268 lines
9.0 KiB
Python
268 lines
9.0 KiB
Python
# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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import os
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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 cv2
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import gc
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import torch
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from comfy.utils import common_upscale,ProgressBar
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from huggingface_hub import hf_hub_download
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import comfy.model_management
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cur_path = os.path.dirname(os.path.abspath(__file__))
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device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
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def cf_unload():
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comfy.model_management.unload_all_models()
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comfy.model_management.soft_empty_cache()
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def cf_prompt_clip(cf_clip,prompt):
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tokens = cf_clip.tokenize(prompt)
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tokens["t5xxl"] = cf_clip.tokenize(prompt)["t5xxl"]
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prompt_embeds = cf_clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True).pop("cond")
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tokens["l"] = cf_clip.tokenize(prompt)["l"]
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pooled_prompt_embeds = cf_clip.encode_from_tokens(tokens, return_dict=True).pop("pooled_output")
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prompt_embeds=prompt_embeds.to(device,torch.bfloat16)
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pooled_prompt_embeds=pooled_prompt_embeds.to(device,torch.bfloat16)
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text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=torch.bfloat16)
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return prompt_embeds,pooled_prompt_embeds,text_ids
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def latent_crop( samples, width, height, x, y):
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s = samples.copy()
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samples = samples['samples']
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x = x // 8
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y = y // 8
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#enfonce minimum size of 64
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if x > (samples.shape[3] - 8):
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x = samples.shape[3] - 8
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if y > (samples.shape[2] - 8):
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y = samples.shape[2] - 8
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new_height = height // 8
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new_width = width // 8
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to_x = new_width + x
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to_y = new_height + y
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s['samples'] = samples[:,:,y:to_y, x:to_x]
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return s
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def cleanup():
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gc.collect()
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torch.cuda.empty_cache()
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def process_image_with_mask(image, mask,width, height,invert=False):
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if invert:
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mask = 1.0 - mask
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mask_tensor=mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
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input_image_resized = tensor2pil_upscale(image, width, height)
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mask_pil=tensor2pil_upscale(mask_tensor, width, height)
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return input_image_resized,mask_pil
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def cv2pil(cv_image):
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"""
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将OpenCV图像转换为PIL图像
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:param cv_image: OpenCV图像
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:return: PIL图像
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"""
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# 将图像从BGR转换为RGB
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rgb_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
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# 使用PIL的Image.fromarray方法将NumPy数组转换为PIL图像
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pil_image = Image.fromarray(rgb_image)
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return pil_image
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def tensor_to_pil(tensor):
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image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
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image = Image.fromarray(image_np, mode='RGB')
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return image
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def tensor2pil_list(image,width,height):
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B,_,_,_=image.size()
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if B==1:
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ref_image_list=[tensor2pil_upscale(image,width,height)]
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else:
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img_list = list(torch.chunk(image, chunks=B))
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ref_image_list = [tensor2pil_upscale(img,width,height) for img in img_list]
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return ref_image_list
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def tensor_upscale(img_tensor, width, height):
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samples = img_tensor.movedim(-1, 1)
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img = common_upscale(samples, width, height, "nearest-exact", "center")
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samples = img.movedim(1, -1)
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return samples
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def tensor2pil_upscale(img_tensor, width, height):
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samples = img_tensor.movedim(-1, 1)
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img = common_upscale(samples, width, height, "nearest-exact", "center")
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samples = img.movedim(1, -1)
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img_pil = tensor_to_pil(samples)
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return img_pil
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def tensor2np_upscale(img_tensor, width, height):
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samples = img_tensor.movedim(-1, 1)
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img = common_upscale(samples, width, height, "nearest-exact", "center")
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samples = img.movedim(1, -1)
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image_np = samples.squeeze().mul(255).clamp(0, 255).byte().numpy()
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return image_np
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def tensor2cv(tensor_image,RGB2BGR=True):
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if len(tensor_image.shape)==4:#bhwc to hwc
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tensor_image=tensor_image.squeeze(0)
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if tensor_image.is_cuda:
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tensor_image = tensor_image.cpu().detach()
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tensor_image=tensor_image.numpy()
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#反归一化
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maxValue=tensor_image.max()
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tensor_image=tensor_image*255/maxValue
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img_cv2=np.uint8(tensor_image)#32 to uint8
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if RGB2BGR:
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img_cv2=cv2.cvtColor(img_cv2,cv2.COLOR_RGB2BGR)
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return img_cv2
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def cvargb2tensor(img):
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assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img))
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img = torch.from_numpy(img.transpose((2, 0, 1)))
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return img.float().div(255).unsqueeze(0) # 255也可以改为256
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def cv2tensor(img):
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assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img))
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = torch.from_numpy(img.transpose((2, 0, 1)))
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return img.float().div(255).unsqueeze(0) # 255也可以改为256
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def images_generator(img_list: list,):
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#get img size
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sizes = {}
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for image_ in img_list:
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if isinstance(image_,Image.Image):
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count = sizes.get(image_.size, 0)
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sizes[image_.size] = count + 1
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elif isinstance(image_,np.ndarray):
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count = sizes.get(image_.shape[:2][::-1], 0)
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sizes[image_.shape[:2][::-1]] = count + 1
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else:
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raise "unsupport image list,must be pil or cv2!!!"
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size = max(sizes.items(), key=lambda x: x[1])[0]
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yield size[0], size[1]
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# any to tensor
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def load_image(img_in):
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if isinstance(img_in, Image.Image):
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img_in=img_in.convert("RGB")
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i = np.array(img_in, dtype=np.float32)
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i = torch.from_numpy(i).div_(255)
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if i.shape[0] != size[1] or i.shape[1] != size[0]:
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i = torch.from_numpy(i).movedim(-1, 0).unsqueeze(0)
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i = common_upscale(i, size[0], size[1], "lanczos", "center")
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i = i.squeeze(0).movedim(0, -1).numpy()
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return i
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elif isinstance(img_in,np.ndarray):
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i=cv2.cvtColor(img_in,cv2.COLOR_BGR2RGB).astype(np.float32)
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i = torch.from_numpy(i).div_(255)
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#print(i.shape)
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return i
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else:
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raise "unsupport image list,must be pil,cv2 or tensor!!!"
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total_images = len(img_list)
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processed_images = 0
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pbar = ProgressBar(total_images)
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images = map(load_image, img_list)
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try:
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prev_image = next(images)
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while True:
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next_image = next(images)
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yield prev_image
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processed_images += 1
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pbar.update_absolute(processed_images, total_images)
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prev_image = next_image
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except StopIteration:
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pass
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if prev_image is not None:
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yield prev_image
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def load_images(img_list: list,):
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gen = images_generator(img_list)
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(width, height) = next(gen)
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images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3)))))
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if len(images) == 0:
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raise FileNotFoundError(f"No images could be loaded .")
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return images
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def tensor2pil(tensor):
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image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
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image = Image.fromarray(image_np, mode='RGB')
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return image
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def pil2narry(img):
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narry = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
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return narry
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def equalize_lists(list1, list2):
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"""
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比较两个列表的长度，如果不一致，则将较短的列表复制以匹配较长列表的长度。
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参数:
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list1 (list): 第一个列表
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list2 (list): 第二个列表
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返回:
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tuple: 包含两个长度相等的列表的元组
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"""
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len1 = len(list1)
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len2 = len(list2)
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if len1 == len2:
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pass
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elif len1 < len2:
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print("list1 is shorter than list2, copying list1 to match list2's length.")
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list1.extend(list1 * ((len2 // len1) + 1)) # 复制list1以匹配list2的长度
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list1 = list1[:len2] # 确保长度一致
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else:
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print("list2 is shorter than list1, copying list2 to match list1's length.")
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list2.extend(list2 * ((len1 // len2) + 1)) # 复制list2以匹配list1的长度
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list2 = list2[:len1] # 确保长度一致
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return list1, list2
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def file_exists(directory, filename):
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# 构建文件的完整路径
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file_path = os.path.join(directory, filename)
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# 检查文件是否存在
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return os.path.isfile(file_path)
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def download_weights(file_dir,repo_id,subfolder="",pt_name=""):
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if subfolder:
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file_path = os.path.join(file_dir,subfolder, pt_name)
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sub_dir=os.path.join(file_dir,subfolder)
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if not os.path.exists(sub_dir):
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os.makedirs(sub_dir)
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if not os.path.exists(file_path):
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file_path = hf_hub_download(
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repo_id=repo_id,
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subfolder=subfolder,
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filename=pt_name,
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local_dir = file_dir,
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)
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return file_path
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else:
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file_path = os.path.join(file_dir, pt_name)
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if not os.path.exists(file_dir):
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os.makedirs(file_dir)
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if not os.path.exists(file_path):
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file_path = hf_hub_download(
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repo_id=repo_id,
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filename=pt_name,
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local_dir=file_dir,
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)
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return file_path
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