From c17dfa7ee5f256d4f8129c2b29c1e6ec08f31156 Mon Sep 17 00:00:00 2001
From: smthem <2102561739@qq.com>
Date: Sat, 7 Sep 2024 11:56:29 +0800
Subject: [PATCH] add new

---
 .../__pycache__/__init__.cpython-311.pyc      |   Bin 0 -> 540 bytes
 .../attention_processor.cpython-311.pyc       |   Bin 0 -> 30255 bytes
 .../__pycache__/ip_adapter.cpython-311.pyc    |   Bin 0 -> 47065 bytes
 .../__pycache__/resampler.cpython-311.pyc     |   Bin 0 -> 8556 bytes
 .../__pycache__/utils.cpython-311.pyc         |   Bin 0 -> 6708 bytes
 CSGO/ip_adapter/ip_adapter.py                 |   258 +-
 README.md                                     |    41 +-
 __pycache__/__init__.cpython-311.pyc          |   Bin 0 -> 316 bytes
 __pycache__/csgo_node.cpython-311.pyc         |   Bin 0 -> 12760 bytes
 configs/config.json                           |    81 +
 csgo_node.py                                  |    61 +-
 example/content_style_img.png                 |   Bin 0 -> 557878 bytes
 example/example.png                           |   Bin 501156 -> 0 bytes
 example/exampleA.png                          |   Bin 505897 -> 0 bytes
 example/txt_only.png                          |   Bin 0 -> 535726 bytes
 example/using blip.png                        |   Bin 0 -> 514892 bytes
 example/workflow.json                         |   314 +
 example/workflow.png                          |   Bin 1044213 -> 0 bytes
 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 +
 35 files changed, 197655 insertions(+), 118 deletions(-)
 create mode 100644 CSGO/ip_adapter/__pycache__/__init__.cpython-311.pyc
 create mode 100644 CSGO/ip_adapter/__pycache__/attention_processor.cpython-311.pyc
 create mode 100644 CSGO/ip_adapter/__pycache__/ip_adapter.cpython-311.pyc
 create mode 100644 CSGO/ip_adapter/__pycache__/resampler.cpython-311.pyc
 create mode 100644 CSGO/ip_adapter/__pycache__/utils.cpython-311.pyc
 create mode 100644 __pycache__/__init__.cpython-311.pyc
 create mode 100644 __pycache__/csgo_node.cpython-311.pyc
 create mode 100644 configs/config.json
 create mode 100644 example/content_style_img.png
 delete mode 100644 example/example.png
 delete mode 100644 example/exampleA.png
 create mode 100644 example/txt_only.png
 create mode 100644 example/using blip.png
 create mode 100644 example/workflow.json
 delete mode 100644 example/workflow.png
 create mode 100644 local_repo/model_index.json
 create mode 100644 local_repo/scheduler/scheduler_config.json
 create mode 100644 local_repo/text_encoder/config.json
 create mode 100644 local_repo/text_encoder_2/config.json
 create mode 100644 local_repo/tokenizer/merges.txt
 create mode 100644 local_repo/tokenizer/special_tokens_map.json
 create mode 100644 local_repo/tokenizer/tokenizer_config.json
 create mode 100644 local_repo/tokenizer/vocab.json
 create mode 100644 local_repo/tokenizer_2/merges.txt
 create mode 100644 local_repo/tokenizer_2/special_tokens_map.json
 create mode 100644 local_repo/tokenizer_2/tokenizer_config.json
 create mode 100644 local_repo/tokenizer_2/vocab.json
 create mode 100644 local_repo/unet/config.json
 create mode 100644 local_repo/vae/config.json
 create mode 100644 local_repo/vae_1_0/config.json
 create mode 100644 local_repo/vae_decoder/config.json
 create mode 100644 local_repo/vae_encoder/config.json

diff --git a/CSGO/ip_adapter/__pycache__/__init__.cpython-311.pyc b/CSGO/ip_adapter/__pycache__/__init__.cpython-311.pyc
new file mode 100644
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diff --git a/CSGO/ip_adapter/__pycache__/attention_processor.cpython-311.pyc b/CSGO/ip_adapter/__pycache__/attention_processor.cpython-311.pyc
new file mode 100644
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diff --git a/CSGO/ip_adapter/ip_adapter.py b/CSGO/ip_adapter/ip_adapter.py
index 1bbaf73..5f99905 100644
--- a/CSGO/ip_adapter/ip_adapter.py
+++ b/CSGO/ip_adapter/ip_adapter.py
@@ -13,6 +13,12 @@ from .utils import is_torch2_available, get_generator
 # import torchvision.transforms.functional as Func
 
 # from .clip_style_models import CSD_CLIP, convert_state_dict
+def tensor2pil(tensor):
+    image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
+    image = Image.fromarray(image_np, mode='RGB')
+    return image
+
+
 
 if is_torch2_available():
     from .attention_processor import (
@@ -73,7 +79,7 @@ class MLPProjModel(torch.nn.Module):
 
 
 class IPAdapter:
-    def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4, target_blocks=["block"]):
+    def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device,encoder_config, num_tokens=4, target_blocks=["block"]):
         self.device = device
         self.image_encoder_path = image_encoder_path
         self.ip_ckpt = ip_ckpt
@@ -84,19 +90,21 @@ class IPAdapter:
         self.set_ip_adapter()
 
         # load image encoder
-        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
-            self.device, dtype=torch.float16
-        )
+        # self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
+        #     self.device, dtype=torch.float16
+        # )
         self.clip_image_processor = CLIPImageProcessor()
         # image proj model
         self.image_proj_model = self.init_proj()
 
         self.load_ip_adapter()
+        self.image_encoder = image_encoder_path.encode_image  # comfy
+        self.encoder_config = encoder_config
 
     def init_proj(self):
         image_proj_model = ImageProjModel(
             cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
-            clip_embeddings_dim=self.image_encoder.config.projection_dim,
+            clip_embeddings_dim=self.encoder_config["projection_dim"],
             clip_extra_context_tokens=self.num_tokens,
         ).to(self.device, dtype=torch.float16)
         return image_proj_model
@@ -248,10 +256,10 @@ class IPAdapter:
 
 
 class IPAdapter_CS:
-    def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_content_tokens=4,
+    def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, encoder_config,num_content_tokens=4,
                  num_style_tokens=4,
                  target_content_blocks=["block"], target_style_blocks=["block"], content_image_encoder_path=None,
-                  controlnet_adapter=False,
+                 controlnet_adapter=False,
                  controlnet_target_content_blocks=None,
                  controlnet_target_style_blocks=None,
                  content_model_resampler=False,
@@ -264,10 +272,9 @@ class IPAdapter_CS:
         self.num_style_tokens = num_style_tokens
         self.content_target_blocks = target_content_blocks
         self.style_target_blocks = target_style_blocks
-
         self.content_model_resampler = content_model_resampler
         self.style_model_resampler = style_model_resampler
-
+       
         self.controlnet_adapter = controlnet_adapter
         self.controlnet_target_content_blocks = controlnet_target_content_blocks
         self.controlnet_target_style_blocks = controlnet_target_style_blocks
@@ -278,20 +285,31 @@ class IPAdapter_CS:
 
 
         # load image encoder
-        if content_image_encoder_path is not None:
-            self.content_image_encoder = AutoModel.from_pretrained(content_image_encoder_path).to(self.device,
-                                                                                                  dtype=torch.float16)
-            self.content_image_processor = AutoImageProcessor.from_pretrained(content_image_encoder_path)
-        else:
-            self.content_image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
-                self.device, dtype=torch.float16
-            )
-            self.content_image_processor = CLIPImageProcessor()
-        # model.requires_grad_(False)
+        # if content_image_encoder_path is not None:
+        #     self.content_image_encoder = AutoModel.from_pretrained(content_image_encoder_path).to(self.device,
+        #                                                                                           dtype=torch.float16)
+        #     self.content_image_processor = AutoImageProcessor.from_pretrained(content_image_encoder_path)
+        # else:
+        #     self.content_image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
+        #         self.device, dtype=torch.float16
+        #     )
+        #     self.content_image_processor = CLIPImageProcessor()
+        # # model.requires_grad_(False)
+        #
+        # self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
+        #     self.device, dtype=torch.float16
+        # )
 
-        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
-            self.device, dtype=torch.float16
-        )
+   
+        self.image_encoder=image_encoder_path.encode_image  #comfy
+        self.clip_image_processor = CLIPImageProcessor()
+        
+        self.content_image_encoder=image_encoder_path.encode_image  #comfy
+        self.content_image_processor = CLIPImageProcessor()
+        
+        self.encoder_config = encoder_config
+        # image_encoder_projection_dim = image_encoder_config["vision_config"]["projection_dim"]
+        
         # if self.use_CSD is not None:
         #     self.style_image_encoder = CSD_CLIP("vit_large", "default",self.use_CSD+"/ViT-L-14.pt")
         #     model_path = self.use_CSD+"/checkpoint.pth"
@@ -307,7 +325,7 @@ class IPAdapter_CS:
         #         normalize,
         #     ])
 
-        self.clip_image_processor = CLIPImageProcessor()
+        
         # image proj model
         self.content_image_proj_model = self.init_proj(self.num_content_tokens, content_or_style_='content',
                                                        model_resampler=self.content_model_resampler)
@@ -319,17 +337,29 @@ class IPAdapter_CS:
     def init_proj(self, num_tokens, content_or_style_='content', model_resampler=False):
 
         # print('@@@@',self.pipe.unet.config.cross_attention_dim,self.image_encoder.config.projection_dim)
+        # if content_or_style_ == 'content' and self.content_image_encoder_path is not None:
+        #     image_proj_model = ImageProjModel(
+        #         cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
+        #         clip_embeddings_dim=self.content_image_encoder.config.projection_dim,
+        #         clip_extra_context_tokens=num_tokens,
+        #     ).to(self.device, dtype=torch.float16)
+        #     return image_proj_model
+
+        # image_proj_model = ImageProjModel(
+        #     cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
+        #     clip_embeddings_dim=self.image_encoder.config.projection_dim,
+        #     clip_extra_context_tokens=num_tokens,
+        # ).to(self.device, dtype=torch.float16)
         if content_or_style_ == 'content' and self.content_image_encoder_path is not None:
             image_proj_model = ImageProjModel(
                 cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
-                clip_embeddings_dim=self.content_image_encoder.config.projection_dim,
+                clip_embeddings_dim=self.encoder_config["projection_dim"],
                 clip_extra_context_tokens=num_tokens,
             ).to(self.device, dtype=torch.float16)
             return image_proj_model
-
         image_proj_model = ImageProjModel(
             cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
-            clip_embeddings_dim=self.image_encoder.config.projection_dim,
+            clip_embeddings_dim=self.encoder_config["projection_dim"],
             clip_extra_context_tokens=num_tokens,
         ).to(self.device, dtype=torch.float16)
         return image_proj_model
@@ -517,36 +547,57 @@ class IPAdapter_CS:
         #     clip_image_embeds = clip_image_embeds - content_prompt_embeds
 
         if content_or_style_ == 'content':
-            if pil_image is not None:
-                if isinstance(pil_image, Image.Image):
-                    pil_image = [pil_image]
+            # if pil_image is not None:
+            #     if isinstance(pil_image, Image.Image):
+            #         pil_image = [pil_image]
+            #     if self.content_image_proj_model is not None:
+            #         clip_image = self.content_image_processor(images=pil_image, return_tensors="pt").pixel_values
+            #         clip_image_embeds = self.content_image_encoder(
+            #             clip_image.to(self.device, dtype=torch.float16)).image_embeds
+            #     else:
+            #         clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+            #         clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
+           
+            if isinstance(pil_image, torch.Tensor):
+                pil_image = pil_image
                 if self.content_image_proj_model is not None:
-                    clip_image = self.content_image_processor(images=pil_image, return_tensors="pt").pixel_values
-                    clip_image_embeds = self.content_image_encoder(
-                        clip_image.to(self.device, dtype=torch.float16)).image_embeds
+                    # clip_image = self.content_image_processor(images=pil_image, return_tensors="pt").pixel_values
+                    # clip_image_embeds = self.content_image_encoder(
+                    #     clip_image.to(self.device, dtype=torch.float16)).image_embeds
+                    clip_image_embeds = self.image_encoder(pil_image)["image_embeds"]
+                    #print(clip_image_embeds.shape,11)
                 else:
-                    clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-                    clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
+                    #clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+                    #clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
+                    clip_image_embeds=self.image_encoder(pil_image)["image_embeds"]
+                    #print(clip_image_embeds.shape,12)
             else:
-                clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
+                clip_image_embeds = clip_image_embeds.clone().detach().to(self.device, dtype=torch.float16)
+                #clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
+                #print(clip_image_embeds.shape, 13)
 
             image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
             uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds))
             return image_prompt_embeds, uncond_image_prompt_embeds
         if content_or_style_ == 'style':
-            if pil_image is not None:
+            # if pil_image is not None:
+            if isinstance(pil_image, torch.Tensor):
                 if self.use_CSD is not None:
-                    clip_image = self.style_preprocess(pil_image).unsqueeze(0).to(self.device, dtype=torch.float32)
-                    clip_image_embeds = self.style_image_encoder(clip_image)
+                    # clip_image = self.style_preprocess(pil_image).unsqueeze(0).to(self.device, dtype=torch.float32)
+                    # clip_image_embeds = self.style_image_encoder(clip_image)
+                    clip_image_embeds = self.image_encoder(pil_image)["penultimate_hidden_states"]
+                    #print(clip_image_embeds.shape,2)
                 else:
-                    if isinstance(pil_image, Image.Image):
-                        pil_image = [pil_image]
-                    clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-                    clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
-
-
+                    # if isinstance(pil_image, Image.Image):
+                    #     pil_image = [pil_image]
+                    # clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+                    # clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
+                    clip_image_embeds = self.image_encoder(pil_image)["image_embeds"]
+                    print(clip_image_embeds.shape,3)
             else:
-                clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
+                #clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
+                clip_image_embeds = clip_image_embeds.clone().detach().to(self.device, dtype=torch.float16)
+                print(clip_image_embeds.shape, 4)
             image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
             uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds))
             return image_prompt_embeds, uncond_image_prompt_embeds
@@ -589,9 +640,10 @@ class IPAdapter_CS:
             **kwargs,
     ):
         self.set_scale(content_scale, style_scale)
-
-        if pil_content_image is not None:
-            num_prompts = 1 if isinstance(pil_content_image, Image.Image) else len(pil_content_image)
+        d1, _, _, _ = pil_content_image.size()
+        
+        if isinstance(pil_content_image,torch.Tensor) :
+            num_prompts = d1
         else:
             num_prompts = clip_content_image_embeds.size(0)
 
@@ -678,8 +730,8 @@ class IPAdapterXL_CS(IPAdapter_CS):
     ):
         self.set_scale(content_scale, style_scale)
         self.device=device
-
-        num_prompts = 1 if isinstance(pil_content_image, Image.Image) else len(pil_content_image)
+        d1, _, _, _ = pil_content_image.size()
+        num_prompts =d1
 
         if prompt is None:
             prompt = "best quality, high quality"
@@ -700,7 +752,10 @@ class IPAdapterXL_CS(IPAdapter_CS):
         style_image_prompt_embeds, uncond_style_image_prompt_embeds = self.get_image_embeds(pil_style_image,
                                                                                             style_image_embeds,
                                                                                             content_or_style_='style')
-
+       
+  
+        
+        torch.cuda.empty_cache()
         bs_embed, seq_len, _ = content_image_prompt_embeds.shape
 
         content_image_prompt_embeds = content_image_prompt_embeds.repeat(1, num_samples, 1)
@@ -753,6 +808,24 @@ class CSGO(IPAdapterXL_CS):
 
     def init_proj(self, num_tokens, content_or_style_='content', model_resampler=False):
         if content_or_style_ == 'content':
+            # if model_resampler:
+            #     image_proj_model = Resampler(
+            #         dim=self.pipe.unet.config.cross_attention_dim,
+            #         depth=4,
+            #         dim_head=64,
+            #         heads=12,
+            #         num_queries=num_tokens,
+            #         embedding_dim=self.content_image_encoder.config.hidden_size,
+            #         output_dim=self.pipe.unet.config.cross_attention_dim,
+            #         ff_mult=4,
+            #     ).to(self.device, dtype=torch.float16)
+            # else:
+            # image_proj_model = ImageProjModel(
+            #     cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
+            #     clip_embeddings_dim=self.image_encoder.config.projection_dim,
+            #     clip_extra_context_tokens=num_tokens,
+            # ).to(self.device, dtype=torch.float16)
+
             if model_resampler:
                 image_proj_model = Resampler(
                     dim=self.pipe.unet.config.cross_attention_dim,
@@ -760,16 +833,34 @@ class CSGO(IPAdapterXL_CS):
                     dim_head=64,
                     heads=12,
                     num_queries=num_tokens,
-                    embedding_dim=self.content_image_encoder.config.hidden_size,
+                    embedding_dim=self.encoder_config["hidden_size"],
                     output_dim=self.pipe.unet.config.cross_attention_dim,
                     ff_mult=4,
                 ).to(self.device, dtype=torch.float16)
             else:
                 image_proj_model = ImageProjModel(
                     cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
-                    clip_embeddings_dim=self.image_encoder.config.projection_dim,
+                    clip_embeddings_dim=self.encoder_config["projection_dim"],
                     clip_extra_context_tokens=num_tokens,
                 ).to(self.device, dtype=torch.float16)
+        # if content_or_style_ == 'style':
+        #     if model_resampler:
+        #         image_proj_model = Resampler(
+        #             dim=self.pipe.unet.config.cross_attention_dim,
+        #             depth=4,
+        #             dim_head=64,
+        #             heads=12,
+        #             num_queries=num_tokens,
+        #             embedding_dim=self.content_image_encoder.config.hidden_size,
+        #             output_dim=self.pipe.unet.config.cross_attention_dim,
+        #             ff_mult=4,
+        #         ).to(self.device, dtype=torch.float16)
+        #     else:
+        #         image_proj_model = ImageProjModel(
+        #             cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
+        #             clip_embeddings_dim=self.image_encoder.config.projection_dim,
+        #             clip_extra_context_tokens=num_tokens,
+        #         ).to(self.device, dtype=torch.float16)
         if content_or_style_ == 'style':
             if model_resampler:
                 image_proj_model = Resampler(
@@ -778,47 +869,55 @@ class CSGO(IPAdapterXL_CS):
                     dim_head=64,
                     heads=12,
                     num_queries=num_tokens,
-                    embedding_dim=self.content_image_encoder.config.hidden_size,
+                    embedding_dim=self.encoder_config["hidden_size"],
                     output_dim=self.pipe.unet.config.cross_attention_dim,
                     ff_mult=4,
                 ).to(self.device, dtype=torch.float16)
             else:
                 image_proj_model = ImageProjModel(
                     cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
-                    clip_embeddings_dim=self.image_encoder.config.projection_dim,
+                    clip_embeddings_dim=self.encoder_config["projection_dim"],
                     clip_extra_context_tokens=num_tokens,
                 ).to(self.device, dtype=torch.float16)
         return image_proj_model
 
     @torch.inference_mode()
     def get_image_embeds(self, pil_image=None, clip_image_embeds=None, content_or_style_=''):
-        if isinstance(pil_image, Image.Image):
-            pil_image = [pil_image]
+       
+        
         if content_or_style_ == 'style':
-
+            if isinstance(pil_image, Image.Image):
+                pil_image = [pil_image]
+            if isinstance(pil_image, torch.Tensor):
+                pil_image = pil_image
             if self.style_model_resampler:
-                clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-                clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16),
-                                                       output_hidden_states=True).hidden_states[-2]
+                # clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+                # clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16),
+                #                                        output_hidden_states=True).hidden_states[-2]
+                clip_image_embeds=self.image_encoder(pil_image)["penultimate_hidden_states"]
+                clip_image_embeds = clip_image_embeds.clone().detach().to(self.device, dtype=torch.float16)
                 image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
                 uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds))
             else:
 
-
-                clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-                clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
+                # clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+                # clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
+                clip_image_embeds = self.image_encoder(pil_image)["image_embeds"]
+                clip_image_embeds = clip_image_embeds.clone().detach().to(self.device, dtype=torch.float16)
                 image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
                 uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds))
             return image_prompt_embeds, uncond_image_prompt_embeds
 
-
         else:
-
+            if isinstance(pil_image, torch.Tensor):
+                pil_image = pil_image
             if self.content_image_encoder_path is not None:
-                clip_image = self.content_image_processor(images=pil_image, return_tensors="pt").pixel_values
-                outputs = self.content_image_encoder(clip_image.to(self.device, dtype=torch.float16),
-                                                     output_hidden_states=True)
-                clip_image_embeds = outputs.last_hidden_state
+                # clip_image = self.content_image_processor(images=pil_image, return_tensors="pt").pixel_values
+                # outputs = self.content_image_encoder(clip_image.to(self.device, dtype=torch.float16),
+                #                                      output_hidden_states=True)
+                # clip_image_embeds = outputs.last_hidden_state
+                clip_image_embeds = self.image_encoder(pil_image)["last_hidden_state"]
+                clip_image_embeds = clip_image_embeds.clone().detach().to(self.device, dtype=torch.float16)
                 image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
 
                 # uncond_clip_image_embeds = self.image_encoder(
@@ -830,10 +929,13 @@ class CSGO(IPAdapterXL_CS):
             else:
                 if self.content_model_resampler:
 
-                    clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-
-                    clip_image = clip_image.to(self.device, dtype=torch.float16)
-                    clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
+                    # clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+                    #
+                    # clip_image = clip_image.to(self.device, dtype=torch.float16)
+                    # clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
+                    
+                    clip_image_embeds = self.image_encoder(pil_image)["penultimate_hidden_states"]
+                    clip_image_embeds = clip_image_embeds.clone().detach().to(self.device,dtype=torch.float16)
                     # clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
                     image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
                     # uncond_clip_image_embeds = self.image_encoder(
@@ -841,11 +943,13 @@ class CSGO(IPAdapterXL_CS):
                     #         ).hidden_states[-2]
                     uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds))
                 else:
-                    clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-                    clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
+                    # clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+                    # clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
+                    clip_image_embeds = self.image_encoder(pil_image)["image_embeds"]
+                    clip_image_embeds = clip_image_embeds.clone().detach().to(self.device, dtype=torch.float16)
                     image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
                     uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds))
-
+                
                 return image_prompt_embeds, uncond_image_prompt_embeds
 
         #     # clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
diff --git a/README.md b/README.md
index 09a436e..caec4c1 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,12 @@ You can using InstantX's CSGO in comfyUI
 
 **CSGO From: [link](https://github.com/instantX-research/CSGO)**
 
+Update
+---
+
+**2024/09/07**
+* fix runway loader error, using single clip_vision weight now；
+* 修复runway跑路导致的diffuser加载报错，限制直接使用IP的SDXL 图片解码模型，请放在clip_vision目录下；
 
 1.Installation
 -----
@@ -19,10 +25,16 @@ pip install -r requirements.txt
 ```
 3 Need  model 
 ----
-3.1 base SDXl ckpt      
+3.1 base SDXl ckpt  and vae and clip_vision      
+ h94/IP-Adapter/sdxl_models [link](https://huggingface.co/h94/IP-Adapter/tree/main/sdxl_models/image_encoder)  
+no need config   
 ```
 ├── ComfyUI/models/checkpoints/
-|      ├──any SDXL weights #for example: Jumpernaut XL_v9-RunDiffusionPhoto_v2.safetensors
+|      ├──any SDXL weights  # for example: Jumpernaut XL_v9-RunDiffusionPhoto_v2.safetensors
+├── ComfyUI/models/vae/
+|      ├──any SDXL vae weights  # sdxl.vae.safetensors 
+├── ComfyUI/models/clip_vision/
+|      ├──model.safetensors  # h94/IP-Adapter/sdxl_models/model.safetensors 
 ```
 3.2 main ckpt      
 CSGO models [link](https://huggingface.co/InstantX/CSGO/tree/main)
@@ -31,18 +43,7 @@ CSGO models [link](https://huggingface.co/InstantX/CSGO/tree/main)
 |      ├──acsgo_4_32.bin  #need token 4/32
 |      ├──acsgo.bin   #need token 4/16
 ```
-3.3 base vae   
-```
-├── ComfyUI/models/vae/
-|      ├──any SDXL vae weights  #sdxl.vae.safetensors 
-```
-3.4 image encoder   
-h94/IP-Adapter/sdxl_models [link](https://huggingface.co/h94/IP-Adapter/tree/main/sdxl_models/image_encoder)
-```
-├── any path dir/
-|             ├── model.safetensors
-|             ├── config.json
-```
+
 3.5 controlnet   
 TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic [link](https://huggingface.co/TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic)
 ```
@@ -50,7 +51,7 @@ TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic [link](https://huggingface.co/T
 |             ├── diffusion_pytorch_model.safetensors  #rename from "TTPLANET_Controlnet_Tile_realistic_v2_fp16.safetensors"
 |             ├── config.json
 ```
-3.6 if using LLM  
+3.6 if using LLM  (unnecessary)   
 Salesforce/blip-image-captioning-large  [link](https://huggingface.co/Salesforce/blip-image-captioning-large/tree/main)
 ```
 ├── any path dir/
@@ -62,10 +63,16 @@ Salesforce/blip-image-captioning-large  [link](https://huggingface.co/Salesforce
 |             ├── tokenizer_config.json
 |             ├── vocab.txt
 ```
+or change prompt input using any other LL.
+
 4 Example
 ----
-![](https://github.com/smthemex/ComfyUI_CSGO_Wrapper/blob/main/example/exampleA.png)  
-![](https://github.com/smthemex/ComfyUI_CSGO_Wrapper/blob/main/example/example.png)
+conternt+style img   
+![](https://github.com/smthemex/ComfyUI_CSGO_Wrapper/blob/main/example/content_style_img.png)  
+style img  + prompt   
+![](https://github.com/smthemex/ComfyUI_CSGO_Wrapper/blob/main/example/txt_only.png)
+conternt+style img  +llm   
+![](https://github.com/smthemex/ComfyUI_CSGO_Wrapper/blob/main/example/using blip.png)
 
 
 5 Function Description of Nodes  
diff --git a/__pycache__/__init__.cpython-311.pyc b/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..895e29ba10c2eb0082051703aaf1b67c984ecf16
GIT binary patch
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diff --git a/configs/config.json b/configs/config.json
new file mode 100644
index 0000000..8c8ae3e
--- /dev/null
+++ b/configs/config.json
@@ -0,0 +1,81 @@
+{
+  "architectures": [
+    "CLIPVisionModelWithProjection"
+  ],
+  "_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,
+  "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,
+  "projection_dim": 1280
+}
diff --git a/csgo_node.py b/csgo_node.py
index 9a5d506..48dd03f 100644
--- a/csgo_node.py
+++ b/csgo_node.py
@@ -3,10 +3,8 @@
 import os
 import numpy as np
 import torch
-import cv2
 from PIL import Image
-
-from transformers import AutoImageProcessor, AutoModel
+from omegaconf import OmegaConf
 from diffusers import (
     AutoencoderKL,
     ControlNetModel,
@@ -19,12 +17,16 @@ from .CSGO.ip_adapter.utils import BLOCKS as BLOCKS
 from .CSGO.ip_adapter.utils import controlnet_BLOCKS as controlnet_BLOCKS
 from .CSGO.ip_adapter.utils import resize_content
 from .CSGO.ip_adapter import CSGO
-MAX_SEED = np.iinfo(np.int32).max
+
+
 import folder_paths
 from comfy.utils import common_upscale
+from comfy.clip_vision import  load
+
+MAX_SEED = np.iinfo(np.int32).max
+
 
 node_cur_path = os.path.dirname(os.path.abspath(__file__))
-#device = torch.device("cuda" if torch.cuda.is_available() else "cpu") #cuda:0
 device = (
     "cuda"
     if torch.cuda.is_available()
@@ -69,7 +71,7 @@ class Blip_Loader:
     RETURN_TYPES = ("MODEL", "MODEL",)
     RETURN_NAMES = ("blip_processor", "blip_model",)
     FUNCTION = "test"
-    CATEGORY = "CSGO_Example_Test"
+    CATEGORY = "CSGO_Wrapper"
 
     def test(self, blip_repo,):
         blip_processor = BlipProcessor.from_pretrained(blip_repo)
@@ -86,7 +88,7 @@ class CSGO_Loader:
         return {
             "required": {
                 "base_cpkt":(["none"]+folder_paths.get_filename_list("checkpoints"),),
-                "image_encoder_repo": ("STRING", {"default": "h94/IP-Adapter"}),
+                "clip_vision":(["none"]+folder_paths.get_filename_list("clip_vision"),),
                 "vae_id":(["none"]+folder_paths.get_filename_list("vae"),),
                 "controlnet_repo": ("STRING", {"default": "TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic" }),
                 "csgo_ckpt": (["none"]+folder_paths.get_filename_list("checkpoints"),),
@@ -110,21 +112,22 @@ class CSGO_Loader:
     RETURN_TYPES = ("MODEL",)
     RETURN_NAMES = ("csgo",)
     FUNCTION = "test"
-    CATEGORY = "CSGO_Example_Test"
+    CATEGORY = "CSGO_Wrapper"
 
-    def test(self,base_cpkt,image_encoder_repo,vae_id,controlnet_repo,csgo_ckpt,num_content_tokens,num_style_tokens):
+    def test(self,base_cpkt,clip_vision,vae_id,controlnet_repo,csgo_ckpt,num_content_tokens,num_style_tokens):
         if csgo_ckpt=="none" or base_cpkt=="none" :
             raise "need weight"
             
         csgo_ckpt=folder_paths.get_full_path("checkpoints",csgo_ckpt) #获取绝对路径
         ckpt_path = folder_paths.get_full_path("checkpoints", base_cpkt)  # 获取绝对路径
-        
-        #vae = AutoencoderKL.from_pretrained(vae, torch_dtype=torch.float16)
+        clip_vision=folder_paths.get_full_path("clip_vision", clip_vision)  # 获取绝对路径
+        img_encoder=load(clip_vision)
+        model_config = os.path.join(node_cur_path, "local_repo")
+        original_config_file = os.path.join(node_cur_path, 'configs', 'sd_xl_base.yaml')
         controlnet = ControlNetModel.from_pretrained(controlnet_repo, torch_dtype=torch.float16, use_safetensors=True)
         vae_id = folder_paths.get_full_path("vae", vae_id)
-        vae = AutoencoderKL.from_single_file(vae_id, torch_dtype=torch.float16)
-        model_config = "stabilityai/stable-diffusion-xl-base-1.0"
-        original_config_file = os.path.join(node_cur_path, 'configs', 'sd_xl_base.yaml')
+        vae_config = os.path.join(node_cur_path, "local_repo","vae")
+        vae = AutoencoderKL.from_single_file(vae_id, config=vae_config,torch_dtype=torch.float16)
         try:
             pipe = StableDiffusionXLControlNetPipeline.from_single_file(
                 ckpt_path,config=model_config, original_config=original_config_file,vae=vae, controlnet=controlnet,
@@ -138,7 +141,6 @@ class CSGO_Loader:
                 raise "load pipe error!,check you diffusers"
         
         pipe.enable_vae_tiling()
-        #pipe.to(device)
         pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
         if device != "mps":
             pipe.enable_model_cpu_offload()
@@ -147,8 +149,10 @@ class CSGO_Loader:
         target_style_blocks = BLOCKS['style']
         controlnet_target_content_blocks = controlnet_BLOCKS['content']
         controlnet_target_style_blocks = controlnet_BLOCKS['style']
-        
-        csgo = CSGO(pipe, image_encoder_repo, csgo_ckpt, device, num_content_tokens=num_content_tokens, num_style_tokens=num_style_tokens,
+        config_path = os.path.join(node_cur_path, "configs", "config.json")
+        image_encoder_config = OmegaConf.load(config_path)
+       
+        csgo = CSGO(pipe, img_encoder, csgo_ckpt, device, image_encoder_config,num_content_tokens=num_content_tokens, num_style_tokens=num_style_tokens,
                     target_content_blocks=target_content_blocks, target_style_blocks=target_style_blocks,
                     controlnet_adapter=True,
                     controlnet_target_content_blocks=controlnet_target_content_blocks,
@@ -220,7 +224,7 @@ class CSGO_Sampler:
                     "display": "number",
                 }),
                 "steps": ("INT", {
-                    "default": 50,
+                    "default": 25,
                     "min": 1,  # Minimum value
                     "max": 100,  # Maximum value
                     "step": 1,  # Slider's step
@@ -251,7 +255,7 @@ class CSGO_Sampler:
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("images",)
     FUNCTION = "test"
-    CATEGORY = "CSGO_Example_Test"
+    CATEGORY = "CSGO_Wrapper"
     
     def test(self,content_image,style_image, csgo, prompt,negative_prompt,num_sample, width,height,content_scale,style_scale,cfg,steps,seed,controlnet_scale,text_only,**kwargs):
         
@@ -259,9 +263,15 @@ class CSGO_Sampler:
         blip_model=kwargs.get("blip_model",None)
         
         #向量转图片
-        style_image= tensor2pil(style_image)
+        #style_image= tensor2pil(style_image)
         #content_image= tensor2pil(content_image)
-        content_image = tensor_upscale2pil(content_image, width, height)
+        content_image_pil = tensor_upscale2pil(content_image, width, height)
+        style_image =tensor_upscale(style_image, width, height)  # torch tensor
+
+        content_image=tensor_upscale(content_image, width, height)  # torch tensor
+        
+        
+        
         #clip vison
         if blip_processor and blip_model:  #判断模型是否存在，不存在则跑文本驱动流程
             with torch.no_grad():
@@ -274,8 +284,11 @@ class CSGO_Sampler:
         else:
             caption = prompt
             if text_only:
-                content_image = Image.fromarray(
-                    np.zeros((content_image.size[0], content_image.size[1], 3), dtype=np.uint8)).convert('RGB')
+                content_image_pil = Image.fromarray(
+                    np.zeros((content_image_pil.size[0], content_image_pil.size[1], 3), dtype=np.uint8)).convert('RGB')
+                content_image=pil2narry(content_image_pil)
+                
+                
         
         #width, height, content_image = resize_content(content_image)#?
         
@@ -291,7 +304,7 @@ class CSGO_Sampler:
                                num_samples=num_sample,
                                num_inference_steps=steps,
                                seed=seed,
-                               image=content_image.convert('RGB'),
+                               image= content_image_pil,
                                controlnet_conditioning_scale=controlnet_scale,
                                )[0]
         images=pil2narry(images)
diff --git a/example/content_style_img.png b/example/content_style_img.png
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diff --git a/example/example.png b/example/example.png
deleted file mode 100644
index 4594b86d3d84a84db39e496aa0df4c8945e6c39d..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/example/exampleA.png b/example/exampleA.png
deleted file mode 100644
index 0198093b26b6899181881f9b5338fe91eeb864e2..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/example/using blip.png b/example/using blip.png
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HcmV?d00001

diff --git a/example/workflow.json b/example/workflow.json
new file mode 100644
index 0000000..c2c5431
--- /dev/null
+++ b/example/workflow.json
@@ -0,0 +1,314 @@
+{
+  "last_node_id": 10,
+  "last_link_id": 11,
+  "nodes": [
+    {
+      "id": 10,
+      "type": "Blip_Loader",
+      "pos": [
+        3337,
+        270
+      ],
+      "size": {
+        "0": 315,
+        "1": 78
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 4,
+      "outputs": [
+        {
+          "name": "blip_processor",
+          "type": "MODEL",
+          "links": null,
+          "shape": 3,
+          "label": "blip_processor"
+        },
+        {
+          "name": "blip_model",
+          "type": "MODEL",
+          "links": null,
+          "shape": 3,
+          "label": "blip_model"
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Blip_Loader"
+      },
+      "widgets_values": [
+        "Salesforce/blip-image-captioning-large"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "CSGO_Loader",
+      "pos": [
+        3313,
+        -9
+      ],
+      "size": {
+        "0": 365.1694030761719,
+        "1": 202
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "csgo",
+          "type": "MODEL",
+          "links": [
+            8
+          ],
+          "slot_index": 0,
+          "shape": 3,
+          "label": "csgo"
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CSGO_Loader"
+      },
+      "widgets_values": [
+        "0SDXL\\sd_xl_base_1.0.safetensors",
+        "IP_img_encoder_sdxl_model.safetensors",
+        "sdxl.vae.safetensors",
+        "F:/test/ComfyUI/models/diffusers/TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic",
+        "csgo_4_32.bin",
+        4,
+        32
+      ]
+    },
+    {
+      "id": 5,
+      "type": "LoadImage",
+      "pos": [
+        2952,
+        -55
+      ],
+      "size": {
+        "0": 315,
+        "1": 314
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            9
+          ],
+          "slot_index": 0,
+          "shape": 3,
+          "label": "IMAGE"
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3,
+          "label": "MASK"
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "girl.jpeg",
+        "image"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "LoadImage",
+      "pos": [
+        2952,
+        -434
+      ],
+      "size": {
+        "0": 315,
+        "1": 314
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            10
+          ],
+          "slot_index": 0,
+          "shape": 3,
+          "label": "IMAGE"
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3,
+          "label": "MASK"
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "3736c69fb7ce5b795ff78a607d3bda37.jpeg",
+        "image"
+      ]
+    },
+    {
+      "id": 6,
+      "type": "SaveImage",
+      "pos": [
+        4169,
+        -145
+      ],
+      "size": {
+        "0": 400.16595458984375,
+        "1": 459.12664794921875
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 11,
+          "label": "images"
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "ComfyUI"
+      ]
+    },
+    {
+      "id": 9,
+      "type": "CSGO_Sampler",
+      "pos": [
+        3729,
+        -164
+      ],
+      "size": {
+        "0": 400,
+        "1": 470
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "content_image",
+          "type": "IMAGE",
+          "link": 10,
+          "label": "content_image"
+        },
+        {
+          "name": "style_image",
+          "type": "IMAGE",
+          "link": 9,
+          "label": "style_image"
+        },
+        {
+          "name": "csgo",
+          "type": "MODEL",
+          "link": 8,
+          "label": "csgo"
+        },
+        {
+          "name": "blip_processor",
+          "type": "MODEL",
+          "link": null,
+          "label": "blip_processor"
+        },
+        {
+          "name": "blip_model",
+          "type": "MODEL",
+          "link": null,
+          "label": "blip_model"
+        }
+      ],
+      "outputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "links": [
+            11
+          ],
+          "slot_index": 0,
+          "shape": 3,
+          "label": "images"
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CSGO_Sampler"
+      },
+      "widgets_values": [
+        "a girl",
+        "text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
+        1,
+        1024,
+        1024,
+        0.5,
+        1,
+        10,
+        25,
+        919524022,
+        "randomize",
+        0.8,
+        false
+      ]
+    }
+  ],
+  "links": [
+    [
+      8,
+      8,
+      0,
+      9,
+      2,
+      "MODEL"
+    ],
+    [
+      9,
+      5,
+      0,
+      9,
+      1,
+      "IMAGE"
+    ],
+    [
+      10,
+      4,
+      0,
+      9,
+      0,
+      "IMAGE"
+    ],
+    [
+      11,
+      9,
+      0,
+      6,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.9646149645000137,
+      "offset": [
+        -2800.1905049143675,
+        506.1924603210764
+      ]
+    }
+  },
+  "version": 0.4
+}
\ No newline at end of file
diff --git a/example/workflow.png b/example/workflow.png
deleted file mode 100644
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diff --git a/local_repo/model_index.json b/local_repo/model_index.json
new file mode 100644
index 0000000..6cc5138
--- /dev/null
+++ b/local_repo/model_index.json
@@ -0,0 +1,34 @@
+{
+  "_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
new file mode 100644
index 0000000..e5bc842
--- /dev/null
+++ b/local_repo/scheduler/scheduler_config.json
@@ -0,0 +1,18 @@
+{
+  "_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
new file mode 100644
index 0000000..cde352a
--- /dev/null
+++ b/local_repo/text_encoder/config.json
@@ -0,0 +1,24 @@
+{
+  "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
new file mode 100644
index 0000000..da1848b
--- /dev/null
+++ b/local_repo/text_encoder_2/config.json
@@ -0,0 +1,24 @@
+{
+  "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
new file mode 100644
index 0000000..76e821f
--- /dev/null
+++ b/local_repo/tokenizer/merges.txt
@@ -0,0 +1,48895 @@
+#version: 0.2
+i n
+t h
+a n
+r e
+a r
+e r
+th e</w>
+in g</w>
+o u
+o n
+s t
+o r
+e n
+o n</w>
+a l
+a t
+e r</w>
+i t
+i n</w>
+t o</w>
+r o
+i s</w>
+l e
+i c
+a t</w>
+an d</w>
+e d</w>
+o f</w>
+c h
+o r</w>
+e s</w>
+i l
+e l
+s t</w>
+a c
+o m
+a m
+l o
+a n</w>
+a y</w>
+s h
+r i
+l i
+t i
+f or</w>
+n e
+ð Ł
+r a
+h a
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diff --git a/local_repo/tokenizer/special_tokens_map.json b/local_repo/tokenizer/special_tokens_map.json
new file mode 100644
index 0000000..2c2130b
--- /dev/null
+++ b/local_repo/tokenizer/special_tokens_map.json
@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
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+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/local_repo/tokenizer/tokenizer_config.json b/local_repo/tokenizer/tokenizer_config.json
new file mode 100644
index 0000000..2e8612a
--- /dev/null
+++ b/local_repo/tokenizer/tokenizer_config.json
@@ -0,0 +1,33 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/local_repo/tokenizer/vocab.json b/local_repo/tokenizer/vocab.json
new file mode 100644
index 0000000..469be27
--- /dev/null
+++ b/local_repo/tokenizer/vocab.json
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diff --git a/local_repo/tokenizer_2/merges.txt b/local_repo/tokenizer_2/merges.txt
new file mode 100644
index 0000000..76e821f
--- /dev/null
+++ b/local_repo/tokenizer_2/merges.txt
@@ -0,0 +1,48895 @@
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new file mode 100644
index 0000000..ae0c5be
--- /dev/null
+++ b/local_repo/tokenizer_2/special_tokens_map.json
@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "normalized": true,
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+    "single_word": false
+  }
+}
diff --git a/local_repo/tokenizer_2/tokenizer_config.json b/local_repo/tokenizer_2/tokenizer_config.json
new file mode 100644
index 0000000..a8438e0
--- /dev/null
+++ b/local_repo/tokenizer_2/tokenizer_config.json
@@ -0,0 +1,33 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
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+  },
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+    "lstrip": false,
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+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "!",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/local_repo/tokenizer_2/vocab.json b/local_repo/tokenizer_2/vocab.json
new file mode 100644
index 0000000..469be27
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diff --git a/local_repo/unet/config.json b/local_repo/unet/config.json
new file mode 100644
index 0000000..c8714c9
--- /dev/null
+++ b/local_repo/unet/config.json
@@ -0,0 +1,69 @@
+{
+  "_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
new file mode 100644
index 0000000..a66a171
--- /dev/null
+++ b/local_repo/vae/config.json
@@ -0,0 +1,32 @@
+{
+  "_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
new file mode 100644
index 0000000..29a4d9f
--- /dev/null
+++ b/local_repo/vae_1_0/config.json
@@ -0,0 +1,31 @@
+{
+  "_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
new file mode 100644
index 0000000..29a4d9f
--- /dev/null
+++ b/local_repo/vae_decoder/config.json
@@ -0,0 +1,31 @@
+{
+  "_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
new file mode 100644
index 0000000..29a4d9f
--- /dev/null
+++ b/local_repo/vae_encoder/config.json
@@ -0,0 +1,31 @@
+{
+  "_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"
+  ]
+}