prereqs

modules/processing.py

@@ -543,12 +543,20 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
         index = position_in_batch + iteration * p.batch_size
     if all_prompts is None:
         all_prompts = p.all_prompts
+    if all_negative_prompts is None:
+        all_negative_prompts = p.all_negative_prompts
     if all_seeds is None:
         all_seeds = p.all_seeds
     if all_subseeds is None:
         all_subseeds = p.all_subseeds
-    if all_negative_prompts is None:
-        all_negative_prompts = p.all_negative_prompts
+    while len(all_prompts) <= index:
+        all_prompts.append(all_prompts[-1])
+    while len(all_seeds) <= index:
+        all_seeds.append(all_seeds[-1])
+    while len(all_subseeds) <= index:
+        all_subseeds.append(all_subseeds[-1])
+    while len(all_negative_prompts) <= index:
+        all_negative_prompts.append(all_negative_prompts[-1])
     comment = ', '.join(comments) if comments is not None and type(comments) is list else None
     ops = list(set(p.ops))
     ops.reverse()
@@ -768,6 +776,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
 
 def validate_sample(tensor):
+    if not isinstance(tensor, np.ndarray) and not isinstance(tensor, torch.Tensor):
+        return tensor
     if tensor.dtype == torch.bfloat16: # numpy does not support bf16
         tensor = tensor.to(torch.float16)
     if shared.backend == shared.Backend.ORIGINAL:

modules/processing_diffusers.py

@@ -478,6 +478,9 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
     p.extra_generation_params["Sampler Eta"] = shared.opts.scheduler_eta if shared.opts.scheduler_eta is not None and shared.opts.scheduler_eta > 0 and shared.opts.scheduler_eta < 1 else None
     try:
         output = shared.sd_model(**base_args) # pylint: disable=not-callable
+        if not hasattr(output, 'images') and hasattr(output, 'frames'):
+            shared.log.debug(f'Generated: frames={len(output.frames[0])}')
+            output.images = output.frames[0]
     except AssertionError as e:
         shared.log.info(e)
     except ValueError as e:

modules/sd_models.py

@@ -637,10 +637,12 @@ def detect_pipeline(f: str, op: str = 'model', warning=True):
             else:
                 guess = 'Stable Diffusion'
             # guess by name
-            if 'LCM_' in f or 'LCM-' or '_LCM' or '-LCM' in f.upper():
+            """
+            if 'LCM_' in f.upper() or 'LCM-' in f.upper() or '_LCM' in f.upper() or '-LCM' in f.upper():
                 if shared.backend == shared.Backend.ORIGINAL:
                     warn(f'Model detected as LCM model, but attempting to load using backend=original: {op}={f} size={size} MB')
                 guess = 'Latent Consistency Model'
+            """
             if 'PixArt' in f:
                 if shared.backend == shared.Backend.ORIGINAL:
                     warn(f'Model detected as PixArt Alpha model, but attempting to load using backend=original: {op}={f} size={size} MB')

requirements.txt

@@ -69,3 +69,4 @@ Pillow==10.1.0
 timm==0.9.7
 pydantic==1.10.13
 typing-extensions==4.8.0
+peft