"""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...
Union# 从 typing 导入类型提示,用于增强代码可读性和类型检查importtorch# 导入 PyTorch 库,用于深度学习importtorch.nnasnn# 从 PyTorch 导入神经网络模块,用于构建模型from...models.attention_processorimport(# 从相对路径导入注意力处理相关类Attention,# 导入注意力机制类AttentionProcessor,# 导入注意力处理器类PAG...
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...
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 ...
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 ...
Describe the bug This error happens with a custom attention processor after setting lora layers Reproduction from diffusers import UNet2DConditionModel from diffusers.models.lora import LoRALinearLayer class AttnProcessorCustom: def __ca...
Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processordefset_attn_processor(self,processor):r"""Sets the attention processor to use to compute attention.Parameters:processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):The instantiated processor ...
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None ) prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, ...
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 ...