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hunyuan3d-paint-v2-0/.gitattributes

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hunyuan3d-paint-v2-0/feature_extractor/preprocessor_config.json

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+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}

hunyuan3d-paint-v2-0/model_index.json

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+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.23.1",
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "requires_safety_checker": false,
+  "safety_checker": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "DDIMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "modules",
+    "UNet2p5DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

hunyuan3d-paint-v2-0/scheduler/scheduler_config.json

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+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.23.1",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "num_train_timesteps": 1000,
+  "prediction_type": "v_prediction",
+  "set_alpha_to_one": true,
+  "steps_offset": 1,
+  "trained_betas": null,
+  "timestep_spacing": "trailing",
+  "rescale_betas_zero_snr": true
+}

hunyuan3d-paint-v2-0/text_encoder/config.json

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+{
+  "_name_or_path": "stabilityai/stable-diffusion-2",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1024,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 23,
+  "pad_token_id": 1,
+  "projection_dim": 512,
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.0.dev0",
+  "vocab_size": 49408
+}

hunyuan3d-paint-v2-0/text_encoder/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c3e254d7b61353497ea0be2c4013df4ea8f739ee88cffa0ba58cd085459ed565
+size 1361671895

hunyuan3d-paint-v2-0/tokenizer/special_tokens_map.json

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+{
+  "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
+  },
+  "pad_token": "!",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

hunyuan3d-paint-v2-0/tokenizer/tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "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,
+  "name_or_path": "stabilityai/stable-diffusion-2",
+  "pad_token": "<|endoftext|>",
+  "special_tokens_map_file": "./special_tokens_map.json",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

hunyuan3d-paint-v2-0/unet/config.json

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+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.10.0.dev0",
+  "act_fn": "silu",
+  "attention_head_dim": [
+    5,
+    10,
+    20,
+    20
+  ],
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "cross_attention_dim": 1024,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dual_cross_attention": false,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "sample_size": 64,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ],
+  "use_linear_projection": true
+}

hunyuan3d-paint-v2-0/unet/diffusion_pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2efb4438e649e1ce152822dde082defe062cdd320bb9f1a27b9a4715d9c56e1a
+size 3663114747

hunyuan3d-paint-v2-0/unet/modules.py

@@ -0,0 +1,437 @@
+import os
+import json
+from typing import Any, Dict, Optional
+from diffusers.models import UNet2DConditionModel
+
+import numpy
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import torch.distributed
+from PIL import Image
+from einops import rearrange
+from typing import Any, Callable, Dict, List, Optional, Union, Tuple
+
+import diffusers
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    DiffusionPipeline,
+    EulerAncestralDiscreteScheduler,
+    UNet2DConditionModel,
+    ImagePipelineOutput
+)
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.attention_processor import Attention, AttnProcessor, XFormersAttnProcessor, AttnProcessor2_0
+from diffusers.utils.import_utils import is_xformers_available
+
+
+from diffusers.utils import deprecate
+
+from diffusers.models.transformers.transformer_2d import BasicTransformerBlock
+
+
+
+def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int):
+    # "feed_forward_chunk_size" can be used to save memory
+    if hidden_states.shape[chunk_dim] % chunk_size != 0:
+        raise ValueError(
+            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+        )
+
+    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
+    ff_output = torch.cat(
+        [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
+        dim=chunk_dim,
+    )
+    return ff_output
+
+
+class Basic2p5DTransformerBlock(torch.nn.Module):
+    def __init__(self, transformer: BasicTransformerBlock, layer_name, use_ma=True, use_ra=True) -> None:
+        super().__init__()
+        self.transformer = transformer
+        self.layer_name = layer_name
+        self.use_ma = use_ma
+        self.use_ra = use_ra
+
+        # multiview attn
+        if self.use_ma:
+            self.attn_multiview = Attention(
+                query_dim=self.dim,
+                heads=self.num_attention_heads,
+                dim_head=self.attention_head_dim,
+                dropout=self.dropout,
+                bias=self.attention_bias,
+                cross_attention_dim=None,
+                upcast_attention=self.attn1.upcast_attention,
+                out_bias=True,
+            )
+
+        # ref attn
+        if self.use_ra:
+            self.attn_refview = Attention(
+                query_dim=self.dim,
+                heads=self.num_attention_heads,
+                dim_head=self.attention_head_dim,
+                dropout=self.dropout,
+                bias=self.attention_bias,
+                cross_attention_dim=None,
+                upcast_attention=self.attn1.upcast_attention,
+                out_bias=True,
+            )
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.transformer, name)
+        
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Self-Attention
+        batch_size = hidden_states.shape[0]
+
+        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        num_in_batch = cross_attention_kwargs.pop('num_in_batch', 1)
+        mode = cross_attention_kwargs.pop('mode', None)
+        mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0)
+        ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0)
+        condition_embed_dict = cross_attention_kwargs.pop("condition_embed_dict", None)
+
+
+        if self.norm_type == "ada_norm":
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.norm_type == "ada_norm_zero":
+            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
+            norm_hidden_states = self.norm1(hidden_states)
+        elif self.norm_type == "ada_norm_continuous":
+            norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
+        elif self.norm_type == "ada_norm_single":
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
+            ).chunk(6, dim=1)
+            norm_hidden_states = self.norm1(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
+        else:
+            raise ValueError("Incorrect norm used")
+
+        if self.pos_embed is not None:
+            norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+        # 1. Prepare GLIGEN inputs
+        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+            attention_mask=attention_mask,
+            **cross_attention_kwargs,
+        )
+
+        if self.norm_type == "ada_norm_zero":
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        elif self.norm_type == "ada_norm_single":
+            attn_output = gate_msa * attn_output
+
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+        
+        # 1.2 Reference Attention
+        if 'w' in mode:
+            condition_embed_dict[self.layer_name] = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch) # B, (N L), C
+
+        if 'r' in mode and self.use_ra:
+            condition_embed = condition_embed_dict[self.layer_name].unsqueeze(1).repeat(1,num_in_batch,1,1) # B N L C
+            condition_embed = rearrange(condition_embed, 'b n l c -> (b n) l c')
+
+            attn_output = self.attn_refview(
+                norm_hidden_states,
+                encoder_hidden_states=condition_embed,
+                attention_mask=None,
+                **cross_attention_kwargs
+            )
+            ref_scale_timing = ref_scale
+            if isinstance(ref_scale, torch.Tensor):
+                ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1)
+                for _ in range(attn_output.ndim - 1):
+                    ref_scale_timing = ref_scale_timing.unsqueeze(-1)
+            hidden_states = ref_scale_timing * attn_output + hidden_states
+            if hidden_states.ndim == 4:
+                hidden_states = hidden_states.squeeze(1)
+            
+
+        # 1.3 Multiview Attention
+        if num_in_batch > 1 and self.use_ma:
+            multivew_hidden_states = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch)
+
+            attn_output = self.attn_multiview(
+                multivew_hidden_states,
+                encoder_hidden_states=multivew_hidden_states,
+                **cross_attention_kwargs
+            )
+
+            attn_output = rearrange(attn_output, 'b (n l) c -> (b n) l c', n=num_in_batch)
+
+            hidden_states = mva_scale * attn_output + hidden_states
+            if hidden_states.ndim == 4:
+                hidden_states = hidden_states.squeeze(1)
+
+        # 1.2 GLIGEN Control
+        if gligen_kwargs is not None:
+            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
+
+        # 3. Cross-Attention
+        if self.attn2 is not None:
+            if self.norm_type == "ada_norm":
+                norm_hidden_states = self.norm2(hidden_states, timestep)
+            elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
+                norm_hidden_states = self.norm2(hidden_states)
+            elif self.norm_type == "ada_norm_single":
+                # For PixArt norm2 isn't applied here:
+                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
+                norm_hidden_states = hidden_states
+            elif self.norm_type == "ada_norm_continuous":
+                norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
+            else:
+                raise ValueError("Incorrect norm")
+
+            if self.pos_embed is not None and self.norm_type != "ada_norm_single":
+                norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+            
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                **cross_attention_kwargs,
+            )
+
+            hidden_states = attn_output + hidden_states
+
+        # 4. Feed-forward
+        # i2vgen doesn't have this norm 🤷‍♂️
+        if self.norm_type == "ada_norm_continuous":
+            norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
+        elif not self.norm_type == "ada_norm_single":
+            norm_hidden_states = self.norm3(hidden_states)
+
+        if self.norm_type == "ada_norm_zero":
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+        if self.norm_type == "ada_norm_single":
+            norm_hidden_states = self.norm2(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
+
+        if self._chunk_size is not None:
+            # "feed_forward_chunk_size" can be used to save memory
+            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
+        else:
+            ff_output = self.ff(norm_hidden_states)
+
+        if self.norm_type == "ada_norm_zero":
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+        elif self.norm_type == "ada_norm_single":
+            ff_output = gate_mlp * ff_output
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        return hidden_states
+
+import copy
+class UNet2p5DConditionModel(torch.nn.Module):
+    def __init__(self, unet: UNet2DConditionModel) -> None:
+        super().__init__()
+        self.unet = unet
+
+        self.use_ma  = True
+        self.use_ra  = True
+        self.use_camera_embedding = True
+        self.use_dual_stream = True
+
+        if self.use_dual_stream:
+            self.unet_dual = copy.deepcopy(unet)
+            self.init_attention(self.unet_dual)
+        self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra)
+        self.init_condition()
+        self.init_camera_embedding()
+
+    
+    @staticmethod
+    def from_pretrained(pretrained_model_name_or_path, **kwargs):
+        torch_dtype = kwargs.pop('torch_dtype', torch.float32)
+        config_path = os.path.join(pretrained_model_name_or_path, 'config.json')
+        unet_ckpt_path = os.path.join(pretrained_model_name_or_path, 'diffusion_pytorch_model.bin')
+        with open(config_path, 'r', encoding='utf-8') as file:
+            config = json.load(file)
+        unet = UNet2DConditionModel(**config)
+        unet = UNet2p5DConditionModel(unet)
+        unet_ckpt = torch.load(unet_ckpt_path, map_location='cpu', weights_only=True)
+        unet.load_state_dict(unet_ckpt, strict=True)
+        unet = unet.to(torch_dtype)
+        return unet
+
+    def init_condition(self):
+        self.unet.conv_in = torch.nn.Conv2d(
+            12, 
+            self.unet.conv_in.out_channels, 
+            kernel_size=self.unet.conv_in.kernel_size, 
+            stride=self.unet.conv_in.stride, 
+            padding=self.unet.conv_in.padding, 
+            dilation=self.unet.conv_in.dilation, 
+            groups=self.unet.conv_in.groups, 
+            bias=self.unet.conv_in.bias is not None)
+        self.unet.learned_text_clip_gen = nn.Parameter(torch.randn(1,77,1024))
+        self.unet.learned_text_clip_ref = nn.Parameter(torch.randn(1,77,1024))
+
+    def init_camera_embedding(self):
+
+        self.max_num_ref_image = 5
+        self.max_num_gen_image = 12*3+4*2
+
+        if self.use_camera_embedding:
+            time_embed_dim = 1280
+            self.unet.class_embedding = nn.Embedding(self.max_num_ref_image+self.max_num_gen_image, time_embed_dim)
+
+
+    def init_attention(self, unet, use_ma=False, use_ra=False):
+
+        for down_block_i, down_block in enumerate(unet.down_blocks):
+            if hasattr(down_block, "has_cross_attention") and down_block.has_cross_attention:
+                for attn_i, attn in enumerate(down_block.attentions):
+                    for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                        if isinstance(transformer, BasicTransformerBlock):
+                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, f'down_{down_block_i}_{attn_i}_{transformer_i}', use_ma, use_ra)
+                            
+
+        if hasattr(unet.mid_block, "has_cross_attention") and unet.mid_block.has_cross_attention:
+            for attn_i, attn in enumerate(unet.mid_block.attentions):
+                for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                    if isinstance(transformer, BasicTransformerBlock):
+                        attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, f'mid_{attn_i}_{transformer_i}', use_ma, use_ra)
+
+        for up_block_i, up_block in enumerate(unet.up_blocks):
+            if hasattr(up_block, "has_cross_attention") and up_block.has_cross_attention:
+                for attn_i, attn in enumerate(up_block.attentions):
+                    for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                        if isinstance(transformer, BasicTransformerBlock):
+                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, f'up_{up_block_i}_{attn_i}_{transformer_i}', use_ma, use_ra)
+
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.unet, name)
+        
+    def forward(
+        self, sample, timestep, encoder_hidden_states,
+        *args, down_intrablock_additional_residuals=None,
+        down_block_res_samples=None, mid_block_res_sample=None,
+        **cached_condition,
+    ):
+        B, N_gen, _, H, W = sample.shape
+        assert H == W
+
+        if self.use_camera_embedding:
+            camera_info_gen = cached_condition['camera_info_gen'] + self.max_num_ref_image
+            camera_info_gen = rearrange(camera_info_gen, 'b n -> (b n)')
+        else:
+            camera_info_gen = None
+
+        sample = [sample]
+        if 'normal_imgs' in cached_condition:
+            sample.append(cached_condition["normal_imgs"])
+        if 'position_imgs' in cached_condition:
+            sample.append(cached_condition["position_imgs"])
+        sample = torch.cat(sample, dim=2)
+
+        sample = rearrange(sample, 'b n c h w -> (b n) c h w')
+
+        encoder_hidden_states_gen = encoder_hidden_states.unsqueeze(1).repeat(1, N_gen, 1, 1)
+        encoder_hidden_states_gen = rearrange(encoder_hidden_states_gen, 'b n l c -> (b n) l c')
+
+        if self.use_ra:
+            if 'condition_embed_dict' in cached_condition:
+                condition_embed_dict = cached_condition['condition_embed_dict']
+            else:
+                condition_embed_dict = {}
+                ref_latents = cached_condition['ref_latents']
+                N_ref = ref_latents.shape[1]
+                if self.use_camera_embedding:
+                    camera_info_ref = cached_condition['camera_info_ref']
+                    camera_info_ref = rearrange(camera_info_ref, 'b n -> (b n)')
+                else:
+                    camera_info_ref = None
+                
+                ref_latents = rearrange(ref_latents, 'b n c h w -> (b n) c h w')
+
+                encoder_hidden_states_ref = self.unet.learned_text_clip_ref.unsqueeze(1).repeat(B, N_ref, 1, 1)
+                encoder_hidden_states_ref = rearrange(encoder_hidden_states_ref, 'b n l c -> (b n) l c')
+
+                noisy_ref_latents = ref_latents
+                timestep_ref = 0
+
+                if self.use_dual_stream:
+                    unet_ref = self.unet_dual
+                else:
+                    unet_ref = self.unet
+                unet_ref(
+                    noisy_ref_latents, timestep_ref,
+                    encoder_hidden_states=encoder_hidden_states_ref,
+                    class_labels=camera_info_ref,
+                    # **kwargs
+                    return_dict=False,
+                    cross_attention_kwargs={
+                        'mode':'w', 'num_in_batch':N_ref, 
+                        'condition_embed_dict':condition_embed_dict},
+                )
+                cached_condition['condition_embed_dict'] = condition_embed_dict
+        else:
+            condition_embed_dict = None
+
+
+        mva_scale = cached_condition.get('mva_scale', 1.0)
+        ref_scale = cached_condition.get('ref_scale', 1.0)
+
+        return self.unet(
+            sample, timestep,
+            encoder_hidden_states_gen, *args,
+            class_labels=camera_info_gen,
+            down_intrablock_additional_residuals=[
+                sample.to(dtype=self.unet.dtype) for sample in down_intrablock_additional_residuals
+            ] if down_intrablock_additional_residuals is not None else None,
+            down_block_additional_residuals=[
+                sample.to(dtype=self.unet.dtype) for sample in down_block_res_samples
+            ] if down_block_res_samples is not None else None,
+            mid_block_additional_residual=(
+                mid_block_res_sample.to(dtype=self.unet.dtype)
+                if mid_block_res_sample is not None else None
+            ),
+            return_dict=False,
+            cross_attention_kwargs={
+                'mode':'r', 'num_in_batch':N_gen, 
+                'condition_embed_dict':condition_embed_dict, 
+                'mva_scale': mva_scale,
+                'ref_scale': ref_scale,
+            },
+        ) 

hunyuan3d-paint-v2-0/vae/config.json

@@ -0,0 +1,29 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.10.0.dev0",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 768,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

hunyuan3d-paint-v2-0/vae/diffusion_pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b4889b6b1d4ce7ae320a02dedaeff1780ad77d415ea0d744b476155c6377ddc
+size 334707217