update changelog

CHANGELOG.md

@@ -2,44 +2,40 @@
 
 ## Pending
 
-- SC Lora
-- HunyuanDiT 1.1
 - Diffusers==0.30.0
 - https://github.com/huggingface/diffusers/issues/8546
 - https://github.com/huggingface/diffusers/pull/8566
 - https://github.com/huggingface/diffusers/pull/8584
 
-## Update for 2024-06-17
+## Update for 2024-06-18
 
-### Highlights for 2024-06-17
+### Highlights for 2024-06-18
 
 Following zero-day **SD3** release, a week later here's a refresh with more than a few improvements.  
 But there's more than SD3:
 - support for **PixArt-Sigma** in small/medium/large variants AND using 4/8/16bit quantized T5 text-encoder!  
+- support for **HunyuanDiT 1.1**  
 - (finally) new release of **Torch-DirectML**  
 
-### Models
+### Model Improvements
 
-#### Stable Diffusion 3
-
-- enable taesd preview and non-full quality mode  
-- enable base LoRA support  
-- add support for 4bit quantized t5 text encoder  
+- **SD3**: enable tiny-VAE (TAESD) preview and non-full quality mode  
+- SD3: enable base LoRA support  
+- SD3: add support for 4bit quantized T5 text encoder  
   simply select in *settings -> model -> text encoder*
-- simplified loading of model in single-file safetensors format  
+- SD3: simplified loading of model in single-file safetensors format  
   loading sd3 can now be performed fully offline  
-- add support for nncf compressed weights, thanks @Disty0!
-- add support for sampler shift for Euler FlowMatch  
+- SD3: add support for nncf compressed weights, thanks @Disty0!
+- SD3: add support for sampler shift for Euler FlowMatch  
   see *settings -> samplers*, also available as param in xyz grid  
   higher shift means model will spend more time on structure and less on details  
-- add support for selecting text encoder in xyz grid
-
-#### Pixart-Σ
-
-- Add *small* (512px) and *large* (2k) variations, in addition to existing *medium* (1k)  
-- Add support for 4/8bit quantized t5 text encoder  
+- SD3: add support for selecting text encoder in xyz grid
+- **Pixart-Σ**: Add *small* (512px) and *large* (2k) variations, in addition to existing *medium* (1k)  
+- Pixart-Σ: Add support for 4/8bit quantized t5 text encoder  
   *note* by default pixart-Σ uses full fp16 t5 encoder with large memory footprint  
   simply select in *settings -> model -> text encoder* before or after model load  
+- **HunyuanDiT**: support for model version 1.1  
+
 
 ### Improvements: General
 
@@ -58,7 +54,7 @@ But there's more than SD3:
 - fix unsaturated outputs, force apply vae config on model load  
 - fix hidiffusion handling of non-square aspect ratios, thanks @ShenZhang-Shin!
 - fix control second pass resize  
-- fix **hunyuandit** set attention processor
+- fix hunyuandit set attention processor
 - fix civitai download without name
 - fix compatibility with latest adetailer
 - fix invalid sampler warning

extensions-builtin/Lora/network_overrides.py

@@ -26,9 +26,6 @@ force_diffusers = [ # forced always
 
 force_models = [ # forced always
     'sd3',
-    'sc',
-    'hunyuandit',
-    'kandinsky',
 ]
 
 force_classes = [ # forced always
@@ -37,7 +34,7 @@ force_classes = [ # forced always
 
 def check_override(shorthash=''):
     force = False
-    force = force or (shared.sd_model_type in force_classes)
+    force = force or (shared.sd_model_type in force_models)
     force = force or (shared.sd_model.__class__.__name__ in force_classes)
     if len(shorthash) < 4:
         return force

extensions-builtin/Lora/networks.py

@@ -49,6 +49,7 @@ def assign_network_names_to_compvis_modules(sd_model):
     network_layer_mapping = {}
     if shared.native:
         if not hasattr(shared.sd_model, 'text_encoder') or not hasattr(shared.sd_model, 'unet'):
+            sd_model.network_layer_mapping = {}
             return
         for name, module in shared.sd_model.text_encoder.named_modules():
             prefix = "lora_te1_" if shared.sd_model_type == "sdxl" else "lora_te_"
@@ -66,6 +67,7 @@ def assign_network_names_to_compvis_modules(sd_model):
             module.network_layer_name = network_name
     else:
         if not hasattr(shared.sd_model, 'cond_stage_model'):
+            sd_model.network_layer_mapping = {}
             return
         for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
             network_name = name.replace(".", "_")
@@ -87,10 +89,14 @@ def load_diffusers(name, network_on_disk, lora_scale=1.0) -> network.Network:
         return cached
     if not shared.native:
         return None
+    if not hasattr(shared.sd_model, 'load_lora_weights'):
+        shared.log.error(f"LoRA load failed: class={shared.sd_model.__class__} does not implement load lora")
+        return None
     try:
         shared.sd_model.load_lora_weights(network_on_disk.filename)
     except Exception as e:
         errors.display(e, "LoRA")
+        return None
     if shared.opts.lora_fuse_diffusers:
         shared.sd_model.fuse_lora(lora_scale=lora_scale)
     net = network.Network(name, network_on_disk)