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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.
###########################################################################
# This pipeline attempts to use a model that has SDXL vae, T5 text encoder,
# and SDXL unet.
# At the present time, there are no pretrained models that give pleasing
# output. So as yet, (2025/06/10) this pipeline is somewhat of a tech
# demo proving that the pieces can at least be put together.
# Hopefully, it will encourage someone with the hardware available to
# throw enough resources into training one up.
from typing import Optional
import torch.nn as nn
from transformers import (
CLIPImageProcessor,
CLIPTokenizer,
CLIPVisionModelWithProjection,
T5EncoderModel,
)
from diffusers import DiffusionPipeline, StableDiffusionXLPipeline
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.schedulers import KarrasDiffusionSchedulers
# 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"
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
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