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Update app.py
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app.py
CHANGED
@@ -24,11 +24,11 @@ import uuid
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DESCRIPTION = '''# Latent Consistency Model OpenVino CPU
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Based on [Latency Consistency Model OpenVINO CPU](https://huggingface.co/deinferno/Latent_Consistency_Model_OpenVino_CPU) HF space
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Converted from [SoteMix](https://huggingface.co/Disty0/SoteMix) to [LCM_SoteMix](https://huggingface.co/Disty0/LCM_SoteMix)
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[Project page](https://latent-consistency-models.github.io)
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<p>Running on CPU
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'''
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MAX_SEED = np.iinfo(np.int32).max
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@@ -39,6 +39,7 @@ batch_size = -1
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width = int(os.getenv("IMAGE_WIDTH", "512"))
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height = int(os.getenv("IMAGE_HEIGHT", "512"))
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num_images = int(os.getenv("NUM_IMAGES", "1"))
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class CustomOVModelVaeDecoder(OVModelVaeDecoder):
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def __init__(
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@@ -105,9 +106,9 @@ def generate(
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examples = [
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo",
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, dark red hair, blue eyes,
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo,
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, abstract, abstract background, (bloom, swirling lights, light particles), floating, romanticized, blush,
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]
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@@ -142,13 +143,6 @@ with gr.Blocks(css="style.css") as demo:
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)
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randomize_seed = gr.Checkbox(label="Randomize seed across runs", value=True)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance scale for base",
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minimum=1,
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maximum=8,
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step=0.1,
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value=1.0,
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps for base",
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minimum=1,
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DESCRIPTION = '''# Latent Consistency Model OpenVino CPU
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Based on [Latency Consistency Model OpenVINO CPU](https://huggingface.co/deinferno/Latent_Consistency_Model_OpenVino_CPU) HF space
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Converted from [SoteMix](https://huggingface.co/Disty0/SoteMix) to [LCM_SoteMix](https://huggingface.co/Disty0/LCM_SoteMix) and then to OpenVINO
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[LCM Project page](https://latent-consistency-models.github.io)
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<p>Running on CPU with OpenVINO.</p>
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'''
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MAX_SEED = np.iinfo(np.int32).max
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width = int(os.getenv("IMAGE_WIDTH", "512"))
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height = int(os.getenv("IMAGE_HEIGHT", "512"))
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num_images = int(os.getenv("NUM_IMAGES", "1"))
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guidance_scale = float(os.getenv("GUIDANCE_SCALE", "1.0"))
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class CustomOVModelVaeDecoder(OVModelVaeDecoder):
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def __init__(
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examples = [
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo",
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, dark red hair, long hair, blue eyes, straight hair, cat ears, medium breasts, mature female, white sweater",
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, scenery, wind, petals, rim lighting, shrine, lens flare, light scatter, depth of field, lens refraction, dark red hair, long hair, blue eyes, straight hair, cat ears, medium breasts, mature female, white sweater",
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, supernova, abstract, abstract background, (bloom, swirling lights, light particles), fire, galaxy, floating, romanticized, blush, looking at viewer, dark red hair, long hair, blue eyes, straight hair, cat ears, medium breasts, mature female, white sweater",
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]
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)
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randomize_seed = gr.Checkbox(label="Randomize seed across runs", value=True)
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with gr.Row():
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num_inference_steps = gr.Slider(
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label="Number of inference steps for base",
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minimum=1,
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