fix auraflow

CHANGELOG.md

@@ -108,8 +108,9 @@ Examples:
   e.g. if generating 1024px image, reducing layers by 1 will result in preview being 512px  
   set via *settings -> live preview -> taesd decode layers*  
 - **xhinker** prompt parser handle offloaded models  
-- **control** better handle offloading
-- **control** upscale will use resize-to if set to non-zero values over resize-by  
+- **control** better handle offloading  
+- **upscale** will use resize-to if set to non-zero values over resize-by  
+  applies to any upscale options, including refine workflow  
 - **networks** add option to choose if mouse-over on network should attempt to fetch additional info  
   option:`extra_networks_fetch` enable/disable in *settings -> networks*  
 - speed up some garbage collection ops  
@@ -131,6 +132,7 @@ Examples:
 - **modernui** update  
 - **zluda** update to 3.8.4, thanks @lshqqytiger
 - update `requirements`
+- fix **AuraFlow**  
 - fix handling of model configs if offline config is not available  
 - fix vae decode in backend original  
 - fix model path typos  

modules/model_auraflow.py

@@ -1,15 +1,17 @@
+import os
 import torch
 import diffusers
+from modules import shared, sd_models, devices
 
 
-repo_id = 'fal/AuraFlow'
+debug = shared.log.trace if os.environ.get('SD_LOAD_DEBUG', None) is not None else lambda *args, **kwargs: None
 
 
-def load_auraflow(_checkpoint_info, diffusers_load_config={}):
-    from modules import shared, devices
+def load_auraflow(checkpoint_info, diffusers_load_config={}):
+    repo_id = sd_models.path_to_repo(checkpoint_info.name)
     if 'torch_dtype' not in diffusers_load_config:
         diffusers_load_config['torch_dtype'] = torch.float16
-
+    debug(f'Loading AuraFlow: repo="{repo_id}" config={diffusers_load_config}')
     pipe = diffusers.AuraFlowPipeline.from_pretrained(
         repo_id,
         cache_dir = shared.opts.diffusers_dir,

modules/sd_models.py

@@ -1236,7 +1236,7 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
                         if model_config is not None:
                             if debug_load:
                                 shared.log.debug(f'Model config: path="{model_config}"')
-                            diffusers_load_config['config_files'] = model_config
+                            diffusers_load_config['config'] = model_config
                 if model_type.startswith('Stable Diffusion 3'):
                     from modules.model_sd3 import load_sd3
                     sd_model = load_sd3(fn=checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, config=diffusers_load_config.get('config', None))
@@ -1566,6 +1566,10 @@ def set_diffusers_attention(pipe):
                 module.set_attn_processor(p.FluxAttnProcessor2_0())
             elif module.__class__.__name__ in ['HunyuanDiT2DModel']:
                 module.set_attn_processor(p.HunyuanAttnProcessor2_0())
+            elif module.__class__.__name__ in ['AuraFlowTransformer2DModel']:
+                module.set_attn_processor(p.AuraFlowAttnProcessor2_0())
+            elif 'Transformer' in module.__class__.__name__:
+                pass # unknown transformer so probably dont want to force attention processor
             else:
                 module.set_attn_processor(attention)