Delete hunyuan3d-paint-v2-0-turbo

hunyuan3d-paint-v2-0-turbo/.gitattributes

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hunyuan3d-paint-v2-0-turbo/README.md

@@ -1,53 +0,0 @@
----
-license: openrail++
-tags:
-- stable-diffusion
-- text-to-image
----
-
-# SD v2.1-base with Zero Terminal SNR (LAION Aesthetic 6+)
-
-This model is used in [Diffusion Model with Perceptual Loss](https://arxiv.org/abs/2401.00110) paper as the MSE baseline.
-
-This model is trained using zero terminal SNR schedule following [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/abs/2305.08891) paper on LAION aesthetic 6+ data.
-
-This model is finetuned from [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base).
-
-This model is meant for research demonstration, not for production use.
-
-## Usage
-
-```python
-from diffusers import StableDiffusionPipeline
-prompt = "A young girl smiling"
-pipe = StableDiffusionPipeline.from_pretrained("ByteDance/sd2.1-base-zsnr-laionaes6").to("cuda")
-pipe(prompt, guidance_scale=7.5, guidance_rescale=0.7).images[0].save("out.jpg")
-```
-
-## Related Models
-
-* [bytedance/sd2.1-base-zsnr-laionaes5](https://huggingface.co/ByteDance/sd2.1-base-zsnr-laionaes5)
-* [bytedance/sd2.1-base-zsnr-laionaes6](https://huggingface.co/ByteDance/sd2.1-base-zsnr-laionaes6)
-* [bytedance/sd2.1-base-zsnr-laionaes6-perceptual](https://huggingface.co/ByteDance/sd2.1-base-zsnr-laionaes6-perceptual)
-
-
-## Cite as
-```
-@misc{lin2024diffusion,
-      title={Diffusion Model with Perceptual Loss}, 
-      author={Shanchuan Lin and Xiao Yang},
-      year={2024},
-      eprint={2401.00110},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-
-@misc{lin2023common,
-      title={Common Diffusion Noise Schedules and Sample Steps are Flawed}, 
-      author={Shanchuan Lin and Bingchen Liu and Jiashi Li and Xiao Yang},
-      year={2023},
-      eprint={2305.08891},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```

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

@@ -1,20 +0,0 @@
-{
-  "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-turbo/image_encoder/config.json

@@ -1,23 +0,0 @@
-{
-  "_name_or_path": "D:\\.cache\\huggingface\\hub\\models--sudo-ai--zero123plus-v1.1\\snapshots\\36df7de980afd15f80b2e1a4e9a920d7020e2654\\vision_encoder",
-  "architectures": [
-    "CLIPVisionModelWithProjection"
-  ],
-  "attention_dropout": 0.0,
-  "dropout": 0.0,
-  "hidden_act": "gelu",
-  "hidden_size": 1280,
-  "image_size": 224,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 5120,
-  "layer_norm_eps": 1e-05,
-  "model_type": "clip_vision_model",
-  "num_attention_heads": 16,
-  "num_channels": 3,
-  "num_hidden_layers": 32,
-  "patch_size": 14,
-  "projection_dim": 1024,
-  "torch_dtype": "float16",
-  "transformers_version": "4.36.0"
-}

hunyuan3d-paint-v2-0-turbo/image_encoder/model.safetensors

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hunyuan3d-paint-v2-0-turbo/image_encoder/preprocessor_config.json

@@ -1,27 +0,0 @@
-{
-  "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
-  }
-}

hunyuan3d-paint-v2-0-turbo/model_index.json

@@ -1,37 +0,0 @@
-{
-  "_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"
-  ],
-  "image_encoder": [
-    "transformers",
-    "CLIPVisionModelWithProjection"
-  ],
-  "unet": [
-    "modules",
-    "UNet2p5DConditionModel"
-  ],
-  "vae": [
-    "diffusers",
-    "AutoencoderKL"
-  ]
-}

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

@@ -1,15 +0,0 @@
-{
-  "_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-turbo/text_encoder/config.json

@@ -1,25 +0,0 @@
-{
-  "_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-turbo/text_encoder/pytorch_model.bin

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

@@ -1,24 +0,0 @@
-{
-  "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-turbo/tokenizer/tokenizer_config.json

@@ -1,34 +0,0 @@
-{
-  "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-turbo/unet/config.json

@@ -1,45 +0,0 @@
-{
-  "_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-turbo/unet/diffusion_pytorch_model.bin

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hunyuan3d-paint-v2-0-turbo/unet/diffusion_pytorch_model.safetensors

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

@@ -1,610 +0,0 @@
-# Open Source Model Licensed under the Apache License Version 2.0
-# and Other Licenses of the Third-Party Components therein:
-# The below Model in this distribution may have been modified by THL A29 Limited
-# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
-
-# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
-# The below software and/or models in this distribution may have been
-# modified by THL A29 Limited ("Tencent Modifications").
-# All Tencent Modifications are Copyright (C) THL A29 Limited.
-
-# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
-# except for the third-party components listed below.
-# Hunyuan 3D does not impose any additional limitations beyond what is outlined
-# in the repsective licenses of these third-party components.
-# Users must comply with all terms and conditions of original licenses of these third-party
-# components and must ensure that the usage of the third party components adheres to
-# all relevant laws and regulations.
-
-# For avoidance of doubts, Hunyuan 3D means the large language models and
-# their software and algorithms, including trained model weights, parameters (including
-# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
-# fine-tuning enabling code and other elements of the foregoing made publicly available
-# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
-
-import copy
-import json
-import os
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from diffusers.models import UNet2DConditionModel
-from diffusers.models.attention_processor import Attention
-from diffusers.models.transformers.transformer_2d import BasicTransformerBlock
-from einops import rearrange
-
-
-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]}"
-            f"has to be divisible by chunk size: {chunk_size}."
-            f" 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, is_turbo=False) -> None:
-        super().__init__()
-        self.transformer = transformer
-        self.layer_name = layer_name
-        self.use_ma = use_ma
-        self.use_ra = use_ra
-        self.is_turbo = is_turbo
-
-        # 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,
-            )
-        if self.is_turbo:
-            self._initialize_attn_weights()
-
-    def _initialize_attn_weights(self):
-
-        if self.use_ma:
-            self.attn_multiview.load_state_dict(self.attn1.state_dict()) 
-            with torch.no_grad():
-                for layer in self.attn_multiview.to_out:
-                    for param in layer.parameters():
-                        param.zero_()
-        if self.use_ra:
-            self.attn_refview.load_state_dict(self.attn1.state_dict()) 
-            with torch.no_grad():
-                for layer in self.attn_refview.to_out:
-                    for param in layer.parameters():
-                        param.zero_()
-
-    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)
-        if not self.is_turbo:
-            mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0)
-            ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0)
-        else:
-            position_attn_mask = cross_attention_kwargs.pop("position_attn_mask", None)
-            position_voxel_indices = cross_attention_kwargs.pop("position_voxel_indices", None)
-            mva_scale = 1.0
-            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
-            )
-            if not self.is_turbo:
-                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)
-
-            if self.is_turbo:
-                position_mask = None
-                if position_attn_mask is not None:
-                    if multivew_hidden_states.shape[1] in position_attn_mask:
-                        position_mask = position_attn_mask[multivew_hidden_states.shape[1]]
-                position_indices = None
-                if position_voxel_indices is not None:
-                    if multivew_hidden_states.shape[1] in position_voxel_indices:
-                        position_indices = position_voxel_indices[multivew_hidden_states.shape[1]]
-                attn_output = self.attn_multiview(
-                    multivew_hidden_states,
-                    encoder_hidden_states=multivew_hidden_states,
-                    attention_mask=position_mask,
-                    position_indices=position_indices,
-                    **cross_attention_kwargs
-                )
-            else:
-                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
-
-@torch.no_grad()
-def compute_voxel_grid_mask(position, grid_resolution=8):
-
-    position = position.half()    
-    B,N,_,H,W = position.shape
-    assert H%grid_resolution==0 and W%grid_resolution==0
-
-    valid_mask = (position != 1).all(dim=2, keepdim=True)
-    valid_mask = valid_mask.expand_as(position)
-    position[valid_mask==False] = 0
-
-    
-    position = rearrange(
-        position,
-        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
-        num_h=grid_resolution, num_w=grid_resolution
-    )
-    valid_mask = rearrange(
-        valid_mask, 
-        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
-        num_h=grid_resolution, num_w=grid_resolution
-    )
-
-    grid_position = position.sum(dim=(-2, -1))
-    count_masked = valid_mask.sum(dim=(-2, -1))
-
-    grid_position = grid_position / count_masked.clamp(min=1)
-    grid_position[count_masked<5] = 0
-
-    grid_position = grid_position.permute(0,1,4,2,3)
-    grid_position = rearrange(grid_position, 'b n c h w -> b n (h w) c')
-
-    grid_position_expanded_1 = grid_position.unsqueeze(2).unsqueeze(4)  # 形状变为 B, N, 1, L, 1, 3
-    grid_position_expanded_2 = grid_position.unsqueeze(1).unsqueeze(3)  # 形状变为 B, 1, N, 1, L, 3
-
-    # 计算欧氏距离
-    distances = torch.norm(grid_position_expanded_1 - grid_position_expanded_2, dim=-1)  # 形状为 B, N, N, L, L
-
-    weights = distances
-    grid_distance = 1.73/grid_resolution
-    
-    #weights = weights*-32
-    #weights = weights.clamp(min=-10000.0)
-    
-    weights = weights< grid_distance
-
-    return weights
-    
-def compute_multi_resolution_mask(position_maps, grid_resolutions=[32, 16, 8]):
-    position_attn_mask = {}
-    with torch.no_grad():
-        for grid_resolution in grid_resolutions:
-            position_mask = compute_voxel_grid_mask(position_maps, grid_resolution)
-            position_mask = rearrange(position_mask, 'b ni nj li lj -> b (ni li) (nj lj)')
-            position_attn_mask[position_mask.shape[1]] = position_mask
-    return position_attn_mask
-
-@torch.no_grad()
-def compute_discrete_voxel_indice(position, grid_resolution=8, voxel_resolution=128):
-
-    position = position.half()    
-    B,N,_,H,W = position.shape
-    assert H%grid_resolution==0 and W%grid_resolution==0
-
-    valid_mask = (position != 1).all(dim=2, keepdim=True)
-    valid_mask = valid_mask.expand_as(position)
-    position[valid_mask==False] = 0
-    
-    position = rearrange(
-        position, 
-        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
-        num_h=grid_resolution, num_w=grid_resolution
-    )
-    valid_mask = rearrange(
-        valid_mask, 
-        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
-        num_h=grid_resolution, num_w=grid_resolution
-    )
-
-    grid_position = position.sum(dim=(-2, -1))
-    count_masked = valid_mask.sum(dim=(-2, -1))
-
-    grid_position = grid_position / count_masked.clamp(min=1)
-    grid_position[count_masked<5] = 0
-
-    grid_position = grid_position.permute(0,1,4,2,3).clamp(0, 1) # B N C H W
-    voxel_indices = grid_position * (voxel_resolution - 1)
-    voxel_indices = torch.round(voxel_indices).long()
-    return voxel_indices
-    
-def compute_multi_resolution_discrete_voxel_indice(
-    position_maps, 
-    grid_resolutions=[64, 32, 16, 8], 
-    voxel_resolutions=[512, 256, 128, 64]
-):
-    voxel_indices = {}
-    with torch.no_grad():
-        for grid_resolution, voxel_resolution in zip(grid_resolutions, voxel_resolutions):
-            voxel_indice = compute_discrete_voxel_indice(position_maps, grid_resolution, voxel_resolution)
-            voxel_indice = rearrange(voxel_indice, 'b n c h w -> b (n h w) c')
-            voxel_indices[voxel_indice.shape[1]] = {'voxel_indices':voxel_indice, 'voxel_resolution':voxel_resolution}
-    return voxel_indices
-
-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
-        self.is_turbo = False
-
-        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, is_turbo=self.is_turbo)
-        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):
-
-        if self.use_camera_embedding:
-            time_embed_dim = 1280
-            self.max_num_ref_image = 5
-            self.max_num_gen_image = 12 * 3 + 4 * 2
-            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, is_turbo=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, is_turbo
-                            )
-
-        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, is_turbo
-                        )
-
-        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, is_turbo
-                            )
-
-    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)
-
-        if self.is_turbo:
-            cross_attention_kwargs_ = {
-                'mode': 'r', 'num_in_batch': N_gen,
-                'condition_embed_dict': condition_embed_dict,
-                'position_attn_mask':position_attn_mask, 
-                'position_voxel_indices':position_voxel_indices,
-                'mva_scale': mva_scale,
-                'ref_scale': ref_scale,
-            }
-        else:
-            cross_attention_kwargs_ = {
-                'mode': 'r', 'num_in_batch': N_gen,
-                'condition_embed_dict': condition_embed_dict,
-                'mva_scale': mva_scale,
-                'ref_scale': ref_scale,
-            }
-        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=cross_attention_kwargs_,
-        )
-

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

@@ -1,29 +0,0 @@
-{
-  "_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-turbo/vae/diffusion_pytorch_model.bin

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