add sdxs

Signed-off-by: vladmandic <mandic00@live.com>

CHANGELOG.md

@@ -1,5 +1,13 @@
 # Change Log for SD.Next
 
+## Update for 2026-04-02
+
+- **Models**
+  - [AiArtLab SDXS-1B](https://huggingface.co/AiArtLab/sdxs-1b) Simple Diffusion XS *(training still in progress)*  
+    this model combines Qwen3.5-1.8B text encoder with SDXL-style UNET with only 1.6B parameters and custom 32ch VAE  
+- **Internal**
+  - additional typing and typechecks, thanks @awsr  
+
 ## Update for 2026-04-01
 
 ### Highlights for 2026-04-01

data/reference.json

@@ -864,6 +864,16 @@
     "extras": "sampler: Default, cfg_scale: 1.5, steps: 50",
     "size": 15.3,
     "date": "2025 January"
+  },
+
+  "AiArtLab SDXS-1B": {
+    "path": "AiArtLab/sdxs-1b",
+    "preview": "AiArtLab--sdxs-1b.jpg",
+    "desc": "Simple Diffusion XS (train in progress) combines Qwen3.5-1.8B text encoder with SDXL-style UNET with only 1.6B parameters and custom 32ch VAE",
+    "skip": true,
+    "extras": "sampler: Default",
+    "size": 15.3,
+    "date": "2026 January"
   }
 
 }

modules/modeldata.py

@@ -100,6 +100,8 @@ def get_model_type(pipe):
         model_type = 'hunyuanimage3'
     elif 'HunyuanImage' in name:
         model_type = 'hunyuanimage'
+    elif 'sdxs-1b' in name:
+        model_type = 'sdxs'
     # video models
     elif "Kandinsky5" in name and '2V' in name:
         model_type = 'kandinsky5video'

modules/processing_class.py

@@ -325,8 +325,8 @@ class StableDiffusionProcessing:
 
         # initializers
         self.prompt = prompt
-        self.seed = seed
-        self.subseed = subseed
+        self.seed = int(seed)
+        self.subseed = int(subseed)
         self.subseed_strength = subseed_strength
         self.seed_resize_from_h = seed_resize_from_h
         self.seed_resize_from_w = seed_resize_from_w

modules/sd_detect.py

@@ -150,6 +150,8 @@ def guess_by_name(fn, current_guess):
         new_guess = 'Ovis-Image'
     elif 'glm-image' in fn.lower():
         new_guess = 'GLM-Image'
+    elif 'sdxs-1b' in fn.lower():
+        new_guess = 'SDXS'
     if debug_load:
         log.trace(f'Autodetect: method=name file="{fn}" previous="{current_guess}" current="{new_guess}"')
     return new_guess or current_guess
@@ -166,6 +168,8 @@ def guess_by_diffusers(fn, current_guess):
         if name is not None and name in exclude_by_name:
             return current_guess, None
         cls = index.get('_class_name', None)
+        if isinstance(cls, list):
+            cls = cls[-1]
         if cls is not None:
             pipeline = getattr(diffusers, cls, None)
             if pipeline is None:

modules/sd_hijack_te.py

@@ -8,7 +8,7 @@ def hijack_encode_prompt(*args, **kwargs):
     jobid = shared.state.begin('TE Encode')
     t0 = time.time()
     if 'max_sequence_length' in kwargs and kwargs['max_sequence_length'] is not None:
-        kwargs['max_sequence_length'] = max(kwargs['max_sequence_length'], os.environ.get('HIDREAM_MAX_SEQUENCE_LENGTH', 256))
+        kwargs['max_sequence_length'] = max(kwargs['max_sequence_length'], os.environ.get('MAX_SEQUENCE_LENGTH', 256))
     try:
         prompt = kwargs.get('prompt', None) or (args[0] if len(args) > 0 else None)
         if prompt is not None:
@@ -20,8 +20,6 @@ def hijack_encode_prompt(*args, **kwargs):
         res = None
     t1 = time.time()
     timer.process.add('te', t1-t0)
-    # if hasattr(shared.sd_model, "maybe_free_model_hooks"):
-    #     shared.sd_model.maybe_free_model_hooks()
     shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
     shared.state.end(jobid)
     return res

modules/sd_models.py

@@ -507,6 +507,10 @@ def load_diffuser_force(detected_model_type, checkpoint_info, diffusers_load_con
             from pipelines.model_glm import load_glm_image
             sd_model = load_glm_image(checkpoint_info, diffusers_load_config)
             allow_post_quant = False
+        elif model_type in ['SDXS']:
+            from pipelines.model_sdxs import load_sdxs
+            sd_model = load_sdxs(checkpoint_info, diffusers_load_config)
+            allow_post_quant = False
     except Exception as e:
         log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
         # if debug_load:

modules/shared_items.py

@@ -65,6 +65,7 @@ pipelines = {
     'HunyuanImage3': getattr(diffusers, 'DiffusionPipeline', None),
     'ChronoEdit': getattr(diffusers, 'DiffusionPipeline', None),
     'Anima': getattr(diffusers, 'DiffusionPipeline', None),
+    'SDXS': getattr(diffusers, 'DiffusionPipeline', None),
 }
 
 

pipelines/model_sdxs.py

@@ -0,0 +1,59 @@
+import time
+import diffusers
+import transformers
+from modules import shared, devices, errors, timer, sd_models, model_quant, sd_hijack_vae
+from modules.logger import log
+from pipelines import generic
+
+
+def hijack_encode_text(prompt: str | list[str]):
+    jobid = shared.state.begin('TE Encode')
+    t0 = time.time()
+    try:
+        prompt = shared.sd_model.refine_prompts(prompt)
+    except Exception as e:
+        log.error(f'Encode prompt: {e}')
+        errors.display(e, 'Encode prompt')
+    try:
+        res = shared.sd_model.orig_encode_text(prompt)
+    except Exception as e:
+        log.error(f'Encode prompt: {e}')
+        errors.display(e, 'Encode prompt')
+        res = None
+    t1 = time.time()
+    timer.process.add('te', t1-t0)
+    shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
+    shared.state.end(jobid)
+    return res
+
+
+def load_sdxs(checkpoint_info, diffusers_load_config=None):
+    if diffusers_load_config is None:
+        diffusers_load_config = {}
+    repo_id = sd_models.path_to_repo(checkpoint_info)
+    sd_models.hf_auth_check(checkpoint_info)
+
+    load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
+    log.debug(f'Load model: type=SDXS repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
+
+    text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen3_5ForConditionalGeneration, load_config=diffusers_load_config, allow_shared=False)
+
+    pipe = diffusers.DiffusionPipeline.from_pretrained(
+        repo_id,
+        text_encoder=text_encoder,
+        cache_dir=shared.opts.diffusers_dir,
+        trust_remote_code=True,
+        **load_args,
+    )
+    pipe.task_args = {
+        'generator': None,
+        'output_type': 'np',
+    }
+
+    pipe.orig_encode_text = pipe.encode_text
+    pipe.encode_text = hijack_encode_text
+    sd_hijack_vae.init_hijack(pipe)
+    del text_encoder
+
+    devices.torch_gc(force=True, reason='load')
+    return pipe