Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [

G: [..., H, W] return: normalized tensor with same shape

Determine the compute mode for the current step. Side effects: - Updates `current['type']` to one of: 'full', 'Taylor', 'ToCa', 'Del

Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is designed to discretize an

Create a horizontal collage from a list of images.

Create a horizontal collage from a list of images.

Return the normalized platform name used for matching mjun0812 wheels.

Keep the first wheel per website key; JSON is expected newest-first.

Build/update Taylor coefficients from the latest feature tensor. Args: cache_dic: Global cache dict storing per-stream/layer/module

Build/update Taylor coefficients for double-stream outputs.

One step for the first-order DPMSolver (equivalent to DDIM). Args: model_output (`torch.Tensor`): The di

r""" Encodes the instruction into text encoder hidden states. Args: instruction (`str` or `List[str]`, *optional*):

Encode an image into the VAE latent space. Args: img: The input image tensor to encode. Returns: to

Flatten patch tokens and pad to batched sequences.

Update `cache_dic['cal_threshold']` for the current denoising step. Args: cache_dic: Mutable cache state dict. Expected keys include

Embed image patches and run the refiner blocks.

Initialize the weights of the double-stream attention processor. Uses Xavier uniform initialization for linear layers and zero initi

Initialize the weights of the double-stream attention processor. Uses Xavier uniform initialization for linear layers and zero initi

Initialize linear weights and modulation parameters.

Initialize linear weights and modulation parameters.

Initialize the weights of the model. Uses Xavier uniform initialization for linear layers.

Yield asset dicts from either a flat asset list or GitHub release list.

This will load the LoRA layers specified in `state_dict` into `prompt_embedding`. Parameters: state_dict (`dict`):

This will load the LoRA layers specified in `state_dict` into `transformer`. Parameters: state_dict (`dict`):

Load pre-fetched mjun0812 asset data from the repository.

# Keep `device_map=None`; no need to set it to `args.device`. # The same applies below (Note: `rewriter` is an external module, so its devi

# Keep `device_map=None`; no need to set it to `args.device`. # The same applies below (Note: `rewriter` is an external module, so its devi

Main function to run the image generation process.

Main function to run the image generation process.

One step for the second-order multistep DPMSolver. Args: model_output_list (`List[torch.Tensor]`): The d

One step for the third-order multistep DPMSolver. Args: model_output_list (`List[torch.Tensor]`): The di

Parse command line arguments.

Parse command line arguments.

Prepare input images for processing by encoding them into the VAE latent space. Args: images: Single image or list of im

Prepare the initial latents for the diffusion process. Args: batch_size: The number of images to generate. n

Preprocess the image input. Args: image (`PipelineImageInput`): The image input, accepted formats are PI

Match exact CPython wheels and compatible abi3 wheels.

Initialize parameters (same logic as RMSNorm)

Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs

Run the image generation pipeline with the given parameters.

Run the image generation pipeline with the given parameters.

Search mjun0812 releases using the same parse/filter model as the site.

Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the multistep DPMSolver.

Reconstruct feature estimate using cached Taylor coefficients. Returns: A tensor with the same shape as cached feature tensors for t

Reconstruct double-stream outputs using cached Taylor coefficients.

Try Dao-AILab official releases (both ABI variants).

Unload optional instruction rewriter model/processor references. Safety rules: 1) If `text_instruction_rewriter` (or its `.m

Process double-stream self-attention computation with flash attention. Args: attn: Attention module img_hidd

Process double-stream self-attention computation with PyTorch's scaled_dot_product_attention. Args: attn: Attention modu

Process attention computation with flash attention. Args: attn: Attention module hidden_states: Hidden state

Process attention computation with flash attention. Args: attn: Attention module hidden_states: Hidden state

Initialize the Boogu-Image pipeline. Args: transformer: Boogu transformer denoiser for latent prediction. va

Initialize the BooguImagePromptTuningPipeline. Args: transformer: Boogu-Image single/dual-stream transformer used as the

Initialize the double-stream attention processor.

Initialize the double-stream attention processor.

Initialize the attention processor.

Initialize the transformer block.

Initialize the double stream transformer block.