add chronoedit prequants and lora loading

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

CHANGELOG.md

@@ -80,6 +80,7 @@ Less than 2 weeks since last release, here's a service-pack style update with a
   - log available attention mechanisms on startup  
   - support for switching back-and-forth **t2i** and **t2v** for *wan-2.x* models  
   - control `api` cache controlnets  
+  - additional model modules **deduplication** for both normal and pre-quant models: *umt5, qwen25-vl*  
 - **Fixes**
   - startup error with `--profile` enabled if using `--skip`  
   - restore orig init image for each batch sequence  

html/reference.json

@@ -1013,9 +1013,19 @@
     "size": 16.10,
     "extras": ""
   },
-  "Tencent HunyuanImage 3.0": {
+  "nVidia ChronoEdit sdnq-svd-uint4": {
+    "path": "Disty0/ChronoEdit-14B-SDNQ-uint4-svd-r32",
+    "preview": "nvidia--ChronoEdit-14B-Diffusers.jpg",
+    "desc": "Quantization of nvidia/ChronoEdit-14B-Diffusers using SDNQ: sdnq-svd 4-bit uint with svd rank 32.",
+    "skip": true,
+    "tags": "quantized",
+    "date": "2025 October",
+    "size": 18.10,
+    "extras": ""
+  },
+  "Tencent HunyuanImage 3.0 sdnq-svd-uint4": {
     "path": "Disty0/HunyuanImage3-SDNQ-uint4-svd-r32",
-    "desc": "HunyuanImage-3.0 is a groundbreaking native multimodal model that unifies multimodal understanding and generation within an autoregressive framework.",
+    "desc": "Quantization of tencent/HunyuanImage-3.0 using SDNQ: sdnq-svd 4-bit uint with svd rank 32.",
     "preview": "Disty0--HunyuanImage3-SDNQ-uint4-svd-r32.jpg",
     "extras": "",
     "skip": true,

modules/lora/lora_load.py

@@ -13,6 +13,9 @@ available_network_aliases = {}
 forbidden_network_aliases = {}
 available_network_hash_lookup = {}
 dump_lora_keys = os.environ.get('SD_LORA_DUMP', None) is not None
+exclude_errors = [
+    "'ChronoEditTransformer3DModel'",
+]
 
 
 def lora_dump(lora, dct):
@@ -285,7 +288,8 @@ def network_load(names, te_multipliers=None, unet_multipliers=None, dyn_dims=Non
                 shared.log.trace(f'Network load: type=LoRA active={sd_model.get_active_adapters()}')
             sd_model.set_adapters(adapter_names=lora_diffusers.diffuser_loaded, adapter_weights=lora_diffusers.diffuser_scales)
         except Exception as e:
-            shared.log.error(f'Network load: type=LoRA action=set {e}')
+            if str(e) not in exclude_errors:
+                shared.log.error(f'Network load: type=LoRA action=strength {str(e)}')
             if l.debug:
                 errors.display(e, 'LoRA')
         try:
@@ -294,7 +298,7 @@ def network_load(names, te_multipliers=None, unet_multipliers=None, dyn_dims=Non
                 sd_model.unload_lora_weights()
             l.timer.activate += time.time() - t1
         except Exception as e:
-            shared.log.error(f'Network load: type=LoRA action=fuse {e}')
+            shared.log.error(f'Network load: type=LoRA action=fuse {str(e)}')
             if l.debug:
                 errors.display(e, 'LoRA')
 

modules/lora/lora_overrides.py

@@ -38,10 +38,12 @@ force_models_diffusers = [ # forced always
     'bria',
     'flite',
     'cosmos',
+    'chrono',
     # video models
     'hunyuanvideo',
     'cogvideo',
     'wanai',
+    'chrono',
     'ltxvideo',
     'mochivideo',
     'allegrovideo',

modules/modeldata.py

@@ -76,8 +76,10 @@ def get_model_type(pipe):
     elif "Allegro" in name:
         model_type = 'allegrovideo'
     # hybrid models
-    elif 'Wan' in name or 'ChronoEdit' in name:
+    elif 'Wan' in name:
         model_type = 'wanai'
+    if 'ChronoEdit' in name:
+        model_type = 'chrono'
     elif 'HDM-xut' in name:
         model_type = 'hdm'
     elif 'HunyuanImage3' in name:

modules/sd_vae_taesd.py

@@ -36,7 +36,7 @@ prev_cls = ''
 prev_type = ''
 prev_model = ''
 lock = threading.Lock()
-supported = ['sd', 'sdxl', 'sd3', 'f1', 'h1', 'lumina2', 'hunyuanvideo', 'wanai', 'mochivideo', 'pixartsigma', 'pixartalpha', 'hunyuandit', 'omnigen', 'qwen']
+supported = ['sd', 'sdxl', 'sd3', 'f1', 'h1', 'lumina2', 'hunyuanvideo', 'wanai', 'chrono', 'mochivideo', 'pixartsigma', 'pixartalpha', 'hunyuandit', 'omnigen', 'qwen']
 
 
 def warn_once(msg, variant=None):
@@ -59,7 +59,7 @@ def get_model(model_type = 'decoder', variant = None):
         model_cls = 'sdxl'
     elif model_cls in {'h1', 'lumina2', 'chroma'}:
         model_cls = 'f1'
-    elif model_cls in {'wanai', 'qwen'}:
+    elif model_cls in {'wanai', 'qwen', 'chrono'}:
         variant = variant or 'TAE WanVideo'
     elif model_cls not in supported:
         warn_once(f'cls={shared.sd_model.__class__.__name__} type={model_cls} unsuppported', variant=variant)

pipelines/model_chrono.py

@@ -1,3 +1,4 @@
+import sys
 import transformers
 from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
 from pipelines import generic
@@ -19,19 +20,31 @@ def load_chrono(checkpoint_info, diffusers_load_config=None):
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
     shared.log.debug(f'Load model: type=ChronoEdit repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
-    from pipelines.chrono.pipeline_chronoedit import ChronoEditPipeline as pipe_cls
-    from pipelines.chrono.transformer_chronoedit import ChronoEditTransformer3DModel
+    from pipelines.chrono import pipeline_chronoedit
+    from pipelines.chrono import transformer_chronoedit
 
-    transformer = generic.load_transformer(repo_id, cls_name=ChronoEditTransformer3DModel, load_config=diffusers_load_config, subfolder="transformer")
+    # monkey patch for <https://huggingface.co/Disty0/ChronoEdit-14B-SDNQ-uint4-svd-r32/blob/main/model_index.json>
+    import pipelines.chrono
+    sys.modules['chronoedit_diffusers'] = pipelines.chrono
+    from diffusers.pipelines import pipeline_loading_utils
+    pipeline_loading_utils.LOADABLE_CLASSES['chronoedit_diffusers.transformer_chronoedit'] = {}
+
+    transformer = generic.load_transformer(repo_id, cls_name=transformer_chronoedit.ChronoEditTransformer3DModel, load_config=diffusers_load_config, subfolder="transformer")
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.UMT5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder")
 
-    pipe = pipe_cls.from_pretrained(
-        repo_id,
-        transformer=transformer,
-        text_encoder=text_encoder,
-        cache_dir=shared.opts.diffusers_dir,
-        **load_args,
-    )
+    try:
+        pipe = pipeline_chronoedit.ChronoEditPipeline.from_pretrained(
+            repo_id,
+            transformer=transformer,
+            text_encoder=text_encoder,
+            cache_dir=shared.opts.diffusers_dir,
+            **load_args,
+        )
+    except Exception as e:
+        import os
+        from modules import errors
+        errors.display(e, 'Chrono')
+        os._exit(1)
     pipe.postprocess = postprocess
     pipe.task_args = {
         'num_temporal_reasoning_steps': shared.opts.model_chrono_temporal_steps,