add model

constants.py

@@ -0,0 +1,211 @@
+DESCRIPTION = """
+# Real Time Latent Consistency Model
+
+This space is using a **CPU runtime**, and it takes about 30 seconds to generate an image.
+For a faster experience, you can duplicate it and use with a **GPU runtime**.
+
+At the meantime you can use **[dynamic 🔥](https://www.fal.ai/dynamic)** by [fal](fal.ai), or **[a hosted space 🤗](https://huggingface.co/spaces/fal-ai/realtime-stable-diffusion)** 
+to generate images in real time.
+"""
+
+
+LOGO = """
+<svg
+    width="100%"
+    height="100%"
+    viewBox="0 0 89 32"
+    fill="none"
+    xmlns="http://www.w3.org/2000/svg"
+>
+    <path
+    d="M52.308 3.07812H57.8465V4.92428H56.0003V6.77043H54.1541V10.4627H57.8465V12.3089H54.1541V25.232H52.308V27.0781H46.7695V25.232H48.6157V12.3089H46.7695V10.4627H48.6157V6.77043H50.4618V4.92428H52.308V3.07812Z"
+    fill="currentColor"
+    ></path>
+    <path
+    d="M79.3849 23.3858H81.2311V25.232H83.0772V27.0781H88.6157V25.232H86.7695V23.3858H84.9234V4.92428H79.3849V23.3858Z"
+    fill="currentColor"
+    ></path>
+    <path
+    d="M57.8465 14.155H59.6926V12.3089H61.5388V10.4627H70.7695V12.3089H74.4618V23.3858H76.308V25.232H78.1541V27.0781H72.6157V25.232H70.7695V23.3858H68.9234V14.155H67.0772V12.3089H65.2311V14.155H63.3849V23.3858H65.2311V25.232H67.0772V27.0781H61.5388V25.232H59.6926V23.3858H57.8465V14.155Z"
+    fill="currentColor"
+    ></path>
+    <path
+    d="M67.0772 25.232V23.3858H68.9234V25.232H67.0772Z"
+    fill="currentColor"
+    ></path>
+    <rect
+    opacity="0.22"
+    x="7.38477"
+    y="29.5391"
+    width="2.46154"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.85"
+    x="2.46094"
+    y="19.6914"
+    width="12.3077"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    x="4.92383"
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+    fill="#5F4CD9"
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+    <rect
+    opacity="0.4"
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+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.7"
+    y="22.1562"
+    width="14.7692"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.5"
+    x="7.38477"
+    y="24.6133"
+    width="7.38462"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.22"
+    x="7.38477"
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+    width="2.46154"
+    height="2.46154"
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+    ></rect>
+    <rect
+    opacity="0.85"
+    x="2.46094"
+    y="2.46094"
+    width="12.3077"
+    height="2.46154"
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+    ></rect>
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+    <rect
+    opacity="0.4"
+    x="7.38477"
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+    width="4.92308"
+    height="2.46154"
+    fill="#5F4CD9"
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+    <rect
+    opacity="0.7"
+    y="4.92188"
+    width="14.7692"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.5"
+    x="7.38477"
+    y="7.38281"
+    width="7.38462"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.22"
+    x="24.6152"
+    y="29.5391"
+    width="2.46154"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.85"
+    x="19.6914"
+    y="19.6914"
+    width="12.3077"
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+    ></rect>
+    <rect
+    x="22.1543"
+    y="17.2305"
+    width="9.84615"
+    height="2.46154"
+    fill="#5F4CD9"
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+    <rect
+    opacity="0.4"
+    x="24.6152"
+    y="27.0781"
+    width="4.92308"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.7"
+    x="17.2305"
+    y="22.1562"
+    width="14.7692"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.5"
+    x="24.6152"
+    y="24.6133"
+    width="7.38462"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.22"
+    x="24.6152"
+    y="12.3086"
+    width="2.46154"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.85"
+    x="19.6914"
+    y="2.46094"
+    width="12.3077"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect x="22.1543" width="9.84615" height="2.46154" fill="#5F4CD9"></rect>
+    <rect
+    opacity="0.4"
+    x="24.6152"
+    y="9.84375"
+    width="4.92308"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.7"
+    x="17.2305"
+    y="4.92188"
+    width="14.7692"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+    <rect
+    opacity="0.5"
+    x="24.6152"
+    y="7.38281"
+    width="7.38462"
+    height="2.46154"
+    fill="#5F4CD9"
+    ></rect>
+</svg>
+"""

model.py

@@ -0,0 +1,43 @@
+from typing import Any
+
+
+def get_pipeline():
+    import torch
+    from diffusers import AutoencoderTiny, AutoPipelineForImage2Image
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+    pipe = AutoPipelineForImage2Image.from_pretrained(
+        "SimianLuo/LCM_Dreamshaper_v7",
+        use_safetensors=True,
+    )
+    pipe.vae = AutoencoderTiny.from_pretrained(
+        "madebyollin/taesd",
+        torch_dtype=torch_dtype,
+        use_safetensors=True,
+    )
+    pipe = pipe.to(device, dtype=torch_dtype)
+    pipe.unet.to(memory_format=torch.channels_last)
+    return pipe
+
+
+def get_test_pipeline():
+    from PIL import Image
+    from dataclasses import dataclass
+    import random
+    import time
+
+    @dataclass
+    class Images:
+        images: list[Image.Image]
+
+    class Pipeline:
+        def __call__(self, *args: Any, **kwds: Any) -> Any:
+            r = random.randint(0, 255)
+            g = random.randint(0, 255)
+            b = random.randint(0, 255)
+
+            return Images(images=[Image.new("RGB", (512, 512), color=(r, g, b))])
+
+    return Pipeline()

requirements.txt

@@ -1,2 +1,4 @@
-fastapi[all]
-uvicorn[standard]
+accelerate
+diffusers[torch]
+transformers
+xformers

utils.py

@@ -0,0 +1,15 @@
+from PIL import Image
+import numpy as np
+
+
+def replace_background(image: Image.Image, new_background_color=(0, 255, 255)):
+    image_np = np.array(image)
+
+    white_threshold = 255 * 3
+    white_pixels = np.sum(image_np, axis=-1) == white_threshold
+
+    image_np[white_pixels] = new_background_color
+
+    result = Image.fromarray(image_np)
+
+    return result