Ruff fix and refiner->is_refiner.

modules/api/api.py

@@ -448,7 +448,7 @@ class Api:
 
     def get_samplers(self):
         return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
-    
+
     def get_sd_vaes(self):
         return [{"model_name": x, "filename": vae_dict[x]} for x in vae_dict.keys()]
 

modules/processing_diffusers.py

@@ -8,6 +8,7 @@ import modules.images as images
 from modules.lora_diffusers import lora_state, unload_diffusers_lora
 from modules.processing import StableDiffusionProcessing
 import modules.prompt_parser_diffusers as prompt_parser_diffusers
+import typing
 
 try:
     import diffusers
@@ -51,7 +52,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
             return latents
 
 
-    def set_pipeline_args(model, prompt, negative_prompt, prompt_2=None, negative_prompt_2=None, refiner=False, **kwargs):
+    def set_pipeline_args(model, prompt: str, negative_prompt: str, prompt_2: typing.Optional[str] =None, negative_prompt_2: typing.Optional[str] = None, is_refiner: bool = False, **kwargs):
         args = {}
         pipeline = model
         signature = inspect.signature(type(pipeline).__call__)
@@ -63,7 +64,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
         negative_embed = None
         negative_pooled = None
         if shared.opts.data['prompt_attention'] != 'Fixed attention':
-            prompt_embed, pooled, negative_embed, negative_pooled = prompt_parser_diffusers.compel_encode_prompt(model, prompt, negative_prompt, prompt_2, negative_prompt_2, refiner)
+            prompt_embed, pooled, negative_embed, negative_pooled = prompt_parser_diffusers.compel_encode_prompt(model, prompt, negative_prompt, prompt_2, negative_prompt_2, is_refiner)
         if 'prompt' in possible:
             if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and prompt_embed is not None:
                 args['prompt_embeds'] = prompt_embed
@@ -157,7 +158,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
         denoising_start=0 if refiner_enabled and p.refiner_start > 0 and p.refiner_start < 1 else None,
         denoising_end=p.refiner_start if refiner_enabled and p.refiner_start > 0 and p.refiner_start < 1 else None,
         output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
-        refiner=False,
+        is_refiner=False,
         **task_specific_kwargs
     )
     output = shared.sd_model(**pipe_args) # pylint: disable=not-callable
@@ -211,7 +212,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
                 denoising_end=1 if p.refiner_start > 0 and p.refiner_start < 1 else None,
                 image=output.images[i],
                 output_type='latent' if hasattr(shared.sd_refiner, 'vae') else 'np',
-                refiner=True
+                is_refiner=True
             )
             refiner_output = shared.sd_refiner(**pipe_args) # pylint: disable=not-callable
             if not shared.state.interrupted and not shared.state.skipped:
@@ -230,4 +231,4 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
 
 
 
-    return results
+    return results

modules/prompt_parser_diffusers.py

@@ -26,7 +26,7 @@ def compel_encode_prompt(pipeline: typing.Any, *args, **kwargs):
       raise TypeError(f"Compel encoding not yet supported for {type(pipeline).__name__}.")
    return compel_encode_fn(pipeline, *args, **kwargs)
 
-def compel_encode_prompt_sdxl(pipeline: diffusers.StableDiffusionXLPipeline, prompt: str, negative_prompt: str, prompt_2: typing.Optional[str]=None, negative_prompt_2: typing.Optional[str]=None, refiner=False):
+def compel_encode_prompt_sdxl(pipeline: diffusers.StableDiffusionXLPipeline, prompt: str, negative_prompt: str, prompt_2: typing.Optional[str]=None, negative_prompt_2: typing.Optional[str]=None, is_refiner: bool = False):
     if shared.opts.data['prompt_attention'] != 'Compel parser':
         prompt = convert_to_compel(prompt)
         negative_prompt = convert_to_compel(negative_prompt)
@@ -46,20 +46,20 @@ def compel_encode_prompt_sdxl(pipeline: diffusers.StableDiffusionXLPipeline, pro
       returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
       requires_pooled=True,
       )
-    if refiner is False:
+    if not is_refiner:
       positive_te1 = compel_te1(prompt)
-      positive_te2, pooled = compel_te2(prompt_2)
+      positive_te2, positive_pooled = compel_te2(prompt_2)
       positive = torch.cat((positive_te1, positive_te2), dim=-1)
 
       negative_te1 = compel_te1(negative_prompt)
       negative_te2, negative_pooled = compel_te2(negative_prompt_2)
       negative = torch.cat((negative_te1, negative_te2), dim=-1)
     else:
-      positive, pooled = compel_te2(prompt)
+      positive, positive_pooled = compel_te2(prompt)
       negative, negative_pooled = compel_te2(negative_prompt)
 
     shared.log.debug(compel_te1.parse_prompt_string(prompt))
     [prompt_embed, negative_embed] = compel_te2.pad_conditioning_tensors_to_same_length([positive, negative])
-    return prompt_embed, pooled, negative_embed, negative_pooled
+    return prompt_embed, positive_pooled, negative_embed, negative_pooled
 
 COMPEL_ENCODE_FN_DICT = {diffusers.StableDiffusionXLPipeline: compel_encode_prompt_sdxl}