refactor(ideogram4): register pipeline and load via from_pretrained

Register the subclass with generic.set_pipeline and build it with
from_pretrained, passing the SDNQ transformers and shared text encoder
while the vae, scheduler, and tokenizer load from the repo.

modules/shared_items.py

@@ -40,7 +40,6 @@ pipelines = {
     'HiDream': getattr(diffusers, 'HiDreamImagePipeline', None),
     'HunyuanDiT': getattr(diffusers, 'HunyuanDiTPipeline', None),
     'HunyuanImage': getattr(diffusers, 'HunyuanImagePipeline', None),
-    'Ideogram4': getattr(diffusers, 'Ideogram4Pipeline', None),
     'JoyEdit': getattr(diffusers, 'JoyImageEditPipeline', None),
     'Kandinsky21': getattr(diffusers, 'KandinskyCombinedPipeline', None),
     'Kandinsky22': getattr(diffusers, 'KandinskyV22CombinedPipeline', None),
@@ -70,6 +69,7 @@ pipelines = {
     'FLEX': None,
     'HiDreamO1': None,
     'HunyuanImage3': None,
+    'Ideogram4': None,
     'Lens': None,
     'LuminaDiMOO': None,
     'Meissonic': None,

pipelines/model_ideogram4.py

@@ -1,8 +1,7 @@
 import json
 import diffusers
-from transformers import AutoTokenizer
 from transformers.models.qwen3_vl import Qwen3VLModel
-from modules import shared, devices, sd_models
+from modules import shared, devices, sd_models, model_quant
 from modules.logger import log
 from pipelines import generic
 
@@ -70,8 +69,10 @@ def load_ideogram4(checkpoint_info, diffusers_load_config=None):
         diffusers_load_config = {}
     repo_id = sd_models.path_to_repo(checkpoint_info)
     sd_models.hf_auth_check(checkpoint_info)
-    log.debug(f'Load model: type=Ideogram4 repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype}')
+    load_args, _ = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
+    log.debug(f'Load model: type=Ideogram4 repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
+    generic.set_pipeline('Ideogram4', Ideogram4Pipeline)
     if repo_id is None or repo_id.lower() == 'none':
         return None
 
@@ -81,19 +82,17 @@ def load_ideogram4(checkpoint_info, diffusers_load_config=None):
     unconditional_transformer = generic.load_transformer(repo_id, cls_name=cls, subfolder="unconditional_transformer", load_config=diffusers_load_config)
     pin_transformers_if_fit(transformer, unconditional_transformer)
     # shared_te_map redirects to the shared Qwen3-VL repo (deduped with VQA + prompt-enhance);
-    # the bundled text_encoder is the fallback when sharing is off.
+    # the bundled text_encoder is the fallback when sharing is off. The vae, tokenizer, and
+    # scheduler load from the repo via from_pretrained.
     text_encoder = generic.load_text_encoder(repo_id, cls_name=Qwen3VLModel, load_config=diffusers_load_config)
-    tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="tokenizer", cache_dir=shared.opts.diffusers_dir)
-    vae = diffusers.AutoencoderKLFlux2.from_pretrained(repo_id, subfolder="vae", cache_dir=shared.opts.diffusers_dir, torch_dtype=devices.dtype)
-    scheduler = diffusers.FlowMatchEulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler", cache_dir=shared.opts.diffusers_dir)
 
-    pipe = Ideogram4Pipeline(
-        scheduler=scheduler,
-        vae=vae,
-        text_encoder=text_encoder,
-        tokenizer=tokenizer,
+    pipe = Ideogram4Pipeline.from_pretrained(
+        repo_id,
+        cache_dir=shared.opts.diffusers_dir,
         transformer=transformer,
         unconditional_transformer=unconditional_transformer,
+        text_encoder=text_encoder,
+        **load_args,
     )
     # The pipeline decodes internally; the CFG scale slider drives guidance_scale, which is
     # mutually exclusive with the pipeline's default per-step guidance_schedule.
@@ -101,6 +100,6 @@ def load_ideogram4(checkpoint_info, diffusers_load_config=None):
     # JSON captions must pass through verbatim; skip styles/wildcards that would strip the braces.
     pipe.keep_prompts = True # pylint: disable=attribute-defined-outside-init
 
-    del transformer, unconditional_transformer, text_encoder, vae
+    del transformer, unconditional_transformer, text_encoder
     devices.torch_gc(force=True, reason='load')
     return pipe