根据 SGM 模式重新映射new_state_dict = {}# 定义内部块映射的列表inner_block_map = ["resnets","attentions","upsamplers"]# 初始化输入、中间和输出块的 ID 集合input_block_ids, middle_block_ids, output_block_ids =set(),set(),set()# 遍历所有层以
fn_recursive_add_processors(name, module, processors)# 返回所有收集到的处理器returnprocessors# 复制自 diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor# 设置用于计算注意力的处理器def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor...
现在创建一个去噪循环,预测噪声较小的样本的残差,并使用scheduler计算噪声较小的样本 importtqdmsample=noisy_samplefori,tinenumerate(tqdm.tqdm(scheduler.timesteps)):# 1. predict noise residualwithtorch.no_grad():residual=model(sample,t).sample# 2. compute less noisy image and set x_t -> x_t-1...
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX. - diffusers/src/diffusers/models/attention.py at main · huggingface/diffusers
然后,加载 LoRA 权重并将其与原始权重融合。lora_scale参数与上面的cross_attention_kwargs={"scale": 0.5}类似,用于控制多大程度上融合 LoRA 的权重。融合时一定要主要设置好这个参数,因为融合后就无法使用cross_attention_kwargs的scale参数来控制了。
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前面的维度不变,后面根据块大小取最小值slice_sizes=list(query.shape[:-3]) + [min(query_chunk_size, num_q), num_heads, q_features],# [...,q,h,d])return(# 返回未使用的下一个块索引chunk_idx + query_chunk_size,# unused ignore it# 调用注意力函数处理当前查询块_query_chunk_attention...
"""# 检查是否启用切片且输入批量大于 1ifself.use_slicingandz.shape[0] >1:# 对输入进行切片解码decoded_slices = [self._decode(z_slice).sampleforz_sliceinz.split(1)]# 将所有解码结果连接成一个张量decoded = torch.cat(decoded_slices)else:# 对整个输入进行解码decoded = self._decode(z).sample...
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX. - diffusers/examples/controlnet/train_controlnet_flax.py at main · huggingface/diffusers
In this case, `attention_head_dim` must be a multiple of `slice_size`. """ if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = self.unet.config.attention_head_dim // 2 self.unet.set_attention_slice(slice_...