update

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,13 @@
+---
+title: StableDiffusionUpscaleImage
+emoji: 💻
+colorFrom: red
+colorTo: yellow
+sdk: gradio
+sdk_version: 4.40.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,22 @@
+import gradio as gr
+from PIL import Image
+import upscale_image
+
+@spaces.GPU(duration=120)
+def gradio_upscale(image):
+    """画像を反転させる関数"""
+    return uscale_image.execute(Image)
+
+
+
+if __name__ == "__main__":
+    # Gradio UIの設定
+    iface = gr.Interface(
+        fn=reverse_image,
+        inputs="image",
+        outputs="image",
+        title="Image Reverser",
+        description="Upload an image to see it reversed.",
+    )
+    # インターフェースを公開
+    iface.launch(share=True)

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -0,0 +1,6 @@
+torch
+diffusers
+pillow
+transformers
+xformers
+spaces

.ipynb_checkpoints/upscale_image-checkpoint.py

@@ -0,0 +1,40 @@
+import torch
+from diffusers import AutoencoderKL
+from diffusers import StableDiffusionUpscalePipeline
+from PIL import Image
+
+device = "cuda"
+
+def execute(input_image):
+    model_id = "stabilityai/stable-diffusion-x4-upscaler"
+      # GPUを使用する場合
+    pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16") #not support vae?
+    pipe = pipe.to(device)
+    pipe.enable_attention_slicing()
+    #pipe.enable_model_cpu_offload()
+    pipe.enable_xformers_memory_efficient_attention()
+    #pipe.enable_vae_tiling()
+    pipe.vae.enable_tiling()
+    # 画像のパスとプロンプト
+    prompt = "beautiful girl"
+    first_resize_w = 128
+    first_resize_h = 128
+    
+        # 画像の読み込みとリサイズ
+    image = input_image.convert("RGB")
+    low_res_img = image
+    if first_resize_w!=0 and first_resize_h!=0:
+        low_res_img = image.resize((first_resize_w, first_resize_h))
+    upscaled_image = upscale(pipe, prompt, low_res_img)
+    return upscaled_image
+
+def upscale(pipe, prompt, img, step=50, guidance_scale=7.5):
+  generator = torch.Generator(device).manual_seed(seed)
+  return pipe(prompt=prompt,generator=generator, image=img, num_inference_steps=step, guidance_scale=guidance_scale).images[0]
+
+
+if __name__ == "__main__":
+    image = Image.open("sample.jpg")
+    upscaled_image = execute(image)
+    upscaled_image.save("output.jpg")
+

requirements.txt

@@ -2,4 +2,5 @@ torch
 diffusers
 pillow
 transformers
-xformers
+xformers
+spaces