cleanup model loaders

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

pipelines/model_lumina.py

@@ -4,9 +4,11 @@ from modules import shared, sd_models, sd_hijack_te, devices, modelloader, model
 from pipelines import generic
 
 
-def load_lumina(_checkpoint_info, diffusers_load_config={}):
+def load_lumina(checkpoint_info, diffusers_load_config={}):
+    repo_id = sd_models.path_to_repo(checkpoint_info)
     modelloader.hf_login()
     load_config, _quant_config = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
+    shared.log.debug(f'Load model: type=LuminaSFT repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
     pipe = diffusers.LuminaText2ImgPipeline.from_pretrained(
         'Alpha-VLLM/Lumina-Next-SFT-diffusers',
         cache_dir = shared.opts.diffusers_dir,
@@ -25,6 +27,7 @@ def load_lumina2(checkpoint_info, diffusers_load_config={}):
         shared.log.debug(f'Transformers cache: type=teacache patch=forward cls={diffusers.Lumina2Transformer2DModel.__name__}')
         diffusers.Lumina2Transformer2DModel.forward = teacache.teacache_lumina2_forward # patch must be done before transformer is loaded
 
+    shared.log.debug(f'Load model: type=Lumina2 repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
     transformer = generic.load_transformer(repo_id, cls_name=diffusers.Lumina2Transformer2DModel, load_config=diffusers_load_config)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Gemma2Model, load_config=diffusers_load_config)
 

pipelines/model_meissonic.py

@@ -12,35 +12,40 @@ def load_meissonic(checkpoint_info, diffusers_load_config={}):
     shared_items.pipelines['Meissonic'] = MeissonicPipeline
 
     modelloader.hf_login()
-    fn = sd_models.path_to_repo(checkpoint_info)
+    repo_id = sd_models.path_to_repo(checkpoint_info)
     cache_dir = shared.opts.diffusers_dir
 
     diffusers_load_config['variant'] = 'fp16'
     diffusers_load_config['trust_remote_code'] = True
 
+    shared.log.debug(f'Load model: type=Meissonic repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
     model = TransformerMeissonic.from_pretrained(
-        fn,
+        repo_id,
         subfolder="transformer",
         cache_dir=cache_dir,
         **diffusers_load_config,
     )
     vqvae = diffusers.VQModel.from_pretrained(
-        fn,
+        repo_id,
         subfolder="vqvae",
         cache_dir=cache_dir,
         **diffusers_load_config,
     )
     text_encoder = transformers.CLIPTextModelWithProjection.from_pretrained(
-        fn,
+        repo_id,
         subfolder="text_encoder",
         cache_dir=cache_dir,
     )
     tokenizer = transformers.CLIPTokenizer.from_pretrained(
-        fn,
+        repo_id,
         subfolder="tokenizer",
         cache_dir=cache_dir,
     )
-    scheduler = MeissonicScheduler.from_pretrained(fn, subfolder="scheduler", cache_dir=cache_dir)
+    scheduler = MeissonicScheduler.from_pretrained(
+        repo_id,
+        subfolder="scheduler",
+        cache_dir=cache_dir,
+    )
     pipe = MeissonicPipeline(
             vqvae=vqvae.to(devices.dtype),
             text_encoder=text_encoder.to(devices.dtype),

pipelines/model_omnigen.py

@@ -7,6 +7,7 @@ def load_omnigen(checkpoint_info, diffusers_load_config={}): # pylint: disable=u
     vae = None
 
     load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='Model')
+    shared.log.debug(f'Load model: type=OmniGen repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
     transformer = diffusers.OmniGenTransformer2DModel.from_pretrained(
         repo_id,
         subfolder="transformer",

pipelines/model_omnigen2.py

@@ -12,6 +12,7 @@ def load_omnigen2(checkpoint_info, diffusers_load_config={}): # pylint: disable=
     diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["omnigen2"] = diffusers.OmniGen2Pipeline
 
     load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='Model')
+    shared.log.debug(f'Load model: type=OmniGen2 repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
     transformer = OmniGen2Transformer2DModel.from_pretrained(
         repo_id,
         subfolder="transformer",

pipelines/model_pixart.py

@@ -18,7 +18,7 @@ def load_pixart(checkpoint_info, diffusers_load_config={}):
         repo_id_pipe = "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS"
 
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
-    shared.log.debug(f'Load model: type=AuraFlow repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
+    shared.log.debug(f'Load model: type=PixArtSigma repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
     transformer = generic.load_transformer(repo_id, cls_name=diffusers.PixArtTransformer2DModel, load_config=diffusers_load_config)
     text_encoder = generic.load_text_encoder(repo_id_tenc, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config)
@@ -33,7 +33,7 @@ def load_pixart(checkpoint_info, diffusers_load_config={}):
 
     del text_encoder
     del transformer
-    sd_hijack_te.init_hijack(pipe)
+    # sd_hijack_te.init_hijack(pipe)
 
     devices.torch_gc(force=True, reason='load')
     return pipe