up

docs/source/en/_toctree.yml

@@ -353,6 +353,8 @@
         title: Flux2Transformer2DModel
       - local: api/models/flux_transformer
         title: FluxTransformer2DModel
+      - local: api/models/glm_image_transformer2d
+        title: GlmImageTransformer2DModel
       - local: api/models/hidream_image_transformer
         title: HiDreamImageTransformer2DModel
       - local: api/models/hunyuan_transformer2d
@@ -541,6 +543,8 @@
         title: Flux2
       - local: api/pipelines/control_flux_inpaint
         title: FluxControlInpaint
+      - local: api/pipelines/glm_image
+        title: GLM-Image
       - local: api/pipelines/hidream
         title: HiDream-I1
       - local: api/pipelines/hunyuandit

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. -->
+
+# GlmImageTransformer2DModel
+
+A Diffusion Transformer model for 2D data from [GlmImageTransformer2DModel]()
+
+## GlmImageTransformer2DModel
+
+[[autodoc]] GlmImageTransformer2DModel

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).
+
+## GlmImagePipeline
+
+[[autodoc]] GlmImagePipeline
+  - all
+  - __call__
+
+## GlmImagePipelineOutput
+
+[[autodoc]] pipelines.cogview4.pipeline_output.GlmImagePipelineOutput

src/diffusers/__init__.py

@@ -223,6 +223,7 @@ else:
             "FluxControlNetModel",
             "FluxMultiControlNetModel",
             "FluxTransformer2DModel",
+            "GlmImageTransformer2DModel",
             "HiDreamImageTransformer2DModel",
             "HunyuanDiT2DControlNetModel",
             "HunyuanDiT2DModel",
@@ -487,6 +488,7 @@ else:
             "FluxKontextPipeline",
             "FluxPipeline",
             "FluxPriorReduxPipeline",
+            "GlmImagePipeline",
             "HiDreamImagePipeline",
             "HunyuanDiTControlNetPipeline",
             "HunyuanDiTPAGPipeline",
@@ -969,6 +971,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             FluxControlNetModel,
             FluxMultiControlNetModel,
             FluxTransformer2DModel,
+            GlmImageTransformer2DModel,
             HiDreamImageTransformer2DModel,
             HunyuanDiT2DControlNetModel,
             HunyuanDiT2DModel,
@@ -1203,6 +1206,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             FluxKontextPipeline,
             FluxPipeline,
             FluxPriorReduxPipeline,
+            GlmImagePipeline,
             HiDreamImagePipeline,
             HunyuanDiTControlNetPipeline,
             HunyuanDiTPAGPipeline,

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"] = ["GlmImageTransformer2DModel"]
     _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,
+            GlmImageTransformer2DModel,
             HiDreamImageTransformer2DModel,
             HunyuanDiT2DModel,
             HunyuanImageTransformer2DModel,

src/diffusers/models/embeddings.py

@@ -1658,6 +1658,37 @@ class CogView3CombinedTimestepSizeEmbeddings(nn.Module):
         return conditioning
 
 
+class GlmImageCombinedTimestepSizeEmbeddings(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 GlmImageTransformer2DModel
     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 +1,567 @@
+# 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 typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+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 GlmImageCombinedTimestepSizeEmbeddings
+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 GlmImageImageProjector(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 GlmImageAdaLayerNormZero(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, encoder_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_encoder_hidden_states = self.norm_context(encoder_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)
+        encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_msa.unsqueeze(1)) + c_shift_msa.unsqueeze(1)
+
+        return (
+            hidden_states,
+            gate_msa,
+            shift_mlp,
+            scale_mlp,
+            gate_mlp,
+            encoder_hidden_states,
+            c_gate_msa,
+            c_shift_mlp,
+            c_scale_mlp,
+            c_gate_mlp,
+        )
+
+
+class GlmImageLayerKVCache:
+    """KV cache for GlmImage model."""
+    def __init__(self):
+        self.k_cache = None
+        self.v_cache = None
+        self.mode: Optional[str] = None # "write", "read", "skip"
+    
+    def store(self, k: torch.Tensor, v: torch.Tensor):
+        if self.k_cache is None:
+            self.k_cache = k
+            self.v_cache = v
+        else:
+            self.k_cache = torch.cat([self.k_cache, k], dim=2)
+            self.v_cache = torch.cat([self.v_cache, v], dim=2)
+
+    def get(self):
+        return self.k_cache, self.v_cache
+    
+    def clear(self):
+        self.k_cache = None
+        self.v_cache = None
+        self.mode = None
+
+
+class GlmImageKVCache:
+    """Container for all layers' KV caches."""
+
+    def __init__(self, num_layers: int):
+        self.num_layers = num_layers
+        self.caches = [GlmImageLayerKVCache() for _ in range(num_layers)]
+    
+    def __getitem__(self, layer_idx: int) -> GlmImageLayerKVCache:
+        return self.caches[layer_idx]
+
+    def set_mode(self, mode: Optional[str]):
+        if mode is not None and mode not in ["write", "read", "skip"]:
+            raise ValueError(f"Invalid mode: {mode}, must be one of 'write', 'read', 'skip'")
+        for cache in self.caches:
+            cache.mode = mode
+    
+    def clear(self):
+        for cache in self.caches:
+            cache.clear()
+
+class GlmImageAttnProcessor:
+    """
+    Processor for implementing scaled dot-product attention for the GlmImage 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("GlmImageAttnProcessor requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.")
+
+    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,
+        kv_cache: Optional[GlmImageLayerKVCache] = 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 kv_cache is not None: 
+            if kv_cache.mode == "write": 
+                kv_cache.store(key, value)
+            elif kv_cache.mode == "read":
+                k_cache, v_cache = kv_cache.get()
+                key = torch.cat([k_cache, key], dim=2) if k_cache is not None else key
+                value = torch.cat([v_cache, value], dim=2) if v_cache is not None else value
+            elif kv_cache.mode == "skip":
+                pass
+
+        # 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 GlmImageTransformerBlock(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 = GlmImageAdaLayerNormZero(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=GlmImageAttnProcessor(),
+        )
+
+        # 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,
+        encoder_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,
+        kv_cache: Optional[GlmImageLayerKVCache] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # 1. Timestep conditioning
+        (
+            norm_hidden_states,
+            gate_msa,
+            shift_mlp,
+            scale_mlp,
+            gate_mlp,
+            norm_encoder_hidden_states,
+            c_gate_msa,
+            c_shift_mlp,
+            c_scale_mlp,
+            c_gate_mlp,
+        ) = self.norm1(hidden_states, encoder_hidden_states, temb)
+
+        # 2. Attention
+        if attention_kwargs is None:
+            attention_kwargs = {}
+
+        attn_hidden_states, attn_encoder_hidden_states = self.attn1(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+            attention_mask=attention_mask,
+            kv_cache=kv_cache,
+            **attention_kwargs,
+        )
+        hidden_states = hidden_states + attn_hidden_states * gate_msa.unsqueeze(1)
+        encoder_hidden_states = encoder_hidden_states + attn_encoder_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_encoder_hidden_states = self.norm2_context(encoder_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_encoder_hidden_states)
+        hidden_states = hidden_states + ff_output * gate_mlp.unsqueeze(1)
+        encoder_hidden_states = encoder_hidden_states + ff_output_context * c_gate_mlp.unsqueeze(1)
+
+        return hidden_states, encoder_hidden_states
+
+
+class GlmImageRotaryPosEmbed(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 GlmImageAdaLayerNormContinuous(nn.Module):
+    """
+    GlmImage-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 GlmImageTransformer2DModel(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 `1472`):
+            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 = [
+        "GlmImageTransformerBlock",
+        "GlmImageImageProjector",
+        "GlmImageImageProjector",
+    ]
+    _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 = 1472,
+        time_embed_dim: int = 512,
+        condition_dim: int = 256,
+        prior_vq_quantizer_codebook_size: int = 16384,
+    ):
+        super().__init__()
+
+        # GlmImage 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 = GlmImageRotaryPosEmbed(attention_head_dim, patch_size, theta=10000.0)
+
+        # 2. Patch & Text-timestep embedding
+        self.image_projector = GlmImageImageProjector(in_channels, inner_dim, patch_size)
+        self.glyph_projector = FeedForward(text_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 = GlmImageCombinedTimestepSizeEmbeddings(
+            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(
+            [
+                GlmImageTransformerBlock(inner_dim, num_attention_heads, attention_head_dim, time_embed_dim)
+                for _ in range(num_layers)
+            ]
+        )
+
+        # 4. Output projection
+        self.norm_out = GlmImageAdaLayerNormContinuous(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,
+        encoder_hidden_states: torch.Tensor,
+        prior_token_id: torch.Tensor,
+        prior_token_drop: torch.Tensor,
+        timestep: torch.LongTensor,
+        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,
+        kv_caches: Optional[GlmImageKVCache] = 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)
+        encoder_hidden_states = self.glyph_projector(encoder_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 idx, block in enumerate(self.transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    attention_mask,
+                    attention_kwargs,
+                    kv_caches[idx] if kv_caches is not None else None,
+                )
+            else:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    attention_mask,
+                    attention_kwargs,
+                    kv_cache=kv_caches[idx] if kv_caches is not None else None,
+                )
+
+        # 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)

src/diffusers/pipelines/auto_pipeline.py

@@ -52,6 +52,7 @@ from .flux import (
     FluxKontextPipeline,
     FluxPipeline,
 )
+from .glm_image import GlmImagePipeline
 from .hunyuandit import HunyuanDiTPipeline
 from .kandinsky import (
     KandinskyCombinedPipeline,
@@ -167,6 +168,7 @@ AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
         ("chroma", ChromaPipeline),
         ("cogview3", CogView3PlusPipeline),
         ("cogview4", CogView4Pipeline),
+        ("glm_image", GlmImagePipeline),
         ("cogview4-control", CogView4ControlPipeline),
         ("qwenimage", QwenImagePipeline),
         ("qwenimage-controlnet", QwenImageControlNetPipeline),

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": ["GlmImagePipelineOutput"]}
+
+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"] = ["GlmImagePipeline"]
+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 GlmImagePipeline
+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 +1,882 @@
+# 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 math import sqrt
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL
+import torch
+from transformers import ByT5Tokenizer, GlmImageForConditionalGeneration, GlmImageProcessor, T5EncoderModel
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...image_processor import VaeImageProcessor
+from ...loaders import CogView4LoraLoaderMixin
+from ...models import AutoencoderKL, GlmImageTransformer2DModel
+from ...models.transformers.transformer_glm_image import GlmImageKVCache
+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 GlmImagePipelineOutput
+
+
+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 GlmImagePipeline
+
+        >>> pipe = GlmImagePipeline.from_pretrained("zai-org/GLM-Image", torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+
+        >>> prompt = "A photo of an astronaut riding a horse on mars<sop>36 24<eop>"
+        >>> 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`.
+    """
+    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 GlmImagePipeline(DiffusionPipeline, CogView4LoraLoaderMixin):
+    r"""
+    Pipeline for text-to-image generation using GLM-Image.
+
+    This pipeline integrates both the AR (autoregressive) model for token generation and the DiT (diffusion
+    transformer) model for image decoding.
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`T5EncoderModel`]):
+            Frozen text-encoder for glyph embeddings.
+        tokenizer (`PreTrainedTokenizer`):
+            Tokenizer for the text encoder.
+        processor (`AutoProcessor`):
+            Processor for the AR model to handle chat templates and tokenization.
+        vision_language_encoder ([`GlmImageForConditionalGeneration`]):
+            The AR model that generates image tokens from text prompts.
+        transformer ([`GlmImageTransformer2DModel`]):
+            A text conditioned transformer to denoise the encoded image latents (DiT).
+        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"]
+
+    def __init__(
+        self,
+        tokenizer: ByT5Tokenizer,
+        processor: GlmImageProcessor,
+        text_encoder: T5EncoderModel,
+        vision_language_encoder: GlmImageForConditionalGeneration,
+        vae: AutoencoderKL,
+        transformer: GlmImageTransformer2DModel,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            tokenizer=tokenizer,
+            processor=processor,
+            text_encoder=text_encoder,
+            vision_language_encoder=vision_language_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 _build_image_grid_thw(
+        self,
+        token_h: int,
+        token_w: int,
+        prev_token_h: int,
+        prev_token_w: int,
+        existing_grid: Optional[torch.Tensor] = None,
+        device: Optional[torch.device] = None,
+    ) -> torch.Tensor:
+        if existing_grid is None or existing_grid.numel() == 0:
+            return torch.tensor(
+                [
+                    [1, token_h, token_w],
+                    [1, prev_token_h, prev_token_w],
+                ],
+                device=device,
+            )
+        else:
+            return torch.cat([existing_grid.to(device), torch.tensor([[1, token_h, token_w]], device=device)], dim=0)
+
+    def _calculate_ar_generation_params(
+        self, token_h: int, token_w: int, prev_token_h: int, prev_token_w: int, is_text_to_image: bool
+    ) -> Tuple[int, int]:
+        """
+        Calculate max_new_tokens and large_image_start_offset for AR generation.
+        """
+        large_image_tokens = token_h * token_w
+        small_image_tokens = prev_token_h * prev_token_w
+
+        if is_text_to_image:
+            max_new_tokens = small_image_tokens + large_image_tokens + 1
+            large_image_start_offset = small_image_tokens
+        else:
+            max_new_tokens = large_image_tokens + 1
+            large_image_start_offset = 0
+
+        return max_new_tokens, large_image_start_offset
+
+    def _extract_large_image_tokens(
+        self, outputs: torch.Tensor, input_length: int, large_image_start_offset: int, large_image_tokens: int
+    ) -> torch.Tensor:
+        generated_tokens = outputs[0][input_length:]
+        large_image_start = large_image_start_offset
+        large_image_end = large_image_start + large_image_tokens
+        return generated_tokens[large_image_start:large_image_end]
+
+    def _upsample_d32_to_d16(self, token_ids: torch.Tensor, token_h: int, token_w: int) -> torch.Tensor:
+        """
+        Upsample token IDs from d32 format to d16 format.
+
+        AR model generates tokens at d32 resolution (each token = 32x32 pixels). DiT expects tokens at d16 resolution
+        (each token = 16x16 pixels). This function performs 2x nearest-neighbor upsampling.
+
+        Args:
+            token_ids: Token IDs of shape [N] where N = token_h * token_w
+            token_h: Height in d32 token units
+            token_w: Width in d32 token units
+
+        Returns:
+            Upsampled token IDs of shape [1, N*4] where N*4 = (token_h*2) * (token_w*2)
+        """
+        token_ids = token_ids.view(1, 1, token_h, token_w)
+        token_ids = torch.nn.functional.interpolate(token_ids.float(), scale_factor=2, mode="nearest").to(
+            dtype=torch.long
+        )
+
+        token_ids = token_ids.view(1, -1)
+        return token_ids
+
+    def _build_prompt_with_shape(
+        self,
+        prompt: str,
+        height: int,
+        width: int,
+        is_text_to_image: bool,
+        factor: int = 32,
+    ) -> Tuple[str, int, int, int, int]:
+        """
+        Build prompt with shape info (<sop>H W<eop>) based on height and width.
+
+        Args:
+            prompt: The raw text prompt without shape info
+            height: Target image height in pixels
+            width: Target image width in pixels
+            is_text_to_image: Whether this is text-to-image (True) or image-to-image (False)
+
+        Returns:
+            Tuple of (expanded_prompt, token_h, token_w, prev_token_h, prev_token_w)
+        """
+        token_h = height // factor
+        token_w = width // factor
+        ratio = token_h / token_w
+        prev_token_h = int(sqrt(ratio) * (factor // 2))
+        prev_token_w = int(sqrt(1 / ratio) * (factor // 2))
+
+        if is_text_to_image:
+            expanded_prompt = f"{prompt}<sop>{token_h} {token_w}<eop><sop>{prev_token_h} {prev_token_w}<eop>"
+        else:
+            expanded_prompt = f"{prompt}<sop>{token_h} {token_w}<eop>"
+
+        return expanded_prompt, token_h, token_w, prev_token_h, prev_token_w
+
+    def generate_prior_tokens(
+        self,
+        prompt: str,
+        height: int,
+        width: int,
+        image: Optional[List[PIL.Image.Image]] = None,
+        factor: int = 32,
+    ) -> Tuple[torch.Tensor, int, int]:
+        """
+        Generate prior tokens using the AR (vision_language_encoder) model.
+
+        Automatically builds the prompt with shape info based on height/width. Users only need to provide the raw text
+        prompt without <sop>...<eop> tags.
+
+        Args:
+            prompt: The raw text prompt (without shape info)
+            height: Target image height in pixels (must be divisible by factor)
+            width: Target image width in pixels (must be divisible by factor)
+            image: Optional list of condition images for image-to-image generation
+            factor: Token size factor (32 for d32 tokens)
+
+        Returns:
+            Tuple of (prior_token_ids, pixel_height, pixel_width)
+            - prior_token_ids: Upsampled to d16 format, shape [1, token_h*token_w*4]
+            - pixel_height: Image height in pixels (aligned to factor)
+            - pixel_width: Image width in pixels (aligned to factor)
+
+        """
+        device = self.vision_language_encoder.device
+        height = (height // factor) * factor
+        width = (width // factor) * factor
+        is_text_to_image = image is None or len(image) == 0
+        expanded_prompt, token_h, token_w, prev_h, prev_w = self._build_prompt_with_shape(
+            prompt, height, width, is_text_to_image
+        )
+        content = []
+        if image is not None:
+            for img in image:
+                content.append({"type": "image", "image": img})
+        content.append({"type": "text", "text": expanded_prompt})
+        messages = [{"role": "user", "content": content}]
+        inputs = self.processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        )
+
+        existing_grid = inputs.get("image_grid_thw")
+        inputs["image_grid_thw"] = self._build_image_grid_thw(
+            token_h,
+            token_w,
+            prev_h,
+            prev_w,
+            existing_grid=existing_grid if not is_text_to_image else None,
+            device=device,
+        )
+
+        max_new_tokens, large_image_offset = self._calculate_ar_generation_params(
+            token_h, token_w, prev_h, prev_w, is_text_to_image
+        )
+        large_image_tokens = token_h * token_w
+
+        inputs = inputs.to(device)
+        input_length = inputs["input_ids"].shape[-1]
+
+        outputs = self.vision_language_encoder.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+        )
+
+        prior_token_ids_d32 = self._extract_large_image_tokens(
+            outputs, input_length, large_image_offset, large_image_tokens
+        )
+        prior_token_ids = self._upsample_d32_to_d16(prior_token_ids_d32, token_h, token_w)
+
+        pixel_height = token_h * factor
+        pixel_width = token_w * factor
+
+        return prior_token_ids, pixel_height, pixel_width
+
+    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.text_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.text_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]],
+        do_classifier_free_guidance: bool = True,
+        num_images_per_prompt: int = 1,
+        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
+            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.
+            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)
+
+        negative_prompt_embeds = None
+        if do_classifier_free_guidance:
+            negative_prompt = ""
+            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,
+        callback_on_step_end_tensor_inputs,
+        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
+
+    @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,
+        prompt: Optional[Union[str, List[str]]] = None,
+        image: Optional[
+            Union[
+                torch.Tensor, PIL.Image.Image, np.ndarray, List[torch.Tensor], List[PIL.Image.Image], List[np.ndarray]
+            ]
+        ] = 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,
+        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[GlmImagePipelineOutput, Tuple]:
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. Must contain shape info in the format '<sop>H
+                W<eop>' where H and W are token dimensions (d32). Example: "A beautiful sunset<sop>36 24<eop>"
+                generates a 1152x768 image.
+            image: Optional condition images for image-to-image generation.
+            height (`int`, *optional*):
+                The height in pixels. If not provided, derived from prompt shape info.
+            width (`int`, *optional*):
+                The width in pixels. If not provided, derived from prompt shape info.
+            num_inference_steps (`int`, *optional*, defaults to `50`):
+                The number of denoising steps for DiT.
+            guidance_scale (`float`, *optional*, defaults to `1.5`):
+                Guidance scale for classifier-free guidance.
+            num_images_per_prompt (`int`, *optional*, defaults to `1`):
+                The number of images to generate per prompt.
+            generator (`torch.Generator`, *optional*):
+                Random generator for reproducibility.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                Output format: "pil", "np", or "latent".
+
+        Examples:
+
+        Returns:
+            [`GlmImagePipelineOutput`] or `tuple`: 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
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            callback_on_step_end_tensor_inputs,
+            prompt_embeds,
+        )
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        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
+
+        ar_condition_images = None
+        if image is not None:
+            if not isinstance(image, list):
+                image = [image]
+            ar_condition_images = []
+            for img in image:
+                if isinstance(img, PIL.Image.Image):
+                    ar_condition_images.append(img)
+                elif isinstance(img, torch.Tensor):
+                    img_np = img.cpu().numpy()
+                    if img_np.ndim == 4:
+                        img_np = img_np[0]
+                    if img_np.shape[0] in [1, 3, 4]:
+                        img_np = img_np.transpose(1, 2, 0)
+                    if img_np.max() <= 1.0:
+                        img_np = (img_np * 255).astype(np.uint8)
+                    ar_condition_images.append(PIL.Image.fromarray(img_np))
+                else:
+                    ar_condition_images.append(PIL.Image.fromarray(img))
+
+        prior_token_id, ar_height, ar_width = self.generate_prior_tokens(
+            prompt=prompt[0] if isinstance(prompt, list) else prompt,
+            image=ar_condition_images,
+            height=height,
+            width=width,
+        )
+
+        height = height or ar_height
+        width = width or ar_width
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt,
+            self.do_classifier_free_guidance,
+            num_images_per_prompt=num_images_per_prompt,
+            prompt_embeds=prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+            dtype=self.dtype,
+        )
+
+        # 4. process images
+        condition_images_prior_token_id = None
+        if image is not None:
+            preprocessed_condition_images = []
+            condition_images_prior_token_id = []
+            for img in image:
+                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)
+            image = preprocessed_condition_images
+
+        # 5. Prepare latents and (optional) image 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,
+        )
+
+        kv_caches = GlmImageKVCache(num_layers=self.transformer.config.num_layers)
+
+        if image is not None and condition_images_prior_token_id is not None:
+            kv_caches.set_mode("write")
+            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(image, 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,
+                    encoder_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),
+                    target_size=torch.tensor([condition_image.shape[-2:]], device=device),
+                    crop_coords=torch.zeros((1, 2), device=device),
+                    attention_kwargs=attention_kwargs,
+                    kv_caches=kv_caches,
+                )
+
+        # 6. Prepare additional timestep conditions
+        target_size = (height, width)
+        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)
+
+        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)
+
+                timestep = t.expand(latents.shape[0]) - 1
+
+                if image is not None:
+                    kv_caches.set_mode("read")
+
+                noise_pred_cond = self.transformer(
+                    hidden_states=latent_model_input,
+                    encoder_hidden_states=prompt_embeds,
+                    prior_token_id=prior_token_id,
+                    prior_token_drop=prior_token_drop_cond,
+                    timestep=timestep,
+                    target_size=target_size,
+                    crop_coords=crops_coords_top_left,
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                    kv_caches=kv_caches,
+                )[0].float()
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    if image is not None:
+                        kv_caches.set_mode("skip")
+                    noise_pred_uncond = self.transformer(
+                        hidden_states=latent_model_input,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        prior_token_id=prior_token_id,
+                        prior_token_drop=prior_token_drop_uncond,
+                        timestep=timestep,
+                        target_size=target_size,
+                        crop_coords=crops_coords_top_left,
+                        attention_kwargs=attention_kwargs,
+                        return_dict=False,
+                        kv_caches=kv_caches,
+                    )[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]
+
+                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)
+
+                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
+        kv_caches.clear()
+
+        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
+            image = self.vae.decode(latents, return_dict=False, generator=generator)[0]
+        else:
+            image = latents
+
+        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 GlmImagePipelineOutput(images=image)

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 GlmImagePipelineOutput(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]

src/diffusers/utils/dummy_pt_objects.py

@@ -967,6 +967,21 @@ class HiDreamImageTransformer2DModel(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class GlmImageTransformer2DModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class HunyuanDiT2DControlNetModel(metaclass=DummyObject):
     _backends = ["torch"]