init

feature_extractor/preprocessor_config.json

@@ -0,0 +1,44 @@
+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "do_center_crop",
+    "crop_size",
+    "do_rescale",
+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "do_convert_rgb",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

image_encoder/config.json

@@ -0,0 +1,23 @@
+{
+  "_name_or_path": "lambdalabs/sd-image-variations-diffusers",
+  "architectures": [
+    "CLIPVisionModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 1024,
+  "image_size": 224,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 24,
+  "patch_size": 14,
+  "projection_dim": 768,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.39.3"
+}

image_encoder/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4b33d864f89a793357a768cb07d0dc18d6a14e6664f4110a0d535ca9ba78da8
+size 607980488

model_index.json

@@ -0,0 +1,30 @@
+{
+  "_class_name": "StableDiffusionImageCustomPipeline",
+  "_diffusers_version": "0.27.2", 
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "image_encoder": [
+    "transformers",
+    "CLIPVisionModelWithProjection"
+  ],
+  "noisy_cond_latents": false,
+  "requires_safety_checker": true,
+  "safety_checker": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "EulerAncestralDiscreteScheduler"
+  ],
+  "unet": [
+    "mv_unet",
+    "UnifieldWrappedUNet"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

@@ -0,0 +1,16 @@
+{
+  "_class_name": "EulerAncestralDiscreteScheduler",
+  "_diffusers_version": "0.27.2",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "rescale_betas_zero_snr": false,
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "linspace",
+  "trained_betas": null
+}

unet/config.json

@@ -0,0 +1,74 @@
+{
+  "_class_name": "UnifieldWrappedUNet",
+  "_diffusers_version": "0.27.2", 
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": 8,
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 768,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "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": null,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 64,
+  "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,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": false, 
+
+  "init_self_attn_ref": true, 
+  "self_attn_ref_position": "attn1",
+  "self_attn_ref_pixel_wise_crosspond": true,
+  "self_attn_ref_effect_on": "all", 
+  "self_attn_ref_chain_pos": "parralle", 
+  "use_simple3d_attn": false 
+}

unet/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5cbaf1d56619345ce78de8cfbb20d94923b3305a364bf6a5b2a2cc422d4b701
+size 3537503456

unet/mv_unet.py

@@ -0,0 +1,314 @@
+import torch
+from typing import Optional, Tuple, Union, Any
+from diffusers import UNet2DConditionModel
+from diffusers.models.attention_processor import Attention
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput
+
+
+def construct_pix2pix_attention(hidden_states_dim, norm_type="none"):
+    if norm_type == "layernorm":
+        norm = torch.nn.LayerNorm(hidden_states_dim)
+    else:
+        norm = torch.nn.Identity()
+    attention = Attention(
+        query_dim=hidden_states_dim,
+        heads=8,
+        dim_head=hidden_states_dim // 8,
+        bias=True,
+    )
+    # NOTE: xformers 0.22 does not support batchsize >= 4096
+    attention.xformers_not_supported = True # hacky solution
+    return norm, attention
+
+
+def switch_extra_processor(model, enable_filter=lambda x:True):
+    def recursive_add_processors(name: str, module: torch.nn.Module):
+        for sub_name, child in module.named_children():
+            recursive_add_processors(f"{name}.{sub_name}", child)
+
+        if isinstance(module, ExtraAttnProc):
+            module.enabled = enable_filter(name)
+
+    for name, module in model.named_children():
+        recursive_add_processors(name, module)
+
+
+def add_extra_processor(model: torch.nn.Module, enable_filter=lambda x:True, **kwargs):
+    return_dict = torch.nn.ModuleDict()
+    proj_in_dim = kwargs.get('proj_in_dim', False)
+    kwargs.pop('proj_in_dim', None)
+
+    def recursive_add_processors(name: str, module: torch.nn.Module):
+        for sub_name, child in module.named_children():
+            if "ref_unet" not in (sub_name + name):
+                recursive_add_processors(f"{name}.{sub_name}", child)
+
+        if isinstance(module, Attention):
+            new_processor = ExtraAttnProc(
+                chained_proc=module.get_processor(),
+                enabled=enable_filter(f"{name}.processor"),
+                name=f"{name}.processor",
+                proj_in_dim=proj_in_dim if proj_in_dim else module.cross_attention_dim,
+                target_dim=module.cross_attention_dim,
+                **kwargs
+            )
+            module.set_processor(new_processor)
+            return_dict[f"{name}.processor".replace(".", "__")] = new_processor
+
+    for name, module in model.named_children():
+        recursive_add_processors(name, module)
+    return return_dict
+
+
+
+class ExtraAttnProc(torch.nn.Module):
+    def __init__(
+        self,
+        chained_proc,
+        enabled=False,
+        name=None,
+        mode='extract',
+        with_proj_in=False,
+        proj_in_dim=768,
+        target_dim=None,
+        pixel_wise_crosspond=False,
+        norm_type="none",   # none or layernorm
+        crosspond_effect_on="all",  # all or first
+        crosspond_chain_pos="parralle",     # before or parralle or after
+        simple_3d=False,
+        views=4,
+    ) -> None:
+        super().__init__()
+        self.enabled = enabled
+        self.chained_proc = chained_proc
+        self.name = name
+        self.mode = mode
+        self.with_proj_in=with_proj_in
+        self.proj_in_dim = proj_in_dim
+        self.target_dim = target_dim or proj_in_dim
+        self.hidden_states_dim = self.target_dim
+        self.pixel_wise_crosspond = pixel_wise_crosspond
+        self.crosspond_effect_on = crosspond_effect_on
+        self.crosspond_chain_pos = crosspond_chain_pos
+        self.views = views
+        self.simple_3d = simple_3d
+        if self.with_proj_in and self.enabled:
+            self.in_linear = torch.nn.Linear(self.proj_in_dim, self.target_dim, bias=False)
+            if self.target_dim == self.proj_in_dim:
+                self.in_linear.weight.data = torch.eye(proj_in_dim)
+        else:
+            self.in_linear = None
+        if self.pixel_wise_crosspond and self.enabled:
+            self.crosspond_norm, self.crosspond_attention = construct_pix2pix_attention(self.hidden_states_dim, norm_type=norm_type)
+    
+    def do_crosspond_attention(self, hidden_states: torch.FloatTensor, other_states: torch.FloatTensor):
+        hidden_states = self.crosspond_norm(hidden_states)
+        
+        batch, L, D = hidden_states.shape
+        assert hidden_states.shape == other_states.shape, f"got {hidden_states.shape} and {other_states.shape}"
+        # to -> batch * L, 1, D
+        hidden_states = hidden_states.reshape(batch * L, 1, D)
+        other_states = other_states.reshape(batch * L, 1, D)
+        hidden_states_catted = other_states
+        hidden_states = self.crosspond_attention(
+            hidden_states,
+            encoder_hidden_states=hidden_states_catted,
+        )
+        return hidden_states.reshape(batch, L, D)
+    
+    def __call__(
+        self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None,
+        ref_dict: dict = None, mode=None, **kwargs
+    ) -> Any:
+        if not self.enabled:
+            return self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        assert ref_dict is not None
+        if (mode or self.mode) == 'extract':
+            ref_dict[self.name] = hidden_states
+            hidden_states1 = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
+            if self.pixel_wise_crosspond and self.crosspond_chain_pos == "after":
+                ref_dict[self.name] = hidden_states1
+            return hidden_states1
+        elif (mode or self.mode) == 'inject':
+            ref_state = ref_dict.pop(self.name)
+            if self.with_proj_in:
+                ref_state = self.in_linear(ref_state)
+            
+            B, L, D = ref_state.shape
+            if hidden_states.shape[0] == B:
+                modalities = 1
+                views = 1
+            else:
+                modalities = hidden_states.shape[0] // B // self.views
+                views = self.views
+            if self.pixel_wise_crosspond:
+                if self.crosspond_effect_on == "all":
+                    ref_state = ref_state[:, None].expand(-1, modalities * views, -1, -1).reshape(-1, *ref_state.shape[-2:])
+                    
+                    if self.crosspond_chain_pos == "before":
+                        hidden_states = hidden_states + self.do_crosspond_attention(hidden_states, ref_state)
+                        
+                    hidden_states1 = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
+                    
+                    if self.crosspond_chain_pos == "parralle":
+                        hidden_states1 = hidden_states1 + self.do_crosspond_attention(hidden_states, ref_state)
+                        
+                    if self.crosspond_chain_pos == "after":
+                        hidden_states1 = hidden_states1 + self.do_crosspond_attention(hidden_states1, ref_state)
+                    return hidden_states1
+                else:
+                    assert self.crosspond_effect_on == "first"
+                    # hidden_states [B * modalities * views, L, D]
+                    # ref_state [B, L, D]
+                    ref_state = ref_state[:, None].expand(-1, modalities, -1, -1).reshape(-1, ref_state.shape[-2], ref_state.shape[-1])  # [B * modalities, L, D]
+                    
+                    def do_paritial_crosspond(hidden_states, ref_state):
+                        first_view_hidden_states = hidden_states.view(-1, views, hidden_states.shape[1], hidden_states.shape[2])[:, 0]  # [B * modalities, L, D]
+                        hidden_states2 = self.do_crosspond_attention(first_view_hidden_states, ref_state) # [B * modalities, L, D]
+                        hidden_states2_padded = torch.zeros_like(hidden_states).reshape(-1, views, hidden_states.shape[1], hidden_states.shape[2])
+                        hidden_states2_padded[:, 0] = hidden_states2
+                        hidden_states2_padded = hidden_states2_padded.reshape(-1, hidden_states.shape[1], hidden_states.shape[2])
+                        return hidden_states2_padded
+                    
+                    if self.crosspond_chain_pos == "before":
+                        hidden_states = hidden_states + do_paritial_crosspond(hidden_states, ref_state)
+                    
+                    hidden_states1 = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)    # [B * modalities * views, L, D]
+                    if self.crosspond_chain_pos == "parralle":
+                        hidden_states1 = hidden_states1 + do_paritial_crosspond(hidden_states, ref_state)
+                    if self.crosspond_chain_pos == "after":
+                        hidden_states1 = hidden_states1 + do_paritial_crosspond(hidden_states1, ref_state)
+                    return hidden_states1
+            elif self.simple_3d:
+                B, L, C = encoder_hidden_states.shape
+                mv = self.views
+                encoder_hidden_states = encoder_hidden_states.reshape(B // mv, mv, L, C)
+                ref_state = ref_state[:, None]
+                encoder_hidden_states = torch.cat([encoder_hidden_states, ref_state], dim=1)
+                encoder_hidden_states = encoder_hidden_states.reshape(B // mv, 1, (mv+1) * L, C)
+                encoder_hidden_states = encoder_hidden_states.repeat(1, mv, 1, 1).reshape(-1, (mv+1) * L, C)
+                return self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
+            else:
+                ref_state = ref_state[:, None].expand(-1, modalities * views, -1, -1).reshape(-1, ref_state.shape[-2], ref_state.shape[-1])
+                encoder_hidden_states = torch.cat([encoder_hidden_states, ref_state], dim=1)
+                return self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
+        else:
+            raise NotImplementedError("mode or self.mode is required to be 'extract' or 'inject'")
+
+
+class UnifieldWrappedUNet(UNet2DConditionModel):
+    def __init__(  
+            self,
+            sample_size: Optional[int] = None,
+            in_channels: int = 4,
+            out_channels: int = 4,
+            center_input_sample: bool = False,
+            flip_sin_to_cos: bool = True,
+            freq_shift: int = 0,
+            down_block_types: Tuple[str] = (
+                "CrossAttnDownBlock2D",
+                "CrossAttnDownBlock2D",
+                "CrossAttnDownBlock2D",
+                "DownBlock2D",
+            ),
+            mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
+            up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+            only_cross_attention: Union[bool, Tuple[bool]] = False,
+            block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+            layers_per_block: Union[int, Tuple[int]] = 2,
+            downsample_padding: int = 1,
+            mid_block_scale_factor: float = 1,
+            dropout: float = 0.0,
+            act_fn: str = "silu",
+            norm_num_groups: Optional[int] = 32,
+            norm_eps: float = 1e-5,
+            cross_attention_dim: Union[int, Tuple[int]] = 1280,
+            transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
+            reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
+            encoder_hid_dim: Optional[int] = None,
+            encoder_hid_dim_type: Optional[str] = None,
+            attention_head_dim: Union[int, Tuple[int]] = 8,
+            num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
+            dual_cross_attention: bool = False,
+            use_linear_projection: bool = False,
+            class_embed_type: Optional[str] = None,
+            addition_embed_type: Optional[str] = None,
+            addition_time_embed_dim: Optional[int] = None,
+            num_class_embeds: Optional[int] = None,
+            upcast_attention: bool = False,
+            resnet_time_scale_shift: str = "default",
+            resnet_skip_time_act: bool = False,
+            resnet_out_scale_factor: float = 1.0,
+            time_embedding_type: str = "positional",
+            time_embedding_dim: Optional[int] = None,
+            time_embedding_act_fn: Optional[str] = None,
+            timestep_post_act: Optional[str] = None,
+            time_cond_proj_dim: Optional[int] = None,
+            conv_in_kernel: int = 3,
+            conv_out_kernel: int = 3,
+            projection_class_embeddings_input_dim: Optional[int] = None,
+            attention_type: str = "default",
+            class_embeddings_concat: bool = False,
+            mid_block_only_cross_attention: Optional[bool] = None,
+            cross_attention_norm: Optional[str] = None,
+            addition_embed_type_num_heads: int = 64,
+            
+            init_self_attn_ref: bool = False,
+            self_attn_ref_other_model_name: str = 'lambdalabs/sd-image-variations-diffusers', 
+            self_attn_ref_position: str = "attn1",
+            self_attn_ref_pixel_wise_crosspond: bool = False,
+            self_attn_ref_effect_on: str = "all",
+            self_attn_ref_chain_pos: str = "parralle",
+            use_simple3d_attn: bool = False,
+            **kwargs
+        ):   
+        super().__init__(**{
+                k: v for k, v in locals().items() if k not in 
+                ["self", "kwargs", "__class__", 
+                    "init_self_attn_ref", "self_attn_ref_other_model_name", "self_attn_ref_position", "self_attn_ref_pixel_wise_crosspond",
+                    "self_attn_ref_effect_on", "self_attn_ref_chain_pos", "use_simple3d_attn"
+                 ]
+            })
+        
+        
+        self.ref_unet: UNet2DConditionModel = UNet2DConditionModel.from_pretrained(
+                self_attn_ref_other_model_name, subfolder="unet", torch_dtype=self.dtype
+            ) 
+        add_extra_processor(
+                model=self.ref_unet, 
+                enable_filter=lambda name: name.endswith(f"{self_attn_ref_position}.processor"), 
+                mode='extract',
+                with_proj_in=False,
+                pixel_wise_crosspond=False,
+            ) 
+        add_extra_processor(
+                model=self,
+                enable_filter=lambda name: name.endswith(f"{self_attn_ref_position}.processor"),
+                mode='inject',
+                with_proj_in=False,
+                pixel_wise_crosspond=self_attn_ref_pixel_wise_crosspond,
+                crosspond_effect_on=self_attn_ref_effect_on,
+                crosspond_chain_pos=self_attn_ref_chain_pos,
+                simple_3d=use_simple3d_attn,
+            )    
+        switch_extra_processor(self, enable_filter=lambda name: name.endswith(f"{self_attn_ref_position}.processor"))
+    
+    def __call__(
+        self,
+        sample: torch.Tensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        condition_latens: torch.Tensor = None,
+        class_labels: Optional[torch.Tensor] = None,  
+    ) -> Union[UNet2DConditionOutput, Tuple]:
+        
+        ref_dict = {}
+        self.ref_unet(condition_latens, timestep, encoder_hidden_states,  cross_attention_kwargs=dict(ref_dict=ref_dict))
+        return self.forward( 
+            sample, timestep, encoder_hidden_states, 
+            class_labels=class_labels, 
+            cross_attention_kwargs=dict(ref_dict=ref_dict, mode='inject'),   
+        ) 
+                

vae/config.json

@@ -0,0 +1,34 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.27.2",
+  "_name_or_path": "lambdalabs/sd-image-variations-diffusers",
+  "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,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 256,
+  "scaling_factor": 0.18215,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

vae/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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