"""dim:int# 内部隐藏状态维度的类型声明n_heads:int# 注意力头数量的类型声明d_head:int# 每个头的隐藏状态维度的类型声明dropout:float=0.0# 随机失活率的默认值only_cross_attention:bool=False# 默认不只应用交叉注意力dtype: jnp.dtype = jnp.float32# 默认数据类型为 jnp.float32use_memory_efficient_at...
# 导入 PyTorch 及其神经网络模块importtorchimporttorch.nnasnn# 从指定路径导入 Attention 处理模块from...models.attention_processorimportAttention# 定义自定义的层归一化类,继承自 nn.LayerNormclassWuerstchenLayerNorm(nn.LayerNorm):# 初始化方法,接收可变参数def__init__(self, *args, **kwargs):# 调用...
dos2unix diffusers_sd3.patch RUN cd /home/ma-user/diffusers && sh prepare.sh RUN cp attention_processor.py /home/ma-user/anaconda3/envs/PyTorch-2.1.0/lib/python3.9/site-packages/diffusers/models/attention_processor.py RUN pip install transformers RUN pip install accelerate RUN pip install ...
U-Net类的attn_processors属性会返回一个词典,它的key是每个Attention运算类所在位置,比如down_blocks.0.attentions.0.transformer_blocks.0.attn1.processor,它的value是每个Attention运算类的实例。默认情况下,每个Attention运算类都是AttnProcessor,它的实现在diffusers/models/attention_processor.py文件中。 为了修改Atte...
File"/Users/aleksandrbobrov/data/sd/sd-local/.venv/lib/python3.11/site-packages/diffusers/models/attention_processor.py", line 1231,in__call__ hidden_states = F.scaled_dot_product_attention( ^^^ RuntimeError: Invalid buffer size: 11.25 GB...
models.attention_processor import AttnProcessor2_0 set_attn(pipe, AttnProcessor2_0()) elif shared.opts.cross_attention_optimization == "xFormers" and hasattr(pipe, 'enable_xformers_memory_efficient_attention'): pipe.enable_xformers_memory_efficient_attention() elif shared.opts.cross_attention_...
这种做法来自于之前有约束图像的扩散模型 Cascaded diffusion models。noise_aug_strength 稍后会作为额外约束输入进 U-Net 里,与去噪时刻的编码相加。 image = self.image_processor.preprocess(image, height=height, width=width).to(device) noise = randn_tensor(image.shape, generator=generator, device=device,...
二、Understanding pipelines, models and schedulers Deconstruct a basic pipeline 这一部分的内容跟上一节的很相似 Deconstruct the Stable Diffusion pipeline Stable Diffusion pipeline比仅包含 UNet model的 DDPM pipeline更复杂。Stable Diffusion model 有三个单独的预训练模型:vae, text encoder, unet ...
Stable Diffusion基于潜在扩散模型(Latent Diffusion Models),专门用于文图生成(Text-to-Image Generation)任务。该模型是由来自 CompVis, Stability AI, LAION以及RunwayML的工程师共同开发完成,目前发布了v1和v2两个版本。v1版本采用了LAION-5B数据集子集(分辨率为 512x512)进行训练,并具有以下架构设置:自动编码器...
models/runwayml/stable-diffusion-v1-5/text_encoder/config.json [2023-03-23 17:07:17,731] [ INFO] - Model config CLIPTextConfig { "_name_or_path": "openai/clip-vit-large-patch14", "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": ...