Stable Diffusion XL为了在解决数据集利用率问题的同时不引入噪声伪影,将U-Net(Base)模型与原始图像分辨率相关联,核心思想是将输入图像的原始高度和宽度 c_{\text size}=(w_\text {origin}, h_\text {origin}) 作为额外的条件嵌入U-Net模型中,height和width分别都用傅里叶特征编码,然后将特征concat后加在Time...
Stable Diffusion XL为了在解决数据集利用率问题的同时不引入噪声伪影,将U-Net(Base)模型与原始图像分辨率相关联,核心思想是将输入图像的原始高度和宽度 作为额外的条件嵌入U-Net模型中,height和width分别都用傅里叶特征编码,然后将特征concat后加在Time Embedding上,将图像尺寸引入训练过程,这样模型在训练过程中能够学习...
Stable Diffusion XL为了在解决数据集利用率问题的同时不引入噪声伪影,将U-Net(Base)模型与原始图像分辨率相关联,核心思想是将输入图像的原始高度和宽度作为额外的条件嵌入U-Net模型中,表示为 C_{size} = (height, width)。height和width都使用傅里叶特征编码进行独立嵌入,然后将特征concat后加在Time Embedding上,将...
在 8 CFG 上采样 50 步。所有涉及图像宽度(width)和高度(height)的参数均应保持一致,即所有宽度一致,所有高度一致。仅使用模型本身,而不使用精炼器(Refiner)。使用基底模型 vae。使用附录部分提供的触发词以活用风格化。在未使用风格化的情况下生成图像的质量将参差 1、露西(赛博朋克2077) 提示词: 这真是一部...
(image.height//2, image.width//2), resample=PIL.Image.NEAREST) original_sizes = [image.height, image.width] image = train_resize(image) y1, x1, h, w = random_crop(image, (1024, 1024)) image = image.crop((y1, x1, h, w)) if True and random.random() < 0.5: # flip x1 =...
base_pipeline=get_base_pipeline()defimagine(prompt,negative_prompt,width,height,scale,steps,seed,upscaler):base_image=get_base_image(prompt,negative_prompt,width,height,scale,steps,seed)refiner_image=refiner_pipeline(prompt=prompt,negative_prompt=negative_prompt,image=base_image).images[0]... ...
@kohya-ss Unfortunately, it's 32 ,But the situation at that time was:min_bucket_reso=256 max_bucket_reso=1024 ,about traindata , both width and height are greater than 1024 WuNein mentioned this issue Aug 8, 2023 SDXL 1.0 Lora 训练报错 Akegarasu/lora-scripts#200 Closed Author Jon...
Steps: 40, Seed: 17748028598464, Model: DreamShaperXL1.0Alpha_half, width: 768, height: 1024, Sampler: DPM++ 2S a Karras, CFG scale: 8 2、画女人 (masterpiece), (extremely intricate:1.3), (realistic), photo of a girl, the most beautiful in the world, (medieval armor), upper body, ...
已为大家提供access_token erniebot.api_type = "yinian" erniebot.access_token = "24.d311c558615a181ebd75090adccd5969.2592000.1704099017.282335-44171052" response = erniebot.Image.create( model="ernie-vilg-v2", prompt="雨后的碎冰蓝玫瑰花,8k,辛烷值渲染", width=512, height=512 ) print(...
image_conditioning=torch.ones(x.shape[0],3,height,width,device=x.device)*0.5 image_conditioning=images_tensor_to_samples(image_conditioning, approximation_indexes.get(opts.sd_vae_encode_method)) # Add the fake full 1s mask to the first dimension. ...