hyper-sd notes

CHANGELOG.md

@@ -19,7 +19,7 @@ Thanks to @BinaryQuantumSoul for his hard work on this project!
 Given huge number of changes with *+3443/-3342 commits diff over the past year, a completely different backend/engine and a change of focus,  
 it is time to give credit to original [author](https://github.com/auTOMATIC1111),  and move on!  
 
-## Update for 2024-05-04
+## Update for 2024-05-06
 
 - **Features**:
   - **ModernUI** preview of the new [ModernUI](https://github.com/BinaryQuantumSoul/sdnext-modernui)  
@@ -81,6 +81,9 @@ it is time to give credit to original [author](https://github.com/auTOMATIC1111)
     sdxs is an extremely fast 1-step generation consistency model that also uses TAESD as quick VAE out-of-the-box  
     to use, simply select from *networks -> models -> SDXS*  
     set parameters: *sampler: CMSI, steps: 1, cfg_scale: 0.0*
+  - [Hyper-SD](https://huggingface.co/ByteDance/Hyper-SD)  
+    sd15 and sdxl 1-step, 2-step, 4-step and 8-step optimized models using lora  
+    set parameters: *sampler: TCD or LCM, steps: 1/2/4/8, cfg_scale: 0.0*
 - **Changes**:
   - Removed built-in extensions: *ControlNet* and *Image-Browser*  
     as both *image-browser* and *controlnet* have native built-in equivalents  

extensions-builtin/Lora/network_overrides.py

@@ -0,0 +1,28 @@
+from modules import shared
+
+
+force_diffusers = [
+    'aaebf6360f7d', # sd15-lcm        
+    '3d18b05e4f56', # sdxl-lcm
+    'b71dcb732467', # sdxl-tcd
+    '813ea5fb1c67', # sdxl-turbo
+    # not really needed, but just in case
+    '5a48ac366664', # hyper-sd15-1step
+    'ee0ff23dcc42', # hyper-sd15-2step
+    'e476eb1da5df', # hyper-sd15-4step
+    'ecb844c3f3b0', # hyper-sd15-8step
+    '1ab289133ebb', # hyper-sd15-8step-cfg
+    '4f494295edb1', # hyper-sdxl-8step
+    'ca14a8c621f8', # hyper-sdxl-8step-cfg
+    '1c88f7295856', # hyper-sdxl-4step
+    'fdd5dcd1d88a', # hyper-sdxl-2step
+    '8cca3706050b', # hyper-sdxl-1step
+]
+
+def check_override(shorthash):
+    if len(shorthash) < 4:
+        return False
+    force = any([x.startswith(shorthash) for x in force_diffusers])
+    if force:
+        shared.log.debug('LoRA override: force diffusers')
+    return force

extensions-builtin/Lora/networks.py

@@ -13,6 +13,7 @@ import network_lokr
 import network_full
 import network_norm
 import network_glora
+import network_overrides
 import lora_convert
 import torch
 import diffusers.models.lora
@@ -188,18 +189,15 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
     for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
         net = None
         if network_on_disk is not None:
+            shorthash = getattr(network_on_disk, 'shorthash', '').lower()
             if debug:
-                shared.log.debug(f'LoRA load start: name="{name}" file="{network_on_disk.filename}"')
+                shared.log.debug(f'LoRA load: name="{name}" file="{network_on_disk.filename}" hash="{shorthash}"')
             try:
                 if recompile_model:
                     shared.compiled_model_state.lora_model.append(f"{name}:{te_multipliers[i] if te_multipliers else 1.0}")
-                shorthash = getattr(network_on_disk, 'shorthash', '').lower()
-                if shared.backend == shared.Backend.DIFFUSERS and (shared.opts.lora_force_diffusers # OpenVINO only works with Diffusers LoRa loading.
-                        or shorthash == 'aaebf6360f7d' # sd15-lcm
-                        or shorthash == '3d18b05e4f56' # sdxl-lcm
-                        or shorthash == 'b71dcb732467' # sdxl-tcd
-                        or shorthash == '813ea5fb1c67' # sdxl-turbo
-                    ):
+                if shared.backend == shared.Backend.DIFFUSERS and shared.opts.lora_force_diffusers: # OpenVINO only works with Diffusers LoRa loading
+                    net = load_diffusers(name, network_on_disk, lora_scale=te_multipliers[i] if te_multipliers else 1.0)
+                elif shared.backend == shared.Backend.DIFFUSERS and network_overrides.check_override(shorthash):
                     net = load_diffusers(name, network_on_disk, lora_scale=te_multipliers[i] if te_multipliers else 1.0)
                 else:
                     net = load_network(name, network_on_disk)