fix omnigen

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

cli/test-all-models.py

@@ -3,6 +3,8 @@
 - fal/AuraFlow-v0.3: layer_class_name=Linear layer_weight_shape=torch.Size([3072, 2, 1024]) weights_dtype=int8 unsupported
 - nvidia/Cosmos-Predict2-2B-Text2Image: mat1 and mat2 shapes cannot be multiplied (512x4096 and 1024x2048)
 - nvidia/Cosmos-Predict2-14B-Text2Image: mat1 and mat2 shapes cannot be multiplied (512x4096 and 1024x5120)
+- HiDream-ai/HiDream-I1-Full: 30+s/it
+- Kwai-Kolors/Kolors-diffusers: `set_input_embeddings` not auto‑handled for ChatGLMModel
 """
 
 import io
@@ -34,17 +36,18 @@ models = [
     "stabilityai/stable-diffusion-3.5-medium",
     "stabilityai/stable-diffusion-3.5-large",
     "fal/AuraFlow-v0.3",
+    "fal/AuraFlow-v0.2",
     "zai-org/CogView4-6B",
     "zai-org/CogView3-Plus-3B",
     # "nvidia/Cosmos-Predict2-2B-Text2Image",
     # "nvidia/Cosmos-Predict2-14B-Text2Image",
     "Qwen/Qwen-Image",
-    "Qwen/Qwen-Lightning",
+    "vladmandic/Qwen-Lightning",
     "Shitao/OmniGen-v1-diffusers",
     "OmniGen2/OmniGen2",
-    "HiDream-ai/HiDream-I1-Full",
+    # "HiDream-ai/HiDream-I1-Full",
     "Kwai-Kolors/Kolors-diffusers",
-    "vladmandic/chroma-unlocked-v50",
+    "lodestones/Chroma1-HD",
     "vladmandic/chroma-unlocked-v50-annealed",
     "vladmandic/chroma-unlocked-v48",
     "vladmandic/chroma-unlocked-v48-detail-calibrated",
@@ -104,6 +107,7 @@ def write_history(model:str, style:str, image:str='', size:tuple=(0,0), duration
     fn = os.path.join(output_folder, 'history.json')
     history.append({
         'model': model,
+        'title': model.split('/')[-1].replace('_diffusers', '').replace('-diffusers', ''),
         'style': style,
         'image': image,
         'size': size,
@@ -160,13 +164,13 @@ def generate(): # pylint: disable=redefined-outer-name
                     image.save(fn)
                     write_history(model=model, style=style, image=fn, size=image.size, duration=round(t1-t0, 3), info=info)
                 else:
-                    write_history(model=model, style=style, duration=round(t1-t0, 3), error='no image')
+                    # write_history(model=model, style=style, duration=round(t1-t0, 3), error='no image')
                     log.error(f' model: error="{model}" style="{style}" no image')
             except Exception as e:
                 if 'Connection refused' in str(e) or 'RemoteDisconnected' in str(e):
                     log.error('server offline')
                     os._exit(1)
-                write_history(model=model, style=style, duration=round(t1-t0, 3), error=str(e))
+                # write_history(model=model, style=style, duration=round(t1-t0, 3), error=str(e))
                 log.error(f' model: error="{model}" style="{style}" exception="{e}"')
 
 

html/reference.json

@@ -141,7 +141,7 @@
   },
 
   "lodestones Chroma Unlocked HD": {
-    "path": "vladmandic/chroma-unlocked-v50",
+    "path": "lodestones/Chroma1-HD",
     "preview": "lodestones--Chroma.jpg",
     "desc": "Chroma is a 8.9B parameter model based on FLUX.1-schnell. It’s fully Apache 2.0 licensed, ensuring that anyone can use, modify, and build on top of it—no corporate gatekeeping. The model is still training right now, and I’d love to hear your thoughts! Your input and feedback are really appreciated.",
     "skip": true,

modules/shared_items.py

@@ -42,7 +42,7 @@ pipelines = {
     'UniDiffuser': getattr(diffusers, 'UniDiffuserPipeline', None),
     'Amused': getattr(diffusers, 'AmusedPipeline', None),
     'HiDream': getattr(diffusers, 'HiDreamImagePipeline', None),
-    'OmniGenPipeline': getattr(diffusers, 'OmniGenPipeline', None),
+    'OmniGen': getattr(diffusers, 'OmniGenPipeline', None),
     'Cosmos': getattr(diffusers, 'Cosmos2TextToImagePipeline', None),
     'WanAI': getattr(diffusers, 'WanPipeline', None),
     'Qwen': getattr(diffusers, 'QwenImagePipeline', None),

pipelines/model_chroma.py

@@ -18,7 +18,7 @@ def load_chroma(checkpoint_info, diffusers_load_config={}):
     transformer = generic.load_transformer(repo_id, cls_name=diffusers.ChromaTransformer2DModel, load_config=diffusers_load_config)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config)
 
-    pipe = diffusers.AuraFlowPipeline.from_pretrained(
+    pipe = diffusers.ChromaPipeline.from_pretrained(
         repo_id,
         transformer=transformer,
         text_encoder=text_encoder,

pipelines/model_qwen.py

@@ -22,7 +22,6 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
         cache_dir=shared.opts.diffusers_dir,
         **load_args,
     )
-    print('HERE4')
     pipe.task_args = {
         'output_type': 'np',
     }