diff --git a/examples/community/pipeline_stable_diffusion_xl_t5.py b/examples/community/pipeline_stable_diffusion_xl_t5.py
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+++ b/examples/community/pipeline_stable_diffusion_xl_t5.py
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+# Copyright Philip Brown, ppbrown@github
+#
+# 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.
+
+# Note: At this time, the intent is to use the T5 encoder mentioned
+# below, with zero changes.
+# Therefore, the model deliberately does not store the T5 encoder model bytes,
+# (Since they are not unique!)
+# but instead takes advantage of huggingface hub cache loading
+
+T5_NAME  = "mcmonkey/google_t5-v1_1-xxl_encoderonly"
+
+
+# Caller is expected to load this, or equivalent, as model name for now
+#   eg: pipe = StableDiffusionXL_T5Pipeline(SDXL_NAME)
+SDXL_NAME = "stabilityai/stable-diffusion-xl-base-1.0"
+
+
+
+from diffusers import StableDiffusionXLPipeline, DiffusionPipeline
+from transformers import T5Tokenizer, T5EncoderModel
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+
+
+from typing import Optional
+
+import torch.nn as nn, torch, types
+
+import torch.nn as nn
+
+class LinearWithDtype(nn.Linear):
+    @property
+    def dtype(self):
+        return self.weight.dtype
+
+
+class StableDiffusionXL_T5Pipeline(StableDiffusionXLPipeline):
+    _expected_modules = [
+        "vae", "unet", "scheduler", "tokenizer",
+        "image_encoder", "feature_extractor",
+        "t5_encoder", "t5_projection", "t5_pooled_projection",
+    ]
+
+    _optional_components = [
+        "image_encoder", "feature_extractor",
+        "t5_encoder", "t5_projection", "t5_pooled_projection",
+    ]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        tokenizer: CLIPTokenizer,
+        t5_encoder=None,
+        t5_projection=None,
+        t5_pooled_projection=None,
+        image_encoder: CLIPVisionModelWithProjection = None,
+        feature_extractor: CLIPImageProcessor = None,
+        force_zeros_for_empty_prompt: bool = True,
+        add_watermarker: Optional[bool] = None,
+    ):
+        DiffusionPipeline.__init__(self)
+
+        if t5_encoder is None:
+            self.t5_encoder     = T5EncoderModel.from_pretrained(T5_NAME,
+                                torch_dtype=unet.dtype)
+        else:
+            self.t5_encoder     = t5_encoder
+
+        # ----- build T5 4096 => 2048 dim projection -----
+        if t5_projection is None:
+            self.t5_projection  = LinearWithDtype(4096, 2048)   # trainable
+        else:
+            self.t5_projection  = t5_projection
+        self.t5_projection.to(dtype=unet.dtype)
+        # ----- build T5 4096 => 1280 dim projection -----
+        if t5_pooled_projection is None:
+            self.t5_pooled_projection  = LinearWithDtype(4096, 1280)   # trainable
+        else:
+            self.t5_pooled_projection  = t5_pooled_projection
+        self.t5_pooled_projection.to(dtype=unet.dtype)
+
+        print("dtype of Linear is ",self.t5_projection.dtype)
+
+        self.register_modules(
+            vae=vae,
+            unet=unet,
+            scheduler=scheduler,
+            tokenizer=tokenizer,
+            t5_encoder=self.t5_encoder,
+            t5_projection=self.t5_projection,
+            t5_pooled_projection=self.t5_pooled_projection,
+            image_encoder=image_encoder,
+            feature_extractor=feature_extractor,
+        )
+        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+        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.unet.config.sample_size
+            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
+            else 128
+        )
+
+        self.watermark = None
+
+        # Parts of original SDXL class complain if these attributes are not
+        # at least PRESENT
+        self.text_encoder = self.text_encoder_2 = None
+
+    # ------------------------------------------------------------------
+    #  Encode a text prompt (T5-XXL + 4096→2048 projection)
+    #  Returns exactly four tensors in the order SDXL’s __call__ expects.
+    # ------------------------------------------------------------------
+    def encode_prompt(
+        self,
+        prompt,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: str | None = None,
+        **_,
+    ):
+        """
+        Returns
+        -------
+        prompt_embeds                : Tensor [B, T, 2048]
+        negative_prompt_embeds       : Tensor [B, T, 2048] | None
+        pooled_prompt_embeds         : Tensor [B, 1280]
+        negative_pooled_prompt_embeds: Tensor [B, 1280]    | None
+        where B = batch * num_images_per_prompt
+        """
+
+        # --- helper to tokenize on the pipeline’s device ----------------
+        def _tok(text: str):
+            tok_out = self.tokenizer(
+                text,
+                return_tensors="pt",
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+            ).to(self.device)
+            return tok_out.input_ids, tok_out.attention_mask
+
+        # ---------- positive stream -------------------------------------
+        ids, mask = _tok(prompt)
+        h_pos = self.t5_encoder(ids, attention_mask=mask).last_hidden_state   # [b, T, 4096]
+        tok_pos = self.t5_projection(h_pos)                                   # [b, T, 2048]
+        pool_pos = self.t5_pooled_projection(h_pos.mean(dim=1))               # [b, 1280]
+
+        # expand for multiple images per prompt
+        tok_pos   = tok_pos.repeat_interleave(num_images_per_prompt, 0)
+        pool_pos  = pool_pos.repeat_interleave(num_images_per_prompt, 0)
+
+        # ---------- negative / CFG stream --------------------------------
+        if do_classifier_free_guidance:
+            neg_text = "" if negative_prompt is None else negative_prompt
+            ids_n, mask_n = _tok(neg_text)
+            h_neg = self.t5_encoder(ids_n, attention_mask=mask_n).last_hidden_state
+            tok_neg = self.t5_projection(h_neg)
+            pool_neg = self.t5_pooled_projection(h_neg.mean(dim=1))
+
+            tok_neg  = tok_neg.repeat_interleave(num_images_per_prompt, 0)
+            pool_neg = pool_neg.repeat_interleave(num_images_per_prompt, 0)
+        else:
+            tok_neg = pool_neg = None
+
+        # ----------------- final ordered return --------------------------
+        # 1) positive token embeddings
+        # 2) negative token embeddings (or None)
+        # 3) positive pooled embeddings
+        # 4) negative pooled embeddings (or None)
+        return tok_pos, tok_neg, pool_pos, pool_neg