exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/omnigen.py

# The code is revised from DiT
import os
import torch
import torch.nn as nn
import numpy as np
import math
from safetensors.torch import load_file
from typing import List, Optional, Tuple, Union
import torch.utils.checkpoint
from huggingface_hub import snapshot_download
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers import Phi3Config, Phi3Model
from transformers.cache_utils import Cache, DynamicCache
from transformers.utils import logging


logger = logging.get_logger(__name__)


class Phi3Transformer(Phi3Model):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
    We only modified the attention mask
    Args:
        config: Phi3Config
    """
    def prefetch_layer(self, layer_idx: int, device: torch.device):
        "Starts prefetching the next layer cache"
        with torch.cuda.stream(self.prefetch_stream):
            # Prefetch next layer tensors to GPU
            for name, param in self.layers[layer_idx].named_parameters():
                param.data = param.data.to(device, non_blocking=True)

    def evict_previous_layer(self, layer_idx: int):
        "Moves the previous layer cache to the CPU"
        prev_layer_idx = layer_idx - 1
        for name, param in self.layers[prev_layer_idx].named_parameters():
            param.data = param.data.to("cpu", non_blocking=True)
            
    def get_offlaod_layer(self, layer_idx: int, device: torch.device):
        # init stream
        if not hasattr(self, "prefetch_stream"):
            self.prefetch_stream = torch.cuda.Stream()

        # delete previous layer
        torch.cuda.current_stream().synchronize()
        self.evict_previous_layer(layer_idx)
        
        # make sure the current layer is ready
        torch.cuda.synchronize(self.prefetch_stream)

        # load next layer
        self.prefetch_layer((layer_idx + 1) % len(self.layers), device)
        

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        offload_model: Optional[bool] = False,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        # kept for BC (non `Cache` `past_key_values` inputs)
        return_legacy_cache = False
        if use_cache and not isinstance(past_key_values, Cache):
            return_legacy_cache = True
            if past_key_values is None:
                past_key_values = DynamicCache()
            else:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
                logger.warning_once(
                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
                )

        # if inputs_embeds is None:
        #     inputs_embeds = self.embed_tokens(input_ids)

        # if cache_position is None:
        #     past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        #     cache_position = torch.arange(
        #         past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
        #     )
        # if position_ids is None:
        #     position_ids = cache_position.unsqueeze(0)

        if attention_mask is not None and attention_mask.dim() == 3:
            dtype = inputs_embeds.dtype
            min_dtype = torch.finfo(dtype).min
            attention_mask = (1 - attention_mask) * min_dtype
            attention_mask = attention_mask.unsqueeze(1).to(inputs_embeds.dtype)
        else:
            raise Exception("attention_mask parameter was unavailable or invalid")
            # causal_mask = self._update_causal_mask(
            #     attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
            # )

        hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None

        layer_idx = -1
        for decoder_layer in self.layers:
            layer_idx += 1

            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    cache_position,
                )
            else:
                if offload_model and not self.training:
                    self.get_offlaod_layer(layer_idx, device=inputs_embeds.device)
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            print('************')
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if return_legacy_cache:
            next_cache = next_cache.to_legacy_cache()

        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


def modulate(x, shift, scale):
    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
 

class TimestepEmbedder(nn.Module):
    """
    Embeds scalar timesteps into vector representations.
    """
    def __init__(self, hidden_size, frequency_embedding_size=256):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size, bias=True),
        )
        self.frequency_embedding_size = frequency_embedding_size

    @staticmethod
    def timestep_embedding(t, dim, max_period=10000):
        """
        Create sinusoidal timestep embeddings.
        :param t: a 1-D Tensor of N indices, one per batch element.
                          These may be fractional.
        :param dim: the dimension of the output.
        :param max_period: controls the minimum frequency of the embeddings.
        :return: an (N, D) Tensor of positional embeddings.
        """
        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
        ).to(device=t.device)
        args = t[:, None].float() * freqs[None]
        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        if dim % 2:
            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
        return embedding

    def forward(self, t, dtype=torch.float32):
        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
        t_emb = self.mlp(t_freq)
        return t_emb


class FinalLayer(nn.Module):
    """
    The final layer of DiT.
    """
    def __init__(self, hidden_size, patch_size, out_channels):
        super().__init__()
        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(),
            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
        )

    def forward(self, x, c):
        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
        x = modulate(self.norm_final(x), shift, scale)
        x = self.linear(x)
        return x


def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=1):
    """
    grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
    [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    if isinstance(grid_size, int):
        grid_size = (grid_size, grid_size)

    grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale
    grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token and extra_tokens > 0:
        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
    return pos_embed


def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    assert embed_dim % 2 == 0

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
    return emb


def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position
    pos: a list of positions to be encoded: size (M,)
    out: (M, D)
    """
    assert embed_dim % 2 == 0
    omega = np.arange(embed_dim // 2, dtype=np.float64)
    omega /= embed_dim / 2.
    omega = 1. / 10000**omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out) # (M, D/2)
    emb_cos = np.cos(out) # (M, D/2)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb


class PatchEmbedMR(nn.Module):
    """ 2D Image to Patch Embedding
    """
    def __init__(
            self,
            patch_size: int = 2,
            in_chans: int = 4,
            embed_dim: int = 768,
            bias: bool = True,
    ):
        super().__init__()
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)

    def forward(self, x):
        x = self.proj(x)
        x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
        return x


class OmniGenOriginalModel(nn.Module):
    """
    Diffusion model with a Transformer backbone.
    """
    def __init__(
        self,
        transformer_config: Phi3Config,
        patch_size=2,
        in_channels=4,
        pe_interpolation: float = 1.0,
        pos_embed_max_size: int = 192,
    ):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = in_channels
        self.patch_size = patch_size
        self.pos_embed_max_size = pos_embed_max_size

        hidden_size = transformer_config.hidden_size

        self.x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True)
        self.input_x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True)

        self.time_token = TimestepEmbedder(hidden_size)
        self.t_embedder = TimestepEmbedder(hidden_size)
        
        self.pe_interpolation = pe_interpolation
        pos_embed = get_2d_sincos_pos_embed(hidden_size, pos_embed_max_size, interpolation_scale=self.pe_interpolation, base_size=64)
        self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=True)

        self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)

        self.initialize_weights()

        self.llm = Phi3Transformer(config=transformer_config)
        self.llm.config.use_cache = False
    
    @classmethod
    def from_pretrained(cls, model_name):
        if not os.path.exists(model_name):
            cache_folder = os.getenv('HF_HUB_CACHE')
            model_name = snapshot_download(repo_id=model_name,
                                           cache_dir=cache_folder,
                                           ignore_patterns=['flax_model.msgpack', 'rust_model.ot', 'tf_model.h5'])
        config = Phi3Config.from_pretrained(model_name)
        model = cls(config)
        if os.path.exists(os.path.join(model_name, 'model.safetensors')):
            print("Loading safetensors")
            ckpt = load_file(os.path.join(model_name, 'model.safetensors'))
        else:
            ckpt = torch.load(os.path.join(model_name, 'model.pt'), map_location='cpu')
        model.load_state_dict(ckpt)
        return model

    def initialize_weights(self):
        assert not hasattr(self, "llama")

        # Initialize transformer layers:
        def _basic_init(module):
            if isinstance(module, nn.Linear):
                torch.nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0)
        self.apply(_basic_init)
        
        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
        w = self.x_embedder.proj.weight.data
        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
        nn.init.constant_(self.x_embedder.proj.bias, 0)

        w = self.input_x_embedder.proj.weight.data
        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
        nn.init.constant_(self.x_embedder.proj.bias, 0)


        # Initialize timestep embedding MLP:
        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
        nn.init.normal_(self.time_token.mlp[0].weight, std=0.02)
        nn.init.normal_(self.time_token.mlp[2].weight, std=0.02)

        # Zero-out output layers:
        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
        nn.init.constant_(self.final_layer.linear.weight, 0)
        nn.init.constant_(self.final_layer.linear.bias, 0)

    def unpatchify(self, x, h, w):
        """
        x: (N, T, patch_size**2 * C)
        imgs: (N, H, W, C)
        """
        c = self.out_channels

        x = x.reshape(shape=(x.shape[0], h//self.patch_size, w//self.patch_size, self.patch_size, self.patch_size, c))
        x = torch.einsum('nhwpqc->nchpwq', x)
        imgs = x.reshape(shape=(x.shape[0], c, h, w))
        return imgs


    def cropped_pos_embed(self, height, width):
        """Crops positional embeddings for SD3 compatibility."""
        if self.pos_embed_max_size is None:
            raise ValueError("`pos_embed_max_size` must be set for cropping.")

        height = height // self.patch_size
        width = width // self.patch_size
        if height > self.pos_embed_max_size:
            raise ValueError(
                f"Height ({height}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}."
            )
        if width > self.pos_embed_max_size:
            raise ValueError(
                f"Width ({width}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}."
            )

        top = (self.pos_embed_max_size - height) // 2
        left = (self.pos_embed_max_size - width) // 2
        spatial_pos_embed = self.pos_embed.reshape(1, self.pos_embed_max_size, self.pos_embed_max_size, -1)
        spatial_pos_embed = spatial_pos_embed[:, top : top + height, left : left + width, :]
        # print(top, top + height, left, left + width, spatial_pos_embed.size())
        spatial_pos_embed = spatial_pos_embed.reshape(1, -1, spatial_pos_embed.shape[-1])
        return spatial_pos_embed


    def patch_multiple_resolutions(self, latents, padding_latent=None, is_input_images:bool=False):
        if isinstance(latents, list):
            return_list = False
            if padding_latent is None:
                padding_latent = [None] * len(latents)
                return_list = True
            patched_latents, num_tokens, shapes = [], [], []
            for latent, padding in zip(latents, padding_latent):
                height, width = latent.shape[-2:]
                if is_input_images:
                    latent = self.input_x_embedder(latent)
                else:
                    latent = self.x_embedder(latent)
                pos_embed = self.cropped_pos_embed(height, width)    
                latent = latent + pos_embed
                if padding is not None:
                    latent = torch.cat([latent, padding], dim=-2)
                patched_latents.append(latent)

                num_tokens.append(pos_embed.size(1))
                shapes.append([height, width])
            if not return_list:
                latents = torch.cat(patched_latents, dim=0)
            else:
                latents = patched_latents
        else:
            height, width = latents.shape[-2:]
            if is_input_images:
                latents = self.input_x_embedder(latents)
            else:
                latents = self.x_embedder(latents)
            pos_embed = self.cropped_pos_embed(height, width)  
            latents = latents + pos_embed
            num_tokens = latents.size(1)
            shapes = [height, width]
        return latents, num_tokens, shapes

    
    def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False):
        """
        
        """
        input_is_list = isinstance(x, list)
        x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent)
        time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1)   
        
        if input_img_latents is not None:
            input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True)
        if input_ids is not None:
            condition_embeds = self.llm.embed_tokens(input_ids).clone()
            input_img_inx = 0
            for b_inx in input_image_sizes.keys():
                for start_inx, end_inx in input_image_sizes[b_inx]:
                    condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx]
                    input_img_inx += 1
            if input_img_latents is not None:
                assert input_img_inx == len(input_latents) 

            input_emb = torch.cat([condition_embeds, time_token, x], dim=1)
        else:
            input_emb = torch.cat([time_token, x], dim=1)
        output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model)
        output, past_key_values = output.last_hidden_state, output.past_key_values
        if input_is_list:
            image_embedding = output[:, -max(num_tokens):]
            time_emb = self.t_embedder(timestep, dtype=x.dtype)
            x = self.final_layer(image_embedding, time_emb)
            latents = []
            for i in range(x.size(0)):
                latent = x[i:i+1, :num_tokens[i]]
                latent = self.unpatchify(latent, shapes[i][0], shapes[i][1])
                latents.append(latent)
        else:
            image_embedding = output[:, -num_tokens:]
            time_emb = self.t_embedder(timestep, dtype=x.dtype)
            x = self.final_layer(image_embedding, time_emb)
            latents = self.unpatchify(x, shapes[0], shapes[1])

        if return_past_key_values:
            return latents, past_key_values
        return latents

    @torch.no_grad()
    def forward_with_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):      
        self.llm.config.use_cache = use_kv_cache
        model_out, past_key_values = self.forward(x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, past_key_values=past_key_values, return_past_key_values=True, offload_model=offload_model)
        if use_img_cfg:
            cond, uncond, img_cond = torch.split(model_out, len(model_out) // 3, dim=0)
            cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
            model_out = [cond, cond, cond]
        else:
            cond, uncond = torch.split(model_out, len(model_out) // 2, dim=0)
            cond = uncond + cfg_scale * (cond - uncond)
            model_out = [cond, cond]
        
        return torch.cat(model_out, dim=0), past_key_values


    @torch.no_grad()
    def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):
        self.llm.config.use_cache = use_kv_cache
        if past_key_values is None:
            past_key_values = [None] * len(attention_mask)

        x = torch.split(x, len(x) // len(attention_mask), dim=0)
        timestep = timestep.to(x[0].dtype)
        timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0)

        model_out, pask_key_values = [], []
        for i in range(len(input_ids)):
            temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model)
            model_out.append(temp_out)
            pask_key_values.append(temp_pask_key_values)

        if len(model_out) == 3:
            cond, uncond, img_cond = model_out
            cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
            model_out = [cond, cond, cond]
        elif len(model_out) == 2:
            cond, uncond = model_out
            cond = uncond + cfg_scale * (cond - uncond)
            model_out = [cond, cond]
        else:
            return model_out[0]
        
        return torch.cat(model_out, dim=0), pask_key_values



class OmniGenTransformer(OmniGenOriginalModel):
    def __init__(self):
        config = {
            "_name_or_path": "Phi-3-vision-128k-instruct",
            "architectures": [
                "Phi3ForCausalLM"
            ],
            "attention_dropout": 0.0,
            "bos_token_id": 1,
            "eos_token_id": 2,
            "hidden_act": "silu",
            "hidden_size": 3072,
            "initializer_range": 0.02,
            "intermediate_size": 8192,
            "max_position_embeddings": 131072,
            "model_type": "phi3",
            "num_attention_heads": 32,
            "num_hidden_layers": 32,
            "num_key_value_heads": 32,
            "original_max_position_embeddings": 4096,
            "rms_norm_eps": 1e-05,
            "rope_scaling": {
                "long_factor": [
                1.0299999713897705,
                1.0499999523162842,
                1.0499999523162842,
                1.0799999237060547,
                1.2299998998641968,
                1.2299998998641968,
                1.2999999523162842,
                1.4499999284744263,
                1.5999999046325684,
                1.6499998569488525,
                1.8999998569488525,
                2.859999895095825,
                3.68999981880188,
                5.419999599456787,
                5.489999771118164,
                5.489999771118164,
                9.09000015258789,
                11.579999923706055,
                15.65999984741211,
                15.769999504089355,
                15.789999961853027,
                18.360000610351562,
                21.989999771118164,
                23.079999923706055,
                30.009998321533203,
                32.35000228881836,
                32.590003967285156,
                35.56000518798828,
                39.95000457763672,
                53.840003967285156,
                56.20000457763672,
                57.95000457763672,
                59.29000473022461,
                59.77000427246094,
                59.920005798339844,
                61.190006256103516,
                61.96000671386719,
                62.50000762939453,
                63.3700065612793,
                63.48000717163086,
                63.48000717163086,
                63.66000747680664,
                63.850006103515625,
                64.08000946044922,
                64.760009765625,
                64.80001068115234,
                64.81001281738281,
                64.81001281738281
                ],
                "short_factor": [
                1.05,
                1.05,
                1.05,
                1.1,
                1.1,
                1.1,
                1.2500000000000002,
                1.2500000000000002,
                1.4000000000000004,
                1.4500000000000004,
                1.5500000000000005,
                1.8500000000000008,
                1.9000000000000008,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.000000000000001,
                2.1000000000000005,
                2.1000000000000005,
                2.2,
                2.3499999999999996,
                2.3499999999999996,
                2.3499999999999996,
                2.3499999999999996,
                2.3999999999999995,
                2.3999999999999995,
                2.6499999999999986,
                2.6999999999999984,
                2.8999999999999977,
                2.9499999999999975,
                3.049999999999997,
                3.049999999999997,
                3.049999999999997
                ],
                "type": "su"
            },
            "rope_theta": 10000.0,
            "sliding_window": 131072,
            "tie_word_embeddings": False,
            "torch_dtype": "bfloat16",
            "transformers_version": "4.38.1",
            "use_cache": True,
            "vocab_size": 32064,
            "_attn_implementation": "sdpa"
        }
        config = Phi3Config(**config)
        super().__init__(config)

    
    def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False):
        input_is_list = isinstance(x, list)
        x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent)
        time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1)   
        
        if input_img_latents is not None:
            input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True)
        if input_ids is not None:
            condition_embeds = self.llm.embed_tokens(input_ids).clone()
            input_img_inx = 0
            for b_inx in input_image_sizes.keys():
                for start_inx, end_inx in input_image_sizes[b_inx]:
                    condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx]
                    input_img_inx += 1
            if input_img_latents is not None:
                assert input_img_inx == len(input_latents) 

            input_emb = torch.cat([condition_embeds, time_token, x], dim=1)
        else:
            input_emb = torch.cat([time_token, x], dim=1)
        output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model)
        output, past_key_values = output.last_hidden_state, output.past_key_values
        if input_is_list:
            image_embedding = output[:, -max(num_tokens):]
            time_emb = self.t_embedder(timestep, dtype=x.dtype)
            x = self.final_layer(image_embedding, time_emb)
            latents = []
            for i in range(x.size(0)):
                latent = x[i:i+1, :num_tokens[i]]
                latent = self.unpatchify(latent, shapes[i][0], shapes[i][1])
                latents.append(latent)
        else:
            image_embedding = output[:, -num_tokens:]
            time_emb = self.t_embedder(timestep, dtype=x.dtype)
            x = self.final_layer(image_embedding, time_emb)
            latents = self.unpatchify(x, shapes[0], shapes[1])

        if return_past_key_values:
            return latents, past_key_values
        return latents
    

    @torch.no_grad()
    def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):
        self.llm.config.use_cache = use_kv_cache
        if past_key_values is None:
            past_key_values = [None] * len(attention_mask)

        x = torch.split(x, len(x) // len(attention_mask), dim=0)
        timestep = timestep.to(x[0].dtype)
        timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0)

        model_out, pask_key_values = [], []
        for i in range(len(input_ids)):
            temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model)
            model_out.append(temp_out)
            pask_key_values.append(temp_pask_key_values)

        if len(model_out) == 3:
            cond, uncond, img_cond = model_out
            cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
            model_out = [cond, cond, cond]
        elif len(model_out) == 2:
            cond, uncond = model_out
            cond = uncond + cfg_scale * (cond - uncond)
            model_out = [cond, cond]
        else:
            return model_out[0]
        
        return torch.cat(model_out, dim=0), pask_key_values
    

    @staticmethod
    def state_dict_converter():
        return OmniGenTransformerStateDictConverter()



class OmniGenTransformerStateDictConverter:
    def __init__(self):
        pass

    def from_diffusers(self, state_dict):
        return state_dict
    
    def from_civitai(self, state_dict):
        return state_dict