83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
from huggingface_hub import snapshot_download
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import torch
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from PIL import Image
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import numpy as np
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import folder_paths
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import os
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SCRIPT_DIR= os.path.dirname(os.path.abspath(__file__))
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PROMPT_STYLES_DIR = os.path.join(SCRIPT_DIR, "style")
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#loras stack number
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MAX_LORA_NUM = 5
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#controlnets stack number
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MAX_CN_NUM = 3
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NEGATIVE_PROMPT = "bad proportions, low resolution,worst quality, low quality, normal quality, lowres, inaccurate limb, bad, ugly, terrible, extra fingers, fewer fingers, missing fingers, extra arms, extra legs, inaccurate eyes, bad composition, bad anatomy, error, extra digit, fewer digits, cropped, low res, jpeg artifacts, trademark,artist's name, username, watermarksignature, watermark,text, words"
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#resolution list
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BASE_RESOLUTIONS = [
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("width", "height"),
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(512, 512),
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(512, 768),
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(576, 1024),
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(768, 512),
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(768, 768),
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(768, 1024),
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(768, 1280),
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(768, 1344),
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(768, 1536),
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(816, 1920),
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(832, 1152),
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(832, 1216),
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(896, 1152),
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(896, 1088),
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(1024, 1024),
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(1024, 576),
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(1024, 768),
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(1080, 1920),
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(1440, 2560),
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(1088, 896),
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(1216, 832),
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(1152, 832),
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(1152, 896),
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(1280, 768),
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(1344, 768),
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(1536, 640),
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(1536, 768),
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(1920, 816),
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(1920, 1080),
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(2560, 1440)
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]
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RESOLUTION_STRINGS = [f"{width} x {height}" if width == 'width' and height == 'height' else f"{width} x {height}" for width, height in BASE_RESOLUTIONS]
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def load_model_from_hug(model):
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model_name = model.rsplit('/', 1)[-1]
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model_path = os.path.join(folder_paths.models_dir, "LLM", model_name)
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if not os.path.exists(model_path):
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print(f"Downloading model to: {model_path}")
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snapshot_download(repo_id=model,
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local_dir=model_path,
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local_dir_use_symlinks=False)
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return model_path
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# Tensor to PIL
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def tensor2pil(image):
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return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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def pil2tensor(image):
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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def resize_image(image, max_size=768):
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"""
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调整图像大小，使得长边不超过max_size像素。
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"""
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width, height = image.size
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if width > height:
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new_width = max_size
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new_height = int(height * (new_width / width))
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elif height > width:
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new_height = max_size
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new_width = int(width * (new_height / height))
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else:
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new_width = max_size
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new_height = max_size
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return image.resize((new_width, new_height), Image.LANCZOS) |