Kandinsky5 model support (#10988)

* Add Kandinsky5 model support

lite and pro T2V tested to work

* Update kandinsky5.py

* Fix fp8

* Fix fp8_scaled text encoder

* Add transformer_options for attention

* Code cleanup, optimizations, use fp32 for all layers originally at fp32

* ImageToVideo -node

* Fix I2V, add necessary latent post process nodes

* Support text to image model

* Support block replace patches (SLG mostly)

* Support official LoRAs

* Don't scale RoPE for lite model as that just doesn't work...

* Update supported_models.py

* Rever RoPE scaling to simpler one

* Fix typo

* Handle latent dim difference for image model in the VAE instead

* Add node to use different prompts for clip_l and qwen25_7b

* Reduce peak VRAM usage a bit

* Further reduce peak VRAM consumption by chunking ffn

* Update chunking

* Update memory_usage_factor

* Code cleanup, don't force the fp32 layers as it has minimal effect

* Allow for stronger changes with first frames normalization

Default values are too weak for any meaningful changes, these should probably be exposed as advanced node options when that's available.

* Add image model's own chat template, remove unused image2video template

* Remove hard error in ReplaceVideoLatentFrames -node

* Update kandinsky5.py

* Update supported_models.py

* Fix typos in prompt template

They were now fixed in the original repository as well

* Update ReplaceVideoLatentFrames

Add tooltips
Make source optional
Better handle negative index

* Rename NormalizeVideoLatentFrames -node

For bit better clarity what it does

* Fix NormalizeVideoLatentStart node out on non-op

comfy/lora.py

@@ -322,6 +322,13 @@ def model_lora_keys_unet(model, key_map={}):
                 key_map["diffusion_model.{}".format(key_lora)] = to
                 key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = to
 
+    if isinstance(model, comfy.model_base.Kandinsky5):
+        for k in sdk:
+            if k.startswith("diffusion_model.") and k.endswith(".weight"):
+                key_lora = k[len("diffusion_model."):-len(".weight")]
+                key_map["{}".format(key_lora)] = k
+                key_map["transformer.{}".format(key_lora)] = k
+
     return key_map