update

model_loading.py

@@ -240,37 +240,37 @@ class DownloadAndLoadCogVideoModel:
         
         #LoRAs
         if lora is not None:
-            # from .lora_utils import merge_lora#, load_lora_into_transformer
-            # if "fun" in model.lower():
-            #     for l in lora:
-            #         log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
-            #         transformer = merge_lora(transformer, l["path"], l["strength"])
-            #else:
-            adapter_list = []
-            adapter_weights = []
-            for l in lora:
-                fuse = True if l["fuse_lora"] else False
-                lora_sd = load_torch_file(l["path"])             
-                for key, val in lora_sd.items():
-                    if "lora_B" in key:
-                        lora_rank = val.shape[1]
-                        break
-                log.info(f"Merging rank {lora_rank} LoRA weights from {l['path']} with strength {l['strength']}")
-                adapter_name = l['path'].split("/")[-1].split(".")[0]
-                adapter_weight = l['strength']
-                pipe.load_lora_weights(l['path'], weight_name=l['path'].split("/")[-1], lora_rank=lora_rank, adapter_name=adapter_name)
-                
-                #transformer = load_lora_into_transformer(lora, transformer)
-                adapter_list.append(adapter_name)
-                adapter_weights.append(adapter_weight)
-            for l in lora:
-                pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
-            if fuse:
-                lora_scale = 1
-                dimension_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
-                if any(item in lora[-1]["path"].lower() for item in dimension_loras):
-                    lora_scale = lora_scale / lora_rank
-                pipe.fuse_lora(lora_scale=lora_scale, components=["transformer"])
+            try:
+                adapter_list = []
+                adapter_weights = []
+                for l in lora:
+                    fuse = True if l["fuse_lora"] else False
+                    lora_sd = load_torch_file(l["path"])             
+                    for key, val in lora_sd.items():
+                        if "lora_B" in key:
+                            lora_rank = val.shape[1]
+                            break
+                    log.info(f"Merging rank {lora_rank} LoRA weights from {l['path']} with strength {l['strength']}")
+                    adapter_name = l['path'].split("/")[-1].split(".")[0]
+                    adapter_weight = l['strength']
+                    pipe.load_lora_weights(l['path'], weight_name=l['path'].split("/")[-1], lora_rank=lora_rank, adapter_name=adapter_name)
+                    
+                    #transformer = load_lora_into_transformer(lora, transformer)
+                    adapter_list.append(adapter_name)
+                    adapter_weights.append(adapter_weight)
+                for l in lora:
+                    pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
+                if fuse:
+                    lora_scale = 1
+                    dimension_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
+                    if any(item in lora[-1]["path"].lower() for item in dimension_loras):
+                        lora_scale = lora_scale / lora_rank
+                    pipe.fuse_lora(lora_scale=lora_scale, components=["transformer"])
+            except: #Fun trainer LoRAs are loaded differently
+                from .lora_utils import merge_lora
+                for l in lora:
+                    log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
+                    transformer = merge_lora(transformer, l["path"], l["strength"])
 
         if "fused" in attention_mode:
             from diffusers.models.attention import Attention

readme.md

@@ -1,5 +1,32 @@
 # WORK IN PROGRESS
 
+## BREAKING Update8
+
+This is big one, and unfortunately to do the necessary cleanup and refactoring this will break every old workflow as they are.
+I apologize for the inconvenience, if I don't do this now I'll keep making it worse until maintaining becomes too much of a chore, so from my pov there was no choice.
+
+*Please either use the new workflows or fix the nodes in your old ones before posting issue reports!*
+
+Old version will be kept in a legacy branch, but not maintained
+
+- Support CogVideoX 1.5 models
+- Major code cleanup (it was bad, still isn't great, wip)
+- Merge Fun -model functionality into main pipeline:
+    - All Fun specific nodes, besides image encode node for Fun -InP models are gone
+    - Main CogVideo Sampler works with Fun models
+    - DimensionX LoRAs now work with Fun models as well
+
+- Remove width/height from the sampler widgets and detect from input instead, this meanst text2vid now requires using empty latents
+- Separate VAE from the model, allow using fp32 VAE
+- Add ability to load some of the non-GGUF models as single files (only few available for now: https://huggingface.co/Kijai/CogVideoX-comfy)
+- Add some torchao quantizations as options
+- Add interpolation as option for the main encode node, old interpolation specific node is gone
+- torch.compile optimizations
+- Remove PAB in favor of FasterCache and cleaner code
+- other smaller things I forgot about at this point
+
+For Fun -model based workflows it's more drastic change, for others migrating generally means re-setting many of the nodes.
+
 ## Update7
 
 - Refactored the Fun version's sampler to accept any resolution, this should make it lot simpler to use with Tora. **BREAKS OLD WORKFLOWS**, old FunSampler nodes need to be remade.