init

docs/source/en/api/models/glm_image_transformer2d.md

@@ -0,0 +1,18 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# GlmImageDecoderTransformer2DModel
+
+A Diffusion Transformer model for 2D data from [GlmImageDecoderTransformer2DModel]()
+
+## GlmImageDecoderTransformer2DModel
+
+[[autodoc]] GlmImageDecoderTransformer2DModel

docs/source/en/api/pipelines/glm_image.md

@@ -0,0 +1,31 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+-->
+
+# GLM-Image
+
+> [!TIP]
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+
+This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/zai-org). The original weights can be found under [hf.co/zai-org](https://huggingface.co/zai-org).
+
+## GlmImageDecoderPipeline
+
+[[autodoc]] GlmImageDecoderPipeline
+  - all
+  - __call__
+
+## GlmImageDecoderPipelineOutput
+
+[[autodoc]] pipelines.cogview4.pipeline_output.GlmImageDecoderPipelineOutput

src/diffusers/models/__init__.py

@@ -96,6 +96,7 @@ if is_torch_available():
     _import_structure["transformers.transformer_easyanimate"] = ["EasyAnimateTransformer3DModel"]
     _import_structure["transformers.transformer_flux"] = ["FluxTransformer2DModel"]
     _import_structure["transformers.transformer_flux2"] = ["Flux2Transformer2DModel"]
+    _import_structure["transformers.transformer_glm_image"] = ["GlmImageDecoderTransformer2DModel"]
     _import_structure["transformers.transformer_hidream_image"] = ["HiDreamImageTransformer2DModel"]
     _import_structure["transformers.transformer_hunyuan_video"] = ["HunyuanVideoTransformer3DModel"]
     _import_structure["transformers.transformer_hunyuan_video15"] = ["HunyuanVideo15Transformer3DModel"]
@@ -203,6 +204,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             EasyAnimateTransformer3DModel,
             Flux2Transformer2DModel,
             FluxTransformer2DModel,
+            GlmImageDecoderTransformer2DModel,
             HiDreamImageTransformer2DModel,
             HunyuanDiT2DModel,
             HunyuanImageTransformer2DModel,

src/diffusers/models/embeddings.py

@@ -1658,6 +1658,37 @@ class CogView3CombinedTimestepSizeEmbeddings(nn.Module):
         return conditioning
 
 
+class GlmImageDecoderCombinedTimestepSizeEmbeddings(nn.Module):
+    def __init__(self, embedding_dim: int, condition_dim: int, pooled_projection_dim: int, timesteps_dim: int = 256):
+        super().__init__()
+
+        self.time_proj = Timesteps(num_channels=timesteps_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.condition_proj = Timesteps(num_channels=condition_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=timesteps_dim, time_embed_dim=embedding_dim)
+        self.condition_embedder = PixArtAlphaTextProjection(pooled_projection_dim, embedding_dim, act_fn="silu")
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        target_size: torch.Tensor,
+        crop_coords: torch.Tensor,
+        hidden_dtype: torch.dtype,
+    ) -> torch.Tensor:
+        timesteps_proj = self.time_proj(timestep)
+
+        crop_coords_proj = self.condition_proj(crop_coords.flatten()).view(crop_coords.size(0), -1)
+        target_size_proj = self.condition_proj(target_size.flatten()).view(target_size.size(0), -1)
+
+        # (B, 2 * condition_dim)
+        condition_proj = torch.cat([crop_coords_proj, target_size_proj], dim=1)
+
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (B, embedding_dim)
+        condition_emb = self.condition_embedder(condition_proj.to(dtype=hidden_dtype))  # (B, embedding_dim)
+
+        conditioning = timesteps_emb + condition_emb
+        return conditioning
+
+
 class HunyuanDiTAttentionPool(nn.Module):
     # Copied from https://github.com/Tencent/HunyuanDiT/blob/cb709308d92e6c7e8d59d0dff41b74d35088db6a/hydit/modules/poolers.py#L6
 

src/diffusers/models/transformers/__init__.py

@@ -27,6 +27,7 @@ if is_torch_available():
     from .transformer_easyanimate import EasyAnimateTransformer3DModel
     from .transformer_flux import FluxTransformer2DModel
     from .transformer_flux2 import Flux2Transformer2DModel
+    from .transformer_glm_image import GlmImageDecoderTransformer2DModel
     from .transformer_hidream_image import HiDreamImageTransformer2DModel
     from .transformer_hunyuan_video import HunyuanVideoTransformer3DModel
     from .transformer_hunyuan_video15 import HunyuanVideo15Transformer3DModel

src/diffusers/models/transformers/transformer_glm_image.py

@@ -0,0 +1,753 @@
+# Copyright 2025 The CogView team, Tsinghua University & ZhipuAI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from enum import Enum
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import PeftAdapterMixin
+from ...utils import logging
+from ...utils.torch_utils import maybe_allow_in_graph
+from ..attention import FeedForward
+from ..attention_processor import Attention
+from ..cache_utils import CacheMixin
+from ..embeddings import GlmImageDecoderCombinedTimestepSizeEmbeddings
+from ..modeling_outputs import Transformer2DModelOutput
+from ..modeling_utils import ModelMixin
+from ..normalization import LayerNorm, RMSNorm
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class GlmImageDecoderImageProjector(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 16,
+        hidden_size: int = 2560,
+        patch_size: int = 2,
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+
+        self.proj = nn.Linear(in_channels * patch_size**2, hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, channel, height, width = hidden_states.shape
+        post_patch_height = height // self.patch_size
+        post_patch_width = width // self.patch_size
+
+        hidden_states = hidden_states.reshape(
+            batch_size, channel, post_patch_height, self.patch_size, post_patch_width, self.patch_size
+        )
+        hidden_states = hidden_states.permute(0, 2, 4, 1, 3, 5).flatten(3, 5).flatten(1, 2)
+        hidden_states = self.proj(hidden_states)
+
+        return hidden_states
+
+
+class GlmImageDecoderAdaLayerNormZero(nn.Module):
+    def __init__(self, embedding_dim: int, dim: int) -> None:
+        super().__init__()
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-5)
+        self.norm_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-5)
+        self.linear = nn.Linear(embedding_dim, 12 * dim, bias=True)
+
+    def forward(
+        self, hidden_states: torch.Tensor, glyph_hidden_states: torch.Tensor, temb: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        dtype = hidden_states.dtype
+        norm_hidden_states = self.norm(hidden_states).to(dtype=dtype)
+        norm_glyph_hidden_states = self.norm_context(glyph_hidden_states).to(dtype=dtype)
+
+        emb = self.linear(temb)
+        (
+            shift_msa,
+            c_shift_msa,
+            scale_msa,
+            c_scale_msa,
+            gate_msa,
+            c_gate_msa,
+            shift_mlp,
+            c_shift_mlp,
+            scale_mlp,
+            c_scale_mlp,
+            gate_mlp,
+            c_gate_mlp,
+        ) = emb.chunk(12, dim=1)
+
+        hidden_states = norm_hidden_states * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1)
+        glyph_hidden_states = norm_glyph_hidden_states * (1 + c_scale_msa.unsqueeze(1)) + c_shift_msa.unsqueeze(1)
+
+        return (
+            hidden_states,
+            gate_msa,
+            shift_mlp,
+            scale_mlp,
+            gate_mlp,
+            glyph_hidden_states,
+            c_gate_msa,
+            c_shift_mlp,
+            c_scale_mlp,
+            c_gate_mlp,
+        )
+
+
+class GlmImageDecoderAttenProcessorState(Enum):
+    ImageGen = "ImageGen"
+    ImageEditWriteKV = "ImageEditWriteKV"
+    ImageEditReadKV = "ImageEditReadKV"
+    ImageEditDontReadKV = "ImageEditNoReadKV"
+
+
+class GlmImageDecoderAttnProcessor:
+    """
+    Processor for implementing scaled dot-product attention for the GlmImageDecoder model. It applies a rotary
+    embedding on query and key vectors, but does not include spatial normalization.
+
+    The processor supports passing an attention mask for text tokens. The attention mask should have shape (batch_size,
+    text_seq_length) where 1 indicates a non-padded token and 0 indicates a padded token.
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "GlmImageDecoderAttnProcessor requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0."
+            )
+        self.processor_state = GlmImageDecoderAttenProcessorState.ImageGen
+        self.k_cache = None
+        self.v_cache = None
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        dtype = encoder_hidden_states.dtype
+
+        batch_size, text_seq_length, embed_dim = encoder_hidden_states.shape
+        batch_size, image_seq_length, embed_dim = hidden_states.shape
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        # 1. QKV projections
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
+        key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
+        value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)
+
+        # 2. QK normalization
+        if attn.norm_q is not None:
+            query = attn.norm_q(query).to(dtype=dtype)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key).to(dtype=dtype)
+
+        # 3. Rotational positional embeddings applied to latent stream
+        if image_rotary_emb is not None:
+            from ..embeddings import apply_rotary_emb
+
+            query[:, :, text_seq_length:, :] = apply_rotary_emb(
+                query[:, :, text_seq_length:, :], image_rotary_emb, use_real_unbind_dim=-2
+            )
+            key[:, :, text_seq_length:, :] = apply_rotary_emb(
+                key[:, :, text_seq_length:, :], image_rotary_emb, use_real_unbind_dim=-2
+            )
+
+        if self.processor_state == GlmImageDecoderAttenProcessorState.ImageEditWriteKV:
+            self.k_cache = key if self.k_cache is None else torch.cat([self.k_cache, key], dim=2)
+            self.v_cache = value if self.v_cache is None else torch.cat([self.v_cache, value], dim=2)
+        elif self.processor_state == GlmImageDecoderAttenProcessorState.ImageEditReadKV:
+            key = torch.cat([self.k_cache, key], dim=2) if self.k_cache is not None else key
+            value = torch.cat([self.v_cache, value], dim=2) if self.v_cache is not None else value
+
+        # 4. Attention
+        if attention_mask is not None:
+            text_attn_mask = attention_mask
+            assert text_attn_mask.dim() == 2, "the shape of text_attn_mask should be (batch_size, text_seq_length)"
+            text_attn_mask = text_attn_mask.float().to(query.device)
+            mix_attn_mask = torch.ones((batch_size, text_seq_length + image_seq_length), device=query.device)
+            mix_attn_mask[:, :text_seq_length] = text_attn_mask
+            mix_attn_mask = mix_attn_mask.unsqueeze(2)
+            attn_mask_matrix = mix_attn_mask @ mix_attn_mask.transpose(1, 2)
+            attention_mask = (attn_mask_matrix > 0).unsqueeze(1).to(query.dtype)
+
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+        hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
+        hidden_states = hidden_states.type_as(query)
+
+        # 5. Output projection
+        hidden_states = attn.to_out[0](hidden_states)
+        hidden_states = attn.to_out[1](hidden_states)
+
+        encoder_hidden_states, hidden_states = hidden_states.split(
+            [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
+        )
+        return hidden_states, encoder_hidden_states
+
+
+@maybe_allow_in_graph
+class GlmImageDecoderTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int = 2560,
+        num_attention_heads: int = 64,
+        attention_head_dim: int = 40,
+        time_embed_dim: int = 512,
+    ) -> None:
+        super().__init__()
+
+        # 1. Attention
+        self.norm1 = GlmImageDecoderAdaLayerNormZero(time_embed_dim, dim)
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            out_dim=dim,
+            bias=True,
+            qk_norm="layer_norm",
+            elementwise_affine=False,
+            eps=1e-5,
+            processor=GlmImageDecoderAttnProcessor(),
+        )
+
+        # 2. Feedforward
+        self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-5)
+        self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-5)
+        self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        glyph_hidden_states: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[
+            Union[Tuple[torch.Tensor, torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]]]
+        ] = None,
+        attention_mask: Optional[Dict[str, torch.Tensor]] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # 1. Timestep conditioning
+        (
+            norm_hidden_states,
+            gate_msa,
+            shift_mlp,
+            scale_mlp,
+            gate_mlp,
+            norm_glyph_hidden_states,
+            c_gate_msa,
+            c_shift_mlp,
+            c_scale_mlp,
+            c_gate_mlp,
+        ) = self.norm1(hidden_states, glyph_hidden_states, temb)
+
+        # 2. Attention
+        if attention_kwargs is None:
+            attention_kwargs = {}
+
+        attn_hidden_states, attn_glyph_hidden_states = self.attn1(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_glyph_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+            attention_mask=attention_mask,
+            **attention_kwargs,
+        )
+        hidden_states = hidden_states + attn_hidden_states * gate_msa.unsqueeze(1)
+        glyph_hidden_states = glyph_hidden_states + attn_glyph_hidden_states * c_gate_msa.unsqueeze(1)
+
+        # 3. Feedforward
+        norm_hidden_states = self.norm2(hidden_states) * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1)
+        norm_glyph_hidden_states = self.norm2_context(glyph_hidden_states) * (
+            1 + c_scale_mlp.unsqueeze(1)
+        ) + c_shift_mlp.unsqueeze(1)
+
+        ff_output = self.ff(norm_hidden_states)
+        ff_output_context = self.ff(norm_glyph_hidden_states)
+        hidden_states = hidden_states + ff_output * gate_mlp.unsqueeze(1)
+        glyph_hidden_states = glyph_hidden_states + ff_output_context * c_gate_mlp.unsqueeze(1)
+
+        return hidden_states, glyph_hidden_states
+
+
+class GlmImageDecoderRotaryPosEmbed(nn.Module):
+    def __init__(self, dim: int, patch_size: int, theta: float = 10000.0) -> None:
+        super().__init__()
+
+        self.dim = dim
+        self.patch_size = patch_size
+        self.theta = theta
+
+    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        batch_size, num_channels, height, width = hidden_states.shape
+        height, width = height // self.patch_size, width // self.patch_size
+
+        dim_h, dim_w = self.dim // 2, self.dim // 2
+        h_inv_freq = 1.0 / (
+            self.theta ** (torch.arange(0, dim_h, 2, dtype=torch.float32)[: (dim_h // 2)].float() / dim_h)
+        )
+        w_inv_freq = 1.0 / (
+            self.theta ** (torch.arange(0, dim_w, 2, dtype=torch.float32)[: (dim_w // 2)].float() / dim_w)
+        )
+        h_seq = torch.arange(height)
+        w_seq = torch.arange(width)
+        freqs_h = torch.outer(h_seq, h_inv_freq)
+        freqs_w = torch.outer(w_seq, w_inv_freq)
+
+        # Create position matrices for height and width
+        # [height, 1, dim//4] and [1, width, dim//4]
+        freqs_h = freqs_h.unsqueeze(1)
+        freqs_w = freqs_w.unsqueeze(0)
+        # Broadcast freqs_h and freqs_w to [height, width, dim//4]
+        freqs_h = freqs_h.expand(height, width, -1)
+        freqs_w = freqs_w.expand(height, width, -1)
+
+        # Concatenate along last dimension to get [height, width, dim//2]
+        freqs = torch.cat([freqs_h, freqs_w], dim=-1)
+        freqs = torch.cat([freqs, freqs], dim=-1)  # [height, width, dim]
+        freqs = freqs.reshape(height * width, -1)
+        return (freqs.cos(), freqs.sin())
+
+
+class GlmImageDecoderAdaLayerNormContinuous(nn.Module):
+    """
+    GlmImageDecoder-only final AdaLN: LN(x) -> Linear(cond) -> chunk -> affine. Matches Megatron: **no activation**
+    before the Linear on conditioning embedding.
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        conditioning_embedding_dim: int,
+        elementwise_affine: bool = True,
+        eps: float = 1e-5,
+        bias: bool = True,
+        norm_type: str = "layer_norm",
+    ):
+        super().__init__()
+        self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)
+        if norm_type == "layer_norm":
+            self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias)
+        elif norm_type == "rms_norm":
+            self.norm = RMSNorm(embedding_dim, eps, elementwise_affine)
+        else:
+            raise ValueError(f"unknown norm_type {norm_type}")
+
+    def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
+        # *** NO SiLU here ***
+        emb = self.linear(conditioning_embedding.to(x.dtype))
+        scale, shift = torch.chunk(emb, 2, dim=1)
+        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
+        return x
+
+
+class GlmImageDecoderTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, CacheMixin):
+    r"""
+    Args:
+        patch_size (`int`, defaults to `2`):
+            The size of the patches to use in the patch embedding layer.
+        in_channels (`int`, defaults to `16`):
+            The number of channels in the input.
+        num_layers (`int`, defaults to `30`):
+            The number of layers of Transformer blocks to use.
+        attention_head_dim (`int`, defaults to `40`):
+            The number of channels in each head.
+        num_attention_heads (`int`, defaults to `64`):
+            The number of heads to use for multi-head attention.
+        out_channels (`int`, defaults to `16`):
+            The number of channels in the output.
+        text_embed_dim (`int`, defaults to `4096`):
+            Input dimension of text embeddings from the text encoder.
+        time_embed_dim (`int`, defaults to `512`):
+            Output dimension of timestep embeddings.
+        condition_dim (`int`, defaults to `256`):
+            The embedding dimension of the input SDXL-style resolution conditions (original_size, target_size,
+            crop_coords).
+        pos_embed_max_size (`int`, defaults to `128`):
+            The maximum resolution of the positional embeddings, from which slices of shape `H x W` are taken and added
+            to input patched latents, where `H` and `W` are the latent height and width respectively. A value of 128
+            means that the maximum supported height and width for image generation is `128 * vae_scale_factor *
+            patch_size => 128 * 8 * 2 => 2048`.
+        sample_size (`int`, defaults to `128`):
+            The base resolution of input latents. If height/width is not provided during generation, this value is used
+            to determine the resolution as `sample_size * vae_scale_factor => 128 * 8 => 1024`
+    """
+
+    _supports_gradient_checkpointing = True
+    _no_split_modules = [
+        "GlmImageDecoderTransformerBlock",
+        "GlmImageDecoderImageProjector",
+        "GlmImageDecoderImageProjector",
+    ]
+    _skip_layerwise_casting_patterns = ["patch_embed", "norm", "proj_out"]
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: int = 2,
+        in_channels: int = 16,
+        out_channels: int = 16,
+        num_layers: int = 30,
+        attention_head_dim: int = 40,
+        num_attention_heads: int = 64,
+        text_embed_dim: int = 4096,
+        glyph_embed_dim: int = 1472,
+        time_embed_dim: int = 512,
+        condition_dim: int = 256,
+        pos_embed_max_size: int = 128,
+        sample_size: int = 128,
+        prior_vq_quantizer_codebook_size: int = 16384,
+    ):
+        super().__init__()
+
+        # GlmImageDecoder uses 2 additional SDXL-like conditions - target_size, crop_coords
+        # Each of these are sincos embeddings of shape 2 * condition_dim
+        pooled_projection_dim = 2 * 2 * condition_dim
+        inner_dim = num_attention_heads * attention_head_dim
+        out_channels = out_channels
+
+        # 1. RoPE
+        self.rope = GlmImageDecoderRotaryPosEmbed(attention_head_dim, patch_size, theta=10000.0)
+
+        # 2. Patch & Text-timestep embedding
+        self.image_projector = GlmImageDecoderImageProjector(in_channels, inner_dim, patch_size)
+        # 这次没有，未来可能有text_projector
+        # self.text_projector = FeedForward(text_embed_dim, inner_dim, activation_fn="gelu")
+        self.glyph_projector = FeedForward(glyph_embed_dim, inner_dim, inner_dim=inner_dim, activation_fn="gelu")
+
+        self.prior_token_embedding = nn.Embedding(prior_vq_quantizer_codebook_size, inner_dim)
+        self.prior_projector = FeedForward(inner_dim, inner_dim, inner_dim=inner_dim, activation_fn="linear-silu")
+
+        self.time_condition_embed = GlmImageDecoderCombinedTimestepSizeEmbeddings(
+            embedding_dim=time_embed_dim,
+            condition_dim=condition_dim,
+            pooled_projection_dim=pooled_projection_dim,
+            timesteps_dim=time_embed_dim,
+        )
+
+        # 3. Transformer blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                GlmImageDecoderTransformerBlock(inner_dim, num_attention_heads, attention_head_dim, time_embed_dim)
+                for _ in range(num_layers)
+            ]
+        )
+
+        # 4. Output projection
+        self.norm_out = GlmImageDecoderAdaLayerNormContinuous(inner_dim, time_embed_dim, elementwise_affine=False)
+        self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels, bias=True)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        glyph_hidden_states: torch.Tensor,
+        prior_token_id: torch.Tensor,
+        prior_token_drop: torch.Tensor,
+        timestep: torch.LongTensor,
+        original_size: torch.Tensor,
+        target_size: torch.Tensor,
+        crop_coords: torch.Tensor,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+        attention_mask: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[
+            Union[Tuple[torch.Tensor, torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]]]
+        ] = None,
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
+        batch_size, num_channels, height, width = hidden_states.shape
+
+        # 1. RoPE
+        if image_rotary_emb is None:
+            image_rotary_emb = self.rope(hidden_states)
+
+        # 2. Patch & Timestep embeddings
+        p = self.config.patch_size
+        post_patch_height = height // p
+        post_patch_width = width // p
+
+        hidden_states = self.image_projector(hidden_states)
+        glyph_hidden_states = self.glyph_projector(glyph_hidden_states)
+        prior_embedding = self.prior_token_embedding(prior_token_id)
+        prior_embedding[prior_token_drop] *= 0.0
+        prior_hidden_states = self.prior_projector(prior_embedding)
+
+        hidden_states = hidden_states + prior_hidden_states
+
+        temb = self.time_condition_embed(timestep, target_size, crop_coords, hidden_states.dtype)
+        temb = F.silu(temb)
+
+        # 3. Transformer blocks
+        for block in self.transformer_blocks:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states, glyph_hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    glyph_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    attention_mask,
+                    attention_kwargs,
+                )
+            else:
+                hidden_states, glyph_hidden_states = block(
+                    hidden_states,
+                    glyph_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    attention_mask,
+                    attention_kwargs,
+                )
+
+        # 4. Output norm & projection
+        hidden_states = self.norm_out(hidden_states, temb)
+        hidden_states = self.proj_out(hidden_states)
+
+        # 5. Unpatchify
+        hidden_states = hidden_states.reshape(batch_size, post_patch_height, post_patch_width, -1, p, p)
+        output = hidden_states.permute(0, 3, 1, 4, 2, 5).flatten(4, 5).flatten(2, 3)
+
+        if not return_dict:
+            return (output,)
+        return Transformer2DModelOutput(sample=output)
+
+    def set_attention_processors_state(self, state: GlmImageDecoderAttenProcessorState):
+        for block in self.transformer_blocks:
+            block.attn1.processor.processor_state = state
+
+    def clear_attention_processors_cache(self):
+        for block in self.transformer_blocks:
+            block.attn1.processor.k_cache = None
+            block.attn1.processor.v_cache = None
+
+    def repeat_attention_processors_cache(self, repeats: int):
+        for block in self.transformer_blocks:
+            if block.attn1.processor.k_cache is None or block.attn1.processor.v_cache is None:
+                continue
+            block.attn1.processor.k_cache = torch.repeat_interleave(block.attn1.processor.k_cache, repeats, dim=2)
+            block.attn1.processor.v_cache = torch.repeat_interleave(block.attn1.processor.v_cache, repeats, dim=2)
+
+
+if __name__ == "__main__":
+
+    def swap_scale_shift(weight, dim):
+        """
+        Swap the scale and shift components in the weight tensor.
+
+        Args:
+            weight (torch.Tensor): The original weight tensor.
+            dim (int): The dimension along which to split.
+
+        Returns:
+            torch.Tensor: The modified weight tensor with scale and shift swapped.
+        """
+        shift, scale = weight.chunk(2, dim=dim)
+        new_weight = torch.cat([scale, shift], dim=dim)
+        return new_weight
+
+    def convert_megatron_transformer_checkpoint_to_diffusers(
+        ckpt_path: str,
+        num_layers: int,
+        num_heads: int,
+        hidden_size: int,
+    ):
+        """
+        Convert a Megatron Transformer checkpoint to Diffusers format.
+
+        Args:
+            ckpt_path (str): Path to the Megatron Transformer checkpoint.
+            num_layers (int): Number of Transformer layers.
+            num_heads (int): Number of attention heads.
+            hidden_size (int): Hidden size of the Transformer.
+
+        Returns:
+            dict: The converted state dictionary compatible with Diffusers.
+        """
+        ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False)
+        mega = ckpt["model"]
+        used_keys = set()
+
+        def get_mega(key):
+            used_keys.add(key)
+            return mega[key]
+
+        new_state_dict = {}
+
+        # Patch Embedding
+        new_state_dict["image_projector.proj.weight"] = get_mega("encoder_expand_linear.weight").reshape(
+            hidden_size, 64
+        )
+        new_state_dict["image_projector.proj.bias"] = get_mega("encoder_expand_linear.bias")
+
+        new_state_dict["glyph_projector.net.0.proj.weight"] = get_mega("glyph_projector.linear_fc1.weight")
+        new_state_dict["glyph_projector.net.0.proj.bias"] = get_mega("glyph_projector.linear_fc1.bias")
+        new_state_dict["glyph_projector.net.2.weight"] = get_mega("glyph_projector.linear_fc2.weight")
+        new_state_dict["glyph_projector.net.2.bias"] = get_mega("glyph_projector.linear_fc2.bias")
+
+        new_state_dict["prior_token_embedding.weight"] = get_mega("xomni_token_id_embedding.weight")
+        new_state_dict["prior_projector.net.0.proj.weight"] = get_mega("prior_condition_embedding.0.weight")
+        new_state_dict["prior_projector.net.0.proj.bias"] = get_mega("prior_condition_embedding.0.bias")
+        new_state_dict["prior_projector.net.2.weight"] = get_mega("prior_condition_embedding.2.weight")
+        new_state_dict["prior_projector.net.2.bias"] = get_mega("prior_condition_embedding.2.bias")
+
+        # Time Condition Embedding
+        new_state_dict["time_condition_embed.timestep_embedder.linear_1.weight"] = get_mega(
+            "time_embedding.time_embed.0.weight"
+        )
+        new_state_dict["time_condition_embed.timestep_embedder.linear_1.bias"] = get_mega(
+            "time_embedding.time_embed.0.bias"
+        )
+        new_state_dict["time_condition_embed.timestep_embedder.linear_2.weight"] = get_mega(
+            "time_embedding.time_embed.2.weight"
+        )
+        new_state_dict["time_condition_embed.timestep_embedder.linear_2.bias"] = get_mega(
+            "time_embedding.time_embed.2.bias"
+        )
+
+        new_state_dict["time_condition_embed.condition_embedder.linear_1.weight"] = get_mega(
+            "label_embedding.label_embed.0.weight"
+        )
+        new_state_dict["time_condition_embed.condition_embedder.linear_1.bias"] = get_mega(
+            "label_embedding.label_embed.0.bias"
+        )
+        new_state_dict["time_condition_embed.condition_embedder.linear_2.weight"] = get_mega(
+            "label_embedding.label_embed.2.weight"
+        )
+        new_state_dict["time_condition_embed.condition_embedder.linear_2.bias"] = get_mega(
+            "label_embedding.label_embed.2.bias"
+        )
+
+        # Convert each Transformer layer
+        from tqdm import tqdm
+
+        for i in tqdm(range(num_layers), desc="Converting layers (Megatron->Diffusers)"):
+            block_prefix = f"transformer_blocks.{i}."
+
+            # AdaLayerNorm
+            new_state_dict[block_prefix + "norm1.linear.weight"] = get_mega(f"decoder.layers.{i}.adaln.weight")
+            new_state_dict[block_prefix + "norm1.linear.bias"] = get_mega(f"decoder.layers.{i}.adaln.bias")
+            qkv_weight = get_mega(f"decoder.layers.{i}.self_attention.linear_qkv.weight")
+            qkv_bias = get_mega(f"decoder.layers.{i}.self_attention.linear_qkv.bias")
+
+            # Reshape to match SAT logic
+            qkv_weight = qkv_weight.view(num_heads, 3, hidden_size // num_heads, hidden_size)
+            qkv_weight = qkv_weight.permute(1, 0, 2, 3).reshape(3 * hidden_size, hidden_size)
+
+            qkv_bias = qkv_bias.view(num_heads, 3, hidden_size // num_heads)
+            qkv_bias = qkv_bias.permute(1, 0, 2).reshape(3 * hidden_size)
+
+            # Assign to Diffusers keys
+            q, k, v = torch.chunk(qkv_weight, 3, dim=0)
+            qb, kb, vb = torch.chunk(qkv_bias, 3, dim=0)
+
+            new_state_dict[block_prefix + "attn1.to_q.weight"] = q
+            new_state_dict[block_prefix + "attn1.to_q.bias"] = qb
+            new_state_dict[block_prefix + "attn1.to_k.weight"] = k
+            new_state_dict[block_prefix + "attn1.to_k.bias"] = kb
+            new_state_dict[block_prefix + "attn1.to_v.weight"] = v
+            new_state_dict[block_prefix + "attn1.to_v.bias"] = vb
+
+            # Attention Output
+            new_state_dict[block_prefix + "attn1.to_out.0.weight"] = get_mega(
+                f"decoder.layers.{i}.self_attention.linear_proj.weight"
+            )
+            new_state_dict[block_prefix + "attn1.to_out.0.bias"] = get_mega(
+                f"decoder.layers.{i}.self_attention.linear_proj.bias"
+            )
+
+            # MLP
+            new_state_dict[block_prefix + "ff.net.0.proj.weight"] = get_mega(
+                f"decoder.layers.{i}.mlp.linear_fc1.weight"
+            )
+            new_state_dict[block_prefix + "ff.net.0.proj.bias"] = get_mega(f"decoder.layers.{i}.mlp.linear_fc1.bias")
+            new_state_dict[block_prefix + "ff.net.2.weight"] = get_mega(f"decoder.layers.{i}.mlp.linear_fc2.weight")
+            new_state_dict[block_prefix + "ff.net.2.bias"] = get_mega(f"decoder.layers.{i}.mlp.linear_fc2.bias")
+
+        # Final Layers
+        new_state_dict["norm_out.linear.weight"] = swap_scale_shift(get_mega("adaln_final.weight"), dim=0)
+        new_state_dict["norm_out.linear.bias"] = swap_scale_shift(get_mega("adaln_final.bias"), dim=0)
+        new_state_dict["proj_out.weight"] = get_mega("output_projector.weight")
+        new_state_dict["proj_out.bias"] = get_mega("output_projector.bias")
+
+        # Check for unused keys
+        all_keys = set(mega.keys())
+        unused_keys = all_keys - used_keys
+        if unused_keys:
+            print(f"\n[WARNING] The following {len(unused_keys)} keys in mega were NOT used:")
+            for key in sorted(unused_keys):
+                print(f"  - {key}")
+            raise ValueError(
+                f"Found {len(unused_keys)} unused keys in Megatron checkpoint. Please update the conversion script to handle these keys."
+            )
+        else:
+            print(f"\n[INFO] All {len(all_keys)} keys in mega were successfully used.")
+
+        return new_state_dict
+
+    transformer = GlmImageDecoderTransformer2DModel(
+        patch_size=2,
+        in_channels=16,
+        num_layers=30,
+        attention_head_dim=128,
+        num_attention_heads=32,
+        out_channels=16,
+        text_embed_dim=4096,
+        time_embed_dim=512,
+        glyph_embed_dim=1472,
+        condition_dim=256,
+        pos_embed_max_size=128,
+    ).to(torch.bfloat16)
+    converted_transformer_state_dict = convert_megatron_transformer_checkpoint_to_diffusers(
+        ckpt_path="/workspace/ckpt/tjy/Glm-train-dev/examples/cogview/ckpts/merge/1+6_0.5+0.5/iter_0000000/mp_rank_00/model_optim_rng.pt",
+        num_layers=30,
+        num_heads=32,
+        hidden_size=4096,
+    )
+    transformer.load_state_dict(converted_transformer_state_dict)
+    transformer.cuda()
+
+    latent = torch.load("/workspace/ckpt/tjy/glm-train-dev/examples/cogview/latent.pt").to(torch.bfloat16)
+    latent = rearrange(latent, "(b h w) (c p q) -> b c (h p) (w q)", b=8, h=72, w=54, p=2, q=2)
+    glyph_hidden_states = torch.load(
+        "/workspace/ckpt/tjy/glm-train-dev/examples/cogview/glyph_condition_embedding.pt"
+    ).to(torch.bfloat16)
+    glyph_hidden_states = rearrange(glyph_hidden_states, "(b n) c -> b n c", b=8, n=2)
+    prior_token_id = torch.load("/workspace/ckpt/tjy/glm-train-dev/examples/cogview/xomni_token_id.pt")
+    prior_token_drop = torch.load("/workspace/ckpt/tjy/glm-train-dev/examples/cogview/xomni_drop.pt")
+    prior_token_id = rearrange(prior_token_id, "(b n) -> b n", b=8)
+    prior_token_drop = rearrange(prior_token_drop, "(b n)-> b n", b=8)
+
+    with torch.no_grad():
+        output = transformer(
+            hidden_states=latent,
+            glyph_hidden_states=glyph_hidden_states,
+            prior_token_id=prior_token_id,
+            prior_token_drop=prior_token_drop,
+            timestep=torch.tensor([999.0] * 8).cuda(),
+            original_size=torch.tensor([[144, 108]] * 8).cuda(),
+            target_size=torch.tensor([[144, 108]] * 8).cuda(),
+            crop_coords=torch.tensor([[0, 0]] * 8).cuda(),
+        )

src/diffusers/pipelines/glm_image/__init__.py

@@ -0,0 +1,47 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_additional_imports = {}
+_import_structure = {"pipeline_output": ["GlmImageDecoderPipelineOutput"]}
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_glm_image"] = ["GlmImageDecoderPipeline"]
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
+    else:
+        from .pipeline_glm_image import GlmImageDecoderPipeline
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)
+    for name, value in _additional_imports.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/glm_image/pipeline_glm_image.py

@@ -0,0 +1,786 @@
+# Copyright 2025 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import re
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL
+import torch
+from transformers import AutoTokenizer, T5EncoderModel
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...image_processor import VaeImageProcessor
+from ...loaders import CogView4LoraLoaderMixin
+from ...models import AutoencoderKL, GlmImageDecoderTransformer2DModel
+from ...models.transformers.transformer_glm_image import GlmImageDecoderAttenProcessorState
+from ...pipelines.pipeline_utils import DiffusionPipeline
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from .pipeline_output import GlmImageDecoderPipelineOutput
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> from diffusers import GlmImageDecoderPipeline
+
+        >>> pipe = GlmImageDecoderPipeline.from_pretrained("THUDM/CogView4-6B", torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+
+        >>> prompt = "A photo of an astronaut riding a horse on mars"
+        >>> image = pipe(prompt).images[0]
+        >>> image.save("output.png")
+        ```
+"""
+
+
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    base_shift: float = 0.25,
+    max_shift: float = 0.75,
+) -> float:
+    m = (image_seq_len / base_seq_len) ** 0.5
+    mu = m * max_shift + base_shift
+    return mu
+
+
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+    accepts_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+
+    if timesteps is not None and sigmas is not None:
+        if not accepts_timesteps and not accepts_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep or sigma schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif timesteps is not None and sigmas is None:
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif timesteps is None and sigmas is not None:
+        if not accepts_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+class GlmImageDecoderPipeline(DiffusionPipeline, CogView4LoraLoaderMixin):
+    r"""
+    Pipeline for text-to-image generation using CogView4.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`GlmModel`]):
+            Frozen text-encoder. CogView4 uses [Glm-4-9b-hf](https://huggingface.co/THUDM/Glm-4-9b-hf).
+        tokenizer (`PreTrainedTokenizer`):
+            Tokenizer of class
+            [PreTrainedTokenizer](https://huggingface.co/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer).
+        transformer ([`CogView4Transformer2DModel`]):
+            A text conditioned `CogView4Transformer2DModel` to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+    """
+
+    _optional_components = []
+    model_cpu_offload_seq = "transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+    def __init__(
+        self,
+        tokenizer: AutoTokenizer,
+        glyph_encoder: T5EncoderModel,
+        vae: AutoencoderKL,
+        transformer: GlmImageDecoderTransformer2DModel,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            tokenizer=tokenizer, glyph_encoder=glyph_encoder, vae=vae, transformer=transformer, scheduler=scheduler
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+        self.default_sample_size = (
+            self.transformer.config.sample_size
+            if hasattr(self, "transformer")
+            and self.transformer is not None
+            and hasattr(self.transformer.config, "sample_size")
+            else 128
+        )
+
+    def get_glyph_texts(
+        self,
+        prompt,
+    ):
+        prompt = prompt[0] if isinstance(prompt, list) else prompt
+        ocr_texts = (
+            re.findall(r"'([^']*)'", prompt)
+            + re.findall(r"“([^“”]*)”", prompt)
+            + re.findall(r'"([^"]*)"', prompt)
+            + re.findall(r"「([^「」]*)」", prompt)
+        )
+        return ocr_texts
+
+    def _get_glyph_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        max_sequence_length: int = 2048,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.glyph_encoder.dtype
+
+        glyph_texts = self.get_glyph_texts(prompt)
+        input_ids = self.tokenizer(
+            glyph_texts if len(glyph_texts) > 0 else [""],
+            max_length=max_sequence_length,
+            truncation=True,
+        ).input_ids
+        input_ids = [
+            [self.tokenizer.pad_token_id] * ((len(input_ids) + 1) % 2) + input_ids_ for input_ids_ in input_ids
+        ]
+        max_length = max(len(input_ids_) for input_ids_ in input_ids)
+        attention_mask = torch.tensor(
+            [[1] * len(input_ids_) + [0] * (max_length - len(input_ids_)) for input_ids_ in input_ids], device=device
+        )
+        input_ids = torch.tensor(
+            [input_ids_ + [self.tokenizer.pad_token_id] * (max_length - len(input_ids_)) for input_ids_ in input_ids],
+            device=device,
+        )
+        outputs = self.glyph_encoder(input_ids, attention_mask=attention_mask)
+        glyph_embeds = outputs.last_hidden_state[attention_mask.bool()].unsqueeze(0)
+
+        return glyph_embeds.to(device=device, dtype=dtype)
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        do_classifier_free_guidance: bool = True,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        max_sequence_length: int = 2048,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            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 `guidance_scale` is
+                less than `1`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                Whether to use classifier free guidance or not.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                Number of images that should be generated per prompt. torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *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.
+            negative_prompt_embeds (`torch.Tensor`, *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.
+            device: (`torch.device`, *optional*):
+                torch device
+            dtype: (`torch.dtype`, *optional*):
+                torch dtype
+            max_sequence_length (`int`, defaults to `2048`):
+                Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results.
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds = self._get_glyph_embeds(prompt, max_sequence_length, device, dtype)
+
+        seq_len = prompt_embeds.size(1)
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds = self._get_glyph_embeds(negative_prompt, max_sequence_length, device, dtype)
+
+            seq_len = negative_prompt_embeds.size(1)
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        if latents is not None:
+            return latents.to(device)
+
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        return latents
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt,
+        callback_on_step_end_tensor_inputs,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if (
+            height is not None
+            and height % (self.vae_scale_factor * self.transformer.config.patch_size) != 0
+            or width is not None
+            and width % (self.transformer.config.patch_size) != 0
+        ):
+            logger.warning(
+                f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are {height} and {width}. Dimensions will be resized accordingly"
+            )
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+    # of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
+    # corresponds to doing no classifier free guidance.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prior_token_id: Optional[torch.LongTensor] = None,
+        prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        condition_images: Optional[
+            Union[
+                torch.Tensor, PIL.Image.Image, np.ndarray, List[torch.Tensor], List[PIL.Image.Image], List[np.ndarray]
+            ]
+        ] = None,
+        condition_images_prior_token_id: Optional[Union[torch.LongTensor, List[torch.LongTensor]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        timesteps: Optional[List[int]] = None,
+        sigmas: Optional[List[float]] = None,
+        guidance_scale: float = 1.5,
+        num_images_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        original_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        output_type: str = "pil",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 2048,
+    ) -> Union[GlmImageDecoderPipelineOutput, Tuple]:
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+                `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
+                numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
+                or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
+                list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
+                latents as `image`, but if passing latents directly it is not encoded again.
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+            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 `guidance_scale` is
+                less than `1`).
+            height (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. If not provided, it is set to 2048.
+            width (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. If not provided it is set to 2048.
+            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.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            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 `5.0`):
+                Guidance scale as defined in [Classifier-Free Diffusion
+                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
+                of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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 be 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.
+            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.
+            original_size (`Tuple[int]`, *optional*, defaults to (2048, 2048)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            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.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            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 `224`):
+                Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput`] or `tuple`:
+            [`~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt,
+            callback_on_step_end_tensor_inputs,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        # 2. Default 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]
+        assert batch_size == 1, "batch_size must be 1"
+
+        device = self._execution_device
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt,
+            negative_prompt,
+            self.do_classifier_free_guidance,
+            num_images_per_prompt=num_images_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+            dtype=self.dtype,
+        )
+
+        # 4. process images
+        if condition_images is not None and not isinstance(condition_images, list):
+            condition_images = [condition_images]
+            condition_images_prior_token_id = [condition_images_prior_token_id]
+        assert condition_images is None or (len(condition_images) == len(condition_images_prior_token_id)), (
+            "image and image_prior_token_id must be the same length"
+        )
+
+        if condition_images is not None:
+            preprocessed_condition_images = []
+            for img in condition_images:
+                image_height, image_width = img.size[::-1] if isinstance(img, PIL.Image.Image) else img.shape[:2]
+                multiple_of = self.vae_scale_factor * self.transformer.config.patch_size
+                image_height = (image_height // multiple_of) * multiple_of
+                image_width = (image_width // multiple_of) * multiple_of
+                img = self.image_processor.preprocess(img, height=image_height, width=image_width)
+                preprocessed_condition_images.append(img)
+                height = height or image_height
+                width = width or image_width
+            condition_images = preprocessed_condition_images
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 5. Prepare latents and (optional) condition_images kv cache
+        latent_channels = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size=batch_size * num_images_per_prompt,
+            num_channels_latents=latent_channels,
+            height=height,
+            width=width,
+            dtype=torch.float32,
+            device=device,
+            generator=generator,
+            latents=latents,
+        )
+
+        if condition_images is not None:
+            self.transformer.set_attention_processors_state(GlmImageDecoderAttenProcessorState.ImageEditWriteKV)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.latent_channels, 1, 1)
+                .to(self.vae.device, self.vae.dtype)
+            )
+            latents_std = (
+                torch.tensor(self.vae.config.latents_std)
+                .view(1, self.vae.config.latent_channels, 1, 1)
+                .to(self.vae.device, self.vae.dtype)
+            )
+            empty_glyph_hiddens = torch.zeros_like(prompt_embeds)[:1, :0, ...]
+            for condition_image, condition_image_prior_token_id in zip(
+                condition_images, condition_images_prior_token_id
+            ):
+                condition_image = condition_image.to(device=device, dtype=self.vae.dtype)
+                condition_latent = retrieve_latents(
+                    self.vae.encode(condition_image), generator=generator, sample_mode="argmax"
+                )
+                condition_latent = (condition_latent - latents_mean) / latents_std
+                _ = self.transformer(
+                    hidden_states=condition_latent,
+                    glyph_hidden_states=empty_glyph_hiddens,
+                    prior_token_id=condition_image_prior_token_id,
+                    prior_token_drop=torch.full_like(condition_image_prior_token_id, False, dtype=torch.bool),
+                    timestep=torch.zeros((1,), device=device),
+                    original_size=torch.tensor([condition_image.shape[-2:]], device=device),
+                    target_size=torch.tensor([condition_image.shape[-2:]], device=device),
+                    crop_coords=torch.zeros((1, 2), device=device),
+                    attention_kwargs=attention_kwargs,
+                )
+
+        # 6. Prepare additional timestep conditions
+        original_size = original_size or (height, width)
+        target_size = (height, width)
+        original_size = torch.tensor([original_size], dtype=prompt_embeds.dtype, device=device)
+        target_size = torch.tensor([target_size], dtype=prompt_embeds.dtype, device=device)
+        crops_coords_top_left = torch.tensor([crops_coords_top_left], dtype=prompt_embeds.dtype, device=device)
+
+        original_size = original_size.repeat(batch_size * num_images_per_prompt, 1)
+        target_size = target_size.repeat(batch_size * num_images_per_prompt, 1)
+        crops_coords_top_left = crops_coords_top_left.repeat(batch_size * num_images_per_prompt, 1)
+
+        # Prepare timesteps
+        image_seq_len = ((height // self.vae_scale_factor) * (width // self.vae_scale_factor)) // (
+            self.transformer.config.patch_size**2
+        )
+        timesteps = (
+            np.linspace(self.scheduler.config.num_train_timesteps, 1.0, num_inference_steps + 1)[:-1]
+            if timesteps is None
+            else np.array(timesteps)
+        )
+        timesteps = timesteps.astype(np.int64).astype(np.float32)
+        sigmas = timesteps / self.scheduler.config.num_train_timesteps if sigmas is None else sigmas
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("base_shift", 0.25),
+            self.scheduler.config.get("max_shift", 0.75),
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas, mu=mu
+        )
+        self._num_timesteps = len(timesteps)
+
+        # 7. Denoising loop
+        transformer_dtype = self.transformer.dtype
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        prior_token_drop_cond = torch.full_like(prior_token_id, False, dtype=torch.bool)
+        prior_token_drop_uncond = torch.full_like(prior_token_id, True, dtype=torch.bool)
+        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
+                latent_model_input = latents.to(transformer_dtype)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]) - 1
+
+                if condition_images is not None:
+                    self.transformer.set_attention_processors_state(GlmImageDecoderAttenProcessorState.ImageEditReadKV)
+
+                noise_pred_cond = self.transformer(
+                    hidden_states=latent_model_input,
+                    glyph_hidden_states=prompt_embeds,
+                    prior_token_id=prior_token_id,
+                    prior_token_drop=prior_token_drop_cond,
+                    timestep=timestep,
+                    original_size=original_size,
+                    target_size=target_size,
+                    crop_coords=crops_coords_top_left,
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                )[0].float()
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    if condition_images is not None:
+                        self.transformer.set_attention_processors_state(
+                            GlmImageDecoderAttenProcessorState.ImageEditDontReadKV
+                        )
+                    noise_pred_uncond = self.transformer(
+                        hidden_states=latent_model_input,
+                        glyph_hidden_states=negative_prompt_embeds,
+                        prior_token_id=prior_token_id,
+                        prior_token_drop=prior_token_drop_uncond,
+                        timestep=timestep,
+                        original_size=original_size,
+                        target_size=target_size,
+                        crop_coords=crops_coords_top_left,
+                        attention_kwargs=attention_kwargs,
+                        return_dict=False,
+                    )[0].float()
+
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)
+                else:
+                    noise_pred = noise_pred_cond
+
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                # call the callback, if provided
+                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, self.scheduler.sigmas[i], callback_kwargs)
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        self._current_timestep = None
+        self.transformer.set_attention_processors_state(GlmImageDecoderAttenProcessorState.ImageGen)
+        self.transformer.clear_attention_processors_cache()
+
+        if not output_type == "latent":
+            latents = latents.to(self.vae.dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.latent_channels, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = (
+                torch.tensor(self.vae.config.latents_std)
+                .view(1, self.vae.config.latent_channels, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents = latents * latents_std + latents_mean
+            condition_images = self.vae.decode(latents, return_dict=False, generator=generator)[0]
+        else:
+            condition_images = latents
+
+        condition_images = self.image_processor.postprocess(condition_images, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (condition_images,)
+
+        return GlmImageDecoderPipelineOutput(images=condition_images)

src/diffusers/pipelines/glm_image/pipeline_output.py

@@ -0,0 +1,21 @@
+from dataclasses import dataclass
+from typing import List, Union
+
+import numpy as np
+import PIL.Image
+
+from ...utils import BaseOutput
+
+
+@dataclass
+class GlmImageDecoderPipelineOutput(BaseOutput):
+    """
+    Output class for CogView3 pipelines.
+
+    Args:
+        images (`List[PIL.Image.Image]` or `np.ndarray`)
+            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
+            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
+    """
+
+    images: Union[List[PIL.Image.Image], np.ndarray]