Update app.py

app.py

@@ -18,77 +18,7 @@ import cv2
 import spaces
 from gradio_imageslider import ImageSlider
 
-js_func = """
-function refresh() {
-    const url = new URL(window.location);
-}
-"""
-
-def nms(x, t, s):
-    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)
-
-    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
-    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
-    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
-    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
-
-    y = np.zeros_like(x)
-
-    for f in [f1, f2, f3, f4]:
-        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
-
-    z = np.zeros_like(y, dtype=np.uint8)
-    z[y > t] = 255
-    return z
-
-def HWC3(x):
-    assert x.dtype == np.uint8
-    if x.ndim == 2:
-        x = x[:, :, None]
-    assert x.ndim == 3
-    H, W, C = x.shape
-    assert C == 1 or C == 3 or C == 4
-    if C == 3:
-        return x
-    if C == 1:
-        return np.concatenate([x, x, x], axis=2)
-    if C == 4:
-        color = x[:, :, 0:3].astype(np.float32)
-        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
-        y = color * alpha + 255.0 * (1.0 - alpha)
-        y = y.clip(0, 255).astype(np.uint8)
-        return y
-
-DESCRIPTION = ''''''
-
-if not torch.cuda.is_available():
-    DESCRIPTION += ""
-
-style_list = [
-    {
-        "name": "(No style)",
-        "prompt": "{prompt}",
-        "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
-    },
-    {
-        "name": "Cinematic",
-        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
-        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
-    },
-    # ... (other styles)
-]
-
-styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
-STYLE_NAMES = list(styles.keys())
-DEFAULT_STYLE_NAME = "(No style)"
-
-def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
-    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
-    return p.replace("{prompt}", positive), n + negative
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
+# ... (keep the existing helper functions like nms, HWC3, etc.)
 
 # Initialize all detectors
 openpose_detector = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
@@ -102,13 +32,13 @@ pidi_detector = PidiNetDetector.from_pretrained('lllyasviel/Annotators')
 normal_detector = NormalBaeDetector.from_pretrained('lllyasviel/Annotators')
 sam_detector = SamDetector.from_pretrained('ybelkada/segment-anything', subfolder='checkpoints')
 
+# ... (keep the existing style_list and other configurations)
+
 controlnet = ControlNetModel.from_pretrained(
     "xinsir/controlnet-union-sdxl-1.0",
     torch_dtype=torch.float16
 )
 
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-
 pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
     controlnet=controlnet,
@@ -118,13 +48,6 @@ pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
 )
 pipe.to(device)
 
-MAX_SEED = np.iinfo(np.int32).max
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
 @spaces.GPU
 def run(
     image: dict,
@@ -355,4 +278,4 @@ with gr.Blocks(css="style.css", js=js_func) as demo:
         fn=run, inputs=inputs, outputs=outputs
     )
 
-demo.queue().launch
+demo.queue().launch(show_error=True, ssl_verify=False)