restore orig init image for each batch sequence

Signed-off-by: Vladimir Mandic <mandic00@live.com>

CHANGELOG.md

@@ -5,6 +5,7 @@
 - **Features**
   - **offline mode**: enable in *settings -> hugginface*  
     enables fully offline mode where previously downloaded models are used as-is  
+    *note*: must be enabled only after all packages have been installed and model has been run online at least once  
 - **Backend**
   - switch to `torch==2.9` for *ipex, rocm and openvino*  
   - switch to `rocm==7.0` for nightlies  
@@ -14,6 +15,7 @@
   - **scheduler** add base and max shift parameters for flow-matching samplers  
 - **Fixes**
   - startup error with `--profile` enabled if using `--skip`  
+  - restore orig init image for each batch sequence  
 
 ## Update for 2025-10-18
 

modules/processing.py

@@ -391,7 +391,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         if not hasattr(p, 'skip_init'):
             p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
         debug(f'Processing inner: args={vars(p)}')
+        p.iter_init_images = p.init_images # required so we use same starting non-processed images for each batch sequence
         for n in range(p.n_iter):
+            p.init_images = p.iter_init_images
             if p.n_iter > 1:
                 shared.log.debug(f'Processing: batch={n+1} total={p.n_iter} progress={(n+1)/p.n_iter:.2f}')
             shared.state.batch_no = n + 1

modules/processing_args.py

@@ -123,11 +123,11 @@ def task_specific_kwargs(p, model):
         }
 
     # model specific args
-    if 'QwenImageEdit' in model_cls and (p.init_images is None or len(p.init_images) == 0):
+    if ('QwenImageEdit' in model_cls) and (p.init_images is None or len(p.init_images) == 0):
         task_args['image'] = [Image.new('RGB', (p.width, p.height), (0, 0, 0))] # monkey-patch so qwen-image-edit pipeline does not error-out on t2i
-    if 'QwenImageEditPlusPipeline' in model_cls and p.init_control is not None and len(p.init_control) > 0:
+    if ('QwenImageEditPlusPipeline' in model_cls) and (p.init_control is not None) and (len(p.init_control) > 0):
         task_args['image'] += p.init_control
-    if 'LatentConsistencyModelPipeline' in model_cls and len(p.init_images) > 0:
+    if ('LatentConsistencyModelPipeline' in model_cls) and (len(p.init_images) > 0):
         p.ops.append('lcm')
         init_latents = [processing_vae.vae_encode(image, model=shared.sd_model, vae_type=p.vae_type).squeeze(dim=0) for image in p.init_images]
         init_latent = torch.stack(init_latents, dim=0).to(shared.device)
@@ -138,6 +138,10 @@ def task_specific_kwargs(p, model):
             'width': p.width,
             'height': p.height,
         }
+    if ('WanImageToVideoPipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
+        task_args['image'] = p.init_images[0]
+    if ('WanVACEPipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
+        task_args['reference_images'] = p.init_images
     if 'BlipDiffusionPipeline' in model_cls:
         if len(p.init_images) == 0:
             shared.log.error('BLiP diffusion requires init image')
@@ -148,10 +152,6 @@ def task_specific_kwargs(p, model):
             'target_subject_category': getattr(p, 'prompt', '').split()[-1],
             'output_type': 'pil',
         }
-    if ('WanImageToVideoPipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
-        task_args['image'] = p.init_images[0]
-    if ('WanVACEPipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
-        task_args['reference_images'] = p.init_images
 
     if debug_enabled:
         debug_log(f'Process task specific args: {task_args}')