Munaf1987 commited on
Commit
adde61a
·
verified ·
1 Parent(s): 7fe7d39

Update app.py

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Files changed (1) hide show
  1. app.py +34 -45
app.py CHANGED
@@ -7,21 +7,6 @@ import io
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  import base64
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  import spaces
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- # Load Ghibli model
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- pipe_ghibli = StableDiffusionImg2ImgPipeline.from_pretrained(
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- "nitrosocke/Ghibli-Diffusion",
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- torch_dtype=torch.float16,
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- use_safetensors=True
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- ).to("cuda")
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-
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- # Load AnimeGANv2 model
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- animegan = torch.hub.load(
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- "bryandlee/animegan2-pytorch:main",
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- "generator",
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- pretrained="face_paint_512_v2"
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- ).to("cuda").eval()
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-
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- # Base64 conversion helpers
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  def pil_to_b64(img: Image.Image) -> str:
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  buf = io.BytesIO()
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  img.save(buf, format="PNG")
@@ -30,46 +15,50 @@ def pil_to_b64(img: Image.Image) -> str:
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  def b64_to_pil(b64: str) -> Image.Image:
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  return Image.open(io.BytesIO(base64.b64decode(b64))).convert("RGB")
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- # AnimeGAN processor
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- def apply_animegan(img: Image.Image) -> Image.Image:
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- transform = transforms.Compose([
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- transforms.Resize((512, 512)),
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- transforms.ToTensor()
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- ])
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- img_tensor = transform(img).unsqueeze(0).to("cuda")
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- with torch.no_grad():
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- out = animegan(img_tensor)[0].clamp(0, 1).cpu()
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- return transforms.ToPILImage()(out)
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- # Unified image processor
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- def process_image(img: Image.Image, effect: str) -> Image.Image:
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  if effect == "ghibli":
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- return pipe_ghibli(prompt="ghibli style", image=img, strength=0.5, guidance_scale=7.5).images[0]
 
 
 
 
 
 
 
 
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  else:
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- return apply_animegan(img)
 
 
 
 
 
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- @spaces.GPU
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- def process_base64(b64: str, effect: str) -> str:
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- img = b64_to_pil(b64)
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- out = process_image(img, effect)
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- return pil_to_b64(out)
 
 
 
 
 
56
 
57
- # Gradio UI
 
 
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  with gr.Blocks() as demo:
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  gr.Markdown("# 🎨 Ghibli & AnimeGAN Effects (ZeroGPU Compatible)")
60
 
61
- with gr.Tab("Web UI"):
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- inp = gr.Image(type="pil", label="Upload Image")
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- eff = gr.Radio(["ghibli", "anime"], label="Select Effect")
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- btn = gr.Button("Apply Effect")
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- out_img = gr.Image(label="Output Image")
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- btn.click(process_image, [inp, eff], out_img)
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-
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  with gr.Tab("Base64 API"):
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  b64_in = gr.Textbox(label="Input Image (Base64)", lines=5)
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- eff2 = gr.Radio(["ghibli", "anime"], label="Select Effect")
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- btn2 = gr.Button("Run API")
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  b64_out = gr.Textbox(label="Output Image (Base64)", lines=5)
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- btn2.click(process_base64, [b64_in, eff2], b64_out)
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75
  demo.launch()
 
7
  import base64
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  import spaces
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  def pil_to_b64(img: Image.Image) -> str:
11
  buf = io.BytesIO()
12
  img.save(buf, format="PNG")
 
15
  def b64_to_pil(b64: str) -> Image.Image:
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  return Image.open(io.BytesIO(base64.b64decode(b64))).convert("RGB")
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+ @spaces.GPU
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+ def process_base64(b64: str, effect: str) -> str:
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+ img = b64_to_pil(b64)
 
 
 
 
 
 
 
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  if effect == "ghibli":
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+ # Load model inside GPU function (ZeroGPU compliant)
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+ pipe_ghibli = StableDiffusionImg2ImgPipeline.from_pretrained(
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+ "nitrosocke/Ghibli-Diffusion",
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+ torch_dtype=torch.float16,
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+ use_safetensors=True
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+ ).to("cuda")
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+
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+ output_image = pipe_ghibli(prompt="ghibli style", image=img, strength=0.5, guidance_scale=7.5).images[0]
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+
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  else:
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+ # Load model inside GPU function (ZeroGPU compliant)
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+ animegan = torch.hub.load(
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+ "bryandlee/animegan2-pytorch:main",
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+ "generator",
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+ pretrained="face_paint_512_v2"
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+ ).to("cuda").eval()
39
 
40
+ transform = transforms.Compose([
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+ transforms.Resize((512, 512)),
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+ transforms.ToTensor()
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+ ])
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+ img_tensor = transform(img).unsqueeze(0).to("cuda")
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+
46
+ with torch.no_grad():
47
+ out = animegan(img_tensor)[0].clamp(0, 1).cpu()
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+
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+ output_image = transforms.ToPILImage()(out)
50
 
51
+ return pil_to_b64(output_image)
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+
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+ # Gradio UI (runs in CPU - ZeroGPU safe)
54
  with gr.Blocks() as demo:
55
  gr.Markdown("# 🎨 Ghibli & AnimeGAN Effects (ZeroGPU Compatible)")
56
 
 
 
 
 
 
 
 
57
  with gr.Tab("Base64 API"):
58
  b64_in = gr.Textbox(label="Input Image (Base64)", lines=5)
59
+ eff = gr.Radio(["ghibli", "anime"], label="Select Effect")
60
+ btn = gr.Button("Run API")
61
  b64_out = gr.Textbox(label="Output Image (Base64)", lines=5)
62
+ btn.click(process_base64, [b64_in, eff], b64_out)
63
 
64
  demo.launch()