main.py

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+import gradio as gr
+from PIL import Image
+from diffusers import StableDiffusionUpscalePipeline
+import torch
+import numpy as np
+
+# Load model and scheduler
+model_id = "stabilityai/stable-diffusion-x4-upscaler"
+pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipeline = pipeline.to("cuda")
+
+def upscale_image(image, prompt):
+    # Convert uploaded image to PIL
+    low_res_img = Image.fromarray(image).convert("RGB")
+
+    # Upscale the image
+    upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
+
+    # Convert upscaled PIL image back to numpy array for Gradio
+    upscaled_image_np = np.array(upscaled_image)
+    
+    return upscaled_image_np
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=upscale_image,
+    inputs=[
+        gr.Image(type="numpy", label="Upload Low-Resolution Image"),
+        gr.Textbox(label="Upscaling Prompt", placeholder="Enter a prompt, e.g., 'a red box with glasses'")
+    ],
+    outputs=gr.Image(type="numpy", label="Upscaled Image"),
+    title="Image Upscaler",
+    description="Upload a low-resolution image and provide a prompt to upscale it using Stable Diffusion."
+)
+
+# Launch the Gradio app
+interface.launch(share=True)

test.py

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+import requests
+from PIL import Image
+from io import BytesIO
+from diffusers import StableDiffusionUpscalePipeline
+import torch
+
+# load model and scheduler
+model_id = "stabilityai/stable-diffusion-x4-upscaler"
+pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipeline = pipeline.to("cuda")
+
+# let's download an  image
+url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
+response = requests.get(url)
+low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
+
+
+low_res_img.save("low_res_cat.png")
+
+prompt = "a white cat"
+
+upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
+upscaled_image.save("upsampled_cat.png")
+
+