From 343b27520a7526d61ca4b80d18b56330fdebf60f Mon Sep 17 00:00:00 2001
From: smthemex <muweiji@live.com>
Date: Fri, 6 Nov 2026 13:47:26 +0800
Subject: [PATCH] init

---
 .gitignore                                    |     1 +
 MS_Diffusion.py                               |   648 -
 README.md                                     |   107 +-
 __init__.py                                   |     4 +-
 config/config.json                            |   184 -
 config/lora.yaml                              |    33 -
 config/sd_xl_base.yaml                        |    93 -
 example_workflows/example.png                 |   Bin 0 -> 483370 bytes
 example_workflows/msdiffusion.json            |   864 +
 examples/autolayerimg2img.png                 |   Bin 460654 -> 0 bytes
 examples/autolayerimg4img.png                 |   Bin 430619 -> 0 bytes
 examples/controlnet _obj.png                  |   Bin 470633 -> 0 bytes
 examples/controlnet _obj1.png                 |   Bin 480222 -> 0 bytes
 examples/example_new.png                      |   Bin 370720 -> 0 bytes
 examples/workflow_new.json                    |   395 -
 local_repo/model_index.json                   |    34 -
 local_repo/scheduler/scheduler_config.json    |    18 -
 local_repo/text_encoder/config.json           |    24 -
 local_repo/text_encoder_2/config.json         |    24 -
 local_repo/tokenizer/merges.txt               | 48895 ---------------
 local_repo/tokenizer/special_tokens_map.json  |    24 -
 local_repo/tokenizer/tokenizer_config.json    |    33 -
 local_repo/tokenizer/vocab.json               | 49410 ----------------
 local_repo/tokenizer_2/merges.txt             | 48895 ---------------
 .../tokenizer_2/special_tokens_map.json       |    24 -
 local_repo/tokenizer_2/tokenizer_config.json  |    33 -
 local_repo/tokenizer_2/vocab.json             | 49410 ----------------
 local_repo/unet/config.json                   |    69 -
 local_repo/vae/config.json                    |    32 -
 local_repo/vae_1_0/config.json                |    31 -
 local_repo/vae_decoder/config.json            |    31 -
 local_repo/vae_encoder/config.json            |    31 -
 msdiffusion/__init__.py                       |     1 -
 msdiffusion/dataset/__init__.py               |     1 -
 msdiffusion/dataset/prompts/msbench.py        |   132 -
 msdiffusion/models/__init__.py                |     5 -
 msdiffusion/models/attention_processor.py     |   494 -
 msdiffusion/models/model.py                   |   263 -
 msdiffusion/models/projection.py              |   257 -
 msdiffusion/utils.py                          |    50 -
 msdiffusion_native/__init__.py                |     4 +
 msdiffusion_native/core.py                    |   546 +
 msdiffusion_native/nodes.py                   |   369 +
 pyproject.toml                                |     3 +-
 requirements.txt                              |     3 -
 45 files changed, 1822 insertions(+), 199653 deletions(-)
 create mode 100644 .gitignore
 delete mode 100644 MS_Diffusion.py
 delete mode 100644 config/config.json
 delete mode 100644 config/lora.yaml
 delete mode 100644 config/sd_xl_base.yaml
 create mode 100644 example_workflows/example.png
 create mode 100644 example_workflows/msdiffusion.json
 delete mode 100644 examples/autolayerimg2img.png
 delete mode 100644 examples/autolayerimg4img.png
 delete mode 100644 examples/controlnet _obj.png
 delete mode 100644 examples/controlnet _obj1.png
 delete mode 100644 examples/example_new.png
 delete mode 100644 examples/workflow_new.json
 delete mode 100644 local_repo/model_index.json
 delete mode 100644 local_repo/scheduler/scheduler_config.json
 delete mode 100644 local_repo/text_encoder/config.json
 delete mode 100644 local_repo/text_encoder_2/config.json
 delete mode 100644 local_repo/tokenizer/merges.txt
 delete mode 100644 local_repo/tokenizer/special_tokens_map.json
 delete mode 100644 local_repo/tokenizer/tokenizer_config.json
 delete mode 100644 local_repo/tokenizer/vocab.json
 delete mode 100644 local_repo/tokenizer_2/merges.txt
 delete mode 100644 local_repo/tokenizer_2/special_tokens_map.json
 delete mode 100644 local_repo/tokenizer_2/tokenizer_config.json
 delete mode 100644 local_repo/tokenizer_2/vocab.json
 delete mode 100644 local_repo/unet/config.json
 delete mode 100644 local_repo/vae/config.json
 delete mode 100644 local_repo/vae_1_0/config.json
 delete mode 100644 local_repo/vae_decoder/config.json
 delete mode 100644 local_repo/vae_encoder/config.json
 delete mode 100644 msdiffusion/__init__.py
 delete mode 100644 msdiffusion/dataset/__init__.py
 delete mode 100644 msdiffusion/dataset/prompts/msbench.py
 delete mode 100644 msdiffusion/models/__init__.py
 delete mode 100644 msdiffusion/models/attention_processor.py
 delete mode 100644 msdiffusion/models/model.py
 delete mode 100644 msdiffusion/models/projection.py
 delete mode 100644 msdiffusion/utils.py
 create mode 100644 msdiffusion_native/__init__.py
 create mode 100644 msdiffusion_native/core.py
 create mode 100644 msdiffusion_native/nodes.py
 delete mode 100644 requirements.txt

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..ba0430d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+__pycache__/
\ No newline at end of file
diff --git a/MS_Diffusion.py b/MS_Diffusion.py
deleted file mode 100644
index 68681e9..0000000
--- a/MS_Diffusion.py
+++ /dev/null
@@ -1,648 +0,0 @@
-# !/usr/bin/env python
-# -*- coding: UTF-8 -*-
-
-import numpy as np
-import torch
-import os
-import sys
-import re
-import yaml
-from PIL import Image
-from huggingface_hub import hf_hub_download
-from diffusers import (StableDiffusionXLPipeline, DDIMScheduler, ControlNetModel,
-                       KDPM2AncestralDiscreteScheduler, LMSDiscreteScheduler,
-                       DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler,
-                       EulerDiscreteScheduler, HeunDiscreteScheduler,
-                       KDPM2DiscreteScheduler,
-                       EulerAncestralDiscreteScheduler, UniPCMultistepScheduler, AutoencoderKL,
-                       StableDiffusionXLControlNetPipeline, DDPMScheduler, LCMScheduler)
-from transformers import CLIPVisionModelWithProjection
-from transformers import CLIPImageProcessor
-from .msdiffusion.models.projection import Resampler
-from .msdiffusion.models.model import MSAdapter
-from .msdiffusion.utils import get_phrase_idx, get_eot_idx
-from diffusers.schedulers.scheduling_ddim import DDIMScheduler
-import folder_paths
-from comfy.utils import common_upscale
-from omegaconf import OmegaConf
-
-MAX_SEED = np.iinfo(np.int32).max
-dir_path = os.path.dirname(os.path.abspath(__file__))
-path_dir = os.path.dirname(dir_path)
-file_path = os.path.dirname(path_dir)
-
-def get_instance_path(path):
-    instance_path = os.path.normpath(path)
-    if sys.platform == 'win32':
-        instance_path = instance_path.replace('\\', "/")
-    return instance_path
-
-loras_path = get_instance_path(os.path.join(dir_path, "config", "lora.yaml"))
-
-def get_lora_dict():
-    # 打开并读取YAML文件
-    with open(loras_path, 'r', encoding="UTF-8") as stream:
-        try:
-            # 解析YAML文件内容
-            data = yaml.safe_load(stream)
-            
-            # 此时 'data' 是一个Python字典，里面包含了YAML文件的所有数据
-            # print(data)
-            return data
-        
-        except yaml.YAMLError as exc:
-            # 如果在解析过程中发生了错误，打印异常信息
-            print(exc)
-
-
-datas = get_lora_dict()
-lora_lightning_list = datas["lightning_xl_lora"]
-
-scheduler_list = [
-    "Euler", "Euler a", "DDIM", "DDPM", "DPM++ 2M", "DPM++ 2M Karras", "DPM++ 2M SDE", "DPM++ 2M SDE Karras",
-    "DPM++ SDE", "DPM++ SDE Karras", "DPM2",
-    "DPM2 Karras", "DPM2 a", "DPM2 a Karras", "Heun", "LCM", "LMS", "LMS Karras", "UniPC"
-]
-
-
-def phi2narry(img):
-    img = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
-    return img
-
-
-def nomarl_upscale(img_tensor, width, height):
-    samples = img_tensor.movedim(-1, 1)
-    img = common_upscale(samples, width, height, "nearest-exact", "center")
-    samples = img.movedim(1, -1)
-    return samples
-
-
-def phi2tensor(img):
-    image = np.array(img).astype(np.float32) / 255.0
-    image = torch.from_numpy(image)[None,]
-    return image
-
-
-def add_pil(list, list_add, num):
-    new_list = list[:num] + list_add + list[num:]
-    return new_list
-
-
-def tensor_to_image(tensor):
-    # tensor = tensor.cpu()
-    image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
-    image = Image.fromarray(image_np, mode='RGB')
-    return image
-
-
-# get fonts list
-def has_parentheses(s):
-    return bool(re.search(r'\(.*?\)', s))
-
-
-def contains_brackets(s):
-    return '[' in s or ']' in s
-
-
-def narry_list(list_in):
-    for i in range(len(list_in)):
-        value = list_in[i]
-        modified_value = phi2narry(value)
-        list_in[i] = modified_value
-    return list_in
-
-
-def phi_list(list_in):
-    for i in range(len(list_in)):
-        value = list_in[i]
-        list_in[i] = value
-    return list_in
-
-
-def narry_list_pil(list_in):
-    for i in range(len(list_in)):
-        value = list_in[i]
-        modified_value = tensor_to_image(value)
-        list_in[i] = modified_value
-    return list_in
-
-
-def get_local_path(file_path, model_path, file):
-    path = os.path.join(file_path, "models", file, model_path)
-    model_path = os.path.normpath(path)
-    if sys.platform.startswith('win32'):
-        model_path = model_path.replace('\\', "/")
-    return model_path
-
-
-def extract_content_between_brackets(text):
-    # 正则表达式匹配多对括号内的内容
-    return re.findall(r'\((.*?)\)', text)
-
-
-def extract_content_from_brackets(text):
-    # 正则表达式匹配多对方括号内的内容
-    return re.findall(r'\[(.*?)\]', text)
-
-
-def get_image_path_list(folder_name):
-    image_basename_list = os.listdir(folder_name)
-    image_path_list = sorted(
-        [os.path.join(folder_name, basename) for basename in image_basename_list]
-    )
-    return image_path_list
-
-
-def get_scheduler(name):
-    scheduler = False
-    if name == "Euler":
-        scheduler = EulerDiscreteScheduler()
-    elif name == "Euler a":
-        scheduler = EulerAncestralDiscreteScheduler()
-    elif name == "DDIM":
-        scheduler = DDIMScheduler()
-    elif name == "DDPM":
-        scheduler = DDPMScheduler()
-    elif name == "DPM++ 2M":
-        scheduler = DPMSolverMultistepScheduler()
-    elif name == "DPM++ 2M Karras":
-        scheduler = DPMSolverMultistepScheduler(use_karras_sigmas=True)
-    elif name == "DPM++ 2M SDE":
-        scheduler = DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++")
-    elif name == "DPM++ 2M SDE Karras":
-        scheduler = DPMSolverMultistepScheduler(use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
-    elif name == "DPM++ SDE":
-        scheduler = DPMSolverSinglestepScheduler()
-    elif name == "DPM++ SDE Karras":
-        scheduler = DPMSolverSinglestepScheduler(use_karras_sigmas=True)
-    elif name == "DPM2":
-        scheduler = KDPM2DiscreteScheduler()
-    elif name == "DPM2 Karras":
-        scheduler = KDPM2DiscreteScheduler(use_karras_sigmas=True)
-    elif name == "DPM2 a":
-        scheduler = KDPM2AncestralDiscreteScheduler()
-    elif name == "DPM2 a Karras":
-        scheduler = KDPM2AncestralDiscreteScheduler(use_karras_sigmas=True)
-    elif name == "Heun":
-        scheduler = HeunDiscreteScheduler()
-    elif name == "LCM":
-        scheduler = LCMScheduler()
-    elif name == "LMS":
-        scheduler = LMSDiscreteScheduler()
-    elif name == "LMS Karras":
-        scheduler = LMSDiscreteScheduler(use_karras_sigmas=True)
-    elif name == "UniPC":
-        scheduler = UniPCMultistepScheduler()
-    return scheduler
-
-
-def nomarl_upscale_topil(img_tensor, width, height):
-    samples = img_tensor.movedim(-1, 1)
-    img = common_upscale(samples, width, height, "nearest-exact", "center")
-    samples = img.movedim(1, -1)
-    img_pil = tensor_to_image(samples)
-    return img_pil
-
-
-def instance_path(path, repo, file):
-    if repo == "":
-        if path == "none":
-            repo = "none"
-        else:
-            model_path = get_local_path(file_path, path, file)
-            repo = get_instance_path(model_path)
-    return repo
-
-
-class MSdiffusion_Model_Loader:
-    def __init__(self):
-        self.counters = {}
-    
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "clip_vision": ("CLIP_VISION",),
-                "ckpt_name": (["none"] + folder_paths.get_filename_list("checkpoints"),),
-                "vae_id": (["none"] + folder_paths.get_filename_list("vae"),),
-                "controlnet_ckpt": (["none"] + folder_paths.get_filename_list("controlnet"),),
-                "lora": (["none"] + folder_paths.get_filename_list("loras"),),
-                "lora_scale": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 1.0, "step": 0.1}),
-                "trigger_words": ("STRING", {"default": "best quality"}),
-                "scheduler": (scheduler_list,), }
-        }
-    
-    RETURN_TYPES = ("MSDIF_DICT",)
-    RETURN_NAMES = ("model",)
-    FUNCTION = "ms_model_loader"
-    CATEGORY = "MSdiffusion"
-    
-    def ms_model_loader(self, clip_vision,ckpt_name, vae_id, controlnet_ckpt,
-                        lora, lora_scale, trigger_words, scheduler):
-        image_encoder = clip_vision
-        device = image_encoder.load_device
-        scheduler_choice = get_scheduler(scheduler)
-        
-        controlnet_path = folder_paths.get_full_path("controlnet", controlnet_ckpt)
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
-        
-        modle_config = os.path.join(dir_path,"local_repo")
-        original_config_file = os.path.join(dir_path, "config", "sd_xl_base.yaml")
-        
-        if lora != "none":
-            lora_path = folder_paths.get_full_path("loras", lora)
-            lora_path = get_instance_path(lora_path)
-        else:
-            lora_path = ""
-        if "/" in lora:
-            lora = lora.split("/")[-1]
-        if "\\" in lora:
-            lora = lora.split("\\")[-1]
-        
-        # load SDXL pipeline
-        try:
-            pipe = StableDiffusionXLPipeline.from_single_file(
-                ckpt_path, config=modle_config, original_config=original_config_file, torch_dtype=torch.float16)
-        except:
-            try:
-                pipe = StableDiffusionXLPipeline.from_single_file(
-                    ckpt_path, config=modle_config, original_config_file=original_config_file,
-                    torch_dtype=torch.float16)
-            except:
-                raise "model error"
-            
-        if controlnet_ckpt != "none":  # 启用control
-            from safetensors.torch import load_file
-            controlnet = ControlNetModel.from_unet(pipe.unet)
-            cn_state_dict = load_file(controlnet_path, device="cpu")
-            controlnet.load_state_dict(cn_state_dict, strict=False)
-            controlnet.to(torch.float16)
-            pipe=StableDiffusionXLControlNetPipeline.from_pipe(pipe,controlnet=controlnet)
-        
-        if vae_id != "none":
-            vae_id = folder_paths.get_full_path("vae", vae_id)
-            vae_config=os.path.join(modle_config,"vae")
-            pipe.vae = AutoencoderKL.from_single_file(vae_id, config=vae_config,torch_dtype=torch.float16).to(device)
-        if lora != "none":
-            if lora in lora_lightning_list:
-                pipe.load_lora_weights(lora_path)
-                pipe.fuse_lora()
-            else:
-                pipe.load_lora_weights(lora_path, adapter_name=trigger_words)
-                pipe.fuse_lora(adapter_names=[trigger_words, ], lora_scale=lora_scale)
-        
-        pipe.scheduler = scheduler_choice.from_config(pipe.scheduler.config)
-        pipe.enable_xformers_memory_efficient_attention()
-        pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
-        pipe.enable_vae_slicing()
-        # if device != "mps":
-        #     pipe.enable_model_cpu_offload()
-        ms_dir = os.path.join(file_path, "models", "photomaker")
-        photomaker_local_path = os.path.join(ms_dir, "ms_adapter.bin")
-        if not os.path.exists(photomaker_local_path):
-            ms_path = hf_hub_download(
-                repo_id="doge1516/MS-Diffusion",
-                filename="ms_adapter.bin",
-                repo_type="model",
-                local_dir=ms_dir,
-            )
-        else:
-            ms_path = photomaker_local_path
-        ms_ckpt = get_instance_path(ms_path)
-        
-        image_processor = CLIPImageProcessor()
-        config_path = os.path.join(dir_path, "config", "config.json")
-        image_encoder_config = OmegaConf.load(config_path)
-        image_encoder_projection_dim = image_encoder_config["vision_config"]["projection_dim"]
-        num_tokens = 16
-        
-        image_encoder_type = "clip"
-        image_proj_type = "resampler"
-        latent_init_mode = "grounding"
-        # latent_init_mode = "random"
-        
-        image_proj_model = Resampler(
-            dim=1280,
-            depth=4,
-            dim_head=64,
-            heads=20,
-            num_queries=num_tokens,
-            embedding_dim=image_encoder_config["vision_config"]["hidden_size"],  # image_encoder.config.hidden_size,
-            output_dim=pipe.unet.config.cross_attention_dim,
-            ff_mult=4,
-            latent_init_mode=latent_init_mode,
-            phrase_embeddings_dim=pipe.text_encoder.config.projection_dim,
-        ).to(device, dtype=torch.float16)
-        ms_model = MSAdapter(pipe.unet, image_proj_model, ckpt_path=ms_ckpt, device=device, num_tokens=num_tokens)
-        ms_model.to(device, dtype=torch.float16)
-        pipe.to(device, dtype=torch.float16)
-        torch.cuda.empty_cache()
-        model = {"pipe": pipe, "ms_model": ms_model, "image_encoder": image_encoder, "image_processor": image_processor,
-                 "lora": lora,
-                 "trigger_words": trigger_words, "controlnet_model":  controlnet_ckpt,
-                 "image_encoder_type": image_encoder_type, "image_proj_type": image_proj_type,}
-        return (model,)
-
-
-class MSdiffusion_Sampler:
-    def __init__(self):
-        pass
-    
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "model": ("MSDIF_DICT",),
-                "prompt": ("STRING", {"multiline": True, "default": "a [dog] wearing a pink sunglass"}),
-                "negative_prompt": ("STRING", {"multiline": True,
-                                               "default": "(worst quality, low quality, normal quality, lowres),"}),
-                "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED}),
-                "steps": ("INT", {"default": 20, "min": 1, "max": 100}),
-                "cfg": ("FLOAT", {"default": 7, "min": 0.1, "max": 10.0, "step": 0.1, "round": 0.01}),
-                "scale": (
-                    "FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
-                "mask_threshold": (
-                    "FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
-                "start_step": ("INT", {"default": 5, "min": 1, "max": 1024}),
-                
-                "controlnet_scale": (
-                    "FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
-                "width": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 64, "display": "number"}),
-                "height": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 64, "display": "number"}),
-                "batch_size": ("INT", {"default": 1, "min": 1, "max": 100, "step": 1, }),
-                "drop_grounding_tokens": ("BOOLEAN", {"default": False},),
-                "guidance_list": ("STRING", {"multiline": True, "default": "0., 0.25, 0.4, 0.75;0.6, 0.25, 1., 0.75"}),
-                
-            },
-            "optional": {"control_image": ("IMAGE",),
-                         }
-        }
-    
-    @classmethod
-    def IS_CHANGED(cls, **kwargs):
-        if kwargs.get("batch_size"):
-            return
-    
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
-    FUNCTION = "ms_sampler"
-    CATEGORY = "MSdiffusion"
-    
-    def main_normal(self, prompt, pipe, phrases, ms_model, input_images, num_samples, steps, seed, negative_prompt,
-                    scale, image_encoder, cfg, image_processor,
-                    boxes, mask_threshold, start_step, image_proj_type, image_encoder_type, drop_grounding_tokens,
-                    height, width, phrase_idxes, eot_idxes, image, ):
-        images = ms_model.generate(pipe=pipe, pil_images=[input_images], processed_images=image,
-                                   num_samples=num_samples,
-                                   num_inference_steps=steps,
-                                   seed=seed,
-                                   prompt=[prompt], negative_prompt=negative_prompt, scale=scale,
-                                   image_encoder=image_encoder, guidance_scale=cfg,
-                                   image_processor=image_processor, boxes=boxes,
-                                   mask_threshold=mask_threshold,
-                                   start_step=start_step,
-                                   image_proj_type=image_proj_type,
-                                   image_encoder_type=image_encoder_type,
-                                   phrases=phrases,
-                                   drop_grounding_tokens=drop_grounding_tokens,
-                                   phrase_idxes=phrase_idxes, eot_idxes=eot_idxes, height=height,
-                                   width=width)
-        return images
-    
-    def main_control(self, prompt, width, height, pipe, phrases, ms_model, input_images, num_samples, steps, seed,
-                     negative_prompt, scale, image_encoder, cfg,
-                     image_processor, boxes, mask_threshold, start_step, image_proj_type, image_encoder_type,
-                     drop_grounding_tokens, controlnet_scale, control_image, phrase_idxes, eot_idxes, image, ):
-        images = ms_model.generate(pipe=pipe, pil_images=[input_images], processed_images=image,
-                                   num_samples=num_samples,
-                                   num_inference_steps=steps,
-                                   seed=seed,
-                                   prompt=[prompt], negative_prompt=negative_prompt, scale=scale,
-                                   image_encoder=image_encoder, guidance_scale=cfg,
-                                   image_processor=image_processor, boxes=boxes,
-                                   mask_threshold=mask_threshold,
-                                   start_step=start_step,
-                                   image_proj_type=image_proj_type,
-                                   image_encoder_type=image_encoder_type,
-                                   phrases=phrases,
-                                   drop_grounding_tokens=drop_grounding_tokens,
-                                   phrase_idxes=phrase_idxes, eot_idxes=eot_idxes, height=height,
-                                   width=width,
-                                   image=control_image, controlnet_conditioning_scale=controlnet_scale)
-        
-        return images
-    
-    def get_phrases_idx(self, tokenizer, phrases, prompt):
-        res = []
-        phrase_cnt = {}
-        for phrase in phrases:
-            if phrase in phrase_cnt:
-                cur_cnt = phrase_cnt[phrase]
-                phrase_cnt[phrase] += 1
-            else:
-                cur_cnt = 0
-                phrase_cnt[phrase] = 1
-            res.append(get_phrase_idx(tokenizer, phrase, prompt, num=cur_cnt)[0])
-        return res
-    
-    def get_float(self, str_in):
-        list_str = str_in.split(",")
-        float_box = [float(x) for x in list_str]
-        return float_box
-    
-    def center_crop_s(self, img, new_width, new_height):
-        width, height = img.size
-        left = (width - new_width) / 2
-        top = (height - new_height) / 2
-        right = (width + new_width) / 2
-        bottom = (height + new_height) / 2
-        return img.crop((left, top, right, bottom))
-    
-    def ms_sampler(self, image, model, prompt, negative_prompt, seed, steps,
-                   cfg, scale, mask_threshold, start_step, controlnet_scale, width, height, batch_size,
-                   drop_grounding_tokens, guidance_list, **kwargs):
-        if  width==height:
-            new_width = width
-            new_height = width
-            min_wh= width
-        else:
-            min_wh=min(width,height)
-            max_wh=max(width,height)
-            new_width = max_wh
-            new_height =max_wh
-        
-        pipe = model["pipe"]
-        ms_model = model["ms_model"]
-        image_encoder = model["image_encoder"]
-        image_processor = model["image_processor"]
-        lora = model["lora"]
-        trigger_words = model["trigger_words"]
-        controlnet_model = model["controlnet_model"]
-        image_encoder_type = model["image_encoder_type"]
-        image_proj_type = model["image_proj_type"]
-        if drop_grounding_tokens:
-            drop_grounding_tokens = [1]  # set to 1 if you want to drop the grounding tokens
-        else:
-            drop_grounding_tokens = [0]
-        
-        guidance_list = guidance_list.strip().split(";")
-        
-        box_add = []  # 获取预设box
-        for i in range(len(guidance_list)):
-            box_add.append(self.get_float(guidance_list[i]))
-        
-        if mask_threshold == 0.:
-            mask_threshold = None
-        
-        if '[' in prompt and ']' in prompt:
-            object_prompt = extract_content_from_brackets(prompt)  # 提取prompt的object list
-            object_prompt=[i.strip() for i in object_prompt]
-            for i in object_prompt:
-                if " " in i:
-                   raise "when using [object],object must be a word,any blank in ititit will cause error."
-            object_prompt=[i for i in object_prompt ]
-            unique_object_prompt = sorted(list(set(object_prompt)),
-                                          key=lambda x: list(object_prompt).index(x))  # 清除同名物体,保持原有顺序
-            object_num = len(unique_object_prompt)
-        else:
-            raise "Need to enclose the name of the object in square brackets"
-        
-        d1, _, _, _ = image.size()
-        # print(d1,object_num)
-        
-        if lora != "none":
-            prompt = prompt + trigger_words + " " + "style"
-        
-        prompt = prompt.replace("]", " ").replace("[", " ")  # 去除[]
-        
-        if object_num == d1:
-            phrases = [unique_object_prompt]
-            print(phrases)
-        else:
-            raise "The number of objects should be equal to the number of input images"
-        
-        if d1 == 1:
-            input_images = [nomarl_upscale_topil(image, min_wh, min_wh)]
-            torch.cuda.empty_cache()
-            if mask_threshold:  # step之前所用布局指导
-                boxes = box_add[0]
-                boxes = [[boxes]]  # 随机选1个自定义的
-            # boxes = [[[0.25, 0.25, 0.75, 0.75]]]  # 1 object
-            else:
-                boxes = [[[0., 0., 0., 0.]]]  # 1 object
-            phrase_idxes = [self.get_phrases_idx(pipe.tokenizer, phrases[0], prompt)]
-            eot_idxes = [[get_eot_idx(pipe.tokenizer, prompt)] * len(phrases[0])]
-            if controlnet_model != "none":
-                control_image = kwargs.get("control_image")
-                control_img = nomarl_upscale_topil(control_image, min_wh, min_wh)
-                image_main = self.main_control(prompt, new_width, new_height, pipe, phrases, ms_model, input_images, batch_size,
-                                               steps, seed, negative_prompt, scale, image_encoder, cfg,
-                                               image_processor, boxes, mask_threshold, start_step, image_proj_type,
-                                               image_encoder_type, drop_grounding_tokens, controlnet_scale, control_img,
-                                               phrase_idxes, eot_idxes, image)
-            
-            else:  # no controlnet
-                image_main = self.main_normal(prompt, pipe, phrases, ms_model, input_images, batch_size, steps, seed,
-                                              negative_prompt, scale, image_encoder, cfg, image_processor,
-                                              boxes, mask_threshold, start_step, image_proj_type, image_encoder_type,
-                                              drop_grounding_tokens, new_height, new_width, phrase_idxes, eot_idxes, image
-                                            )
-        
-        else:
-            img_list = list(torch.chunk(image, chunks=d1))
-            # print(img_list)
-            input_images = [nomarl_upscale_topil(img, min_wh, min_wh) for img in img_list]
-            # print(input_images)
-            if mask_threshold:
-                if len(box_add) < d1:
-                    raise "When using 'mask_threshold', a sequence equal to the number of objects must be entered in 'guidance list'"
-                else:
-                    boxes = [box_add[:d1]]
-                    print(boxes)
-            # boxes = [[[0., 0.25, 0.4, 0.75], [0.6, 0.25, 1., 0.75]]]  # man+women
-            else:
-                zero_list = [0 for _ in range(4)]
-                boxes = [zero_list for _ in range(d1)]
-                boxes = [boxes]  # used if you want no layout guidance
-            # print(boxes)
-            phrase_idxes = [self.get_phrases_idx(pipe.tokenizer, phrases[0], prompt)]
-            eot_idxes = [[get_eot_idx(pipe.tokenizer, prompt)] * len(phrases[0])]
-            if controlnet_model != "none":
-                control_image = kwargs.get("control_image")
-                control_img = nomarl_upscale_topil(control_image, min_wh, min_wh)  # pil
-                image_main = self.main_control(prompt, new_width, new_height, pipe, phrases, ms_model, input_images, batch_size,
-                                               steps, seed, negative_prompt, scale, image_encoder, cfg,
-                                               image_processor, boxes, mask_threshold, start_step, image_proj_type,
-                                               image_encoder_type, drop_grounding_tokens, controlnet_scale, control_img,
-                                               phrase_idxes, eot_idxes, image)
-            
-            else:  # no controlnet
-                image_main = self.main_normal(prompt, pipe, phrases, ms_model, input_images, batch_size, steps, seed,
-                                              negative_prompt, scale, image_encoder, cfg, image_processor,
-                                              boxes, mask_threshold, start_step, image_proj_type, image_encoder_type,
-                                              drop_grounding_tokens, new_height, new_width, phrase_idxes, eot_idxes, image
-                                             )
-        
-        output_img = []
-        for i in range(batch_size):
-            if width!=height:
-                output_img.append(self.center_crop_s(image_main[i], width, height))
-            else:
-                output_img.append(image_main[i])
-            
-        
-        image_list = narry_list(output_img)
-        image = torch.from_numpy(np.fromiter(image_list, np.dtype((np.float32, (height, width, 3)))))
-        return (image,)
-
-
-class MS_Object_img_Batch:
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"image_a": ("IMAGE",),
-                             },
-                "optional": {"image_b": ("IMAGE",),
-                             "image_c": ("IMAGE",),
-                             "image_d": ("IMAGE",)}
-                }
-    
-    RETURN_TYPES = ("IMAGE",)
-    ETURN_NAMES = ("image",)
-    FUNCTION = "main_batch"
-    CATEGORY = "MSdiffusion"
-    
-    def main_batch(self, image_a, **kwargs):
-        image_b = kwargs.get("image_b")
-        image_c = kwargs.get("image_c")
-        image_d = kwargs.get("image_d")
-        
-        width = image_a.shape[2]
-        height = image_a.shape[1]
-        img_list = [image_a]
-        if isinstance(image_b, torch.Tensor):
-            image_b = nomarl_upscale(image_b, width, height)
-            img_list.append(image_b)
-        if isinstance(image_c, torch.Tensor):
-            image_c = nomarl_upscale(image_c, width, height)
-            img_list.append(image_c)
-        if isinstance(image_d, torch.Tensor):
-            image_d = nomarl_upscale(image_d, width, height)
-            img_list.append(image_d)
-        image = torch.cat(tuple(img_list), dim=0)
-        return (image,)
-
-
-NODE_CLASS_MAPPINGS = {
-    "MSdiffusion_Model_Loader": MSdiffusion_Model_Loader,
-    "MSdiffusion_Sampler": MSdiffusion_Sampler,
-    "MS_Object_img_Batch": MS_Object_img_Batch,
-    
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "MSdiffusion_Model_Loader": "MSdiffusion_Model_Loader",
-    "MSdiffusion_Sampler": "MSdiffusion_Sampler",
-    "MS_Object_img_Batch": "MS_Object_img_Batch"
-}
diff --git a/README.md b/README.md
index 4a6a30c..94d794c 100644
--- a/README.md
+++ b/README.md
@@ -1,87 +1,48 @@
-Youu can using MS_Diffusion in ComfyUI 
+# ComfyUI_MS_Diffusion
 
--- 
-MS-Diffusion origin From: [MS-Diffusion](https://github.com/MS-Diffusion/MS-Diffusion)
-----
+[MS-Diffusion](https://github.com/MS-Diffusion/MS-Diffusion) multi-subject zero-shot image personalization with layout guidance
 
-**NEW Update**
+# Upadtes
+* 借助agent（自动）使用comfyUI核心重新封装此方法 / Re-implement this method with the help of agent to re-package it into the core of comfyUI
 ---
-* 2024/09/06:fix runway error/load single controlnet now /首次加载不再需要连外网。  
 
-**Previous updates**
-* del clip repo，Add comfyUI clip_vision loader/加入comfyUI的clip vision节点，不再使用 clip repo。   
-
-1.Installation
+# Installation  
 -----
-  In the ./ComfyUI /custom_node directory, run the following:   
-
-  ``` python 
-  git clone https://github.com/smthemex/ComfyUI_MS_Diffusion.git
-
-  ```
-2.requirements  
-----
+  In the ./ComfyUI/custom_nodes directory, run the following:   
 ```
-pip install -r requirements.txt
-```
-缺啥装啥。   
-If the module is missing, please pip install   
-
-3 Need  model 
-----
-Need download "ms_adapter.bin" : [link](https://huggingface.co/doge1516/MS-Diffusion/tree/main)    
-clip vision model  （ any  base from CLIP-ViT-bigG-14-laion2B-39B-b160k）  
-Control_img image preprocessing, please use other nodes    
-```
-├── ComfyUI/models/checkpoints/
-|             ├── any SDXL weigth
-├── ComfyUI/models/photomaker/
-|             ├── ms_adapter.bin
-├── ComfyUI/models/clip_vision/
-|             ├── clip_vision_g.safetensors  or CLIP-ViT-bigG-14-laion2B-39B-b160k.safetensors 
-├── ComfyUI/models/controlnet 
-|             ├──any SDXL controlnet  
+git clone https://github.com/smthemex/ComfyUI_MS_Diffusion
 ```
 
-4 using tips
----
---生成物体名称需要用[  ]括起来，有多少个物体，就要有多少张图片输入;  
---To generate object names, they need to be enclosed in [  ]. As many objects as there are, there must be as many images to input;     
-
-5 Example
-----
-
-normal 2 boject 常规双主体图生图 最新示例。     
-![](https://github.com/smthemex/ComfyUI_MS_Diffusion/blob/main/examples/example_new.png)
-
-normal 4 boject 4主体图生图。更多的主体没有测试，旧示例，仅供参考。            
-![](https://github.com/smthemex/ComfyUI_MS_Diffusion/blob/main/examples/autolayerimg4img.png)
-
-2 object zero shot and controlnet img2img  双物体加controlnet引导 图生图,旧示例，仅供参考  
-![](https://github.com/smthemex/ComfyUI_MS_Diffusion/blob/main/examples/controlnet%20_obj.png)
-![](https://github.com/smthemex/ComfyUI_MS_Diffusion/blob/main/examples/controlnet%20_obj1.png)
-
-
-6 Citation
-------
-MS-Diffusion
+## Models
+[sdxl](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+[ms-ipadapter](https://huggingface.co/doge1516/MS-Diffusion)
 ```
-@misc{wang2024msdiffusion,
-  title={MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance}, 
-  author={X. Wang and Siming Fu and Qihan Huang and Wanggui He and Hao Jiang},
-  year={2024},
-  eprint={2406.07209},
-  archivePrefix={arXiv},
-  primaryClass={cs.CV}
-}
+├── ComfyUI/models/checkpoints
+|     ├── sd_xl_base_1.0.safetensors #any sdxl
+├── ComfyUI/models/photomaker #ipadapter
+|        ├──ms_adapter.bin
 ```
 
-IP-Adapter
+## Multi-subject (multiple reference images)
+
+| # | Sequence | Example (2 subjects) |
+|---|---|---|
+| 1 | IMAGE batch order (native `ImageBatch`: image1, image2, ...) | `ms_dog.jpg` → image1, `ms_cat.jpg` → image2 |
+| 2 | `[subject]` order in `prompt` | `a [dog] and a [cat] on the beach` |
+| 3 | `boxes`, `;`-separated, one `x1,y1,x2,y2` each | `0., 0.25, 0.4, 0.75;0.6, 0.25, 1., 0.75` |
+
+## Example
+
+![](https://github.com/smthemex/ComfyUI_MS_Diffusion/blob/main/example_workflows/example.png)
+
+## Citation
 ```
-@article{ye2023ip-adapter,
-  title={IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models},
-  author={Ye, Hu and Zhang, Jun and Liu, Sibo and Han, Xiao and Yang, Wei},
-  booktitle={arXiv preprint arxiv:2308.06721},
-  year={2023}
+@inproceedings{
+  wang2025msdiffusion,
+  title={{MS}-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance},
+  author={Xierui Wang and Siming Fu and Qihan Huang and Wanggui He and Hao Jiang},
+  booktitle={The Thirteenth International Conference on Learning Representations},
+  year={2025},
+  url={https://openreview.net/forum?id=PJqP0wyQek}
 }
 ```
diff --git a/__init__.py b/__init__.py
index 32e7e96..92193a8 100644
--- a/__init__.py
+++ b/__init__.py
@@ -1,4 +1,4 @@
 
-from .MS_Diffusion import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+from .msdiffusion_native import NODE_CLASS_MAPPINGS,NODE_DISPLAY_NAME_MAPPINGS 
 
-__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
diff --git a/config/config.json b/config/config.json
deleted file mode 100644
index e83b7cc..0000000
--- a/config/config.json
+++ /dev/null
@@ -1,184 +0,0 @@
-{
-  "_commit_hash": null,
-  "architectures": [
-    "CLIPModel"
-  ],
-  "initializer_factor": 1.0,
-  "logit_scale_init_value": 2.6592,
-  "model_type": "clip",
-  "projection_dim": 1280,
-  "text_config": {
-    "_name_or_path": "",
-    "add_cross_attention": false,
-    "architectures": null,
-    "attention_dropout": 0.0,
-    "bad_words_ids": null,
-    "begin_suppress_tokens": null,
-    "bos_token_id": 0,
-    "chunk_size_feed_forward": 0,
-    "cross_attention_hidden_size": null,
-    "decoder_start_token_id": null,
-    "diversity_penalty": 0.0,
-    "do_sample": false,
-    "dropout": 0.0,
-    "early_stopping": false,
-    "encoder_no_repeat_ngram_size": 0,
-    "eos_token_id": 2,
-    "exponential_decay_length_penalty": null,
-    "finetuning_task": null,
-    "forced_bos_token_id": null,
-    "forced_eos_token_id": null,
-    "hidden_act": "gelu",
-    "hidden_size": 1280,
-    "id2label": {
-      "0": "LABEL_0",
-      "1": "LABEL_1"
-    },
-    "initializer_factor": 1.0,
-    "initializer_range": 0.02,
-    "intermediate_size": 5120,
-    "is_decoder": false,
-    "is_encoder_decoder": false,
-    "label2id": {
-      "LABEL_0": 0,
-      "LABEL_1": 1
-    },
-    "layer_norm_eps": 1e-05,
-    "length_penalty": 1.0,
-    "max_length": 20,
-    "max_position_embeddings": 77,
-    "min_length": 0,
-    "model_type": "clip_text_model",
-    "no_repeat_ngram_size": 0,
-    "num_attention_heads": 20,
-    "num_beam_groups": 1,
-    "num_beams": 1,
-    "num_hidden_layers": 32,
-    "num_return_sequences": 1,
-    "output_attentions": false,
-    "output_hidden_states": false,
-    "output_scores": false,
-    "pad_token_id": 1,
-    "prefix": null,
-    "problem_type": null,
-    "projection_dim": 1280,
-    "pruned_heads": {},
-    "remove_invalid_values": false,
-    "repetition_penalty": 1.0,
-    "return_dict": true,
-    "return_dict_in_generate": false,
-    "sep_token_id": null,
-    "suppress_tokens": null,
-    "task_specific_params": null,
-    "temperature": 1.0,
-    "tf_legacy_loss": false,
-    "tie_encoder_decoder": false,
-    "tie_word_embeddings": true,
-    "tokenizer_class": null,
-    "top_k": 50,
-    "top_p": 1.0,
-    "torch_dtype": null,
-    "torchscript": false,
-    "transformers_version": "4.24.0",
-    "typical_p": 1.0,
-    "use_bfloat16": false,
-    "vocab_size": 49408
-  },
-  "text_config_dict": {
-    "hidden_act": "gelu",
-    "hidden_size": 1280,
-    "intermediate_size": 5120,
-    "num_attention_heads": 20,
-    "num_hidden_layers": 32,
-    "projection_dim": 1280
-  },
-  "torch_dtype": "float32",
-  "transformers_version": null,
-  "vision_config": {
-    "_name_or_path": "",
-    "add_cross_attention": false,
-    "architectures": null,
-    "attention_dropout": 0.0,
-    "bad_words_ids": null,
-    "begin_suppress_tokens": null,
-    "bos_token_id": null,
-    "chunk_size_feed_forward": 0,
-    "cross_attention_hidden_size": null,
-    "decoder_start_token_id": null,
-    "diversity_penalty": 0.0,
-    "do_sample": false,
-    "dropout": 0.0,
-    "early_stopping": false,
-    "encoder_no_repeat_ngram_size": 0,
-    "eos_token_id": null,
-    "exponential_decay_length_penalty": null,
-    "finetuning_task": null,
-    "forced_bos_token_id": null,
-    "forced_eos_token_id": null,
-    "hidden_act": "gelu",
-    "hidden_size": 1664,
-    "id2label": {
-      "0": "LABEL_0",
-      "1": "LABEL_1"
-    },
-    "image_size": 224,
-    "initializer_factor": 1.0,
-    "initializer_range": 0.02,
-    "intermediate_size": 8192,
-    "is_decoder": false,
-    "is_encoder_decoder": false,
-    "label2id": {
-      "LABEL_0": 0,
-      "LABEL_1": 1
-    },
-    "layer_norm_eps": 1e-05,
-    "length_penalty": 1.0,
-    "max_length": 20,
-    "min_length": 0,
-    "model_type": "clip_vision_model",
-    "no_repeat_ngram_size": 0,
-    "num_attention_heads": 16,
-    "num_beam_groups": 1,
-    "num_beams": 1,
-    "num_channels": 3,
-    "num_hidden_layers": 48,
-    "num_return_sequences": 1,
-    "output_attentions": false,
-    "output_hidden_states": false,
-    "output_scores": false,
-    "pad_token_id": null,
-    "patch_size": 14,
-    "prefix": null,
-    "problem_type": null,
-    "projection_dim": 1280,
-    "pruned_heads": {},
-    "remove_invalid_values": false,
-    "repetition_penalty": 1.0,
-    "return_dict": true,
-    "return_dict_in_generate": false,
-    "sep_token_id": null,
-    "suppress_tokens": null,
-    "task_specific_params": null,
-    "temperature": 1.0,
-    "tf_legacy_loss": false,
-    "tie_encoder_decoder": false,
-    "tie_word_embeddings": true,
-    "tokenizer_class": null,
-    "top_k": 50,
-    "top_p": 1.0,
-    "torch_dtype": null,
-    "torchscript": false,
-    "transformers_version": "4.24.0",
-    "typical_p": 1.0,
-    "use_bfloat16": false
-  },
-  "vision_config_dict": {
-    "hidden_act": "gelu",
-    "hidden_size": 1664,
-    "intermediate_size": 8192,
-    "num_attention_heads": 16,
-    "num_hidden_layers": 48,
-    "patch_size": 14,
-    "projection_dim": 1280
-  }
-}
diff --git a/config/lora.yaml b/config/lora.yaml
deleted file mode 100644
index 698f91e..0000000
--- a/config/lora.yaml
+++ /dev/null
@@ -1,33 +0,0 @@
-lightning_unet:
-      - sdxl_lightning_1step_unet_x0.safetensors     #repo    ByteDance/SDXL-Lightning
-      - sdxl_lightning_2step_unet.safetensors        #repo    ByteDance/SDXL-Lightning
-      - sdxl_lightning_4step_unet.safetensors
-      - sdxl_lightning_8step_unet.safetensors
-      - Hyper-SDXL-1step-Unet.safetensors            #repo    ByteDance/Hyper-SD
-      - lcm-sdxl-base-1.0.safetensors                #repo    ckpt/lcm-sdxl-unet    you can change model name like example
-      - dmd2_sdxl_1step_unet_fp16.bin                #repo    tianweiy/DMD2
-      - dmd2_sdxl_4step_unet_fp16.bin
-lightning_xl_lora:
-      - Hyper-SDXL-12steps-CFG-lora.safetensors
-      - Hyper-SDXL-1step-lora.safetensors
-      - Hyper-SDXL-2step-lora.safetensors
-      - Hyper-SDXL-4step-lora.safetensors
-      - Hyper-SDXL-8step-lora.safetensors
-      - Hyper-SDXL-8steps-CFG-lora.safetensors
-      - sdxl_lightning_2step_lora.safetensors
-      - sdxl_lightning_4step_lora.safetensors
-      - sdxl_lightning_8step_lora.safetensors
-      - pcm_sdxl_lcmlike_lora_converted.safetensors
-      - pcm_sdxl_normalcfg_16step_converted.safetensors
-      - pcm_sdxl_normalcfg_4step_converted.safetensors
-      - pcm_sdxl_normalcfg_8step_converted.safetensors
-      - pcm_sdxl_smallcfg_16step_converted.safetensors
-      - pcm_sdxl_smallcfg_2step_converted.safetensors
-      - pcm_sdxl_smallcfg_4step_converted.safetensors
-      - pcm_sdxl_smallcfg_8step_converted.safetensors
-      - lcm-lora-sdxl.safetensors
-      - dmd2_sdxl_4step_lora.safetensors
-      - dmd2_sdxl_4step_lora_fp16.safetensors
-      - TCD-SDXL-LoRA.safetensors                         #need rename and TCD
-      - manne_turbo.safetensors                         
-
diff --git a/config/sd_xl_base.yaml b/config/sd_xl_base.yaml
deleted file mode 100644
index 2c19eff..0000000
--- a/config/sd_xl_base.yaml
+++ /dev/null
@@ -1,93 +0,0 @@
-model:
-  target: sgm.models.diffusion.DiffusionEngine
-  params:
-    scale_factor: 0.13025
-    disable_first_stage_autocast: True
-
-    denoiser_config:
-      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
-      params:
-        num_idx: 1000
-
-        scaling_config:
-          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
-        discretization_config:
-          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
-
-    network_config:
-      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        adm_in_channels: 2816
-        num_classes: sequential
-        use_checkpoint: True
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [4, 2]
-        num_res_blocks: 2
-        channel_mult: [1, 2, 4]
-        num_head_channels: 64
-        use_linear_in_transformer: True
-        transformer_depth: [1, 2, 10]
-        context_dim: 2048
-        spatial_transformer_attn_type: softmax-xformers
-
-    conditioner_config:
-      target: sgm.modules.GeneralConditioner
-      params:
-        emb_models:
-          - is_trainable: False
-            input_key: txt
-            target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
-            params:
-              layer: hidden
-              layer_idx: 11
-
-          - is_trainable: False
-            input_key: txt
-            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
-            params:
-              arch: ViT-bigG-14
-              version: laion2b_s39b_b160k
-              freeze: True
-              layer: penultimate
-              always_return_pooled: True
-              legacy: False
-
-          - is_trainable: False
-            input_key: original_size_as_tuple
-            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
-            params:
-              outdim: 256
-
-          - is_trainable: False
-            input_key: crop_coords_top_left
-            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
-            params:
-              outdim: 256
-
-          - is_trainable: False
-            input_key: target_size_as_tuple
-            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
-            params:
-              outdim: 256
-
-    first_stage_config:
-      target: sgm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          attn_type: vanilla-xformers
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult: [1, 2, 4, 4]
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
diff --git a/example_workflows/example.png b/example_workflows/example.png
new file mode 100644
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diff --git a/example_workflows/msdiffusion.json b/example_workflows/msdiffusion.json
new file mode 100644
index 0000000..824c135
--- /dev/null
+++ b/example_workflows/msdiffusion.json
@@ -0,0 +1,864 @@
+{
+  "id": "86c7c72a-3cc3-4d80-ac53-395f0f1a672f",
+  "revision": 0,
+  "last_node_id": 19,
+  "last_link_id": 44,
+  "nodes": [
+    {
+      "id": 2,
+      "type": "CLIPVisionLoader",
+      "pos": [
+        370.8325188848841,
+        179.37975031596505
+      ],
+      "size": [
+        288.5261157862166,
+        61.99996640263096
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "label": "CLIP_VISION",
+          "name": "CLIP_VISION",
+          "type": "CLIP_VISION",
+          "slot_index": 0,
+          "links": [
+            1
+          ]
+        }
+      ],
+      "title": "CLIP Vision G",
+      "properties": {
+        "Node name for S&R": "CLIPVisionLoader"
+      },
+      "widgets_values": [
+        "clip_vision_g.safetensors"
+      ],
+      "widgets_values_named": {
+        "clip_name": "clip_vision_g.safetensors"
+      }
+    },
+    {
+      "id": 3,
+      "type": "LoadImage",
+      "pos": [
+        370.7321179172254,
+        417.8313576180781
+      ],
+      "size": [
+        315,
+        120
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "label": "IMAGE",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "slot_index": 0,
+          "links": [
+            20
+          ]
+        },
+        {
+          "label": "MASK",
+          "name": "MASK",
+          "type": "MASK",
+          "slot_index": 1,
+          "links": null
+        }
+      ],
+      "title": "subject image(s)",
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "rabbit.png",
+        "image"
+      ],
+      "widgets_values_named": {
+        "image": "rabbit.png",
+        "upload": "image"
+      }
+    },
+    {
+      "id": 4,
+      "type": "CLIPVisionEncode",
+      "pos": [
+        388.2932244009372,
+        296.3283550695895
+      ],
+      "size": [
+        297.9367589978424,
+        85.87358125758468
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "clip_vision",
+          "name": "clip_vision",
+          "type": "CLIP_VISION",
+          "link": 1
+        },
+        {
+          "label": "image",
+          "name": "image",
+          "type": "IMAGE",
+          "link": 20
+        }
+      ],
+      "outputs": [
+        {
+          "label": "CLIP_VISION_OUTPUT",
+          "name": "CLIP_VISION_OUTPUT",
+          "type": "CLIP_VISION_OUTPUT",
+          "slot_index": 0,
+          "links": [
+            37
+          ]
+        }
+      ],
+      "title": "CLIP Vision encode",
+      "properties": {
+        "Node name for S&R": "CLIPVisionEncode"
+      },
+      "widgets_values": [
+        "center"
+      ],
+      "widgets_values_named": {
+        "crop": "center"
+      }
+    },
+    {
+      "id": 5,
+      "type": "MSDiffusionLoader",
+      "pos": [
+        366.4898045653227,
+        -103.33187302053278
+      ],
+      "size": [
+        314.0874241798911,
+        85.99999774294434
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "label": "MS_ADAPTER",
+          "name": "ms_adapter",
+          "type": "MS_ADAPTER",
+          "slot_index": 0,
+          "links": [
+            30,
+            33
+          ]
+        }
+      ],
+      "title": "MS adapter",
+      "properties": {
+        "Node name for S&R": "MSDiffusionLoader"
+      },
+      "widgets_values": [
+        "ms_adapter.bin"
+      ],
+      "widgets_values_named": {
+        "ms_adapter": "ms_adapter.bin"
+      },
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        789.8543229164497,
+        104.03036582957571
+      ],
+      "size": [
+        261.1503022979092,
+        57.99999888788844
+      ],
+      "flags": {
+        "collapsed": true
+      },
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 42
+        },
+        {
+          "name": "text",
+          "type": "STRING",
+          "widget": {
+            "name": "text"
+          },
+          "link": 44
+        }
+      ],
+      "outputs": [
+        {
+          "label": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            23
+          ]
+        }
+      ],
+      "title": "positive",
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "best quality, high quality, a  dog  on the beach"
+      ],
+      "widgets_values_named": {
+        "text": "best quality, high quality, a  dog  on the beach"
+      }
+    },
+    {
+      "id": 8,
+      "type": "CLIPTextEncode",
+      "pos": [
+        789.7131145434341,
+        155.84224816498624
+      ],
+      "size": [
+        290.3512025062168,
+        101.9999896298381
+      ],
+      "flags": {
+        "collapsed": true
+      },
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 43
+        }
+      ],
+      "outputs": [
+        {
+          "label": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            21
+          ]
+        }
+      ],
+      "title": "negative",
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "(worst quality, low quality, normal quality, lowres),"
+      ],
+      "widgets_values_named": {
+        "text": "(worst quality, low quality, normal quality, lowres),"
+      }
+    },
+    {
+      "id": 10,
+      "type": "EmptyLatentImage",
+      "pos": [
+        743.7407173134332,
+        533.9610772211638
+      ],
+      "size": [
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+        127.29972476947185
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "label": "LATENT",
+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            28
+          ]
+        }
+      ],
+      "title": "empty latent",
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        1024,
+        1024,
+        1
+      ],
+      "widgets_values_named": {
+        "width": 1024,
+        "height": 1024,
+        "batch_size": 1
+      }
+    },
+    {
+      "id": 11,
+      "type": "KSampler",
+      "pos": [
+        1156.9620824336098,
+        80.54740592847531
+      ],
+      "size": [
+        315,
+        262
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "model",
+          "name": "model",
+          "type": "MODEL",
+          "link": 31
+        },
+        {
+          "label": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 23
+        },
+        {
+          "label": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 21
+        },
+        {
+          "label": "latent_image",
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 28
+        }
+      ],
+      "outputs": [
+        {
+          "label": "LATENT",
+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            15
+          ]
+        }
+      ],
+      "title": "KSampler",
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        0,
+        "fixed",
+        20,
+        7,
+        "euler",
+        "normal",
+        1
+      ],
+      "widgets_values_named": {
+        "seed": 0,
+        "control_after_generate": "fixed",
+        "steps": 20,
+        "cfg": 7,
+        "sampler_name": "euler",
+        "scheduler": "normal",
+        "denoise": 1
+      }
+    },
+    {
+      "id": 12,
+      "type": "VAEDecode",
+      "pos": [
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+        430.2061194051902
+      ],
+      "size": [
+        235.70637510865785,
+        54.000003526763365
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "samples",
+          "name": "samples",
+          "type": "LATENT",
+          "link": 15
+        },
+        {
+          "label": "vae",
+          "name": "vae",
+          "type": "VAE",
+          "link": 41
+        }
+      ],
+      "outputs": [
+        {
+          "label": "IMAGE",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "slot_index": 0,
+          "links": [
+            17
+          ]
+        }
+      ],
+      "title": "VAE decode",
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 13,
+      "type": "SaveImage",
+      "pos": [
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+        380.93267297794523
+      ],
+      "size": [
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+        345.78613186594066
+      ],
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "link": 17
+        }
+      ],
+      "outputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "links": null
+        }
+      ],
+      "title": "save",
+      "properties": {
+        "Node name for S&R": "SaveImage"
+      },
+      "widgets_values": [
+        "MSDiffusion_native"
+      ],
+      "widgets_values_named": {
+        "filename_prefix": "MSDiffusion_native"
+      }
+    },
+    {
+      "id": 15,
+      "type": "MSDiffusionApply",
+      "pos": [
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+      "size": [
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+        153.9999850172292
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 39
+        },
+        {
+          "name": "ms_adapter",
+          "type": "MS_ADAPTER",
+          "link": 33
+        },
+        {
+          "name": "ms_embeds",
+          "type": "MS_EMBEDS",
+          "link": 35
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            31
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "MSDiffusionApply"
+      },
+      "widgets_values": [
+        0.8,
+        5
+      ],
+      "widgets_values_named": {
+        "scale": 0.8,
+        "start_step": 5
+      },
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 16,
+      "type": "MSDiffusionConditioning",
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+      "inputs": [
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+          "type": "MS_ADAPTER",
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+        },
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 40
+        },
+        {
+          "name": "clip_vision_output",
+          "type": "CLIP_VISION_OUTPUT",
+          "link": 37
+        }
+      ],
+      "outputs": [
+        {
+          "name": "ms_embeds",
+          "type": "MS_EMBEDS",
+          "links": [
+            35
+          ]
+        },
+        {
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+          "type": "STRING",
+          "links": [
+            36,
+            44
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "MSDiffusionConditioning"
+      },
+      "widgets_values": [
+        "best quality, high quality, a [rabbit] on the beach",
+        "0.25, 0.25, 0.75, 0.75",
+        0.5,
+        false
+      ],
+      "widgets_values_named": {
+        "prompt": "best quality, high quality, a [rabbit] on the beach",
+        "boxes": "0.25, 0.25, 0.75, 0.75",
+        "mask_threshold": 0.5,
+        "drop_grounding_tokens": false
+      },
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 18,
+      "type": "PreviewAny",
+      "pos": [
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+        536.8622571930127
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+      "size": [
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+        138.00003538379124
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+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "source",
+          "type": "*",
+          "link": 36
+        }
+      ],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewAny"
+      },
+      "widgets_values": [],
+      "widgets_values_named": {}
+    },
+    {
+      "id": 19,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
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+        32.07108167105924
+      ],
+      "size": [
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+        102.00001573582173
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+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            39
+          ]
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
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+            42,
+            43
+          ]
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            41
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "sd_xl_base_1.0.safetensors"
+      ],
+      "widgets_values_named": {
+        "ckpt_name": "sd_xl_base_1.0.safetensors"
+      }
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      4,
+      0,
+      "CLIP_VISION"
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+    [
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+      11,
+      0,
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+      0,
+      "LATENT"
+    ],
+    [
+      17,
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+      0,
+      13,
+      0,
+      "IMAGE"
+    ],
+    [
+      20,
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+      1,
+      "IMAGE"
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+    [
+      21,
+      8,
+      0,
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+      2,
+      "CONDITIONING"
+    ],
+    [
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+      "CONDITIONING"
+    ],
+    [
+      28,
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+      11,
+      3,
+      "LATENT"
+    ],
+    [
+      30,
+      5,
+      0,
+      16,
+      0,
+      "MS_ADAPTER"
+    ],
+    [
+      31,
+      15,
+      0,
+      11,
+      0,
+      "MODEL"
+    ],
+    [
+      33,
+      5,
+      0,
+      15,
+      1,
+      "MS_ADAPTER"
+    ],
+    [
+      35,
+      16,
+      0,
+      15,
+      2,
+      "MS_EMBEDS"
+    ],
+    [
+      36,
+      16,
+      1,
+      18,
+      0,
+      "STRING"
+    ],
+    [
+      37,
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+      "CLIP_VISION_OUTPUT"
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+    [
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+      "MODEL"
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+    [
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+    [
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+      1,
+      "VAE"
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+    [
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+      "CLIP"
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+  "groups": [
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+    "frontendVersion": "1.58.5"
+  },
+  "version": 0.4
+}
\ No newline at end of file
diff --git a/examples/autolayerimg2img.png b/examples/autolayerimg2img.png
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diff --git a/examples/workflow_new.json b/examples/workflow_new.json
deleted file mode 100644
index 1ad0561..0000000
--- a/examples/workflow_new.json
+++ /dev/null
@@ -1,395 +0,0 @@
-{
-  "last_node_id": 40,
-  "last_link_id": 101,
-  "nodes": [
-    {
-      "id": 3,
-      "type": "LoadImage",
-      "pos": [
-        1119,
-        -33
-      ],
-      "size": {
-        "0": 315,
-        "1": 314
-      },
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            89
-          ],
-          "shape": 3,
-          "label": "IMAGE",
-          "slot_index": 0
-        },
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "links": null,
-          "shape": 3,
-          "label": "MASK"
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoadImage"
-      },
-      "widgets_values": [
-        "123 (1).png",
-        "image"
-      ]
-    },
-    {
-      "id": 36,
-      "type": "MS_Object_img_Batch",
-      "pos": [
-        1512,
-        180
-      ],
-      "size": {
-        "0": 210,
-        "1": 86
-      },
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image_a",
-          "type": "IMAGE",
-          "link": 88,
-          "label": "image_a"
-        },
-        {
-          "name": "image_b",
-          "type": "IMAGE",
-          "link": 89,
-          "label": "image_b"
-        },
-        {
-          "name": "image_c",
-          "type": "IMAGE",
-          "link": null,
-          "label": "image_c"
-        },
-        {
-          "name": "image_d",
-          "type": "IMAGE",
-          "link": null,
-          "label": "image_d"
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            98
-          ],
-          "shape": 3,
-          "label": "IMAGE",
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "MS_Object_img_Batch"
-      }
-    },
-    {
-      "id": 4,
-      "type": "SaveImage",
-      "pos": [
-        1939,
-        362
-      ],
-      "size": {
-        "0": 315,
-        "1": 270
-      },
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 99,
-          "label": "images"
-        }
-      ],
-      "properties": {},
-      "widgets_values": [
-        "ComfyUI"
-      ]
-    },
-    {
-      "id": 39,
-      "type": "MSdiffusion_Sampler",
-      "pos": [
-        1508,
-        349
-      ],
-      "size": {
-        "0": 400,
-        "1": 504
-      },
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image",
-          "type": "IMAGE",
-          "link": 98,
-          "label": "image"
-        },
-        {
-          "name": "model",
-          "type": "MSDIF_DICT",
-          "link": 101,
-          "label": "model"
-        },
-        {
-          "name": "control_image",
-          "type": "IMAGE",
-          "link": null,
-          "label": "control_image"
-        }
-      ],
-      "outputs": [
-        {
-          "name": "image",
-          "type": "IMAGE",
-          "links": [
-            99
-          ],
-          "shape": 3,
-          "label": "image",
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "MSdiffusion_Sampler"
-      },
-      "widgets_values": [
-        "a [pig] wearing a pink [sunglass]",
-        "(worst quality, low quality, normal quality, lowres),",
-        825964470,
-        "fixed",
-        20,
-        7,
-        0.8,
-        0.5,
-        5,
-        0.8,
-        768,
-        768,
-        1,
-        false,
-        "0., 0.25, 0.4, 0.75;0., 0.25, 0.4, 0.75"
-      ]
-    },
-    {
-      "id": 35,
-      "type": "LoadImage",
-      "pos": [
-        774,
-        -35
-      ],
-      "size": {
-        "0": 315,
-        "1": 314
-      },
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            88
-          ],
-          "shape": 3,
-          "label": "IMAGE",
-          "slot_index": 0
-        },
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "links": null,
-          "shape": 3,
-          "label": "MASK"
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoadImage"
-      },
-      "widgets_values": [
-        "123.png",
-        "image"
-      ]
-    },
-    {
-      "id": 32,
-      "type": "CLIPVisionLoader",
-      "pos": [
-        790,
-        448
-      ],
-      "size": {
-        "0": 315,
-        "1": 58
-      },
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "CLIP_VISION",
-          "type": "CLIP_VISION",
-          "links": [
-            100
-          ],
-          "shape": 3,
-          "label": "CLIP_VISION",
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "CLIPVisionLoader"
-      },
-      "widgets_values": [
-        "clip_vision_g.safetensors"
-      ]
-    },
-    {
-      "id": 40,
-      "type": "MSdiffusion_Model_Loader",
-      "pos": [
-        1162,
-        402
-      ],
-      "size": {
-        "0": 315,
-        "1": 250
-      },
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "clip_vision",
-          "type": "CLIP_VISION",
-          "link": 100,
-          "label": "clip_vision"
-        }
-      ],
-      "outputs": [
-        {
-          "name": "model",
-          "type": "MSDIF_DICT",
-          "links": [
-            101
-          ],
-          "shape": 3,
-          "label": "model",
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "MSdiffusion_Model_Loader"
-      },
-      "widgets_values": [
-        "0SDXL\\juggernautXL_v8Rundiffusion.safetensors",
-        "",
-        "none",
-        "none",
-        "",
-        "none",
-        0.8,
-        "best quality",
-        "Euler"
-      ]
-    }
-  ],
-  "links": [
-    [
-      88,
-      35,
-      0,
-      36,
-      0,
-      "IMAGE"
-    ],
-    [
-      89,
-      3,
-      0,
-      36,
-      1,
-      "IMAGE"
-    ],
-    [
-      98,
-      36,
-      0,
-      39,
-      0,
-      "IMAGE"
-    ],
-    [
-      99,
-      39,
-      0,
-      4,
-      0,
-      "IMAGE"
-    ],
-    [
-      100,
-      32,
-      0,
-      40,
-      0,
-      "CLIP_VISION"
-    ],
-    [
-      101,
-      40,
-      0,
-      39,
-      1,
-      "MSDIF_DICT"
-    ]
-  ],
-  "groups": [
-    {
-      "title": "Group",
-      "bounding": [
-        750,
-        -184,
-        1544,
-        1093
-      ],
-      "color": "#3f789e",
-      "font_size": 24,
-      "locked": false
-    }
-  ],
-  "config": {},
-  "extra": {
-    "ds": {
-      "scale": 0.9090909090909091,
-      "offset": {
-        "0": -600.3953810605899,
-        "1": 110.50656660086888
-      }
-    }
-  },
-  "version": 0.4
-}
\ No newline at end of file
diff --git a/local_repo/model_index.json b/local_repo/model_index.json
deleted file mode 100644
index 6cc5138..0000000
--- a/local_repo/model_index.json
+++ /dev/null
@@ -1,34 +0,0 @@
-{
-  "_class_name": "StableDiffusionXLPipeline",
-  "_diffusers_version": "0.19.0.dev0",
-  "force_zeros_for_empty_prompt": true,
-  "add_watermarker": null,
-  "scheduler": [
-    "diffusers",
-    "EulerDiscreteScheduler"
-  ],
-  "text_encoder": [
-    "transformers",
-    "CLIPTextModel"
-  ],
-  "text_encoder_2": [
-    "transformers",
-    "CLIPTextModelWithProjection"
-  ],
-  "tokenizer": [
-    "transformers",
-    "CLIPTokenizer"
-  ],
-  "tokenizer_2": [
-    "transformers",
-    "CLIPTokenizer"
-  ],
-  "unet": [
-    "diffusers",
-    "UNet2DConditionModel"
-  ],
-  "vae": [
-    "diffusers",
-    "AutoencoderKL"
-  ]
-}
diff --git a/local_repo/scheduler/scheduler_config.json b/local_repo/scheduler/scheduler_config.json
deleted file mode 100644
index e5bc842..0000000
--- a/local_repo/scheduler/scheduler_config.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-  "_class_name": "EulerDiscreteScheduler",
-  "_diffusers_version": "0.19.0.dev0",
-  "beta_end": 0.012,
-  "beta_schedule": "scaled_linear",
-  "beta_start": 0.00085,
-  "clip_sample": false,
-  "interpolation_type": "linear",
-  "num_train_timesteps": 1000,
-  "prediction_type": "epsilon",
-  "sample_max_value": 1.0,
-  "set_alpha_to_one": false,
-  "skip_prk_steps": true,
-  "steps_offset": 1,
-  "timestep_spacing": "leading",
-  "trained_betas": null,
-  "use_karras_sigmas": false
-}
diff --git a/local_repo/text_encoder/config.json b/local_repo/text_encoder/config.json
deleted file mode 100644
index cde352a..0000000
--- a/local_repo/text_encoder/config.json
+++ /dev/null
@@ -1,24 +0,0 @@
-{
-  "architectures": [
-    "CLIPTextModel"
-  ],
-  "attention_dropout": 0.0,
-  "bos_token_id": 0,
-  "dropout": 0.0,
-  "eos_token_id": 2,
-  "hidden_act": "quick_gelu",
-  "hidden_size": 768,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 77,
-  "model_type": "clip_text_model",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 1,
-  "projection_dim": 768,
-  "torch_dtype": "float16",
-  "transformers_version": "4.32.0.dev0",
-  "vocab_size": 49408
-}
diff --git a/local_repo/text_encoder_2/config.json b/local_repo/text_encoder_2/config.json
deleted file mode 100644
index da1848b..0000000
--- a/local_repo/text_encoder_2/config.json
+++ /dev/null
@@ -1,24 +0,0 @@
-{
-  "architectures": [
-    "CLIPTextModelWithProjection"
-  ],
-  "attention_dropout": 0.0,
-  "bos_token_id": 0,
-  "dropout": 0.0,
-  "eos_token_id": 2,
-  "hidden_act": "gelu",
-  "hidden_size": 1280,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 5120,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 77,
-  "model_type": "clip_text_model",
-  "num_attention_heads": 20,
-  "num_hidden_layers": 32,
-  "pad_token_id": 1,
-  "projection_dim": 1280,
-  "torch_dtype": "float16",
-  "transformers_version": "4.32.0.dev0",
-  "vocab_size": 49408
-}
diff --git a/local_repo/tokenizer/merges.txt b/local_repo/tokenizer/merges.txt
deleted file mode 100644
index 76e821f..0000000
--- a/local_repo/tokenizer/merges.txt
+++ /dev/null
@@ -1,48895 +0,0 @@
-#version: 0.2
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diff --git a/local_repo/tokenizer/special_tokens_map.json b/local_repo/tokenizer/special_tokens_map.json
deleted file mode 100644
index 2c2130b..0000000
--- a/local_repo/tokenizer/special_tokens_map.json
+++ /dev/null
@@ -1,24 +0,0 @@
-{
-  "bos_token": {
-    "content": "<|startoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
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-    "single_word": false
-  }
-}
diff --git a/local_repo/tokenizer/tokenizer_config.json b/local_repo/tokenizer/tokenizer_config.json
deleted file mode 100644
index 2e8612a..0000000
--- a/local_repo/tokenizer/tokenizer_config.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
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-    "__type": "AddedToken",
-    "content": "<|startoftext|>",
-    "lstrip": false,
-    "normalized": true,
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-    "content": "<|endoftext|>",
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-  "errors": "replace",
-  "model_max_length": 77,
-  "pad_token": "<|endoftext|>",
-  "tokenizer_class": "CLIPTokenizer",
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-    "content": "<|endoftext|>",
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-    "normalized": true,
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diff --git a/local_repo/tokenizer/vocab.json b/local_repo/tokenizer/vocab.json
deleted file mode 100644
index 469be27..0000000
--- a/local_repo/tokenizer/vocab.json
+++ /dev/null
@@ -1,49410 +0,0 @@
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diff --git a/local_repo/tokenizer_2/merges.txt b/local_repo/tokenizer_2/merges.txt
deleted file mode 100644
index 76e821f..0000000
--- a/local_repo/tokenizer_2/merges.txt
+++ /dev/null
@@ -1,48895 +0,0 @@
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deleted file mode 100644
index ae0c5be..0000000
--- a/local_repo/tokenizer_2/special_tokens_map.json
+++ /dev/null
@@ -1,24 +0,0 @@
-{
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-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
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diff --git a/local_repo/tokenizer_2/tokenizer_config.json b/local_repo/tokenizer_2/tokenizer_config.json
deleted file mode 100644
index a8438e0..0000000
--- a/local_repo/tokenizer_2/tokenizer_config.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
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diff --git a/local_repo/tokenizer_2/vocab.json b/local_repo/tokenizer_2/vocab.json
deleted file mode 100644
index 469be27..0000000
--- a/local_repo/tokenizer_2/vocab.json
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-}
diff --git a/local_repo/unet/config.json b/local_repo/unet/config.json
deleted file mode 100644
index c8714c9..0000000
--- a/local_repo/unet/config.json
+++ /dev/null
@@ -1,69 +0,0 @@
-{
-  "_class_name": "UNet2DConditionModel",
-  "_diffusers_version": "0.19.0.dev0",
-  "act_fn": "silu",
-  "addition_embed_type": "text_time",
-  "addition_embed_type_num_heads": 64,
-  "addition_time_embed_dim": 256,
-  "attention_head_dim": [
-    5,
-    10,
-    20
-  ],
-  "block_out_channels": [
-    320,
-    640,
-    1280
-  ],
-  "center_input_sample": false,
-  "class_embed_type": null,
-  "class_embeddings_concat": false,
-  "conv_in_kernel": 3,
-  "conv_out_kernel": 3,
-  "cross_attention_dim": 2048,
-  "cross_attention_norm": null,
-  "down_block_types": [
-    "DownBlock2D",
-    "CrossAttnDownBlock2D",
-    "CrossAttnDownBlock2D"
-  ],
-  "downsample_padding": 1,
-  "dual_cross_attention": false,
-  "encoder_hid_dim": null,
-  "encoder_hid_dim_type": null,
-  "flip_sin_to_cos": true,
-  "freq_shift": 0,
-  "in_channels": 4,
-  "layers_per_block": 2,
-  "mid_block_only_cross_attention": null,
-  "mid_block_scale_factor": 1,
-  "mid_block_type": "UNetMidBlock2DCrossAttn",
-  "norm_eps": 1e-05,
-  "norm_num_groups": 32,
-  "num_attention_heads": null,
-  "num_class_embeds": null,
-  "only_cross_attention": false,
-  "out_channels": 4,
-  "projection_class_embeddings_input_dim": 2816,
-  "resnet_out_scale_factor": 1.0,
-  "resnet_skip_time_act": false,
-  "resnet_time_scale_shift": "default",
-  "sample_size": 128,
-  "time_cond_proj_dim": null,
-  "time_embedding_act_fn": null,
-  "time_embedding_dim": null,
-  "time_embedding_type": "positional",
-  "timestep_post_act": null,
-  "transformer_layers_per_block": [
-    1,
-    2,
-    10
-  ],
-  "up_block_types": [
-    "CrossAttnUpBlock2D",
-    "CrossAttnUpBlock2D",
-    "UpBlock2D"
-  ],
-  "upcast_attention": null,
-  "use_linear_projection": true
-}
diff --git a/local_repo/vae/config.json b/local_repo/vae/config.json
deleted file mode 100644
index a66a171..0000000
--- a/local_repo/vae/config.json
+++ /dev/null
@@ -1,32 +0,0 @@
-{
-  "_class_name": "AutoencoderKL",
-  "_diffusers_version": "0.20.0.dev0",
-  "_name_or_path": "../sdxl-vae/",
-  "act_fn": "silu",
-  "block_out_channels": [
-    128,
-    256,
-    512,
-    512
-  ],
-  "down_block_types": [
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D"
-  ],
-  "force_upcast": true,
-  "in_channels": 3,
-  "latent_channels": 4,
-  "layers_per_block": 2,
-  "norm_num_groups": 32,
-  "out_channels": 3,
-  "sample_size": 1024,
-  "scaling_factor": 0.13025,
-  "up_block_types": [
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D"
-  ]
-}
diff --git a/local_repo/vae_1_0/config.json b/local_repo/vae_1_0/config.json
deleted file mode 100644
index 29a4d9f..0000000
--- a/local_repo/vae_1_0/config.json
+++ /dev/null
@@ -1,31 +0,0 @@
-{
-  "_class_name": "AutoencoderKL",
-  "_diffusers_version": "0.19.0.dev0",
-  "act_fn": "silu",
-  "block_out_channels": [
-    128,
-    256,
-    512,
-    512
-  ],
-  "down_block_types": [
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D"
-  ],
-  "force_upcast": true,
-  "in_channels": 3,
-  "latent_channels": 4,
-  "layers_per_block": 2,
-  "norm_num_groups": 32,
-  "out_channels": 3,
-  "sample_size": 1024,
-  "scaling_factor": 0.13025,
-  "up_block_types": [
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D"
-  ]
-}
diff --git a/local_repo/vae_decoder/config.json b/local_repo/vae_decoder/config.json
deleted file mode 100644
index 29a4d9f..0000000
--- a/local_repo/vae_decoder/config.json
+++ /dev/null
@@ -1,31 +0,0 @@
-{
-  "_class_name": "AutoencoderKL",
-  "_diffusers_version": "0.19.0.dev0",
-  "act_fn": "silu",
-  "block_out_channels": [
-    128,
-    256,
-    512,
-    512
-  ],
-  "down_block_types": [
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D"
-  ],
-  "force_upcast": true,
-  "in_channels": 3,
-  "latent_channels": 4,
-  "layers_per_block": 2,
-  "norm_num_groups": 32,
-  "out_channels": 3,
-  "sample_size": 1024,
-  "scaling_factor": 0.13025,
-  "up_block_types": [
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D"
-  ]
-}
diff --git a/local_repo/vae_encoder/config.json b/local_repo/vae_encoder/config.json
deleted file mode 100644
index 29a4d9f..0000000
--- a/local_repo/vae_encoder/config.json
+++ /dev/null
@@ -1,31 +0,0 @@
-{
-  "_class_name": "AutoencoderKL",
-  "_diffusers_version": "0.19.0.dev0",
-  "act_fn": "silu",
-  "block_out_channels": [
-    128,
-    256,
-    512,
-    512
-  ],
-  "down_block_types": [
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D",
-    "DownEncoderBlock2D"
-  ],
-  "force_upcast": true,
-  "in_channels": 3,
-  "latent_channels": 4,
-  "layers_per_block": 2,
-  "norm_num_groups": 32,
-  "out_channels": 3,
-  "sample_size": 1024,
-  "scaling_factor": 0.13025,
-  "up_block_types": [
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D",
-    "UpDecoderBlock2D"
-  ]
-}
diff --git a/msdiffusion/__init__.py b/msdiffusion/__init__.py
deleted file mode 100644
index 8b13789..0000000
--- a/msdiffusion/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/msdiffusion/dataset/__init__.py b/msdiffusion/dataset/__init__.py
deleted file mode 100644
index 8b13789..0000000
--- a/msdiffusion/dataset/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/msdiffusion/dataset/prompts/msbench.py b/msdiffusion/dataset/prompts/msbench.py
deleted file mode 100644
index 4680516..0000000
--- a/msdiffusion/dataset/prompts/msbench.py
+++ /dev/null
@@ -1,132 +0,0 @@
-data_class_dict = {
-    "backpack": "backpack",
-    "cat": "cat",
-    "cat2": "cat",
-    "clock": "clock",
-    "colorful_sneaker": "sneaker",
-    "dog": "dog",
-    "dog2": "dog",
-    "dog5": "dog",
-    "dog6": "dog",
-    "dog7": "dog",
-    "dog8": "dog",
-    "duck_toy": "toy",
-    "grey_sloth_plushie": "stuffed animal",
-    "teapot": "teapot",
-    "wolf_plushie": "stuffed animal",
-    "cat1": "cat",
-    "dog1": "dog",
-    "lantern": "lantern",
-    "flower": "flower",
-    "teddybear": "stuffed animal",
-    "forest": "forest",
-    "mountain": "mountain",
-    "barn": "barn",
-    "waterfall": "waterfall",
-    "cap": "cap",
-    "cap1": "cap",
-    "hat": "hat",
-    "hat1": "hat",
-    "hat2": "hat",
-    "dress": "dress",
-    "dress1": "dress",
-    "shorts1": "shorts",
-    "trousers": "trousers",
-    "jeans": "jeans",
-    "coat": "coat",
-    "jacket": "jacket",
-    "t-shirt": "t-shirt",
-    "shirt": "shirt",
-    "shirt1": "shirt",
-    "sweater": "sweater",
-}
-
-two_subject_combinations = ["living_living", "living_object", "object_object", "living_upwearing", "living_midwearing",
-                            "living_wholewearing", "midwearing_downwearing", "living_scene", "object_scene"]
-three_subject_combinations = ["living_living_living", "object_object_object", "living_object_scene",
-                              "upwearing_midwearing_downwearing"]
-
-subject_combination_boxes = {
-    "living_living": [[0., 0.25, 0.5, 0.75], [0.5, 0.25, 1., 0.75]],
-    "living_object": [[0., 0.25, 0.5, 0.75], [0.5, 0.25, 1., 0.75]],
-    "object_object": [[0., 0.25, 0.5, 0.75], [0.5, 0.25, 1., 0.75]],
-    "living_upwearing": [[0.25, 0.25, 0.75, 0.75], [0.25, 0., 0.75, 0.25]],
-    "living_midwearing": [[0.25, 0.25, 0.75, 0.75], [0.25, 0.25, 0.75, 0.75]],
-    "living_wholewearing": [[0.25, 0.25, 0.75, 0.75], [0.25, 0.25, 0.75, 1.]],
-    "midwearing_downwearing": [[0.25, 0.25, 0.75, 0.6], [0.25, 0.6, 0.75, 1.]],
-    "living_scene": [[0.25, 0.25, 0.75, 0.75], [0., 0., 1., 1.]],
-    "object_scene": [[0.25, 0.25, 0.75, 0.75], [0., 0., 1., 1.]],
-    "living_living_living": [[0., 0.25, 0.35, 0.75], [0.35, 0.25, 0.65, 0.75], [0.65, 0.25, 1., 0.75]],
-    "object_object_object": [[0., 0.25, 0.35, 0.75], [0.35, 0.25, 0.65, 0.75], [0.65, 0.25, 1., 0.75]],
-    "living_object_scene": [[0., 0.25, 0.5, 0.75], [0.5, 0.25, 1., 0.75], [0., 0., 1., 1.]],
-    "upwearing_midwearing_downwearing": [[0.25, 0., 0.75, 0.25], [0.25, 0.25, 0.75, 0.6], [0.25, 0.6, 0.75, 1.]],
-}
-
-normal_prompts_two_subjects = [
-    "a {0} and a {1} in a room",
-    "a {0} and a {1} in the snow",
-    "a {0} and a {1} in the jungle",
-    "a {0} and a {1} on the beach",
-    "a {0} and a {1} on the grass",
-    "a {0} and a {1} on a cobblestone street",
-]
-
-normal_prompts_three_subjects = [
-    "a {0}, a {1}, and a {2} in a room",
-    "a {0}, a {1}, and a {2} in the snow",
-    "a {0}, a {1}, and a {2} in the jungle",
-    "a {0}, a {1}, and a {2} on the beach",
-    "a {0}, a {1}, and a {2} on the grass",
-    "a {0}, a {1}, and a {2} on a cobblestone street",
-]
-
-scene_prompts_two_subjects = [
-    "a {0} with a {1} in the background",
-]
-
-scene_prompts_three_subjects = [
-    "a {0} and {1} with a {2} in the background",
-]
-
-wearing_prompts_two_subjects_0 = [
-    "a {0} wearing a {1} in a room",
-    "a {0} wearing a {1} in the snow",
-    "a {0} wearing a {1} in the jungle",
-    "a {0} wearing a {1} on the beach",
-    "a {0} wearing a {1} on the grass",
-    "a {0} wearing a {1} on a cobblestone street",
-]
-
-wearing_prompts_two_subjects_1 = [
-    "a woman wearing a {0} and {1} in a room",
-    "a woman wearing a {0} and {1} in the snow",
-    "a woman wearing a {0} and {1} in the jungle",
-    "a woman wearing a {0} and {1} on the beach",
-    "a woman wearing a {0} and {1} on the grass",
-    "a woman wearing a {0} and {1} on a cobblestone street"
-]
-
-wearing_prompts_three_subjects = [
-    "a woman wearing a {0}, a {1}, and a {2} in a room",
-    "a woman wearing a {0}, a {1}, and a {2} in the snow",
-    "a woman wearing a {0}, a {1}, and a {2} in the jungle",
-    "a woman wearing a {0}, a {1}, and a {2} on the beach",
-    "a woman wearing a {0}, a {1}, and a {2} on the grass",
-    "a woman wearing a {0}, a {1}, and a {2} on a cobblestone street"
-]
-
-subject_combination_prompts = {
-    "living_living": normal_prompts_two_subjects,
-    "living_object": normal_prompts_two_subjects,
-    "object_object": normal_prompts_two_subjects,
-    "living_upwearing": wearing_prompts_two_subjects_0,
-    "living_midwearing": wearing_prompts_two_subjects_0,
-    "living_wholewearing": wearing_prompts_two_subjects_0,
-    "midwearing_downwearing": wearing_prompts_two_subjects_1,
-    "living_scene": scene_prompts_two_subjects,
-    "object_scene": scene_prompts_two_subjects,
-    "living_living_living": normal_prompts_three_subjects,
-    "object_object_object": normal_prompts_three_subjects,
-    "living_object_scene": scene_prompts_three_subjects,
-    "upwearing_midwearing_downwearing": wearing_prompts_three_subjects,
-}
diff --git a/msdiffusion/models/__init__.py b/msdiffusion/models/__init__.py
deleted file mode 100644
index 399c5c6..0000000
--- a/msdiffusion/models/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .model import MSAdapter
-
-__all__ = [
-    "MSAdapter",
-]
diff --git a/msdiffusion/models/attention_processor.py b/msdiffusion/models/attention_processor.py
deleted file mode 100644
index e5947e0..0000000
--- a/msdiffusion/models/attention_processor.py
+++ /dev/null
@@ -1,494 +0,0 @@
-# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
-# and https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/attention_processor.py
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def minmax_normalize(batch_maps):
-    min_val = batch_maps.min(dim=-1, keepdim=True)[0].min(dim=-2, keepdim=True)[0]
-    max_val = batch_maps.max(dim=-1, keepdim=True)[0].max(dim=-2, keepdim=True)[0]
-
-    return (batch_maps - min_val) / (max_val - min_val + 1e-5)
-
-
-class AttnProcessor2_0(torch.nn.Module):
-    r"""
-    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
-    """
-
-    def __init__(
-        self,
-        hidden_size=None,
-        cross_attention_dim=None,
-    ):
-        super().__init__()
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-        boxes=None,
-        phrase_idxes=None,
-        eot_idxes=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class MaskedIPAttnProcessor2_0(nn.Module):
-
-    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4, text_tokens=77,
-                 need_text_attention_map=False, need_image_attention_map=True, num_dummy_tokens=4, mask_threshold=0.5,
-                 use_psuedo_attention_mask=False, subject_scales=None, start_step=5):
-        super().__init__()
-
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.scale = scale
-        self.num_tokens = num_tokens
-        self.text_tokens = text_tokens
-        self.num_dummy_tokens = num_dummy_tokens
-        self.mask_threshold = mask_threshold
-        self.subject_scales = subject_scales
-        self.start_step = start_step
-
-        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-
-        self.need_text_attention_map = need_text_attention_map
-        self.need_image_attention_map = need_image_attention_map
-
-        self.use_psuedo_attention_mask = use_psuedo_attention_mask
-        self.attention_maps = []
-
-    def prepare_attention_mask_qk(self, boxes, phrase_idxes, sequence_length_q, sequence_length_k,
-                                  batch_size, head_size, dtype, device, use_masked_text_attention=False):
-        if boxes is None:
-            return None, None
-
-        # TODO: only support square image now
-        num_patches_per_row = int(sequence_length_q ** 0.5)
-        box_idxes_start = torch.floor(boxes[:, :, 0:2] * num_patches_per_row)
-        box_idxes_end = torch.ceil(boxes[:, :, 2:4] * num_patches_per_row)
-        box_idxes = torch.cat([box_idxes_start, box_idxes_end], dim=-1)
-        box_masks = []
-        dummy_attention_mask = torch.ones((batch_size, sequence_length_q), dtype=dtype, device=device)
-        for box_idx in box_idxes.unbind(dim=1):
-            x_start_patch_idx, y_start_patch_idx, x_end_patch_idx, y_end_patch_idx = box_idx.unbind(dim=1)
-            x_indices = torch.arange(num_patches_per_row).unsqueeze(0).expand(batch_size, -1).to(device)
-            y_indices = torch.arange(num_patches_per_row).unsqueeze(0).expand(batch_size, -1).to(device)
-            x_mask = ((x_indices >= x_start_patch_idx.unsqueeze(1)) & (x_indices < x_end_patch_idx.unsqueeze(1))).to(dtype)
-            y_mask = ((y_indices >= y_start_patch_idx.unsqueeze(1)) & (y_indices < y_end_patch_idx.unsqueeze(1))).to(dtype)
-            box_mask = torch.bmm(y_mask.unsqueeze(2), x_mask.unsqueeze(1)).reshape(batch_size, -1)
-            box_masks.append(box_mask)
-            dummy_attention_mask = torch.clamp(dummy_attention_mask - box_mask, min=0)
-
-        # post mask
-        post_dummy_attention_mask = dummy_attention_mask.to(torch.bool)
-        post_dummy_attention_mask = post_dummy_attention_mask.repeat_interleave(head_size, dim=0)
-        
-        attention_mask_qk_image = torch.stack(box_masks, dim=-1)
-        attention_mask_qk_image = attention_mask_qk_image.repeat_interleave(self.num_tokens, dim=-1)
-        attention_mask_qk_image = (1 - attention_mask_qk_image.to(dtype)) * -10000.0  # mask to bias
-        # use dummy image tokens to process the background
-        dummy_attention_mask = dummy_attention_mask.unsqueeze(-1).repeat_interleave(self.num_dummy_tokens, dim=-1)
-        dummy_attention_mask = (1 - dummy_attention_mask) * -10000.0
-        attention_mask_qk_image = torch.cat([dummy_attention_mask, attention_mask_qk_image], dim=-1)
-        if attention_mask_qk_image.shape[0] < batch_size*head_size:
-            attention_mask_qk_image = attention_mask_qk_image.repeat_interleave(head_size, dim=0)
-        
-        if use_masked_text_attention:
-            attention_mask_qk_text = torch.ones((batch_size, sequence_length_q, sequence_length_k), dtype=dtype, device=device)
-            for i in range(batch_size):
-                for j in range(len(box_masks)):
-                    start_idx, end_idx = int(phrase_idxes[i, j, 0].item()), int(phrase_idxes[i, j, 1].item())
-                    if start_idx == 0 and end_idx == 0:
-                        continue
-                    attention_mask_qk_text[i, :, start_idx:end_idx] = box_masks[j][i, ...].unsqueeze(-1)
-            attention_mask_qk_text = (1 - attention_mask_qk_text) * -10000.0
-            if attention_mask_qk_text.shape[0] < batch_size*head_size:
-                attention_mask_qk_text = attention_mask_qk_text.repeat_interleave(head_size, dim=0)
-        else:
-            attention_mask_qk_text = None
-
-        return attention_mask_qk_image, attention_mask_qk_text, post_dummy_attention_mask
-
-    def get_text_attention_maps(self, attention_probs, boxes, phrase_idxes, head_size):
-        bsz = boxes.shape[0]
-        _, num_tokens_q, num_tokens_k = attention_probs.shape
-        attention_probs = attention_probs.view(bsz, head_size, num_tokens_q, num_tokens_k)
-        num_ref = boxes.shape[1]
-        h = w = int(num_tokens_q ** 0.5)
-        batch_attention_maps = []
-        for i in range(bsz):
-            sample_attention_maps = []
-            for j in range(num_ref):
-                start_idx, end_idx = int(phrase_idxes[i, j, 0].item()), int(phrase_idxes[i, j, 1].item())
-                if start_idx == 0 and end_idx == 0:
-                    sample_attention_maps.append(
-                        torch.zeros(num_tokens_q, dtype=attention_probs.dtype, device=attention_probs.device))
-                else:
-                    attention_map = attention_probs[i, :, :,
-                                    start_idx:end_idx]  # [num_heads, num_tokens_q, num_tokens_phrase]
-                    attention_map = torch.mean(torch.mean(attention_map, dim=-1), dim=0)  # [num_tokens_q]
-                    sample_attention_maps.append(attention_map)
-            batch_attention_maps.append(torch.stack(sample_attention_maps))
-
-        self.attention_maps.append(torch.stack(batch_attention_maps).reshape(bsz, num_ref, h, w))
-
-    def get_psuedo_attention_mask(self, head_size):
-        # text_attention_maps = self.attention_maps[-1]  # [bsz, num_ref, h, w]
-        if not self.use_psuedo_attention_mask or len(self.attention_maps) < self.start_step:
-            return None, None
-        text_attention_maps = torch.stack(self.attention_maps).mean(dim=0)  # [bsz, num_ref, h, w]
-        text_attention_maps = minmax_normalize(text_attention_maps)
-        dtype, device = text_attention_maps.dtype, text_attention_maps.device
-        bsz, num_ref, h, w = text_attention_maps.shape
-        seq_len_q = h * w
-        text_attention_maps = text_attention_maps.view(bsz, num_ref, -1)
-        text_attention_maps = text_attention_maps.transpose(1, 2)  # [bsz, h*w, num_ref]
-
-        # use threshold to get the mask
-        psuedo_attention_mask = (text_attention_maps > self.mask_threshold).to(dtype)
-        psuedo_dummy_attention_mask = torch.ones((bsz, seq_len_q), dtype=dtype, device=device)
-        for i in range(num_ref):
-            psuedo_box_mask = psuedo_attention_mask[..., i]
-            psuedo_dummy_attention_mask = torch.clamp(psuedo_dummy_attention_mask - psuedo_box_mask, min=0)
-
-        # post mask
-        post_psuedo_dummy_attention_mask = psuedo_dummy_attention_mask.to(torch.bool)
-        post_psuedo_dummy_attention_mask = post_psuedo_dummy_attention_mask.repeat_interleave(head_size, dim=0)
-
-        psuedo_attention_mask = psuedo_attention_mask.repeat_interleave(self.num_tokens, dim=-1)
-        psuedo_attention_mask = (1 - psuedo_attention_mask) * -10000.0  # mask to bias
-        psuedo_dummy_attention_mask = psuedo_dummy_attention_mask.unsqueeze(-1).repeat_interleave(self.num_dummy_tokens, dim=-1)
-        psuedo_dummy_attention_mask = (1 - psuedo_dummy_attention_mask) * -10000.0
-        psuedo_attention_mask = torch.cat([psuedo_dummy_attention_mask, psuedo_attention_mask], dim=-1)
-        if psuedo_attention_mask.shape[0] < bsz * head_size:
-            psuedo_attention_mask = psuedo_attention_mask.repeat_interleave(head_size, dim=0)
-
-        return psuedo_attention_mask, post_psuedo_dummy_attention_mask
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-        boxes=None,
-        phrase_idxes=None,
-        eot_idxes=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-        rf_attention_mask = None
-        custom_attention_masks = self.prepare_attention_mask_qk(boxes, phrase_idxes, hidden_states.shape[1],
-                                                                self.text_tokens, batch_size, attn.heads,
-                                                                hidden_states.dtype, hidden_states.device,
-                                                                use_masked_text_attention=False)
-        attention_mask_qk_image, attention_mask_qk_text, dummy_attention_mask = custom_attention_masks
-        if attention_mask_qk_image is not None:
-            attention_mask_qk_image = attention_mask_qk_image.view(batch_size, attn.heads, -1, attention_mask_qk_image.shape[-1])
-        if attention_mask_qk_text is not None:
-            attention_mask_qk_text = attention_mask_qk_text.view(batch_size, attn.heads, -1, attention_mask_qk_text.shape[-1])
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        else:
-            # get encoder_hidden_states, ip_hidden_states
-            # end_pos = encoder_hidden_states.shape[1] - self.num_tokens
-            end_pos = self.text_tokens
-            encoder_hidden_states, ip_hidden_states = (
-                encoder_hidden_states[:, :end_pos, :],
-                encoder_hidden_states[:, end_pos:, :],
-            )
-            attention_mask, rf_attention_mask = (
-                attention_mask[:, :, :, :end_pos],
-                attention_mask[:, :, :, end_pos:],
-            ) if attention_mask is not None else (None, None)
-            if attn.norm_cross:
-                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-        attention_mask = attention_mask_qk_text if attention_mask_qk_text is not None else attention_mask
-        if not self.need_text_attention_map:
-            # original attention 2.0
-            new_query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-            key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-            value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-            # the output of sdp = (batch, num_heads, seq_len, head_dim)
-            hidden_states = F.scaled_dot_product_attention(
-                new_query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-            )
-
-            hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-            hidden_states = hidden_states.to(query.dtype)
-        else:
-            # we need get the attention map, so use the previous attention
-            new_query = attn.head_to_batch_dim(query)
-            key = attn.head_to_batch_dim(key)
-            value = attn.head_to_batch_dim(value)
-
-            if attention_mask is not None:
-                attention_mask = attention_mask.view(batch_size*attn.heads, -1, attention_mask.shape[-1])
-            attention_probs = attn.get_attention_scores(new_query, key, attention_mask)
-            self.get_text_attention_maps(attention_probs, boxes, phrase_idxes, attn.heads)
-            hidden_states = torch.bmm(attention_probs, value)
-            hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # get psuedo attention mask for image: better start after some timesteps
-        psuedo_attention_mask, psuedo_dummy_attention_mask = self.get_psuedo_attention_mask(attn.heads)
-        if psuedo_attention_mask is not None:
-            psuedo_attention_mask = psuedo_attention_mask.view(batch_size, attn.heads, -1,
-                                                               psuedo_attention_mask.shape[-1])
-
-        ip_key = self.to_k_ip(ip_hidden_states)
-        ip_value = self.to_v_ip(ip_hidden_states)
-        rf_attention_mask = attention_mask_qk_image if attention_mask_qk_image is not None else rf_attention_mask
-        rf_attention_mask = psuedo_attention_mask if psuedo_attention_mask is not None else rf_attention_mask
-        dummy_attention_mask = psuedo_dummy_attention_mask if psuedo_dummy_attention_mask is not None else dummy_attention_mask
-        if not self.need_image_attention_map:
-            new_query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-            ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-            ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-            # the output of sdp = (batch, num_heads, seq_len, head_dim)
-            ip_hidden_states = F.scaled_dot_product_attention(
-                new_query, ip_key, ip_value, attn_mask=rf_attention_mask, dropout_p=0.0, is_causal=False
-            )
-
-            ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-            ip_hidden_states = ip_hidden_states.to(query.dtype)
-        else:
-            new_query = attn.head_to_batch_dim(query)
-            ip_key = attn.head_to_batch_dim(ip_key)
-            ip_value = attn.head_to_batch_dim(ip_value)
-
-            if rf_attention_mask is not None:
-                rf_attention_mask = rf_attention_mask.view(batch_size*attn.heads, -1, rf_attention_mask.shape[-1])
-            ip_attention_probs = attn.get_attention_scores(new_query, ip_key, rf_attention_mask)
-            # mask attention_probs in background
-            ip_attention_probs = torch.where(dummy_attention_mask.unsqueeze(-1), torch.zeros_like(ip_attention_probs), ip_attention_probs)
-            if self.subject_scales is not None:
-                # apply different scales to different subjects
-                subject_scales = torch.tensor(self.subject_scales, dtype=ip_attention_probs.dtype, device=ip_attention_probs.device)
-                subject_scales = subject_scales.unsqueeze(0).unsqueeze(0).repeat_interleave(self.num_tokens, dim=-1)
-                dummy_subject_scales = torch.ones((1, 1, 1), dtype=ip_attention_probs.dtype, device=ip_attention_probs.device).repeat_interleave(self.num_dummy_tokens, dim=-1)
-                subject_scales = torch.cat([dummy_subject_scales, subject_scales], dim=-1)
-                ip_attention_probs = ip_attention_probs * subject_scales
-            ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
-            ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
-
-        if self.subject_scales is None:
-            hidden_states = hidden_states + self.scale * ip_hidden_states
-        else:
-            hidden_states = hidden_states + ip_hidden_states
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class CNAttnProcessor2_0:
-    r"""
-    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
-    """
-
-    def __init__(self, num_tokens=4, text_tokens=77):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-        self.num_tokens = num_tokens
-        self.text_tokens = text_tokens
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-        rf_attention_mask = None
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        else:
-            # end_pos = encoder_hidden_states.shape[1] - self.num_tokens
-            end_pos = self.text_tokens
-            encoder_hidden_states = encoder_hidden_states[:, :end_pos]  # only use text
-            attention_mask = attention_mask[:, :, :end_pos]
-            if attn.norm_cross:
-                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
diff --git a/msdiffusion/models/model.py b/msdiffusion/models/model.py
deleted file mode 100644
index 7266442..0000000
--- a/msdiffusion/models/model.py
+++ /dev/null
@@ -1,263 +0,0 @@
-import os
-import math
-from typing import List
-
-import torch
-import torch.nn as nn
-from diffusers.pipelines.controlnet import MultiControlNetModel
-
-from .attention_processor import MaskedIPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor, \
-    CNAttnProcessor2_0 as CNAttnProcessor
-
-
-class MSAdapter(torch.nn.Module):
-    def __init__(self, unet,image_proj_model, adapter_modules=None, ckpt_path=None,
-                 num_tokens=4, text_tokens=77, max_rn=4, num_dummy_tokens=4,
-                 device="cuda", controlnet=None):
-        super().__init__()
-        self.unet = unet
-        self.image_proj_model = image_proj_model
-        self.adapter_modules = adapter_modules
-        self.num_tokens = num_tokens
-        self.num_dummy_tokens = num_dummy_tokens
-        self.text_tokens = text_tokens
-        self.max_rn = max_rn
-        self.device = device
-        self.controlnet = controlnet
-        self.cross_attention_dim = self.unet.config.cross_attention_dim
-        
-        # set attention processor when inference
-        if self.adapter_modules is None:
-            self.set_ms_adapter()
-        
-        # dummy image tokens
-        self.dummy_image_tokens = nn.Parameter(torch.randn(1, self.num_dummy_tokens, self.cross_attention_dim))
-        
-        if ckpt_path is not None:
-            self.load_from_checkpoint(ckpt_path)
-    
-    def load_from_checkpoint(self, ckpt_path: str):
-        if os.path.isdir(ckpt_path):
-            ckpt_path = os.path.join(ckpt_path, "ms_adapter.bin")
-        
-        # Calculate original checksums
-        orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
-        orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
-        
-        state_dict = torch.load(ckpt_path, map_location="cpu")
-        # Load state dict for image_proj_model and adapter_modules when using resampler
-        self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=False)
-        self.adapter_modules.load_state_dict(state_dict["ms_adapter"], strict=False)
-        self.load_state_dict({"dummy_image_tokens": state_dict["dummy_image_tokens"]}, strict=False)
-        
-        # Calculate new checksums
-        new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
-        new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
-        
-        # Verify if the weights have changed
-        assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
-        assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!"
-        
-        print(f"Successfully loaded weights from checkpoint {ckpt_path}")
-    
-    def set_ms_adapter(self, weight_dtype=torch.float16, cache_attention_maps=True):
-        # set attention processor
-        attn_procs = {}
-        for name in self.unet.attn_processors.keys():
-            cross_attention_dim = None if name.endswith("attn1.processor") else self.unet.config.cross_attention_dim
-            if name.startswith("mid_block"):
-                hidden_size = self.unet.config.block_out_channels[-1]
-            elif name.startswith("up_blocks"):
-                block_id = int(name[len("up_blocks.")])
-                hidden_size = list(reversed(self.unet.config.block_out_channels))[block_id]
-            elif name.startswith("down_blocks"):
-                block_id = int(name[len("down_blocks.")])
-                hidden_size = self.unet.config.block_out_channels[block_id]
-            if cross_attention_dim is None:
-                attn_procs[name] = AttnProcessor()
-            else:
-                attn_procs[name] = IPAttnProcessor(
-                    hidden_size=hidden_size,
-                    cross_attention_dim=cross_attention_dim,
-                    num_tokens=self.num_tokens,
-                    text_tokens=self.text_tokens,
-                ).to(self.device, dtype=weight_dtype)
-        self.unet.set_attn_processor(attn_procs)
-        self.adapter_modules = torch.nn.ModuleList(self.unet.attn_processors.values())
-        if self.controlnet is not None:
-            if isinstance(self.controlnet, MultiControlNetModel):
-                for controlnet in self.controlnet.nets:
-                    controlnet.set_attn_processor(
-                        CNAttnProcessor(text_tokens=self.text_tokens, num_tokens=self.num_tokens))
-            else:
-                self.controlnet.set_attn_processor(
-                    CNAttnProcessor(text_tokens=self.text_tokens, num_tokens=self.num_tokens))
-    
-    @torch.inference_mode()
-    def get_image_embeds(self, processed_images, image_encoder=None, image_proj_type="linear",
-                         image_encoder_type="clip", weight_dtype=torch.float16,):
-        # get image embeds
-        #processed_images: [bsz, rn, ...]
-        # if use_repo:
-        #     processed_images = processed_images.view(-1, processed_images.shape[-3], processed_images.shape[-2],
-        #                                              processed_images.shape[-1])  # (bsz*rn, ...)
-        #     #print(processed_images.shape)  # torch.Size([1, 3, 224, 224])
-
-        if image_proj_type == "resampler":
-            # if use_repo:
-            #     image_embeds = image_encoder(processed_images.to(self.device, dtype=weight_dtype),
-            #                                  output_hidden_states=True).hidden_states[
-            #         -2]  # (bsz*rn, num_tokens, embedding_dim)
-            # else:
-            image_encoder = image_encoder.encode_image
-            processed_images.to(self.device, dtype=weight_dtype)
-            #image_embeds = image_encoder(processed_images)["image_embeds"]
-            #image_embeds = image_encoder(processed_images)["last_hidden_state"]
-            image_embeds=image_encoder(processed_images)["penultimate_hidden_states"]
-            # print(image_embeds.shape)
-        else:
-            image_embeds = image_encoder(
-                processed_images.to(self.device, dtype=weight_dtype)).image_embeds  # (bsz*rn, embedding_dim)
-        
-        return image_embeds  # [bsz*rn, ...]
-    
-    def set_scale(self, scale, subject_scales):
-        for attn_processor in self.pipe.unet.attn_processors.values():
-            if isinstance(attn_processor, IPAttnProcessor):
-                attn_processor.scale = scale
-                attn_processor.subject_scales = subject_scales
-    
-    def enable_psuedo_attention_mask(self, mask_threshold=0.5, start_step=5):
-        for attn_processor in self.pipe.unet.attn_processors.values():
-            if isinstance(attn_processor, IPAttnProcessor):
-                attn_processor.mask_threshold = mask_threshold
-                attn_processor.start_step = start_step
-                attn_processor.use_psuedo_attention_mask = True
-                attn_processor.need_text_attention_map = True
-                attn_processor.attention_maps = []  # clear attention maps
-    
-    def generate(self, pipe, pil_images=None, processed_images=None, prompt=None, negative_prompt=None, scale=1.0,
-                 num_samples=4, seed=None, guidance_scale=7.5, num_inference_steps=50, image_processor=None,
-                 image_encoder=None, image_proj_type="linear", image_encoder_type="clip", weight_dtype=torch.float16,
-                 boxes=None, phrases=None, drop_grounding_tokens=None, phrase_idxes=None,
-                 eot_idxes=None, height=1024, width=1024, subject_scales=None, mask_threshold=None, start_step=5,
-                 **kwargs):
-        # generate images (validation&inference)
-        self.pipe = pipe
-        self.set_scale(scale, subject_scales)
-        if mask_threshold is not None:
-            self.enable_psuedo_attention_mask(mask_threshold, start_step)
-        
-        # pil_images: [[xxx, xxx, xxx], [xxx, xxx, xxx], ...]
-        bsz = len(pil_images)  # only support bsz=1 now
-        if processed_images is None:
-            # write in this way to promise it can be extended to batch in the future
-            processed_images = []
-            for pil_image in pil_images:
-                processed_image = image_processor(images=pil_image, return_tensors="pt").pixel_values
-                processed_images.append(processed_image)
-            processed_images = torch.stack(processed_images, dim=0)
-        
-        num_prompts = bsz
-        if prompt is None:
-            prompt = "best quality, high quality"
-        if negative_prompt is None:
-            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"  # duplicate
-        if not isinstance(prompt, List):
-            prompt = [prompt] * num_prompts
-        if not isinstance(negative_prompt, List):
-            negative_prompt = [negative_prompt] * num_prompts
-        
-        cross_attention_kwargs = None
-        grounding_kwargs = None
-        if boxes is not None:
-            boxes = torch.tensor(boxes).to(self.device, weight_dtype)
-            if phrases is not None:
-                drop_grounding_tokens = drop_grounding_tokens if drop_grounding_tokens is not None else [0] * bsz
-                batch_boxes = boxes.view(bsz * boxes.shape[1], -1)
-                # write in this way to promise it can be extended to batch in the future
-                phrase_input_ids = []
-                for phrase in phrases:
-                    phrase_input_id = pipe.tokenizer(phrase, max_length=pipe.tokenizer.model_max_length,
-                                                     padding="max_length", truncation=True,
-                                                     return_tensors="pt").input_ids
-                    phrase_input_ids.append(phrase_input_id)
-                phrase_input_ids = torch.stack(phrase_input_ids)
-                phrase_input_ids = phrase_input_ids.view(-1, phrase_input_ids.shape[-1])
-                phrase_embeds = pipe.text_encoder(phrase_input_ids.to(self.device)).pooler_output
-                #print(phrase_embeds.shape,batch_boxes.shape)#torch.Size([1, 768]) torch.Size([1, 4])
-                grounding_kwargs = {"boxes": batch_boxes, "phrase_embeds": phrase_embeds,
-                                    "drop_grounding_tokens": drop_grounding_tokens}
-            else:
-                grounding_kwargs = None
-            boxes = torch.repeat_interleave(boxes, repeats=num_samples, dim=0)
-            uncond_boxes = torch.zeros_like(boxes)
-            boxes = torch.cat([uncond_boxes, boxes], dim=0)
-            cross_attention_kwargs = {"boxes": boxes}
-        
-        if phrase_idxes is not None:
-            phrase_idxes = torch.tensor(phrase_idxes).to(self.device, torch.int)
-            eot_idxes = torch.tensor(eot_idxes).to(self.device, torch.int)
-            phrase_idxes = torch.repeat_interleave(phrase_idxes, repeats=num_samples, dim=0)
-            eot_idxes = torch.repeat_interleave(eot_idxes, repeats=num_samples, dim=0)
-            uncond_phrase_idxes = torch.zeros_like(phrase_idxes)
-            uncond_eot_idxes = torch.zeros_like(eot_idxes)
-            phrase_idxes = torch.cat([uncond_phrase_idxes, phrase_idxes], dim=0)
-            eot_idxes = torch.cat([uncond_eot_idxes, eot_idxes], dim=0)
-            if cross_attention_kwargs is None:
-                cross_attention_kwargs = {"phrase_idxes": phrase_idxes, "eot_idxes": eot_idxes}
-            else:
-                cross_attention_kwargs["phrase_idxes"] = phrase_idxes
-                cross_attention_kwargs["eot_idxes"] = eot_idxes
-        
-        with (torch.inference_mode()):
-            image_embeds = self.get_image_embeds(processed_images, image_encoder, image_proj_type=image_proj_type,
-                                                 image_encoder_type=image_encoder_type, weight_dtype=weight_dtype,)
-            #print(image_embeds.shape) #torch.Size([1, 257, 1664])
-            
-            image_embeds=image_embeds.clone().detach().to(self.device, weight_dtype)
-            image_prompt_embeds = self.image_proj_model(image_embeds, grounding_kwargs=grounding_kwargs)
-            del image_embeds
-            image_prompt_embeds = image_prompt_embeds.view(bsz, -1, image_prompt_embeds.shape[-2],
-                                                           image_prompt_embeds.shape[
-                                                               -1])  # (bsz, rn, num_tokens, cross_attention_dim)
-            image_prompt_embeds = image_prompt_embeds.view(bsz,
-                                                           image_prompt_embeds.shape[-3] * image_prompt_embeds.shape[
-                                                               -2],
-                                                           image_prompt_embeds.shape[
-                                                               -1])  # (bsz, total_num_tokens*rn, cross_attention_dim)
-            image_prompt_embeds = torch.cat([self.dummy_image_tokens, image_prompt_embeds], dim=1)
-            uncond_image_prompt_embeds = torch.zeros_like(image_prompt_embeds)
-            bs_embed, seq_len, _ = image_prompt_embeds.shape
-            image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
-            image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-            uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
-            uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-            
-            prompt_embeds_, negative_prompt_embeds_, pooled_prompt_embeds, negative_pooled_prompt_embeds = pipe.encode_prompt(
-                prompt,
-                device=self.device,
-                num_images_per_prompt=num_samples,
-                do_classifier_free_guidance=True,
-                negative_prompt=negative_prompt,
-            )
-            prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
-            negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
-        
-        generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
-        
-        images = pipe(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            generator=generator,
-            cross_attention_kwargs=cross_attention_kwargs,
-            height=height,
-            width=width,
-            **kwargs,
-        ).images
-        
-        return images
diff --git a/msdiffusion/models/projection.py b/msdiffusion/models/projection.py
deleted file mode 100644
index c3f7999..0000000
--- a/msdiffusion/models/projection.py
+++ /dev/null
@@ -1,257 +0,0 @@
-# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
-# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
-# and https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/ip_adapter.py
-# and https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/resampler.py
-
-import math
-
-import torch
-import torch.nn as nn
-from einops import rearrange
-from einops.layers.torch import Rearrange
-
-
-class FourierEmbedder(nn.Module):
-    def __init__(self, num_freqs=64, temperature=100):
-        super().__init__()
-
-        self.num_freqs = num_freqs
-        self.temperature = temperature
-
-        freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs)
-        freq_bands = freq_bands[None, None]
-        self.register_buffer("freq_bands", freq_bands, persistent=False)
-
-    def __call__(self, x):
-        x = self.freq_bands * x.unsqueeze(-1)
-        return torch.stack((x.sin(), x.cos()), dim=-1).permute(0, 2, 3, 1).reshape(x.shape[0], -1)
-
-
-class ImageProjModel(torch.nn.Module):
-    """Projection Model"""
-
-    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
-        super().__init__()
-
-        self.cross_attention_dim = cross_attention_dim
-        self.clip_extra_context_tokens = clip_extra_context_tokens
-        self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
-        self.norm = torch.nn.LayerNorm(cross_attention_dim)
-
-    def forward(self, image_embeds):
-        embeds = image_embeds
-        clip_extra_context_tokens = self.proj(embeds).reshape(
-            -1, self.clip_extra_context_tokens, self.cross_attention_dim
-        )
-        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
-        return clip_extra_context_tokens
-
-
-# FFN
-def FeedForward(dim, mult=4):
-    inner_dim = int(dim * mult)
-    return nn.Sequential(
-        nn.LayerNorm(dim),
-        nn.Linear(dim, inner_dim, bias=False),
-        nn.GELU(),
-        nn.Linear(inner_dim, dim, bias=False),
-        # nn.LayerNorm(dim),
-    )
-
-
-def reshape_tensor(x, heads):
-    bs, length, width = x.shape
-    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
-    x = x.view(bs, length, heads, -1)
-    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
-    x = x.transpose(1, 2)
-    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
-    x = x.reshape(bs, heads, length, -1)
-    return x
-
-
-class PerceiverAttention(nn.Module):
-    def __init__(self, *, dim, dim_head=64, heads=8):
-        super().__init__()
-        self.scale = dim_head**-0.5
-        self.dim_head = dim_head
-        self.heads = heads
-        inner_dim = dim_head * heads
-
-        self.norm1 = nn.LayerNorm(dim)
-        self.norm2 = nn.LayerNorm(dim)
-
-        self.to_q = nn.Linear(dim, inner_dim, bias=False)
-        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
-        self.to_out = nn.Linear(inner_dim, dim, bias=False)
-
-    def forward(self, x, latents):
-        """
-        Args:
-            x (torch.Tensor): image features
-                shape (b, n1, D)
-            latent (torch.Tensor): latent features
-                shape (b, n2, D)
-        """
-        x = self.norm1(x)
-        latents = self.norm2(latents)
-
-        b, l, _ = latents.shape
-
-        q = self.to_q(latents)
-        kv_input = torch.cat((x, latents), dim=-2)
-        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
-
-        q = reshape_tensor(q, self.heads)
-        k = reshape_tensor(k, self.heads)
-        v = reshape_tensor(v, self.heads)
-
-        # attention
-        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
-        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
-        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
-        out = weight @ v
-
-        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
-
-        return self.to_out(out)
-
-
-class Resampler(nn.Module):
-    def __init__(
-        self,
-        dim=1024,
-        depth=8,
-        dim_head=64,
-        heads=16,
-        num_queries=8,
-        embedding_dim=768,
-        output_dim=1024,
-        ff_mult=4,
-        max_seq_len: int = 257,  # CLIP tokens + CLS token
-        apply_pos_emb: bool = False,
-        num_latents_mean_pooled: int = 0,  # number of latents derived from mean pooled representation of the sequence
-        latent_init_mode: str = "random",
-        phrase_embeddings_dim: int = 1024,
-        fourier_freqs: int = 8,
-    ):
-        super().__init__()
-        self.num_queries = num_queries
-        self.grounding_token_num = self.num_queries
-        self.dim = dim
-        self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
-
-        self.latent_init_mode = latent_init_mode
-        if latent_init_mode == "random":
-            self.latents = nn.Parameter(torch.randn(1, self.latents_token_num, dim) / dim**0.5)
-            self.fourier_embedder = None
-            self.latent_proj = None
-            self.latent_norm = None
-        elif latent_init_mode == "grounding":
-            self.latents = None
-            self.grounding_latents = nn.Parameter(torch.randn(1, self.grounding_token_num, dim) / dim ** 0.5)
-            self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)
-            grounding_embedding_dim = phrase_embeddings_dim + fourier_freqs * 2 * 4  # 2: sin/cos, 4: xyxy
-            self.latent_proj = torch.nn.Sequential(
-                torch.nn.Linear(grounding_embedding_dim, grounding_embedding_dim * 2),
-                torch.nn.GELU(),
-                torch.nn.Linear(grounding_embedding_dim * 2, dim * self.grounding_token_num),
-            )
-            self.latent_norm = nn.LayerNorm(dim)
-        else:
-            raise ValueError(f"Invalid latent_init_mode: {latent_init_mode}")
-
-        self.proj_in = nn.Linear(embedding_dim, dim)
-        self.attention_norm = nn.LayerNorm(dim)
-
-        self.proj_out = nn.Linear(dim, output_dim)
-        self.norm_out = nn.LayerNorm(output_dim)
-
-        self.to_latents_from_mean_pooled_seq = (
-            nn.Sequential(
-                nn.LayerNorm(dim),
-                nn.Linear(dim, dim * num_latents_mean_pooled),
-                Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
-            )
-            if num_latents_mean_pooled > 0
-            else None
-        )
-
-        self.layers = nn.ModuleList([])
-        for _ in range(depth):
-            self.layers.append(
-                nn.ModuleList(
-                    [
-                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
-                        FeedForward(dim=dim, mult=ff_mult),
-                    ]
-                )
-            )
-
-    def forward(self, x, grounding_kwargs=None, shortcut=False, scale=1.0):
-        if self.pos_emb is not None:
-            n, device = x.shape[1], x.device
-            pos_emb = self.pos_emb(torch.arange(n, device=device))
-            x = x + pos_emb
-
-        if self.latent_init_mode == "random":
-            latents = self.latents.repeat(x.size(0), 1, 1)
-        elif self.latent_init_mode == "grounding":
-            boxes = grounding_kwargs["boxes"]
-            phrase_embeds = grounding_kwargs["phrase_embeds"]
-            fourier_embeds = self.fourier_embedder(boxes)
-            grounding_embeds = torch.cat((phrase_embeds, fourier_embeds), dim=-1)
-
-            drop_grounding_tokens = grounding_kwargs["drop_grounding_tokens"]
-            num_ref = x.shape[0] // len(drop_grounding_tokens)
-            drop_grounding_tokens = [item for item in drop_grounding_tokens for _ in range(num_ref)]
-
-            latents = self.latent_proj(grounding_embeds)
-            latents = latents.view(-1, self.grounding_token_num, self.dim)
-            latents = self.latent_norm(latents)
-
-            # drop grounding tokens to learnable latents
-            drop_num = len([item for item in drop_grounding_tokens if item == 1])
-            if drop_num > 0:
-                latents_ = []
-                learnable_latents = self.grounding_latents.repeat(drop_num, 1, 1)
-                cur_idx = 0
-                for latent, drop_grounding_token in zip(latents, drop_grounding_tokens):
-                    if drop_grounding_token == 1:
-                        latent = learnable_latents[cur_idx]
-                        cur_idx += 1
-                    latents_.append(latent)
-                latents = torch.stack(latents_)
-        else:
-            raise ValueError(f"Invalid latent_init_mode: {self.latent_init_mode}")
-
-        x = self.proj_in(x)
-
-        if self.to_latents_from_mean_pooled_seq:
-            meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
-            meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
-            latents = torch.cat((meanpooled_latents, latents), dim=-2)
-
-        init_latents = latents
-
-        for attn, ff in self.layers:
-            latents = attn(x, latents) + latents
-            latents = ff(latents) + latents
-
-        latents = self.attention_norm(latents)
-        latents = self.proj_out(latents)
-        if shortcut:
-            latents = init_latents + latents * scale
-
-        return self.norm_out(latents)
-
-
-def masked_mean(t, *, dim, mask=None):
-    if mask is None:
-        return t.mean(dim=dim)
-
-    denom = mask.sum(dim=dim, keepdim=True)
-    mask = rearrange(mask, "b n -> b n 1")
-    masked_t = t.masked_fill(~mask, 0.0)
-
-    return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)
diff --git a/msdiffusion/utils.py b/msdiffusion/utils.py
deleted file mode 100644
index 42d40ce..0000000
--- a/msdiffusion/utils.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import re
-
-
-def get_phrase_idx(tokenizer, phrase, prompt, get_last_word=False, num=0):
-    def is_equal_words(pr_words, ph_words):
-        if len(pr_words) != len(ph_words):
-            return False
-        for pr_word, ph_word in zip(pr_words, ph_words):
-            if "-"+ph_word not in pr_word and ph_word != re.sub(r'[.!?,:]$', '', pr_word):
-                return False
-        return True
-
-    phrase_words = phrase.split()
-    if len(phrase_words) == 0:
-        return [0, 0], None
-    if get_last_word:
-        phrase_words = phrase_words[-1:]
-    # prompt_words = re.findall(r'\b[\w\'-]+\b', prompt)
-    prompt_words = prompt.split()
-    start = 1
-    end = 0
-    res_words = phrase_words
-    for i in range(len(prompt_words)):
-        if is_equal_words(prompt_words[i:i+len(phrase_words)], phrase_words):
-            if num != 0:
-                # skip this one
-                num -= 1
-                continue
-            end = start
-            res_words = prompt_words[i:i+len(phrase_words)]
-            res_words = [re.sub(r'[.!?,:]$', '', w) for w in res_words]
-            prompt_words[i+len(phrase_words)-1] = res_words[-1]  # remove the last punctuation
-            for j in range(i, i+len(phrase_words)):
-                end += len(tokenizer.encode(prompt_words[j])) - 2
-            break
-        else:
-            start += len(tokenizer.encode(prompt_words[i])) - 2
-
-    if end == 0:
-        return [0, 0], None
-
-    return [start, end], res_words
-
-
-def get_eot_idx(tokenizer, prompt):
-    words = prompt.split()
-    start = 1
-    for w in words:
-        start += len(tokenizer.encode(w)) - 2
-    return start
diff --git a/msdiffusion_native/__init__.py b/msdiffusion_native/__init__.py
new file mode 100644
index 0000000..5aed92b
--- /dev/null
+++ b/msdiffusion_native/__init__.py
@@ -0,0 +1,4 @@
+"""Native MS-Diffusion nodes: pure torch + ComfyUI core."""
+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
diff --git a/msdiffusion_native/core.py b/msdiffusion_native/core.py
new file mode 100644
index 0000000..55420d6
--- /dev/null
+++ b/msdiffusion_native/core.py
@@ -0,0 +1,546 @@
+"""MS-Diffusion native core.
+
+Pure torch + ComfyUI core APIs. No diffusers, no transformers, no einops,
+no omegaconf, no PIL. Everything here is a line-by-line port of the upstream
+MS-Diffusion inference path (D:/Downloads/MS-Diffusion-main):
+
+  msdiffusion/models/projection.py          -> FourierEmbedder / Resampler
+  msdiffusion/models/attention_processor.py-> MaskedIPAttnProcessor2_0
+  msdiffusion/models/model.py               -> MSAdapter.generate (conditioning assembly)
+
+The SDXL UNet itself, text encoding, CLIP-Vision, KSampler and VAE are all
+left to ComfyUI native nodes. This file only supplies the *patch* that turns
+native attn2 into the MS-Diffusion dual-branch cross-attention.
+
+Key semantic facts (verified against real weights on 2026-11-06):
+  *_ms_adapter.bin* has exactly 70 (to_k_ip, to_v_ip) pairs, indexed
+  1,3,...,139 in diffusers attn_processors order. That order is
+  down_blocks -> up_blocks -> mid_block (module *registration* order),
+  whereas ComfyUI *executes* input_blocks -> middle_block -> output_blocks.
+  The two must be permuted, see build_block_weights().
+  * diffusers and ComfyUI use the SAME head partition for SDXL:
+    inner_dim 640 -> 10 heads, 1280 -> 20 heads, head_dim 64.
+"""
+
+import math
+import os
+import uuid
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+NUM_TOKENS = 16           # image tokens per subject
+NUM_DUMMY_TOKENS = 4      # learnable "background" image tokens
+TEXT_TOKENS = 77          # upstream hard-codes this split point
+VISION_DIM = 1664         # CLIP-ViT-bigG/14 penultimate hidden size
+CROSS_DIM = 2048          # SDXL cross_attention_dim
+PHRASE_DIM = 768          # CLIP-L pooled output
+RESAMPLER_DIM = 1280
+FOURIER_FREQS = 8
+HEAD_DIM = 64             # SDXL attention head dim (both frameworks)
+
+
+# ---------------------------------------------------------------------------
+# projection.py port (einops removed; runtime never touches masked_mean/Rearrange)
+# ---------------------------------------------------------------------------
+
+class FourierEmbedder(nn.Module):
+    def __init__(self, num_freqs=64, temperature=100):
+        super().__init__()
+        self.num_freqs = num_freqs
+        self.temperature = temperature
+        freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs)
+        self.register_buffer("freq_bands", freq_bands[None, None], persistent=False)
+
+    def __call__(self, x):
+        x = self.freq_bands * x.unsqueeze(-1)
+        return torch.stack((x.sin(), x.cos()), dim=-1).permute(0, 2, 3, 1).reshape(x.shape[0], -1)
+
+
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    x = x.view(bs, length, heads, -1).transpose(1, 2)
+    return x.reshape(bs, heads, length, -1)
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head ** -0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+        b, l, _ = latents.shape
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+        # upstream stability trick: scale BOTH operands, float32 softmax
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+        return self.to_out(out)
+
+
+def feed_forward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+class Resampler(nn.Module):
+    """Grounding-mode Resampler, key-for-key identical to upstream projection.py."""
+
+    def __init__(self, dim=RESAMPLER_DIM, depth=4, dim_head=64, heads=20,
+                 num_queries=NUM_TOKENS, embedding_dim=VISION_DIM,
+                 output_dim=CROSS_DIM, ff_mult=4, max_seq_len=257,
+                 phrase_embeddings_dim=PHRASE_DIM, fourier_freqs=FOURIER_FREQS):
+        super().__init__()
+        self.num_queries = num_queries
+        self.grounding_token_num = num_queries
+        self.dim = dim
+        self.latent_init_mode = "grounding"
+        self.grounding_latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim ** 0.5)
+        self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)
+        grounding_embedding_dim = phrase_embeddings_dim + fourier_freqs * 2 * 4
+        self.latent_proj = nn.Sequential(
+            nn.Linear(grounding_embedding_dim, grounding_embedding_dim * 2),
+            nn.GELU(),
+            nn.Linear(grounding_embedding_dim * 2, dim * num_queries),
+        )
+        self.latent_norm = nn.LayerNorm(dim)
+        self.proj_in = nn.Linear(embedding_dim, dim)
+        self.attention_norm = nn.LayerNorm(dim)
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                feed_forward(dim=dim, mult=ff_mult),
+            ]))
+
+    def forward(self, x, grounding_kwargs=None, shortcut=False, scale=1.0):
+        boxes = grounding_kwargs["boxes"]
+        phrase_embeds = grounding_kwargs["phrase_embeds"]
+        fourier_embeds = self.fourier_embedder(boxes)
+        grounding_embeds = torch.cat((phrase_embeds, fourier_embeds), dim=-1)
+
+        drop_grounding_tokens = grounding_kwargs["drop_grounding_tokens"]
+        num_ref = x.shape[0] // len(drop_grounding_tokens)
+        drop_grounding_tokens = [item for item in drop_grounding_tokens for _ in range(num_ref)]
+
+        latents = self.latent_proj(grounding_embeds)
+        latents = latents.view(-1, self.grounding_token_num, self.dim)
+        latents = self.latent_norm(latents)
+
+        drop_num = len([item for item in drop_grounding_tokens if item == 1])
+        if drop_num > 0:
+            latents_ = []
+            learnable_latents = self.grounding_latents.repeat(drop_num, 1, 1)
+            cur_idx = 0
+            for latent, flag in zip(latents, drop_grounding_tokens):
+                if flag == 1:
+                    latent = learnable_latents[cur_idx]
+                    cur_idx += 1
+                latents_.append(latent)
+            latents = torch.stack(latents_)
+
+        x = self.proj_in(x)
+        init_latents = latents
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+        latents = self.attention_norm(latents)
+        latents = self.proj_out(latents)
+        if shortcut:
+            latents = init_latents + latents * scale
+        return self.norm_out(latents)
+
+
+class IPProjection(nn.Module):
+    def __init__(self, hidden_size):
+        super().__init__()
+        self.to_k_ip = nn.Linear(CROSS_DIM, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(CROSS_DIM, hidden_size, bias=False)
+
+
+class MSAdapter(nn.Module):
+    """Self-contained adapter. Keys are 1:1 with ms_adapter.bin after index remap."""
+
+    def __init__(self, hidden_sizes):
+        super().__init__()
+        self.hidden_sizes = list(hidden_sizes)
+        self.image_proj = Resampler()
+        self.adapters = nn.ModuleList([IPProjection(h) for h in self.hidden_sizes])
+        self.dummy_image_tokens = nn.Parameter(torch.randn(1, NUM_DUMMY_TOKENS, CROSS_DIM))
+        self._placed = None
+
+    def load(self, path):
+        if os.path.isdir(path):
+            path = os.path.join(path, "ms_adapter.bin")
+        sd = torch.load(path, map_location="cpu", weights_only=True)
+        missing_top = {"image_proj", "ms_adapter", "dummy_image_tokens"} - set(sd)
+        if missing_top:
+            raise RuntimeError("MS-Diffusion checkpoint missing groups: %s" % sorted(missing_top))
+
+        self.image_proj.load_state_dict(sd["image_proj"], strict=True)
+        proj_sd = {}
+        for i in range(len(self.hidden_sizes)):
+            src = 2 * i + 1                       # 1,3,...,139
+            for name in ("to_k_ip", "to_v_ip"):
+                proj_sd["%d.%s.weight" % (i, name)] = sd["ms_adapter"]["%d.%s.weight" % (src, name)]
+        self.adapters.load_state_dict(proj_sd, strict=True)
+        assert sd["dummy_image_tokens"].shape == self.dummy_image_tokens.shape
+        with torch.no_grad():
+            self.dummy_image_tokens.copy_(sd["dummy_image_tokens"])
+        for i, h in enumerate(self.hidden_sizes):
+            w = self.adapters[i].to_k_ip.weight
+            if w.shape != (h, CROSS_DIM):
+                raise RuntimeError("adapter %d shape %s != (%d,%d)" % (i, tuple(w.shape), h, CROSS_DIM))
+        return self
+
+    @torch.no_grad()
+    def ensure_on(self, device, dtype):
+        tag = (str(device), str(dtype))
+        if self._placed == tag:
+            return
+        self.to(device=device, dtype=dtype)
+        self._placed = tag
+
+
+# ---------------------------------------------------------------------------
+# Conditioning assembly (port of MSAdapter.generate, without diffusers pipeline)
+# ---------------------------------------------------------------------------
+
+class MSEmbeds:
+    """Everything the attn2 patch needs, independent of CONDITIONING."""
+
+    def __init__(self, image_prompt_embeds, boxes, phrase_idxes, num_ref,
+                 scale, mask_threshold, start_step, subject_scales=None):
+        self.image_prompt_embeds = image_prompt_embeds   # [1, 4+16*num_ref, 2048] fp32
+        self.boxes = boxes                               # [1, num_ref, 4] fp32
+        self.phrase_idxes = phrase_idxes                 # [1, num_ref, 2] int
+        self.num_ref = num_ref
+        self.scale = float(scale)
+        self.mask_threshold = mask_threshold             # None disables pseudo mask
+        self.start_step = int(start_step)
+        self.subject_scales = subject_scales
+
+
+def minmax_normalize(batch_maps):
+    min_val = batch_maps.min(dim=-1, keepdim=True)[0].min(dim=-2, keepdim=True)[0]
+    max_val = batch_maps.max(dim=-1, keepdim=True)[0].max(dim=-2, keepdim=True)[0]
+    return (batch_maps - min_val) / (max_val - min_val + 1e-5)
+
+
+def build_box_masks(boxes, seq_len_q, dtype, device):
+    """Port of MaskedIPAttnProcessor2_0.prepare_attention_mask_qk (boxes path)."""
+    if boxes is None:
+        return None, None, None
+    batch_size, num_ref, _ = boxes.shape
+    nrow = int(seq_len_q ** 0.5)
+    box_idxes_start = torch.floor(boxes[:, :, 0:2] * nrow)
+    box_idxes_end = torch.ceil(boxes[:, :, 2:4] * nrow)
+    box_idxes = torch.cat([box_idxes_start, box_idxes_end], dim=-1)
+    box_masks = []
+    dummy_attention_mask = torch.ones((batch_size, seq_len_q), dtype=dtype, device=device)
+    for box_idx in box_idxes.unbind(dim=1):
+        xs, ys, xe, ye = box_idx.unbind(dim=1)
+        x_indices = torch.arange(nrow).unsqueeze(0).expand(batch_size, -1).to(device)
+        y_indices = torch.arange(nrow).unsqueeze(0).expand(batch_size, -1).to(device)
+        x_mask = ((x_indices >= xs.unsqueeze(1)) & (x_indices < xe.unsqueeze(1))).to(dtype)
+        y_mask = ((y_indices >= ys.unsqueeze(1)) & (y_indices < ye.unsqueeze(1))).to(dtype)
+        box_mask = torch.bmm(y_mask.unsqueeze(2), x_mask.unsqueeze(1)).reshape(batch_size, -1)
+        box_masks.append(box_mask)
+        dummy_attention_mask = torch.clamp(dummy_attention_mask - box_mask, min=0)
+
+    post_dummy = dummy_attention_mask.to(torch.bool)
+    image_mask = torch.stack(box_masks, dim=-1)
+    image_mask = image_mask.repeat_interleave(NUM_TOKENS, dim=-1)
+    image_mask = (1 - image_mask.to(dtype)) * -10000.0
+    dummy_mask = dummy_attention_mask.unsqueeze(-1).repeat_interleave(NUM_DUMMY_TOKENS, dim=-1)
+    dummy_mask = (1 - dummy_mask) * -10000.0
+    image_mask = torch.cat([dummy_mask, image_mask], dim=-1)
+    return image_mask, post_dummy, box_masks
+
+
+def build_pseudo_masks(maps, mask_threshold, seq_len_q, dtype, device, num_ref):
+    """Port of get_psuedo_attention_mask(). `maps` is a list of [B,num_ref,h,w]."""
+    text_maps = minmax_normalize(torch.stack(maps).mean(dim=0))
+    bsz, nr, h, w = text_maps.shape
+    seq_len_q = h * w
+    text_maps = text_maps.view(bsz, nr, -1).transpose(1, 2)
+    pseudo = (text_maps > mask_threshold).to(dtype)
+    pseudo_dummy = torch.ones((bsz, seq_len_q), dtype=dtype, device=device)
+    for i in range(nr):
+        pseudo_dummy = torch.clamp(pseudo_dummy - pseudo[..., i], min=0)
+    post_dummy = pseudo_dummy.to(torch.bool)
+    pseudo = pseudo.repeat_interleave(NUM_TOKENS, dim=-1)
+    pseudo = (1 - pseudo) * -10000.0
+    pseudo_dummy = pseudo_dummy.unsqueeze(-1).repeat_interleave(NUM_DUMMY_TOKENS, dim=-1)
+    pseudo_dummy = (1 - pseudo_dummy) * -10000.0
+    return torch.cat([pseudo_dummy, pseudo], dim=-1), post_dummy
+
+
+# ---------------------------------------------------------------------------
+# attn2 patch
+# ---------------------------------------------------------------------------
+
+# Module-level registry: ComfyUI's ModelPatcher.clone() deep-copies dict/list in
+# model_options but passes arbitrary objects (functions, dicts of state) through
+# unchanged. Closures capture only a string id, so cloned patchers share state.
+_STATES = {}
+
+
+def new_state(adapter, embeds):
+    sid = uuid.uuid4().hex
+    _STATES[sid] = {
+        "adapter": adapter,
+        "embeds": embeds,
+        "maps": {},              # per layer index -> list of text attention maps
+        "last_sigma": -1.0,
+        "calls": 0,
+    }
+    # keep the registry bounded across many node executions
+    if len(_STATES) > 64:
+        for k in list(_STATES)[:-32]:
+            _STATES.pop(k, None)
+    return sid
+
+
+def _select_per_batch(t, batch, cond_or_uncond, zero_for_uncond=True):
+    """Expand a [1,...] tensor to `batch` rows, zeroing uncond rows.
+
+    Upstream builds `cat([zeros_like(x), x])` for negative/positive. ComfyUI
+    instead reports the role of every batch chunk in transformer_options
+    ["cond_or_uncond"] (0=cond, 1=uncond), so we build the same layout without
+    relying on a fixed chunk order.
+    """
+    t = t.expand(batch, *t.shape[1:])
+    if not zero_for_uncond or cond_or_uncond is None:
+        return t
+    labels = list(cond_or_uncond)
+    if len(labels) == 0:
+        return t
+    if len(labels) != batch:
+        repeat = batch // len(labels)
+        if repeat * len(labels) != batch:
+            return t
+        labels = [x for x in labels for _ in range(repeat)]
+    keep = torch.tensor([1.0 if int(c) == 0 else 0.0 for c in labels],
+                        device=t.device, dtype=t.dtype).view(batch, *([1] * (t.dim() - 1)))
+    return t * keep
+
+
+def make_attn2_patch(state_id, layer_index, is_sentinel=False):
+    """Return a ComfyUI `patches_replace["attn2"][(block, n, depth)]` callable.
+
+    Signature is (q, k, v, extra_options); q/k/v are already projected flat
+    tensors [B, L, inner_dim]. The enclosing BasicTransformerBlock applies
+    attn2.to_out and the residual, exactly like diffusers' processor.
+    """
+
+    def attn2_patch(q, k, v, extra_options):
+        st = _STATES[state_id]
+        adapter = st["adapter"]
+        emb = st["embeds"]
+
+        # Detect a fresh sampling run by the (monotonically decreasing) sigma.
+        # All 70 patches see this; the first one resets, the rest no-op.
+        sigmas = extra_options.get("sigmas")
+        if sigmas is not None:
+            try:
+                sig = float(torch.as_tensor(sigmas).flatten()[0])
+                if sig > st["last_sigma"] + 1e-6:
+                    st["maps"] = {}
+                    st["calls"] = 0
+                st["last_sigma"] = sig
+            except Exception:
+                pass
+        if is_sentinel:
+            st["calls"] += 1
+
+        device, dtype = q.device, q.dtype
+        adapter.ensure_on(device, dtype)
+        bsz, seq_q, inner_dim = q.shape
+        heads = inner_dim // HEAD_DIM
+        head_dim = inner_dim // heads
+        scale_q = head_dim ** -0.5
+        use_pseudo = emb.mask_threshold is not None
+
+        def split_heads(t):
+            return t.view(t.shape[0], -1, heads, head_dim).transpose(1, 2)
+
+        qt = split_heads(q)
+
+        # ---- text branch -------------------------------------------------
+        kt, vt = split_heads(k), split_heads(v)
+        if not use_pseudo:
+            text_out = F.scaled_dot_product_attention(qt, kt, vt)
+        else:
+            scores = torch.matmul(qt * scale_q, kt.transpose(-1, -2))
+            text_probs = torch.softmax(scores.float(), dim=-1).to(dtype)
+            # upstream get_text_attention_maps() works on [B*heads, Lq, Lk]
+            flat = text_probs.reshape(bsz * heads, seq_q, -1).view(bsz, heads, seq_q, -1)
+            # Upstream builds cat([zeros, real]) for uncond/cond: uncond boxes
+            # and phrase indices are all zero. Reproduce that exactly.
+            boxes = _select_per_batch(emb.boxes.to(device=device, dtype=dtype), bsz,
+                                      extra_options.get("cond_or_uncond"))
+            pidx = _select_per_batch(emb.phrase_idxes.to(device=device, dtype=dtype), bsz,
+                                     extra_options.get("cond_or_uncond")).to(torch.int)
+            num_ref = boxes.shape[1]
+            h = w = int(seq_q ** 0.5)
+            maps = []
+            for i in range(bsz):
+                per_ref = []
+                for j in range(num_ref):
+                    start_idx = int(pidx[i, j, 0].item())
+                    end_idx = int(pidx[i, j, 1].item())
+                    if start_idx == 0 and end_idx == 0:
+                        per_ref.append(torch.zeros(seq_q, dtype=dtype, device=device))
+                    else:
+                        amap = flat[i, :, :, start_idx:end_idx]
+                        per_ref.append(torch.mean(torch.mean(amap, dim=-1), dim=0))
+                maps.append(torch.stack(per_ref))
+            st["maps"].setdefault(layer_index, []).append(
+                torch.stack(maps).reshape(bsz, num_ref, h, w))
+            text_out = torch.matmul(text_probs, vt)
+        text_out = text_out.transpose(1, 2).reshape(bsz, seq_q, inner_dim).to(dtype)
+
+        # ---- image branch ------------------------------------------------
+        ip = emb.image_prompt_embeds.to(device=device, dtype=dtype)
+        ip = _select_per_batch(ip, bsz, extra_options.get("cond_or_uncond"))
+        ipk = split_heads(F.linear(ip, adapter.adapters[layer_index].to_k_ip.weight))
+        ipv = split_heads(F.linear(ip, adapter.adapters[layer_index].to_v_ip.weight))
+
+        boxes = _select_per_batch(emb.boxes.to(device=device, dtype=dtype), bsz,
+                                  extra_options.get("cond_or_uncond"))
+        rf_mask, dummy_mask, _ = build_box_masks(boxes, seq_q, dtype, device)
+        if use_pseudo:
+            maps_here = st["maps"].get(layer_index, [])
+            if len(maps_here) >= emb.start_step:
+                pm, pd = build_pseudo_masks(maps_here, emb.mask_threshold,
+                                            seq_q, dtype, device, boxes.shape[1])
+                rf_mask, dummy_mask = pm, pd
+        # [B, Lq, N] -> [B*heads, Lq, N]
+        if rf_mask is not None:
+            rf_mask = rf_mask.repeat_interleave(heads, dim=0).reshape(bsz * heads, seq_q, -1)
+        if dummy_mask is not None:
+            dummy_mask = dummy_mask.repeat_interleave(heads, dim=0)
+
+        qf = qt.reshape(bsz * heads, seq_q, head_dim)
+        kf = ipk.reshape(bsz * heads, -1, head_dim)
+        vf = ipv.reshape(bsz * heads, -1, head_dim)
+        scores = torch.bmm(qf * scale_q, kf.transpose(-1, -2))
+        if rf_mask is not None:
+            scores = scores + rf_mask
+        ip_probs = torch.softmax(scores.float(), dim=-1).to(dtype)
+        if dummy_mask is not None:
+            ip_probs = torch.where(dummy_mask.unsqueeze(-1), torch.zeros_like(ip_probs), ip_probs)
+        if emb.subject_scales is not None:
+            ss = torch.tensor(emb.subject_scales, dtype=dtype, device=device)
+            ss = ss.unsqueeze(0).unsqueeze(0).repeat_interleave(NUM_TOKENS, dim=-1)
+            dss = torch.ones((1, 1, 1), dtype=dtype, device=device).repeat_interleave(NUM_DUMMY_TOKENS, dim=-1)
+            ip_probs = ip_probs * torch.cat([dss, ss], dim=-1)
+        ip_out = torch.bmm(ip_probs, vf)
+        ip_out = ip_out.reshape(bsz, heads, seq_q, head_dim).transpose(1, 2).reshape(bsz, seq_q, inner_dim)
+
+        if emb.subject_scales is None:
+            return text_out + emb.scale * ip_out
+        return text_out + ip_out
+
+    return attn2_patch
+
+
+# ---------------------------------------------------------------------------
+# Weight -> ComfyUI block mapping
+# ---------------------------------------------------------------------------
+
+def enumerate_attn2(diffusion_model):
+    """Return attn2 descriptors in ComfyUI *execution* order.
+
+    Each item: (key3, inner_dim, section) where key3 is the exact
+    (block_name, block_number, transformer_index) key ComfyUI looks up in
+    patches_replace["attn2"].
+    """
+    import re
+    out = []
+    for name, module in diffusion_model.named_modules():
+        if not name.endswith(".attn2"):
+            continue
+        m = re.match(r"^(input_blocks|middle_block|output_blocks)\.(\d+)\..*?transformer_blocks\.(\d+)\.attn2$", name)
+        if m is None:
+            continue
+        section = {"input_blocks": "input", "middle_block": "middle", "output_blocks": "output"}[m.group(1)]
+        block_name = section
+        block_number = 0 if section == "middle" else int(m.group(2))
+        depth = int(m.group(3))
+        inner_dim = module.to_q.out_features
+        out.append(((block_name, block_number, depth), inner_dim, section))
+    # named_modules() already yields registration order == execution order
+    return out
+
+
+def build_block_weights(attn2_desc, hidden_sizes):
+    """Permute the 70 checkpoint groups into ComfyUI execution order.
+
+    Checkpoint order (diffusers registration):  input | output | middle
+    ComfyUI execution order:                    input | middle | output
+    """
+    n_input = sum(1 for x in attn2_desc if x[2] == "input")
+    n_middle = sum(1 for x in attn2_desc if x[2] == "middle")
+    n_output = sum(1 for x in attn2_desc if x[2] == "output")
+    total = n_input + n_middle + n_output
+    if total != len(hidden_sizes):
+        raise RuntimeError(
+            "MS-Diffusion: UNet has %d attn2 modules (%d input / %d middle / %d output) "
+            "but the checkpoint has %d adapter groups. This adapter only supports "
+            "stock SDXL (70 cross-attention layers)." % (total, n_input, n_middle, n_output, len(hidden_sizes)))
+    reordered = (list(range(0, n_input))
+                 + list(range(n_input + n_output, n_input + n_output + n_middle))
+                 + list(range(n_input, n_input + n_output)))
+    for pos, ckpt_idx in enumerate(reordered):
+        want = attn2_desc[pos][1]
+        got = hidden_sizes[ckpt_idx]
+        if want != got:
+            raise RuntimeError(
+                "MS-Diffusion: adapter mapping mismatch at attn2 #%d: UNet inner_dim=%d "
+                "but checkpoint group %d has hidden_size=%d. Refusing to run with wrong weights."
+                % (pos, want, ckpt_idx, got))
+    return reordered
+
+
+def adapter_hidden_sizes(checkpoint_path):
+    """Read just the 70 to_k_ip shapes so the loader node can build the module."""
+    sd = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
+    if not isinstance(sd, dict) or "ms_adapter" not in sd or "image_proj" not in sd:
+        raise RuntimeError(
+            "MS-Diffusion: %s is not an ms_adapter.bin checkpoint (needs "
+            "'image_proj', 'ms_adapter' and 'dummy_image_tokens' groups). "
+            "Pick ms_adapter.bin, not a generic IP-Adapter file." % checkpoint_path)
+    group = sd["ms_adapter"]
+    idx = sorted({int(k.split(".")[0]) for k in group})
+    try:
+        return [group["%d.to_k_ip.weight" % i].shape[0] for i in idx]
+    except KeyError as e:
+        raise RuntimeError("MS-Diffusion: unexpected ms_adapter key layout (%s). "
+                           "Expected 70 to_k_ip/to_v_ip pairs." % e) from e
diff --git a/msdiffusion_native/nodes.py b/msdiffusion_native/nodes.py
new file mode 100644
index 0000000..414f891
--- /dev/null
+++ b/msdiffusion_native/nodes.py
@@ -0,0 +1,369 @@
+"""ComfyUI nodes for the native MS-Diffusion chain.
+
+Intended graph (everything except the three MS nodes is a stock ComfyUI node):
+
+  CheckpointLoaderSimple -> MODEL / CLIP / VAE
+  LoadImage xN -> native ImageBatch (chained N-1 times) -> CLIPVisionEncode
+                                                      -> CLIP_VISION_OUTPUT
+  CLIPTextEncode x2 -> positive / negative CONDITIONING
+  MSDiffusionLoader ------------------------------> MS_ADAPTER
+  MSDiffusionConditioning ------------------------> MS_EMBEDS (+ clean prompt)
+  MSDiffusionApply(MODEL, MS_ADAPTER, MS_EMBEDS) --> patched MODEL
+  EmptyLatentImage -> KSampler -> VAEDecode -> SaveImage
+
+No diffusers / transformers / omegaconf / einops / PIL imports anywhere.
+"""
+
+import os
+import re
+
+import torch
+import folder_paths
+
+from .core import (MSAdapter, MSEmbeds, adapter_hidden_sizes, build_block_weights,
+                   enumerate_attn2, make_attn2_patch, new_state, NUM_TOKENS,
+                   NUM_DUMMY_TOKENS)
+
+ADAPTER_FOLDER = "ms_adapter"
+_ADAPTER_EXTS = (".bin", ".safetensors", ".pt", ".pth", ".ckpt")
+
+
+def _register_folders():
+    """Register models/ipadapter and models/photomaker as one virtual folder.
+
+    The upstream plugin hard-coded models/photomaker; the user's actual file is
+    in models/ipadapter. Support both without touching ComfyUI source.
+    """
+    try:
+        if ADAPTER_FOLDER not in folder_paths.folder_names_and_paths:
+            folder_paths.folder_names_and_paths[ADAPTER_FOLDER] = ([], set())
+        for sub in ("ipadapter", "photomaker"):
+            p = os.path.join(folder_paths.models_dir, sub)
+            if os.path.isdir(p):
+                folder_paths.add_model_folder_path(ADAPTER_FOLDER, p)
+    except Exception:
+        pass
+
+
+_register_folders()
+
+
+def _adapter_files():
+    try:
+        files = [f for f in folder_paths.get_filename_list(ADAPTER_FOLDER)
+                 if f.lower().endswith(_ADAPTER_EXTS)]
+    except Exception:
+        files = []
+    if not files:
+        # fallback scan, keeps the node usable even if registration was blocked
+        for sub in ("ipadapter", "photomaker"):
+            d = os.path.join(folder_paths.models_dir, sub)
+            if os.path.isdir(d):
+                files += [sub + "/" + f for f in sorted(os.listdir(d))
+                          if f.lower().endswith(_ADAPTER_EXTS)]
+    files = sorted(set(files)) or ["none"]
+    # models/ipadapter often holds many unrelated IP-Adapter weights; put the
+    # real MS-Diffusion checkpoint first so the default is the correct file.
+    preferred = [f for f in files if os.path.basename(f).lower().startswith("ms_adapter")]
+    return preferred + [f for f in files if f not in preferred]
+
+
+def _adapter_path(name):
+    try:
+        return folder_paths.get_full_path_or_raise(ADAPTER_FOLDER, name)
+    except Exception:
+        return os.path.join(folder_paths.models_dir, name.replace("/", os.sep))
+
+
+# ---------------------------------------------------------------------------
+# phrase token position (port of msdiffusion/utils.py, no transformers import)
+# ---------------------------------------------------------------------------
+
+def _token_encoder(clip):
+    """Return f(word) -> len(HF tokenizer.encode(word)).
+
+    ComfyUI already owns a CLIP tokenizer instance; we just borrow it, so this
+    plugin adds no transformers import of its own.
+    """
+    try:
+        tk = clip.tokenizer.clip_l.tokenizer
+        return lambda w: len(tk.encode(w))
+    except Exception:
+        pass
+    # fallback: derive from ComfyUI's own token/weight pairs
+    def from_comfy(w):
+        pairs = clip.tokenize(w)["l"][0]
+        n = 0
+        for tid, _ in pairs:
+            if int(tid) == 49407:      # EOS == pad token
+                break
+            n += 1
+        return max(n, 1) + 1          # [BOS] + word tokens => n-1 + 2
+    return from_comfy
+
+
+def _is_equal_words(pr_words, ph_words):
+    if len(pr_words) != len(ph_words):
+        return False
+    for pr, ph in zip(pr_words, ph_words):
+        if "-" + ph not in pr and ph != re.sub(r'[.!?,:]$', '', pr):
+            return False
+    return True
+
+
+def _get_phrase_idx(enc_len, phrase, prompt, num=0):
+    phrase_words = phrase.split()
+    if len(phrase_words) == 0:
+        return [0, 0]
+    prompt_words = prompt.split()
+    start, end = 1, 0
+    for i in range(len(prompt_words)):
+        if _is_equal_words(prompt_words[i:i + len(phrase_words)], phrase_words):
+            if num != 0:
+                num -= 1
+                continue
+            end = start
+            prompt_words[i + len(phrase_words) - 1] = re.sub(
+                r'[.!?,:]$', '', prompt_words[i + len(phrase_words) - 1])
+            for j in range(i, i + len(phrase_words)):
+                end += enc_len(prompt_words[j]) - 2
+            break
+        else:
+            start += enc_len(prompt_words[i]) - 2
+    if end == 0:
+        return [0, 0]
+    return [start, end]
+
+
+def _parse_boxes(text, num_ref, enabled):
+    """Upstream: mask_threshold == 0 disables layout guidance -> all-zero boxes."""
+    zeros = [[0.0, 0.0, 0.0, 0.0] for _ in range(num_ref)]
+    if not enabled or not text.strip():
+        return zeros
+    groups = [g.strip() for g in text.strip().split(";") if g.strip()]
+    parsed = []
+    for g in groups:
+        vals = [float(x.strip()) for x in g.split(",")]
+        if len(vals) != 4:
+            raise ValueError("MS-Diffusion: each box must have 4 floats (x1,y1,x2,y2); got %r" % g)
+        parsed.append(vals)
+    if len(parsed) < num_ref:
+        # A zero box masks that subject's 16 image tokens completely (-10000 bias),
+        # silently dropping it. Upstream raises here instead of guessing; do the same.
+        raise ValueError(
+            "MS-Diffusion: %d subjects but only %d boxes. Provide one "
+            "'x1,y1,x2,y2' per subject separated by ';' (or set mask_threshold=0 "
+            "to disable layout guidance)." % (num_ref, len(parsed)))
+    return parsed[:num_ref]
+
+
+def _clip_l_pooled(clip, phrase):
+    """CLIP-L 768-d pooled output (the grounding phrase embedding).
+
+    SDXL's CONDITIONING pooled_output is clip_g (1280) and cannot be used here.
+    """
+    clip.load_model()
+    dev = clip.patcher.load_device
+    try:
+        clip.cond_stage_model.clip_l.set_clip_options({"execution_device": dev})
+    except Exception:
+        pass
+    tokens = clip.tokenize(phrase)["l"]
+    with torch.no_grad():
+        out = clip.cond_stage_model.clip_l.encode_token_weights(tokens)
+    pooled = out[1]
+    return pooled.to(torch.float32).cpu()
+
+
+# ---------------------------------------------------------------------------
+# Nodes
+# ---------------------------------------------------------------------------
+
+class MSDiffusionLoader:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {"ms_adapter": (_adapter_files(),)}}
+
+    RETURN_TYPES = ("MS_ADAPTER",)
+    RETURN_NAMES = ("ms_adapter",)
+    FUNCTION = "load"
+    CATEGORY = "MSdiffusion/Native"
+    DESCRIPTION = ("Load ms_adapter.bin from models/ipadapter or models/photomaker. "
+                   "Pure torch; no diffusers/transformers needed.")
+
+    def load(self, ms_adapter):
+        if ms_adapter == "none":
+            raise RuntimeError("MS-Diffusion: no ms_adapter.bin found in models/ipadapter or models/photomaker")
+        path = _adapter_path(ms_adapter)
+        sizes = adapter_hidden_sizes(path)
+        adapter = MSAdapter(sizes).load(path)
+        print("[MS-Diffusion] loaded %s (%d cross-attn groups)" % (path, len(sizes)))
+        return (adapter,)
+
+
+class MSDiffusionConditioning:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+            "ms_adapter": ("MS_ADAPTER",),
+            "clip": ("CLIP",),
+            "clip_vision_output": ("CLIP_VISION_OUTPUT",),
+            "prompt": ("STRING", {"multiline": True,
+                                  "default": "best quality, high quality, a [dog] on the beach"}),
+            "boxes": ("STRING", {"multiline": True,
+                                 "default": "0.25, 0.25, 0.75, 0.75",
+                                 "tooltip": "one 'x1,y1,x2,y2' per subject, separated by ';'. "
+                                            "Ignored when mask_threshold is 0."}),
+            "mask_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05,
+                                         "tooltip": "0 disables layout guidance entirely (upstream behaviour). "
+                                                    ">0 enables box guidance + pseudo attention mask."}),
+            "drop_grounding_tokens": ("BOOLEAN", {"default": False}),
+        }}
+
+    RETURN_TYPES = ("MS_EMBEDS", "STRING")
+    RETURN_NAMES = ("ms_embeds", "prompt_clean")
+    FUNCTION = "encode"
+    CATEGORY = "MSdiffusion/Native"
+    DESCRIPTION = ("Build MS-Diffusion image prompt tokens from CLIP-Vision penultimate "
+                   "hidden states + CLIP-L phrase pooled embeddings + layout boxes. "
+                   "Connect prompt_clean to your positive CLIPTextEncode so the two match.")
+
+    def encode(self, ms_adapter, clip, clip_vision_output, prompt, boxes,
+               mask_threshold, drop_grounding_tokens):
+        import comfy.model_management as mm
+
+        phrases = [p.strip() for p in re.findall(r'\[(.*?)\]', prompt)]
+        if not phrases:
+            raise ValueError("MS-Diffusion: subject names must be enclosed in [ ] inside the prompt")
+        for p in phrases:
+            if " " in p:
+                raise ValueError("MS-Diffusion: subject %r must be a single word" % p)
+        phrases = list(dict.fromkeys(phrases))
+        clean_prompt = prompt.replace("]", " ").replace("[", " ")
+
+        hidden = clip_vision_output["penultimate_hidden_states"]
+        if hidden.dim() != 3:
+            raise ValueError("MS-Diffusion: CLIP-Vision output must be penultimate_hidden_states [B,257,1664]")
+        num_ref = hidden.shape[0]
+        if num_ref != len(phrases):
+            raise ValueError(
+                "MS-Diffusion: got %d subject images but %d [subject] names in prompt (%s)"
+                % (num_ref, len(phrases), phrases))
+        if hidden.shape[-1] != 1664:
+            raise ValueError("MS-Diffusion: expected CLIP-ViT-bigG hidden size 1664, got %d. "
+                             "Use clip_vision_g.safetensors." % hidden.shape[-1])
+
+        layout_on = mask_threshold > 0.0
+        box_vals = _parse_boxes(boxes, num_ref, layout_on)
+        boxes_t = torch.tensor([box_vals], dtype=torch.float32)          # [1, num_ref, 4]
+        batch_boxes = boxes_t.view(num_ref, 4)                           # Resampler grounding input
+
+        phrase_embeds = torch.cat([_clip_l_pooled(clip, p) for p in phrases], dim=0)
+        if phrase_embeds.shape != (num_ref, 768):
+            raise RuntimeError("MS-Diffusion: CLIP-L pooled shape %s, expected (%d,768)"
+                               % (tuple(phrase_embeds.shape), num_ref))
+
+        enc_len = _token_encoder(clip)
+        seen = {}
+        pidx = []
+        for p in phrases:
+            n = seen.get(p, 0)
+            seen[p] = n + 1
+            pidx.append(_get_phrase_idx(enc_len, p, clean_prompt, num=n))
+        phrase_idxes = torch.tensor([pidx], dtype=torch.int32)           # [1, num_ref, 2]
+
+        dev = mm.get_torch_device()
+        dtype = torch.float16
+        with torch.no_grad():
+            ms_adapter.image_proj.to(device=dev, dtype=dtype)
+            x = hidden.to(device=dev, dtype=dtype)
+            grounding = {
+                "boxes": batch_boxes.to(device=dev, dtype=dtype),
+                "phrase_embeds": phrase_embeds.to(device=dev, dtype=dtype),
+                "drop_grounding_tokens": [1 if drop_grounding_tokens else 0],
+            }
+            ip_tokens = ms_adapter.image_proj(x, grounding_kwargs=grounding)
+            ip_tokens = ip_tokens.view(1, -1, ip_tokens.shape[-2], ip_tokens.shape[-1])
+            ip_tokens = ip_tokens.view(1, ip_tokens.shape[-3] * ip_tokens.shape[-2], ip_tokens.shape[-1])
+            dummy = ms_adapter.dummy_image_tokens.to(device=dev, dtype=dtype)
+            image_prompt_embeds = torch.cat([dummy, ip_tokens], dim=1).float().cpu()
+            ms_adapter.image_proj.to(device=torch.device("cpu"))
+            ms_adapter._placed = None
+
+        assert image_prompt_embeds.shape == (1, NUM_DUMMY_TOKENS + NUM_TOKENS * num_ref, 2048)
+        embeds = MSEmbeds(
+            image_prompt_embeds=image_prompt_embeds,
+            boxes=boxes_t,
+            phrase_idxes=phrase_idxes,
+            num_ref=num_ref,
+            scale=1.0,                                   # set by MSDiffusionApply
+            mask_threshold=None if not layout_on else float(mask_threshold),
+            start_step=5,                               # set by MSDiffusionApply
+        )
+        return (embeds, clean_prompt)
+
+
+class MSDiffusionApply:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+            "model": ("MODEL",),
+            "ms_adapter": ("MS_ADAPTER",),
+            "ms_embeds": ("MS_EMBEDS",),
+            "scale": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.05}),
+            "start_step": ("INT", {"default": 5, "min": 0, "max": 1024,
+                                   "tooltip": "pseudo attention mask kicks in after this many "
+                                              "denoising steps (only when mask_threshold > 0)"}),
+        }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply"
+    CATEGORY = "MSdiffusion/Native"
+    DESCRIPTION = ("Patch every native SDXL attn2 with MS-Diffusion dual-branch "
+                   "cross-attention. Output is a normal MODEL: use stock KSampler / "
+                   "ControlNet / FreeU / LoRA nodes downstream.")
+
+    def apply(self, model, ms_adapter, ms_embeds, scale, start_step):
+        import copy
+        m = model.clone()
+        desc = enumerate_attn2(m.model.diffusion_model)
+        if not desc:
+            raise RuntimeError("MS-Diffusion: no attn2 modules found. This adapter requires a stock SDXL UNet.")
+        perm = build_block_weights(desc, ms_adapter.hidden_sizes)
+
+        emb = copy.copy(ms_embeds)
+        emb.scale = float(scale)
+        emb.start_step = int(start_step)
+        sid = new_state(ms_adapter, emb)
+
+        # IMPORTANT: set_model_patch_replace() REPLACES the whole
+        # transformer_options dict and silently drops previously attached keys.
+        # Build patches_replace in place on the cloned options instead.
+        to = m.model_options["transformer_options"]
+        pr = dict(to.get("patches_replace", {}))
+        a2 = dict(pr.get("attn2", {}))
+        for pos, (key3, _inner, _section) in enumerate(desc):
+            a2[key3] = make_attn2_patch(sid, perm[pos], is_sentinel=(pos == 0))
+        pr["attn2"] = a2
+        to["patches_replace"] = pr
+        print("[MS-Diffusion] patched %d attn2 layers (scale=%.2f, layout=%s)"
+              % (len(desc), emb.scale, "on" if emb.mask_threshold is not None else "off"))
+        return (m,)
+
+
+# No custom image-batch node: ComfyUI's native `ImageBatch` already stacks
+# images into [N,H,W,3] and aligns size/channels. Chain it for N > 2.
+# (The old `MS_Object_img_Batch` / `MSDiffusionImageBatch` ids are intentionally
+#  gone; replace them with the native ImageBatch node in old graphs.)
+
+NODE_CLASS_MAPPINGS = {
+    "MSDiffusionLoader": MSDiffusionLoader,
+    "MSDiffusionConditioning": MSDiffusionConditioning,
+    "MSDiffusionApply": MSDiffusionApply,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "MSDiffusionLoader": "MS-Diffusion Loader (Native)",
+    "MSDiffusionConditioning": "MS-Diffusion Conditioning (Native)",
+    "MSDiffusionApply": "MS-Diffusion Apply to MODEL (Native)",
+}
diff --git a/pyproject.toml b/pyproject.toml
index 1603daf..57d73f6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,8 @@ name = "comfyui_ms_diffusion"
 description = "you can make story in comfyUI using MS-diffusion"
 version = "1.1.0"
 license = "LICENSE"
-dependencies = ["tensorboard", "scipy", "opencv-python", "PyYAML"]
+# Native chain depends only on torch, which ComfyUI already provides.
+dependencies = []
 
 [project.urls]
 Repository = "https://github.com/smthemex/ComfyUI_MS_Diffusion"
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index c26d7b8..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-tensorboard
-scipy
-PyYAML