add kandinsky5

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

html/reference.json

@@ -770,7 +770,7 @@
     "size": 5.15,
     "date": "2023 July"
   },
-  "Kandinsky 3": {
+  "Kandinsky 3.0": {
     "path": "kandinsky-community/kandinsky-3",
     "desc": "Kandinsky 3.0 is an open-source text-to-image diffusion model built upon the Kandinsky2-x model family. In comparison to its predecessors, Kandinsky 3.0 incorporates more data and specifically related to Russian culture, which allows to generate pictures related to Russin culture. Furthermore, enhancements have been made to the text understanding and visual quality of the model, achieved by increasing the size of the text encoder and Diffusion U-Net models, respectively.",
     "preview": "kandinsky-community--kandinsky-3.jpg",
@@ -779,6 +779,22 @@
     "size": 27.72,
     "date": "2023 November"
   },
+  "Kandinsky 5.0 T2I Lite": {
+    "path": "kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers",
+    "desc": "Kandinsky 5.0 Image Lite is a 6B image generation models 1K resulution, high visual quality and strong text-writing",
+    "preview": "kandinskylab--Kandinsky-5.0-T2I-Lite-sft-Diffusers.jpg",
+    "skip": true,
+    "size": 33.20,
+    "date": "2025 November"
+  },
+  "Kandinsky 5.0 I2I Lite": {
+    "path": "kandinskylab/Kandinsky-5.0-I2I-Lite-sft-Diffusers",
+    "desc": "Kandinsky 5.0 Image Lite is a 6B image editing models 1K resulution, high visual quality and strong text-writing",
+    "preview": "kandinskylab--Kandinsky-5.0-I2I-Lite-sft-Diffusers.jpg",
+    "skip": true,
+    "size": 33.20,
+    "date": "2025 November"
+  },
 
   "Playground v1": {
     "path": "playgroundai/playground-v1",

modules/lora/lora_overrides.py

@@ -29,6 +29,8 @@ force_models_diffusers = [ # forced always
     # 'sd3',
     'sc',
     'h1',
+    'kandinsky5',
+    'kandinsky3',
     'kandinsky',
     'hunyuandit',
     'hunyuanimage',
@@ -43,6 +45,7 @@ force_models_diffusers = [ # forced always
     'f2',
     # video models
     'hunyuanvideo',
+    'hunyuanvideo15'
     'cogvideo',
     'wanai',
     'chrono',

modules/modeldata.py

@@ -22,6 +22,10 @@ def get_model_type(pipe):
         model_type = 'sd' # instaflow is compatible with sd
     elif "AnimateDiffPipeline" in name:
         model_type = 'sd' # animatediff is compatible with sd
+    elif "Kandinsky5" in name:
+        model_type = 'kandinsky5'
+    elif "Kandinsky3" in name:
+        model_type = 'kandinsky3'
     elif "Kandinsky" in name:
         model_type = 'kandinsky'
     elif "HunyuanDiT" in name:
@@ -75,6 +79,8 @@ def get_model_type(pipe):
     # video models
     elif "CogVideo" in name:
         model_type = 'cogvideo'
+    elif 'HunyuanVideo15':
+        model_type = 'hunyuanvideo15'
     elif 'HunyuanVideoPipeline' in name or 'HunyuanSkyreels' in name:
         model_type = 'hunyuanvideo'
     elif 'LTX' in name:

modules/sd_models.py

@@ -422,6 +422,10 @@ def load_diffuser_force(detected_model_type, checkpoint_info, diffusers_load_con
             from pipelines.model_kandinsky import load_kandinsky3
             sd_model = load_kandinsky3(checkpoint_info, diffusers_load_config)
             allow_post_quant = False
+        elif model_type in ['Kandinsky 5.0']:
+            from pipelines.model_kandinsky import load_kandinsky5
+            sd_model = load_kandinsky5(checkpoint_info, diffusers_load_config)
+            allow_post_quant = False
         elif model_type in ['NextStep']:
             from pipelines.model_nextstep import load_nextstep
             sd_model = load_nextstep(checkpoint_info, diffusers_load_config) # pylint: disable=assignment-from-none

pipelines/model_kandinsky.py

@@ -71,3 +71,40 @@ def load_kandinsky3(checkpoint_info, diffusers_load_config=None):
 
     devices.torch_gc(force=True, reason='load')
     return pipe
+
+
+def load_kandinsky5(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)
+    shared.log.debug(f'Load model: type=Kandinsky50 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.Kandinsky5Transformer3DModel, load_config=diffusers_load_config)
+    text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen2_5_VLForConditionalGeneration, load_config=diffusers_load_config)
+
+    if 'I2I' in repo_id:
+        cls = diffusers.Kandinsky5I2IPipeline
+    else:
+        cls = diffusers.Kandinsky5T2IPipeline
+
+    pipe = cls.from_pretrained(
+        repo_id,
+        transformer=transformer,
+        text_encoder=text_encoder,
+        cache_dir=shared.opts.diffusers_dir,
+        **load_args,
+    )
+    pipe.task_args = {
+        'output_type': 'np',
+    }
+
+    del text_encoder
+    del transformer
+    sd_hijack_te.init_hijack(pipe)
+    sd_hijack_vae.init_hijack(pipe)
+
+    devices.torch_gc(force=True, reason='load')
+    return pipe