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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

config.json

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+{
+    "architectures": [
+        "XOmniForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "auto_map": {
+        "AutoConfig": "modeling_xomni.XOmniConfig",
+        "AutoModel": "modeling_xomni.XOmniModel",
+        "AutoModelForCausalLM": "modeling_xomni.XOmniForCausalLM"
+    },
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+    "hidden_act": "silu",
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+    "max_position_embeddings": 8192,
+    "max_window_layers": 28,
+    "mm_special_tokens": [
+        "<SOM>",
+        "<EOM>",
+        "<IMAGE>"
+    ],
+    "mm_vocab_size": 16448,
+    "model_type": "x-omni",
+    "num_attention_heads": 28,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 4,
+    "num_mm_adap_layers": 4,
+    "num_mm_head_layers": 4,
+    "pad_token_id": 151643,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 1000000.0,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.52.0",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vision_config":{
+        "transform": {
+            "short_size": 384,
+            "long_size": 1152,
+            "patch_size": 16,
+            "random_ratio": null,
+            "min_short_size": 128,
+            "max_aspect_ratio": 3.0,
+            "filtering": false
+        },
+        "encoder": {
+            "siglip_name": "siglip2_giant_patch16_384",
+            "siglip_path": "vit/vit_g.pth",
+            "projector_path": "vit/siglip_vq.pt",
+            "with_norm": true,
+            "z_channels": 1536,
+            "codebook_size": 16384,
+            "codebook_dim": 2048
+        },
+        "decoder": {
+            "model_path": "diffusers",
+            "num_inference_steps": 28,
+            "cfg_scale": 1.5,
+            "cfg_scale_2": 1.5,
+            "upscale_factor": 16
+        },
+        "dtype": "bfloat16"
+    },
+    "vocab_size": 151936
+}

configuration_xomni.py

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+from transformers import AutoConfig, Qwen2Config
+from typing import Tuple
+
+
+class XOmniConfig(Qwen2Config):
+    model_type = "x-omni"
+
+    def __init__(
+        self,
+        num_mm_adap_layers: int = 4,
+        num_mm_head_layers: int = 4,
+        mm_vocab_size: int = 16448,
+        image_vocab_size: int = 16384,
+        mm_special_tokens: Tuple[str] = ('<SOM>', '<EOM>', '<IMAGE>'),
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.num_mm_adap_layers = num_mm_adap_layers
+        self.num_mm_head_layers = num_mm_head_layers
+        self.mm_vocab_size = mm_vocab_size
+        self.image_vocab_size = image_vocab_size
+        self.mm_special_tokens = mm_special_tokens
+
+
+AutoConfig.register("x-omni", XOmniConfig)

diffusers/config.json

@@ -0,0 +1,21 @@
+{
+  "_diffusers_version": "0.33.0.dev0",
+  "attention_head_dim": 128,
+  "axes_dims_rope": [
+    16,
+    56,
+    56
+  ],
+  "drop_token_prob": 0.0,
+  "guidance_embeds": true,
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+  "in_channels": 64,
+  "joint_attention_dim": 4096,
+  "num_attention_heads": 24,
+  "num_layers": 19,
+  "num_single_layers": 38,
+  "out_channels": null,
+  "patch_size": 1,
+  "pooled_projection_dim": 768,
+  "siglip_channels": null
+}

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+}

modeling_siglip_flux.py

@@ -0,0 +1,841 @@
+import torch
+import numpy as np
+
+from typing import Any, Callable, Dict, Tuple, List, Optional, Union
+from diffusers import FluxTransformer2DModel
+from diffusers.configuration_utils import register_to_config
+from diffusers.utils import logging, USE_PEFT_BACKEND, scale_lora_layers, unscale_lora_layers
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
+from diffusers.image_processor import PipelineImageInput
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def drop_token(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0], x.shape[1], 1)
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0 and scale_by_keep:
+        random_tensor.div_(keep_prob)
+    return x * random_tensor
+
+
+class FluxTransformer2DModelWithSigLIP(FluxTransformer2DModel):
+    @register_to_config
+    def __init__(
+            self,
+            patch_size: int = 1,
+            in_channels: int = 64,
+            out_channels: Optional[int] = None,
+            num_layers: int = 19,
+            num_single_layers: int = 38,
+            attention_head_dim: int = 128,
+            num_attention_heads: int = 24,
+            joint_attention_dim: int = 4096,
+            pooled_projection_dim: int = 768,
+            guidance_embeds: bool = False,
+            axes_dims_rope: Tuple[int] = (16, 56, 56),
+            siglip_channels: Optional[int] = None,
+            drop_token_prob: float = 0.,
+    ):
+        super().__init__(
+            patch_size=patch_size,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            num_layers=num_layers,
+            num_single_layers=num_single_layers,
+            attention_head_dim=attention_head_dim,
+            num_attention_heads=num_attention_heads,
+            joint_attention_dim=joint_attention_dim,
+            pooled_projection_dim=pooled_projection_dim,
+            guidance_embeds=guidance_embeds,
+            axes_dims_rope=axes_dims_rope,
+        )
+        self.drop_token_prob = drop_token_prob
+        if siglip_channels is not None:
+            self.init_siglip_embed(siglip_channels)
+
+    def init_siglip_embed(self, siglip_channels):
+        self.siglip_embed = torch.nn.Linear(siglip_channels, self.inner_dim, bias=False)
+        torch.nn.init.zeros_(self.siglip_embed.weight)
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            encoder_hidden_states: torch.Tensor = None,
+            pooled_projections: torch.Tensor = None,
+            timestep: torch.LongTensor = None,
+            img_ids: torch.Tensor = None,
+            txt_ids: torch.Tensor = None,
+            guidance: torch.Tensor = None,
+            siglip_tensor: Optional[torch.Tensor] = None,
+            joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+            controlnet_block_samples=None,
+            controlnet_single_block_samples=None,
+            return_dict: bool = True,
+            controlnet_blocks_repeat: bool = False,
+    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
+        """
+        The [`FluxTransformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
+                Input `hidden_states`.
+            encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
+                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+            pooled_projections (`torch.Tensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+                from the embeddings of input conditions.
+            timestep ( `torch.LongTensor`):
+                Used to indicate denoising step.
+            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+                A list of tensors that if specified are added to the residuals of transformer blocks.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        if joint_attention_kwargs is not None:
+            joint_attention_kwargs = joint_attention_kwargs.copy()
+            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+
+        hidden_states = self.x_embedder(hidden_states)
+
+        timestep = timestep.to(hidden_states.dtype) * 1000
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype) * 1000
+        else:
+            guidance = None
+
+        temb = (
+            self.time_text_embed(timestep, pooled_projections)
+            if guidance is None
+            else self.time_text_embed(timestep, guidance, pooled_projections)
+        )
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        if txt_ids.ndim == 3:
+            logger.warning(
+                "Passing `txt_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            txt_ids = txt_ids[0]
+        if img_ids.ndim == 3:
+            logger.warning(
+                "Passing `img_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            img_ids = img_ids[0]
+
+        ids = torch.cat((txt_ids, img_ids), dim=0)
+        image_rotary_emb = self.pos_embed(ids)
+
+        if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
+            ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")
+            ip_hidden_states = self.encoder_hid_proj(ip_adapter_image_embeds)
+            joint_attention_kwargs.update({"ip_hidden_states": ip_hidden_states})
+
+        for index_block, block in enumerate(self.transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_block_samples is not None:
+                interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                # For Xlabs ControlNet.
+                if controlnet_blocks_repeat:
+                    hidden_states = (
+                            hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
+                    )
+                else:
+                    hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+
+        if siglip_tensor is not None:
+            siglip_tensor = drop_token(siglip_tensor, self.drop_token_prob, training=self.training)
+            hidden_states = hidden_states + self.siglip_embed(siglip_tensor)
+
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_single_block_samples is not None:
+                interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                hidden_states[:, encoder_hidden_states.shape[1]:, ...] = (
+                        hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+                        + controlnet_single_block_samples[index_block // interval_control]
+                )
+
+        hidden_states = hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+
+        hidden_states = self.norm_out(hidden_states, temb)
+        output = self.proj_out(hidden_states)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)
+
+
+def teacache_forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_block_samples=None,
+        controlnet_single_block_samples=None,
+        return_dict: bool = True,
+        controlnet_blocks_repeat: bool = False,
+        siglip_tensor: Optional[torch.Tensor] = None,
+) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+    """
+    The [`FluxTransformer2DModel`] forward method.
+
+    Args:
+        hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+            Input `hidden_states`.
+        encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+            Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+        pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+            from the embeddings of input conditions.
+        timestep ( `torch.LongTensor`):
+            Used to indicate denoising step.
+        block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+            A list of tensors that if specified are added to the residuals of transformer blocks.
+        joint_attention_kwargs (`dict`, *optional*):
+            A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+            `self.processor` in
+            [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+        return_dict (`bool`, *optional*, defaults to `True`):
+            Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+            tuple.
+
+    Returns:
+        If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+        `tuple` where the first element is the sample tensor.
+    """
+    if joint_attention_kwargs is not None:
+        joint_attention_kwargs = joint_attention_kwargs.copy()
+        lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+    else:
+        lora_scale = 1.0
+
+    if USE_PEFT_BACKEND:
+        # weight the lora layers by setting `lora_scale` for each PEFT layer
+        scale_lora_layers(self, lora_scale)
+    else:
+        if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+            logger.warning(
+                "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+            )
+
+    batch_size, seq_len, channels = hidden_states.shape
+    device, dtype = hidden_states.device, hidden_states.dtype
+    hidden_states = self.x_embedder(hidden_states)
+
+    timestep = timestep.to(hidden_states.dtype) * 1000
+    if guidance is not None:
+        guidance = guidance.to(hidden_states.dtype) * 1000
+    else:
+        guidance = None
+
+    temb = (
+        self.time_text_embed(timestep, pooled_projections)
+        if guidance is None
+        else self.time_text_embed(timestep, guidance, pooled_projections)
+    )
+    encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+    if txt_ids.ndim == 3:
+        logger.warning(
+            "Passing `txt_ids` 3d torch.Tensor is deprecated."
+            "Please remove the batch dimension and pass it as a 2d torch Tensor"
+        )
+        txt_ids = txt_ids[0]
+    if img_ids.ndim == 3:
+        logger.warning(
+            "Passing `img_ids` 3d torch.Tensor is deprecated."
+            "Please remove the batch dimension and pass it as a 2d torch Tensor"
+        )
+        img_ids = img_ids[0]
+
+    ids = torch.cat((txt_ids, img_ids), dim=0)
+    image_rotary_emb = self.pos_embed(ids)
+
+    if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
+        ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")
+        ip_hidden_states = self.encoder_hid_proj(ip_adapter_image_embeds)
+        joint_attention_kwargs.update({"ip_hidden_states": ip_hidden_states})
+
+    if self.enable_teacache:
+        inp = hidden_states.clone()
+        temb_ = temb.clone()
+        modulated_inp, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.transformer_blocks[0].norm1(inp, emb=temb_)
+        if self.cnt == 0 or self.cnt == self.num_steps - 1:
+            should_calc = True
+            self.accumulated_rel_l1_distance = 0
+        else:
+            coefficients = [4.98651651e+02, -2.83781631e+02, 5.58554382e+01, -3.82021401e+00, 2.64230861e-01]
+            rescale_func = np.poly1d(coefficients)
+            # rescale_func = Polynomial(coefficients.reverse())
+            self.accumulated_rel_l1_distance += rescale_func(((modulated_inp - self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item())
+            if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
+                should_calc = False
+            else:
+                should_calc = True
+                self.accumulated_rel_l1_distance = 0
+        self.previous_modulated_input = modulated_inp
+        self.cnt += 1
+        if self.cnt == self.num_steps:
+            self.cnt = 0
+
+    if self.enable_teacache:
+        if not should_calc:
+            hidden_states += self.previous_residual
+        else:
+            ori_hidden_states = hidden_states.clone()
+            for index_block, block in enumerate(self.transformer_blocks):
+                if torch.is_grad_enabled() and self.gradient_checkpointing:
+                    encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
+                        block,
+                        hidden_states,
+                        encoder_hidden_states,
+                        temb,
+                        image_rotary_emb,
+                    )
+
+                else:
+                    encoder_hidden_states, hidden_states = block(
+                        hidden_states=hidden_states,
+                        encoder_hidden_states=encoder_hidden_states,
+                        temb=temb,
+                        image_rotary_emb=image_rotary_emb,
+                        joint_attention_kwargs=joint_attention_kwargs,
+                    )
+
+                # controlnet residual
+                if controlnet_block_samples is not None:
+                    interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                    interval_control = int(np.ceil(interval_control))
+                    # For Xlabs ControlNet.
+                    if controlnet_blocks_repeat:
+                        hidden_states = (
+                                hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
+                        )
+                    else:
+                        hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+
+            if siglip_tensor is not None:
+                siglip_tensor = drop_token(siglip_tensor, self.drop_token_prob, training=self.training)
+                hidden_states = hidden_states + self.siglip_embed(siglip_tensor)
+            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+            for index_block, block in enumerate(self.single_transformer_blocks):
+                if torch.is_grad_enabled() and self.gradient_checkpointing:
+                    hidden_states = self._gradient_checkpointing_func(
+                        block,
+                        hidden_states,
+                        temb,
+                        image_rotary_emb,
+                    )
+
+                else:
+                    hidden_states = block(
+                        hidden_states=hidden_states,
+                        temb=temb,
+                        image_rotary_emb=image_rotary_emb,
+                        joint_attention_kwargs=joint_attention_kwargs,
+                    )
+
+                # controlnet residual
+                if controlnet_single_block_samples is not None:
+                    interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                    interval_control = int(np.ceil(interval_control))
+                    hidden_states[:, encoder_hidden_states.shape[1]:, ...] = (
+                            hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+                            + controlnet_single_block_samples[index_block // interval_control]
+                    )
+
+            hidden_states = hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+            self.previous_residual = hidden_states - ori_hidden_states
+    else:
+        for index_block, block in enumerate(self.transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                )
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_block_samples is not None:
+                interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                # For Xlabs ControlNet.
+                if controlnet_blocks_repeat:
+                    hidden_states = (
+                            hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
+                    )
+                else:
+                    hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+        if siglip_tensor is not None:
+            siglip_tensor = drop_token(siglip_tensor, self.drop_token_prob, training=self.training)
+            hidden_states = hidden_states + self.siglip_embed(siglip_tensor)
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_single_block_samples is not None:
+                interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                hidden_states[:, encoder_hidden_states.shape[1]:, ...] = (
+                        hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+                        + controlnet_single_block_samples[index_block // interval_control]
+                )
+
+        hidden_states = hidden_states[:, encoder_hidden_states.shape[1]:, ...]
+
+    hidden_states = self.norm_out(hidden_states, temb)
+    output = self.proj_out(hidden_states)
+
+    if USE_PEFT_BACKEND:
+        # remove `lora_scale` from each PEFT layer
+        unscale_lora_layers(self, lora_scale)
+
+    if not return_dict:
+        return (output,)
+
+    return Transformer2DModelOutput(sample=output)
+
+
+class FluxPipelineWithSigLIP(FluxPipeline):
+   
+    @torch.no_grad()
+    def __call__(
+            self,
+            siglip_tensor: torch.Tensor,
+            prompt: Union[str, List[str]] = None,
+            prompt_2: Optional[Union[str, List[str]]] = None,
+            negative_prompt: Union[str, List[str]] = None,
+            negative_prompt_2: Optional[Union[str, List[str]]] = None,
+            true_cfg_scale: float = 1.0,
+            true_cfg_scale_2: float = 1.0,
+            height: Optional[int] = None,
+            width: Optional[int] = None,
+            num_inference_steps: int = 28,
+            sigmas: Optional[List[float]] = None,
+            guidance_scale: float = 3.5,
+            num_images_per_prompt: Optional[int] = 1,
+            generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+            latents: Optional[torch.FloatTensor] = None,
+            prompt_embeds: Optional[torch.FloatTensor] = None,
+            pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+            ip_adapter_image: Optional[PipelineImageInput] = None,
+            ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+            negative_ip_adapter_image: Optional[PipelineImageInput] = None,
+            negative_ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+            negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+            output_type: Optional[str] = "pil",
+            return_dict: bool = True,
+            joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+            callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+            callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+            max_sequence_length: int = 512,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                will be used instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is
+                not greater than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
+            true_cfg_scale (`float`, *optional*, defaults to 1.0):
+                When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            guidance_scale (`float`, *optional*, defaults to 3.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            negative_ip_adapter_image:
+                (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            negative_ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+        assert true_cfg_scale == true_cfg_scale_2
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        has_neg_prompt = negative_prompt is not None or (
+                negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
+        )
+        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+        assert do_true_cfg
+        (
+            negative_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            _,
+        ) = self.encode_prompt(
+            prompt=negative_prompt,
+            prompt_2=negative_prompt_2,
+            prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
+        image_seq_len = latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.15),
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0] * 2)
+        else:
+            guidance = None
+
+        if (ip_adapter_image is not None or ip_adapter_image_embeds is not None) and (
+                negative_ip_adapter_image is None and negative_ip_adapter_image_embeds is None
+        ):
+            negative_ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)
+            negative_ip_adapter_image = [negative_ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters
+
+        elif (ip_adapter_image is None and ip_adapter_image_embeds is None) and (
+                negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None
+        ):
+            ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)
+            ip_adapter_image = [ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters
+
+        if self.joint_attention_kwargs is None:
+            self._joint_attention_kwargs = {}
+
+        image_embeds = None
+        negative_image_embeds = None
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+            )
+        if negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None:
+            negative_image_embeds = self.prepare_ip_adapter_image_embeds(
+                negative_ip_adapter_image,
+                negative_ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+            )
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                self._current_timestep = t
+                if image_embeds is not None:
+                    self._joint_attention_kwargs["ip_adapter_image_embeds"] = image_embeds
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0] * 2).to(latents.dtype)
+
+                batch_noise_pred = self.transformer(
+                    hidden_states=torch.cat([latents, latents], dim=0),
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=torch.cat([pooled_prompt_embeds, negative_pooled_prompt_embeds.expand_as(pooled_prompt_embeds)], dim=0),
+                    encoder_hidden_states=torch.cat([prompt_embeds, negative_prompt_embeds.expand_as(prompt_embeds)], dim=0),
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    siglip_tensor=torch.cat([siglip_tensor, torch.zeros_like(siglip_tensor)], dim=0),
+                    return_dict=False,
+                )[0]
+                noise_pred, neg_noise_pred = batch_noise_pred.chunk(2)
+                noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        self._current_timestep = None
+
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents, return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)

modeling_siglip_tokenizer.py

@@ -0,0 +1,231 @@
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import einsum
+from torchvision import transforms
+
+from PIL import Image
+from einops import rearrange
+
+from .modeling_vit import create_siglip_vit
+
+
+def create_anyres_preprocess(
+    short_size=384, 
+    long_size=1152, 
+    patch_size=16, 
+    random_ratio=None, 
+    min_short_size=128, 
+    max_aspect_ratio=3., 
+    filtering=True
+):
+
+    def resize_and_filtering(pil_image):
+        pil_image = pil_image.convert('RGB')
+        width, height = pil_image.size
+        ss, ls = min(width, height), max(width, height)
+        aspect_ratio = ls / ss
+        if filtering and (ss < min_short_size or aspect_ratio > max_aspect_ratio):
+            return None
+        target_width, target_height = width, height
+        if random_ratio is not None:
+            log_ratio = torch.log(torch.tensor(random_ratio))
+            sqrt_ratio = torch.exp(0.5 * torch.empty(1).uniform_(log_ratio[0], log_ratio[1])).item()
+            target_width = int(round(target_width * sqrt_ratio))
+            target_height = int(round(target_height / sqrt_ratio))
+        
+        ss = min(target_width, target_height)
+        if ss < short_size:
+            target_width = target_width * (short_size / ss)
+            target_height = target_height * (short_size / ss)
+        
+        ls = max(target_width, target_height)
+        if ls > long_size:
+            target_width = target_width * (long_size / ls)
+            target_height = target_height * (long_size / ls)
+        
+        target_width = int(round(target_width / patch_size)) * patch_size
+        target_height = int(round(target_height / patch_size)) * patch_size
+        pil_image = pil_image.resize((target_width, target_height), resample=Image.BICUBIC)
+        
+        to_tensor = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+        ])
+        return to_tensor(pil_image)
+
+    transform = transforms.Lambda(resize_and_filtering)
+    return transform
+
+
+class IBQ(nn.Module):
+    def __init__(self, n_e, e_dim, skip_quantization_prob=0.0, quantization_temp=2.0, beta=0.25, sane_index_shape=False, l2_norm=True):
+        super().__init__()
+        self.n_e = n_e
+        self.e_dim = e_dim
+        self.quantization_temp = quantization_temp
+        self.skip_quantization_prob = skip_quantization_prob
+        self.beta = beta
+        self.sane_index_shape = sane_index_shape
+        self.l2_norm = l2_norm
+
+        self.embedding = nn.Embedding(self.n_e, self.e_dim)
+        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
+        if self.l2_norm:
+            self.embedding.weight.data = F.normalize(self.embedding.weight.data, p=2, dim=-1)
+    
+    def forward(self, z, temp=None, rescale_logits=False, return_logits=False, **kwargs):
+        assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
+        assert rescale_logits == False, "Only for interface compatible with Gumbel"
+        assert return_logits == False, "Only for interface compatible with Gumbel"
+        # reshape z -> (batch, height, width, channel) and flatten
+        z = rearrange(z, 'b c h w -> b h w c').contiguous()
+        assert z.shape[-1] == self.e_dim
+        z_flattened = z.view(-1, self.e_dim)
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+
+        if self.l2_norm:
+            z = F.normalize(z, p=2, dim=-1)
+            z_flattened = F.normalize(z_flattened, p=2, dim=-1)
+            embedding = F.normalize(self.embedding.weight, p=2, dim=-1)
+        else:
+            embedding = self.embedding.weight
+
+        d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
+            torch.sum(embedding**2, dim=1) - 2 * \
+            torch.einsum('bd,dn->bn', z_flattened, torch.einsum('n d -> d n', embedding))
+        
+        if self.training:
+            logits = -d / self.quantization_temp
+            soft_one_hot = F.softmax(logits, dim=1)
+            min_encoding_indices = soft_one_hot.max(1, keepdim=True)[1]
+            hard_one_hot = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(1, min_encoding_indices, 1.0)
+            one_hot = hard_one_hot - soft_one_hot.detach() + soft_one_hot
+
+            z_q = einsum('b n, n d -> b d', one_hot, self.embedding.weight).view(z.shape)
+            z_q_2 = einsum('b n, n d -> b d', hard_one_hot, self.embedding.weight).view(z.shape)
+
+            # compute loss for embedding
+            commit_loss = torch.mean((z_q - z) ** 2) + torch.mean((z_q_2.detach() - z) ** 2) + self.beta * \
+                        torch.mean((z_q_2 - z.detach()) ** 2)
+        else:
+            min_encoding_indices = torch.argmin(d, dim=1)
+            z_q = embedding[min_encoding_indices].view(z.shape)
+            commit_loss = None
+        
+        if self.training and self.skip_quantization_prob > 0.0:
+            z_q = torch.where(
+                torch.rand_like(z_q[:, 0:1, 0:1, 0:1]).expand_as(z_q) <= self.skip_quantization_prob,
+                z, z_q,
+            )
+        
+        # reshape back to match original input shape
+        z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
+
+        if self.sane_index_shape:
+            min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3])
+
+        return (z_q, None, min_encoding_indices), commit_loss
+
+    def get_codebook_entry(self, indices, bhwc):
+        # shape specifying (batch, height, width, channel)
+        # get quantized latent vectors
+        z_q = self.embedding(indices)
+
+        if bhwc is not None:
+            z_q = z_q.view(bhwc)
+            # reshape back to match original input shape
+            z_q = z_q.permute(0, 3, 1, 2).contiguous()
+
+        return z_q
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, channels, num_groups=32):
+        super().__init__()
+        self.conv1 = nn.Conv2d(channels, channels, 3, padding='same')
+        self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=channels)
+        self.activate = nn.GELU()
+        self.conv2 = nn.Conv2d(channels, channels, 3, padding='same')
+        self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=channels)
+    
+    def forward(self, x):
+        res = x
+        x = self.norm1(x)
+        x = self.activate(x)
+        x = self.conv1(x)
+        x = self.norm2(x)
+        x = self.activate(x)
+        x = self.conv2(x)
+        return x + res
+
+
+class VQConvProjector(nn.Module):
+    def __init__(
+        self, 
+        z_channels=1536, 
+        codebook_size=16384, 
+        codebook_dim=2048, 
+        conv_layers=2,
+        with_norm=True,
+        skip_quant_prob=0.1,
+    ):
+        super().__init__()
+        self.quant_conv = nn.Conv2d(z_channels, codebook_dim, 1)
+        self.quantize = IBQ(codebook_size, codebook_dim, skip_quant_prob, sane_index_shape=True)
+        self.post_quant_conv = nn.Conv2d(codebook_dim, z_channels, 1)
+        block = ResidualBlock
+        self.post_conv = nn.Sequential(*[block(z_channels) for _ in range(conv_layers)])
+    
+    def forward(self, x, h, w):
+        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
+        z = self.quant_conv(x)
+        (z_q, _, _), codebook_loss = self.quantize(z)
+        z = self.post_quant_conv(z_q)
+        z = self.post_conv(z)
+        z = rearrange(z, 'b c h w -> b (h w) c')
+        return z, codebook_loss
+    
+    def encode(self, x, h, w):
+        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
+        z = self.quant_conv(x)
+        (_, _, tokens), _ = self.quantize(z)
+        return tokens
+    
+    def decode(self, tokens, bhwc):
+        z_q = self.quantize.get_codebook_entry(tokens, bhwc)
+        z = self.post_quant_conv(z_q)
+        z = self.post_conv(z)        
+        return z
+
+
+class SiglipTokenizer(nn.Module):
+    def __init__(
+        self, 
+        siglip_name, 
+        siglip_path, 
+        projector_path, 
+        z_channels=1536, 
+        codebook_size=16384, 
+        codebook_dim=2048, 
+        with_norm=True
+    ):
+        super().__init__()
+        self.vit = create_siglip_vit(model_name=siglip_name, path=siglip_path)
+        self.vqproj = VQConvProjector(
+            z_channels=z_channels, 
+            codebook_size=codebook_size, 
+            codebook_dim=codebook_dim, 
+            with_norm=with_norm
+        )
+        self.vqproj.load_state_dict(torch.load(projector_path, map_location='cpu'), strict=True)
+
+    def encode(self, x):
+        features, (h, w), _ = self.vit(x)
+        tokens = self.vqproj.encode(features, h, w)
+        return tokens
+    
+    def decode(self, tokens, bhwc):
+        return self.vqproj.decode(tokens, bhwc)

modeling_vit.py

@@ -0,0 +1,699 @@
+import math
+import warnings
+from dataclasses import dataclass
+from functools import partial
+from typing import (
+    Callable, Dict, Final, List, Literal, Optional,
+    Sequence, Set, Tuple, Type, Union,
+)
+
+from torch.utils.checkpoint import checkpoint
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from timm.layers import (
+    DropPath, LayerType, Mlp, PatchDropout,
+    PatchEmbed, resample_abs_pos_embed,
+)
+from timm.models._manipulate import checkpoint_seq, named_apply
+
+from flash_attn import flash_attn_func, flash_attn_varlen_func
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2,
+        )
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)  # noqa: E741
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.0))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
+    # type: (torch.Tensor, float, float, float, float) -> torch.Tensor
+    r"""The original timm.models.layers.weight_init.trunc_normal_ can not handle bfloat16 yet, here we first
+    convert the tensor to float32, apply the trunc_normal_() in float32, and then convert it back to its orignal dtype.
+    Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn
+    from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+
+    with torch.no_grad():
+        dtype = tensor.dtype
+        tensor_fp32 = tensor.float()
+        tensor_fp32 = _no_grad_trunc_normal_(tensor_fp32, mean, std, a, b)
+        tensor_dtype = tensor_fp32.to(dtype=dtype)
+        tensor.copy_(tensor_dtype)
+
+
+def init_weights(self):
+    if self.pos_embed is not None:
+        trunc_normal_(self.pos_embed, std=self.pos_embed.shape[1] ** -0.5)
+    trunc_normal_(self.latent, std=self.latent_dim**-0.5)
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = "") -> None:
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+    elif hasattr(module, "init_weights"):
+        module.init_weights()
+
+
+class Attention(nn.Module):
+    fused_attn: Final[bool]
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        qk_norm: bool = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        norm_layer: nn.Module = nn.LayerNorm,
+    ) -> None:
+        super().__init__()
+        assert dim % num_heads == 0, "dim should be divisible by num_heads"
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim**-0.5
+        # self.fused_attn = use_fused_attn()
+        self.fused_attn = True
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop) if proj_drop > 0.0 else nn.Identity()
+
+    def forward(self, x: torch.Tensor, cu_slens=None) -> torch.Tensor:
+        B, N, C = x.shape
+        qkv = (
+            self.qkv(x)
+            .reshape(B, N, 3, self.num_heads, self.head_dim)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = qkv.unbind(0)
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        if cu_slens is not None:
+            q = q.permute(0, 2, 1, 3)   # B, num_heads, N, C -> B, N, num_heads, C
+            k = k.permute(0, 2, 1, 3)
+            v = v.permute(0, 2, 1, 3)
+            max_seqlen = torch.max(cu_slens[1:] - cu_slens[:-1]).item()
+            x = flash_attn_varlen_func(
+                q.squeeze(0),
+                k.squeeze(0),
+                v.squeeze(0),
+                cu_seqlens_q=cu_slens,
+                cu_seqlens_k=cu_slens,
+                max_seqlen_q=max_seqlen,
+                max_seqlen_k=max_seqlen,
+                softmax_scale=self.scale,
+                causal=False,
+                )
+
+            x = x.reshape(B, N, -1)
+            x = self.proj(x)
+            x = self.proj_drop(x)
+
+        else:
+            q = q.permute(0, 2, 1, 3)   # B, num_heads, N, C -> B, N, num_heads, C
+            k = k.permute(0, 2, 1, 3)
+            v = v.permute(0, 2, 1, 3)
+            x = flash_attn_func(q, k, v, softmax_scale=self.scale) # -> b, n, h, c
+
+            x = x.reshape(B, N, -1)
+            x = self.proj(x)
+            x = self.proj_drop(x)
+        return x
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: float = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        qk_norm: bool = False,
+        proj_drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values: Optional[float] = None,
+        drop_path: float = 0.0,
+        act_layer: nn.Module = nn.GELU,
+        norm_layer: nn.Module = nn.LayerNorm,
+        mlp_layer: nn.Module = Mlp,
+    ) -> None:
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop,
+            norm_layer=norm_layer,
+        )
+        self.ls1 = (
+            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        )
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = mlp_layer(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=proj_drop,
+        )
+        self.ls2 = (
+            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        )
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+    def forward(self, x: torch.Tensor, cu_slens=None) -> torch.Tensor:
+        x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x), cu_slens=cu_slens)))
+        x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
+        return x
+
+
+class VisionTransformer(nn.Module):
+    """Vision Transformer
+
+    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
+        - https://arxiv.org/abs/2010.11929
+    """
+
+    dynamic_img_size: Final[bool]
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        num_classes: int = 1000,
+        global_pool: Literal["", "avg", "token", "map"] = "token",
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        qk_norm: bool = False,
+        init_values: Optional[float] = None,
+        class_token: bool = True,
+        no_embed_class: bool = False,
+        reg_tokens: int = 0,
+        pre_norm: bool = False,
+        fc_norm: Optional[bool] = None,
+        dynamic_img_size: bool = False,
+        dynamic_img_pad: bool = False,
+        drop_rate: float = 0.0,
+        pos_drop_rate: float = 0.0,
+        patch_drop_rate: float = 0.0,
+        proj_drop_rate: float = 0.0,
+        attn_drop_rate: float = 0.0,
+        drop_path_rate: float = 0.0,
+        weight_init: Literal["skip", "jax", "jax_nlhb", "moco", ""] = "",
+        embed_layer: Callable = PatchEmbed,
+        norm_layer: Optional[LayerType] = None,
+        act_layer: Optional[LayerType] = None,
+        strict_img_size: bool = False,
+        block_fn: Type[nn.Module] = Block,
+        mlp_layer: Type[nn.Module] = Mlp,
+        ignore_head: bool = False,
+    ) -> None:
+        """
+        Args:
+            img_size: Input image size.
+            patch_size: Patch size.
+            in_chans: Number of image input channels.
+            num_classes: Mumber of classes for classification head.
+            global_pool: Type of global pooling for final sequence (default: 'token').
+            embed_dim: Transformer embedding dimension.
+            depth: Depth of transformer.
+            num_heads: Number of attention heads.
+            mlp_ratio: Ratio of mlp hidden dim to embedding dim.
+            qkv_bias: Enable bias for qkv projections if True.
+            init_values: Layer-scale init values (layer-scale enabled if not None).
+            class_token: Use class token.
+            no_embed_class: Don't include position embeddings for class (or reg) tokens.
+            reg_tokens: Number of register tokens.
+            fc_norm: Pre head norm after pool (instead of before), if None, enabled when global_pool == 'avg'.
+            drop_rate: Head dropout rate.
+            pos_drop_rate: Position embedding dropout rate.
+            attn_drop_rate: Attention dropout rate.
+            drop_path_rate: Stochastic depth rate.
+            weight_init: Weight initialization scheme.
+            embed_layer: Patch embedding layer.
+            norm_layer: Normalization layer.
+            act_layer: MLP activation layer.
+            block_fn: Transformer block layer.
+        """
+        super().__init__()
+        assert global_pool in ("", "avg", "token", "map")
+        assert class_token or global_pool != "token"
+        use_fc_norm = global_pool == "avg" if fc_norm is None else fc_norm
+        # norm_layer = get_norm_layer(norm_layer) or partial(nn.LayerNorm, eps=1e-6)
+        # act_layer = get_act_layer(act_layer) or nn.GELU
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        act_layer = nn.GELU
+
+        self.num_classes = num_classes
+        self.global_pool = global_pool
+        self.num_features = self.embed_dim = (
+            embed_dim  # num_features for consistency with other models
+        )
+        self.num_prefix_tokens = 1 if class_token else 0
+        self.num_prefix_tokens += reg_tokens
+        self.num_reg_tokens = reg_tokens
+        self.has_class_token = class_token
+        self.no_embed_class = (
+            no_embed_class  # don't embed prefix positions (includes reg)
+        )
+        self.dynamic_img_size = dynamic_img_size
+        self.grad_checkpointing = False
+        self.ignore_head = ignore_head
+
+        embed_args = {}
+        if dynamic_img_size:
+            # flatten deferred until after pos embed
+            embed_args.update(dict(strict_img_size=False, output_fmt="NHWC"))
+        self.patch_embed = embed_layer(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            bias=not pre_norm,  # disable bias if pre-norm is used (e.g. CLIP)
+            dynamic_img_pad=dynamic_img_pad,
+            strict_img_size=strict_img_size,
+            **embed_args,
+        )
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = (
+            nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
+        )
+        self.reg_token = (
+            nn.Parameter(torch.zeros(1, reg_tokens, embed_dim)) if reg_tokens else None
+        )
+        embed_len = (
+            num_patches if no_embed_class else num_patches + self.num_prefix_tokens
+        )
+        self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * 0.02)
+        self.pos_drop = nn.Dropout(p=pos_drop_rate)
+        if patch_drop_rate > 0:
+            self.patch_drop = PatchDropout(
+                patch_drop_rate,
+                num_prefix_tokens=self.num_prefix_tokens,
+            )
+        else:
+            self.patch_drop = nn.Identity()
+        self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
+
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, depth)
+        ]  # stochastic depth decay rule
+        self.blocks = nn.Sequential(
+            *[
+                block_fn(
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_norm=qk_norm,
+                    init_values=init_values,
+                    proj_drop=proj_drop_rate,
+                    attn_drop=attn_drop_rate,
+                    drop_path=dpr[i],
+                    norm_layer=norm_layer,
+                    act_layer=act_layer,
+                    mlp_layer=mlp_layer,
+                )
+                for i in range(depth)
+            ]
+        )
+
+    def init_weights(self, mode: Literal["jax", "jax_nlhb", "moco", ""] = "") -> None:
+        assert mode in ("jax", "jax_nlhb", "moco", "")
+        # head_bias = -math.log(self.num_classes) if "nlhb" in mode else 0.0
+        trunc_normal_(self.pos_embed, std=0.02)
+        if self.cls_token is not None:
+            nn.init.normal_(self.cls_token, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    @torch.jit.ignore
+    def no_weight_decay(self) -> Set:
+        return {"pos_embed", "cls_token", "dist_token"}
+
+    @torch.jit.ignore
+    def group_matcher(self, coarse: bool = False) -> Dict:
+        return dict(
+            stem=r"^cls_token|pos_embed|patch_embed",  # stem and embed
+            blocks=[(r"^blocks\.(\d+)", None), (r"^norm", (99999,))],
+        )
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable: bool = True) -> None:
+        self.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def get_classifier(self) -> nn.Module:
+        return self.head
+
+    def reset_classifier(self, num_classes: int, global_pool=None) -> None:
+        self.num_classes = num_classes
+        if global_pool is not None:
+            assert global_pool in ("", "avg", "token", "map")
+            if global_pool == "map" and self.attn_pool is None:
+                assert (
+                    False
+                ), "Cannot currently add attention pooling in reset_classifier()."
+            elif global_pool != "map " and self.attn_pool is not None:
+                self.attn_pool = None  # remove attention pooling
+            self.global_pool = global_pool
+        self.head = (
+            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+        )
+
+    def rescale_positional_embedding(self, out_size):
+        h, w = out_size
+        pos_embed_shape = int((self.pos_embed.shape[1]) ** 0.5)
+        if (h, w) == (pos_embed_shape, pos_embed_shape):
+            return self.pos_embed
+        rescaled_positional_embedding = \
+            self.pos_embed.new_zeros(1, h*w, self.pos_embed.shape[2])
+        pe_2d = self.pos_embed[0].T.contiguous().view(1, -1, pos_embed_shape, pos_embed_shape)
+        pe_2d = F.interpolate(pe_2d, out_size, mode='bilinear', align_corners=False).view(-1, h*w)
+        rescaled_positional_embedding[0] = pe_2d.T.contiguous()
+        return rescaled_positional_embedding
+
+    def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:
+        if self.dynamic_img_size:
+            B, H, W, C = x.shape
+            pos_embed = resample_abs_pos_embed(
+                self.pos_embed,
+                (H, W),
+                num_prefix_tokens=0 if self.no_embed_class else self.num_prefix_tokens,
+            )
+            x = x.view(B, -1, C)
+        else:
+            pos_embed = self.pos_embed
+
+        to_cat = []
+        if self.cls_token is not None:
+            to_cat.append(self.cls_token.expand(x.shape[0], -1, -1))
+        if self.reg_token is not None:
+            to_cat.append(self.reg_token.expand(x.shape[0], -1, -1))
+
+        if self.no_embed_class:
+            # deit-3, updated JAX (big vision)
+            # position embedding does not overlap with class token, add then concat
+            x = x + pos_embed
+            if to_cat:
+                x = torch.cat(to_cat + [x], dim=1)
+        else:
+            # original timm, JAX, and deit vit impl
+            # pos_embed has entry for class token, concat then add
+            if to_cat:
+                x = torch.cat(to_cat + [x], dim=1)
+            x = x + pos_embed
+
+        return self.pos_drop(x)
+
+    def _intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,
+    ) -> List[torch.Tensor]:
+        outputs, num_blocks = [], len(self.blocks)
+        take_indices = set(
+            range(num_blocks - n, num_blocks) if isinstance(n, int) else n
+        )
+
+        # forward pass
+        x = self.patch_embed(x)
+        x = self._pos_embed(x)
+        x = self.patch_drop(x)
+        x = self.norm_pre(x)
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in take_indices:
+                outputs.append(x)
+
+        return outputs
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,
+        reshape: bool = False,
+        return_prefix_tokens: bool = False,
+        norm: bool = False,
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        """Intermediate layer accessor (NOTE: This is a WIP experiment).
+        Inspired by DINO / DINOv2 interface
+        """
+        # take last n blocks if n is an int, if in is a sequence, select by matching indices
+        outputs = self._intermediate_layers(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        prefix_tokens = [out[:, 0 : self.num_prefix_tokens] for out in outputs]
+        outputs = [out[:, self.num_prefix_tokens :] for out in outputs]
+
+        if reshape:
+            grid_size = self.patch_embed.grid_size
+            outputs = [
+                out.reshape(x.shape[0], grid_size[0], grid_size[1], -1)
+                .permute(0, 3, 1, 2)
+                .contiguous()
+                for out in outputs
+            ]
+
+        if return_prefix_tokens:
+            return tuple(zip(outputs, prefix_tokens))
+        return tuple(outputs)
+
+    def forward_features_list(self, x_list):
+        x_all = []
+        image_sizes = []
+        for x in x_list:
+            bs, _, h, w = x.shape
+
+            # fix patch size=14 in datasets 
+            pad_h = (self.patch_embed.patch_size[0] - h % self.patch_embed.patch_size[0]) % self.patch_embed.patch_size[0]
+            pad_w = (self.patch_embed.patch_size[1] - w % self.patch_embed.patch_size[1]) % self.patch_embed.patch_size[1]
+            x = F.pad(x, (0, pad_w, 0, pad_h))
+
+            bs, _, h, w = x.shape
+
+            h = h // self.patch_embed.patch_size[0]
+            w = w // self.patch_embed.patch_size[1]
+
+            x = self.patch_embed(x)
+            x = x + self.rescale_positional_embedding(out_size=(h, w))
+            x = self.patch_drop(x)
+            x = self.norm_pre(x)
+            x_all.append(x)
+            image_sizes.append((h, w))
+
+        slen = [xi.size(1) for xi in x_all]
+        x = torch.cat(x_all, dim=1)
+
+        cu_indices = [0, ]
+        for i in slen:
+            cu_indices.append(cu_indices[-1] + i)
+
+        cu_slens = torch.tensor(cu_indices, dtype=torch.int32).to(x.device)
+        for idx, blk in enumerate(self.blocks):
+            if self.grad_checkpointing and not torch.jit.is_scripting():
+                x = checkpoint(blk, x, cu_slens, use_reentrant=True)
+            else:
+                x = blk(x, cu_slens=cu_slens)
+        feats = x.split(slen, dim=1) #[(1, slen, c)]
+        return feats, image_sizes
+
+    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
+        bs, _, h, w = x.shape
+        h = h // self.patch_embed.patch_size[0]
+        w = w // self.patch_embed.patch_size[1]
+        
+        x = self.patch_embed(x)
+        # x = self._pos_embed(x)
+        x = x + self.rescale_positional_embedding(out_size=(h, w))
+        x = self.patch_drop(x)
+        x = self.norm_pre(x)
+        if self.grad_checkpointing and not torch.jit.is_scripting():
+            x = checkpoint_seq(self.blocks, x)
+        else:
+            x = self.blocks(x)
+        return x, (h, w)
+
+    def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
+        x = self.norm(x)
+        if self.attn_pool is not None:
+            x = self.attn_pool(x)
+        elif self.global_pool == "avg":
+            x = x[:, self.num_prefix_tokens :].mean(dim=1)
+        elif self.global_pool:
+            x = x[:, 0]  # class token
+        x = self.fc_norm(x)
+        x = self.head_drop(x)
+        return x if pre_logits else self.head(x)
+
+    def forward(self, x, cal_attn_pool=False):
+        if type(x) is list:
+            x, image_sizes = self.forward_features_list(x)
+            return x, image_sizes, None
+        else:
+            x, image_sizes = self.forward_features(x)
+            return x, image_sizes, None
+
+@dataclass
+class SigLIPVisionCfg:
+    width: int = 1152
+    layers: Union[Tuple[int, int, int, int], int] = 27
+    heads: int = 16
+    patch_size: int = 14
+    image_size: Union[Tuple[int, int], int] = 336
+    global_pool: str = "map"
+    mlp_ratio: float = 3.7362
+    class_token: bool = False
+    num_classes: int = 0
+    use_checkpoint: bool = False
+
+
+SigLIP_MODEL_CONFIG = {
+    "siglip_so400m_patch16_384": {
+        "image_size": 384,
+        "patch_size": 16,
+        "width": 1152,
+        "layers": 27,
+        "heads": 16,
+        "mlp_ratio": 3.7362,
+        "global_pool": "map",
+        "use_checkpoint": False,
+    },
+    "siglip2_giant_patch16_384":{
+        "image_size": 384,
+        "patch_size": 16,
+        "width": 1536,
+        "layers": 40,
+        "heads": 16,
+        "mlp_ratio": 4,
+        "global_pool": "map",
+        "use_checkpoint": False,
+    },
+}
+
+
+def resize_evaclip_pos_embed(model: VisionTransformer, interpolation: str = 'bicubic'):
+    # interpolate position embedding
+    orig_size = 24
+    new_size = 128
+    pos_tokens = model.pos_embed
+    pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, model.embed_dim).permute(0, 3, 1, 2)
+    pos_tokens = torch.nn.functional.interpolate(
+        pos_tokens, size=(new_size, new_size), mode=interpolation, align_corners=False)
+    pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+    model.pos_embed = nn.Parameter(pos_tokens, requires_grad=True)
+    return model 
+
+
+def create_siglip_vit(
+    model_name: str = "siglip_so400m_patch14_384",
+    select_layer: int = -1,
+    path: str = "",
+    gradient_checkpointing: bool = False,
+    **kwargs,
+):
+    vision_cfg = SigLIPVisionCfg(**SigLIP_MODEL_CONFIG[model_name])
+
+    if select_layer <= 0:
+        layers = min(vision_cfg.layers, vision_cfg.layers + select_layer + 1)
+    else:
+        layers = min(vision_cfg.layers, select_layer)
+
+    model = VisionTransformer(
+        img_size=2048,
+        patch_size=16,
+        embed_dim=vision_cfg.width,
+        depth=layers,
+        num_heads=vision_cfg.heads,
+        mlp_ratio=vision_cfg.mlp_ratio,
+        class_token=vision_cfg.class_token,
+        global_pool=vision_cfg.global_pool,
+        dynamic_img_pad=False,
+        strict_img_size=False,
+        ignore_head=kwargs.get("ignore_head", False),
+        weight_init=kwargs.get("weight_init", "skip"),
+        num_classes=0
+    )
+    model.config = vision_cfg
+    state_dict = torch.load(path, map_location="cpu")
+    model.load_state_dict(state_dict, strict=False)
+
+    if gradient_checkpointing:
+        model.set_grad_checkpointing(True)
+    return model

modeling_xomni.py

@@ -0,0 +1,315 @@
+import os
+from types import SimpleNamespace
+from typing import Tuple, List, Optional, Union
+
+import torch
+import torch.nn as nn
+
+from huggingface_hub import hf_hub_download
+from transformers import Qwen2ForCausalLM, AutoModel, AutoModelForCausalLM
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm, Qwen2RotaryEmbedding, Qwen2DecoderLayer, Qwen2Model, Qwen2PreTrainedModel
+
+from .configuration_xomni import XOmniConfig
+from .modeling_siglip_tokenizer import create_anyres_preprocess, SiglipTokenizer
+from .modeling_siglip_flux import FluxTransformer2DModelWithSigLIP, FluxPipelineWithSigLIP
+from .modeling_vit import create_siglip_vit
+
+
+class XOmniDecoderLayer(Qwen2DecoderLayer):
+    def __init__(self, config: XOmniConfig, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.layer_idx = layer_idx
+        self.is_lm_layer = config.num_mm_adap_layers <= layer_idx < config.num_hidden_layers - config.num_mm_head_layers
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        hidden_states, multimodal_mask = torch.split(hidden_states, hidden_states.shape[-1] // 2, dim=-1)
+        if self.is_lm_layer:
+            output_hidden_states, *others = super().forward(hidden_states, **kwargs)
+            output_hidden_states = torch.cat([output_hidden_states, multimodal_mask], dim=-1)
+            return output_hidden_states, *others
+        
+        # mm_hidden_states = torch.where(multimodal_mask.bool(), hidden_states, torch.zeros_like(hidden_states))
+        output_hidden_states, *others = super().forward(hidden_states, **kwargs)
+        output_hidden_states = torch.where(multimodal_mask.bool(), output_hidden_states, hidden_states)
+        output_hidden_states = torch.cat([output_hidden_states, multimodal_mask], dim=-1)
+        return output_hidden_states, *others
+
+
+class XOmniModel(Qwen2Model, Qwen2PreTrainedModel):
+    model_type = "x-omni"
+    config_class = XOmniConfig
+
+    def __init__(self, config: XOmniConfig):
+        Qwen2PreTrainedModel.__init__(self, config)
+        self.padding_idx = -1
+        self.vocab_size = config.vocab_size
+        
+        self.lm_embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.mm_embed_tokens = nn.Embedding(config.mm_vocab_size, config.hidden_size, self.padding_idx)
+
+        self.layers = nn.ModuleList(
+            [XOmniDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.lm_norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.mm_norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Qwen2RotaryEmbedding(config=config)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.lm_embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.lm_embed_tokens = value
+
+    def embed_tokens(self, input_ids):
+        (B, L), C = input_ids.shape, self.config.hidden_size
+        multimodal_mask = input_ids >= self.config.vocab_size
+        lm_input_ids = input_ids[~multimodal_mask][None, :]
+        mm_input_ids = input_ids[multimodal_mask][None, :] - self.config.vocab_size
+        lm_embeds = self.lm_embed_tokens(lm_input_ids)
+        mm_embeds = self.mm_embed_tokens(mm_input_ids)
+
+        inputs_embeds = lm_embeds.new_empty((B, L, C))
+        multimodal_mask = multimodal_mask[:, :, None].expand_as(inputs_embeds)
+        inputs_embeds[~multimodal_mask] = lm_embeds.reshape(-1)
+        inputs_embeds[multimodal_mask] = mm_embeds.reshape(-1)
+
+        inputs_embeds = torch.cat([inputs_embeds, multimodal_mask.to(inputs_embeds.dtype)], dim=-1)
+        return inputs_embeds
+
+    def norm(self, hidden_states):
+        hidden_states, multimodal_mask = torch.split(hidden_states, hidden_states.shape[-1] // 2, dim=-1)
+        return torch.where(multimodal_mask.bool(), self.mm_norm(hidden_states), self.lm_norm(hidden_states))
+
+
+class XOmniForCausalLM(Qwen2ForCausalLM):
+    model_type = "x-omni"
+    config_class = XOmniConfig
+    
+    _keys_to_ignore_on_load_missing = r'image_tokenizer\.*'
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = XOmniModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.mm_head = nn.Linear(config.hidden_size, config.mm_vocab_size, bias=False)
+        
+        self.generation_mode = 'text'
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @property
+    def device(self):
+        return next(iter(self.parameters())).device
+    
+    def init_vision(self, flux_pipe_path):
+        self.som_token = self.config.mm_special_tokens[0]
+        self.eom_token = self.config.mm_special_tokens[1]
+        self.img_token = self.config.mm_special_tokens[2]
+
+        self.vision_config = SimpleNamespace(**self.config.vision_config)
+        self.transform_config = SimpleNamespace(**self.vision_config.transform)
+        self.encoder_config = SimpleNamespace(**self.vision_config.encoder)
+        self.decoder_config = SimpleNamespace(**self.vision_config.decoder)
+
+        dtype_map = {'float32': torch.float32, 'float16': torch.float16, 'bfloat16': torch.bfloat16}
+        self.vision_dtype = dtype_map[self.vision_config.dtype]
+
+        self.image_transform = create_anyres_preprocess(**self.vision_config.transform)
+        
+        self.encoder_config.siglip_path = os.path.join(self.name_or_path, self.encoder_config.siglip_path) if os.path.isdir(self.name_or_path) else hf_hub_download(repo_id=self.name_or_path, filename=self.encoder_config.siglip_path)
+        self.encoder_config.projector_path = os.path.join(self.name_or_path, self.encoder_config.projector_path) if os.path.isdir(self.name_or_path) else hf_hub_download(repo_id=self.name_or_path, filename=self.encoder_config.projector_path)
+
+        self.image_tokenizer = SiglipTokenizer(**vars(self.encoder_config))
+        self.image_tokenizer.to(self.device, self.vision_dtype)
+
+        self.decoder_pipe = FluxPipelineWithSigLIP.from_pretrained(
+            flux_pipe_path, 
+            torch_dtype=self.vision_dtype,
+        )
+        self.decoder_pipe.transformer = FluxTransformer2DModelWithSigLIP.from_pretrained(
+            self.name_or_path, 
+            siglip_channels=self.encoder_config.z_channels, 
+            torch_dtype=self.vision_dtype,
+            subfolder=self.decoder_config.model_path,
+        )
+
+        self.decoder_pipe.set_progress_bar_config(disable=True)
+        self.decoder_pipe.to(self.device)
+
+    def set_generation_mode(self, mode):
+        assert mode in ('text', 'image'), f'Invalid generation mode: {mode}'
+        self.generation_mode = mode
+
+    def mmencode(self, tokenizer, texts=None, images=None, **kwargs):
+        texts = texts or []
+        images = images or []
+        doc = ''
+        while len(texts) > 0 or len(images) > 0:
+            if len(texts) > 0:
+                doc += texts.pop(0)
+            if len(images) > 0:
+                doc += self.tokenize_image(images.pop(0))
+        return tokenizer.encode(doc, **kwargs)
+    
+    def mmdecode(self, tokenizer, token_ids, force_text=None, **kwargs):
+        force_text = force_text or []
+        if isinstance(token_ids, torch.Tensor):
+            if len(token_ids.shape) == 2:
+                assert token_ids.shape[0] == 1
+                token_ids = token_ids[0]
+            assert len(token_ids.shape) == 1
+        else:
+            if not isinstance(token_ids[0], int):
+                assert len(token_ids) == 1
+                token_ids = token_ids[0]
+            assert isinstance(token_ids[0], int)
+        
+        doc = tokenizer.decode(token_ids, **kwargs)
+        doc = doc.replace(tokenizer.pad_token, '')
+        doc = doc.replace('<SEP>', '')
+        texts, images = [], []
+        text_image_chunks = doc.split(self.eom_token)
+        for chunk in text_image_chunks:
+            text, image_str = chunk.split(self.som_token) \
+                if self.som_token in chunk else (chunk, '')
+            texts.append(text)
+            if self.img_token in image_str:
+                image_meta, token_str = image_str.split(self.img_token)
+                H, W = tuple(map(int, image_meta.split(' ')))
+                token_ids = list(map(
+                    lambda x: int(x.split('>')[0]),
+                    token_str.split('<MM-Token-')[1:H*W+1],
+                ))
+                if len(force_text) > 0:
+                    image = self.detokenize_image([force_text.pop(0)], images, token_ids, (H, W))
+                else:
+                    image = self.detokenize_image(texts, images, token_ids, (H, W))
+                images.append(image)
+        return texts, images
+    
+    @torch.no_grad()
+    def tokenize_image(self, image):
+        assert hasattr(self, 'image_tokenizer'), 'Please call "init_vision" before that.'
+
+        image_str = self.som_token
+        image = self.image_transform(image)
+        assert image is not None, f'Unsupported image aspect ratio (max {self.transform_config.max_aspect_ratio}) or image resolution is too low (min {self.transform_config.min_short_size})'
+
+        image = image[None, ...].to(self.device, self.vision_dtype)
+        tokens = self.image_tokenizer.encode(image)
+        B, H, W = tokens.shape
+        tokens = tokens.view(B, -1).cpu().tolist()[0]
+        token_str = ''.join(map(lambda x: '<MM-Token-{token_id}>'.format(token_id=x), tokens))
+        image_str = f'{self.som_token}{H} {W}{self.img_token}{token_str}{self.eom_token}'
+        return image_str
+    
+    @torch.no_grad()
+    def detokenize_image(self, texts, images, token_ids, shape):
+        assert hasattr(self, 'image_tokenizer'), 'Please call "init_vision" before that.'
+        assert len(texts) == 1 and len(images) == 0, 'Only support one image per sample.'
+        H, W = shape
+        tokens = torch.tensor(token_ids, device=self.device, dtype=torch.long)
+        latents = self.image_tokenizer.decode(tokens, (1, H, W, self.encoder_config.codebook_dim))
+        upscale_factor = self.decoder_config.upscale_factor
+        latents = latents.reshape(*latents.shape[:2], -1).transpose(1, 2).contiguous()
+        image = self.decoder_pipe(
+            latents,
+            [texts[0]],
+            negative_prompt=[''],
+            height=H * upscale_factor, width=W * upscale_factor,
+            num_inference_steps=self.decoder_config.num_inference_steps, 
+            guidance_scale=1.0,
+            true_cfg_scale=self.decoder_config.cfg_scale,
+            true_cfg_scale_2=self.decoder_config.cfg_scale_2,
+        ).images[0]
+
+
+        return image
+    
+    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,
+        labels: Optional[torch.LongTensor] = 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,
+        num_logits_to_keep: int = 0,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        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
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        hidden_states = hidden_states[:, -num_logits_to_keep:, :]
+        logits = hidden_states.new_full(
+            (*hidden_states.shape[:-1], self.config.vocab_size + self.config.mm_vocab_size), 
+            torch.finfo(hidden_states.dtype).min
+        )
+        if self.generation_mode == 'text':
+            logits[:, :, :self.config.vocab_size] = self.lm_head(hidden_states)
+        else:
+            logits[:, :, self.config.vocab_size:self.config.vocab_size + self.config.image_vocab_size] = self.mm_head(hidden_states)[:, :, :self.config.image_vocab_size]
+        
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+AutoModel.register(XOmniConfig, XOmniModel)
+AutoModelForCausalLM.register(XOmniConfig, XOmniForCausalLM)

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f0f6cd9880d5d8ffdd4761e68e770a51845e4021966bc93d1558c67d83e55fe8
+size 14625209

vit/siglip_vq.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:225aa2d41094ee6c83d2d184d2b6cc9cdadb4f72c1a94501f916cffd42d3b567
+size 249612138

vit/vit_g.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:250be9ba1a52a5b1365cfd79276cbf022df63577eed5d9cc234f8603d06ef626
+size 2376032176