support klein 4b models

diffsynth/configs/model_configs.py

@@ -510,6 +510,13 @@ flux2_series = [
         "model_name": "flux2_vae",
         "model_class": "diffsynth.models.flux2_vae.Flux2VAE",
     },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "3bde7b817fec8143028b6825a63180df",
+        "model_name": "flux2_dit",
+        "model_class": "diffsynth.models.flux2_dit.Flux2DiT",
+        "extra_kwargs": {"guidance_embeds": False, "joint_attention_dim": 7680, "num_attention_heads": 24, "num_layers": 5, "num_single_layers": 20}
+    },
 ]
 
 z_image_series = [

diffsynth/models/flux2_dit.py

@@ -823,7 +823,13 @@ class Flux2PosEmbed(nn.Module):
 
 
 class Flux2TimestepGuidanceEmbeddings(nn.Module):
-    def __init__(self, in_channels: int = 256, embedding_dim: int = 6144, bias: bool = False):
+    def __init__(
+        self,
+        in_channels: int = 256,
+        embedding_dim: int = 6144,
+        bias: bool = False,
+        guidance_embeds: bool = True,
+    ):
         super().__init__()
 
         self.time_proj = Timesteps(num_channels=in_channels, flip_sin_to_cos=True, downscale_freq_shift=0)
@@ -831,20 +837,24 @@ class Flux2TimestepGuidanceEmbeddings(nn.Module):
             in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
         )
 
-        self.guidance_embedder = TimestepEmbedding(
-            in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
-        )
+        if guidance_embeds:
+            self.guidance_embedder = TimestepEmbedding(
+                in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
+            )
+        else:
+            self.guidance_embedder = None
 
     def forward(self, timestep: torch.Tensor, guidance: torch.Tensor) -> torch.Tensor:
         timesteps_proj = self.time_proj(timestep)
         timesteps_emb = self.timestep_embedder(timesteps_proj.to(timestep.dtype))  # (N, D)
 
-        guidance_proj = self.time_proj(guidance)
-        guidance_emb = self.guidance_embedder(guidance_proj.to(guidance.dtype))  # (N, D)
-
-        time_guidance_emb = timesteps_emb + guidance_emb
-
-        return time_guidance_emb
+        if guidance is not None and self.guidance_embedder is not None:
+            guidance_proj = self.time_proj(guidance)
+            guidance_emb = self.guidance_embedder(guidance_proj.to(guidance.dtype))  # (N, D)
+            time_guidance_emb = timesteps_emb + guidance_emb
+            return time_guidance_emb
+        else:
+            return timesteps_emb
 
 
 class Flux2Modulation(nn.Module):
@@ -882,6 +892,7 @@ class Flux2DiT(torch.nn.Module):
         axes_dims_rope: Tuple[int, ...] = (32, 32, 32, 32),
         rope_theta: int = 2000,
         eps: float = 1e-6,
+        guidance_embeds: bool = True,
     ):
         super().__init__()
         self.out_channels = out_channels or in_channels
@@ -892,7 +903,10 @@ class Flux2DiT(torch.nn.Module):
 
         # 2. Combined timestep + guidance embedding
         self.time_guidance_embed = Flux2TimestepGuidanceEmbeddings(
-            in_channels=timestep_guidance_channels, embedding_dim=self.inner_dim, bias=False
+            in_channels=timestep_guidance_channels,
+            embedding_dim=self.inner_dim,
+            bias=False,
+            guidance_embeds=guidance_embeds,
         )
 
         # 3. Modulation (double stream and single stream blocks share modulation parameters, resp.)
@@ -953,34 +967,9 @@ class Flux2DiT(torch.nn.Module):
         txt_ids: torch.Tensor = None,
         guidance: torch.Tensor = None,
         joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
         use_gradient_checkpointing=False,
         use_gradient_checkpointing_offload=False,
-    ) -> Union[torch.Tensor]:
-        """
-        The [`FluxTransformer2DModel`] forward method.
-
-        Args:
-            hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
-                Input `hidden_states`.
-            encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
-                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
-            timestep ( `torch.LongTensor`):
-                Used to indicate denoising step.
-            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
-                A list of tensors that if specified are added to the residuals of transformer blocks.
-            joint_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
-            `tuple` where the first element is the sample tensor.
-        """
+    ):
         # 0. Handle input arguments
         if joint_attention_kwargs is not None:
             joint_attention_kwargs = joint_attention_kwargs.copy()
@@ -992,7 +981,9 @@ class Flux2DiT(torch.nn.Module):
 
         # 1. Calculate timestep embedding and modulation parameters
         timestep = timestep.to(hidden_states.dtype) * 1000
-        guidance = guidance.to(hidden_states.dtype) * 1000
+
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype) * 1000
 
         temb = self.time_guidance_embed(timestep, guidance)
 

diffsynth/pipelines/flux2_image.py

@@ -10,7 +10,7 @@ from ..diffusion import FlowMatchScheduler
 from ..core import ModelConfig, gradient_checkpoint_forward
 from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
 
-from transformers import AutoProcessor
+from transformers import AutoProcessor, AutoTokenizer
 from ..models.flux2_text_encoder import Flux2TextEncoder
 from ..models.flux2_dit import Flux2DiT
 from ..models.flux2_vae import Flux2VAE
@@ -53,11 +53,12 @@ class Flux2ImagePipeline(BasePipeline):
         
         # Fetch models
         pipe.text_encoder = model_pool.fetch_model("flux2_text_encoder")
+        pipe.text_encoder_qwen3 = model_pool.fetch_model("z_image_text_encoder")
         pipe.dit = model_pool.fetch_model("flux2_dit")
         pipe.vae = model_pool.fetch_model("flux2_vae")
         if tokenizer_config is not None:
             tokenizer_config.download_if_necessary()
-            pipe.tokenizer = AutoProcessor.from_pretrained(tokenizer_config.path)
+            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
         
         # VRAM Management
         pipe.vram_management_enabled = pipe.check_vram_management_state()