allow different lora in hires

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

CHANGELOG.md

@@ -11,6 +11,7 @@
   - pipelines: add **ZImageInpaint**, thanks @CalamitousFelicitousness  
   - add `--remote` command line flag that reduces client/server chatter and improves link stability  
     for long-running generates, useful when running on remote servers  
+  - hires: allow using different lora in refiner prompt  
 - **UI**
   - ui: **themes** add *CTD-NT64Light* and *CTD-NT64Dark*, thanks @resonantsky  
   - ui: **gallery** add option to auto-refresh gallery, thanks @awsr  

modules/extra_networks.py

@@ -156,7 +156,6 @@ def parse_prompt(prompt: str | None) -> tuple[str, defaultdict[str, list[ExtraNe
     if prompt is None:
         return "", res
     if isinstance(prompt, list):
-        shared.log.warning(f"parse_prompt was called with a list instead of a string: {prompt}")
         return parse_prompts(prompt)
 
     def found(m: re.Match[str]):
@@ -168,13 +167,17 @@ def parse_prompt(prompt: str | None) -> tuple[str, defaultdict[str, list[ExtraNe
     return updated_prompt, res
 
 
-def parse_prompts(prompts: list[str]):
+def parse_prompts(prompts: list[str], extra_data=None):
     updated_prompt_list: list[str] = []
-    extra_data: defaultdict[str, list[ExtraNetworkParams]] = defaultdict(list)
+    extra_data: defaultdict[str, list[ExtraNetworkParams]] = extra_data or defaultdict(list)
     for prompt in prompts:
         updated_prompt, parsed_extra_data = parse_prompt(prompt)
         if not extra_data:
             extra_data = parsed_extra_data
+        elif parsed_extra_data:
+            extra_data = parsed_extra_data
+        else:
+            pass
         updated_prompt_list.append(updated_prompt)
 
     return updated_prompt_list, extra_data

modules/processing.py

@@ -272,7 +272,6 @@ def process_init(p: StableDiffusionProcessing):
             p.all_prompts, p.all_negative_prompts = shared.prompt_styles.apply_styles_to_prompts(p.all_prompts, p.all_negative_prompts, p.styles, p.all_seeds)
             p.prompts = p.all_prompts[(p.iteration * p.batch_size):((p.iteration+1) * p.batch_size)]
             p.negative_prompts = p.all_negative_prompts[(p.iteration * p.batch_size):((p.iteration+1) * p.batch_size)]
-        p.prompts, _ = extra_networks.parse_prompts(p.prompts)
 
 
 def process_samples(p: StableDiffusionProcessing, samples):

modules/processing_diffusers.py

@@ -171,6 +171,7 @@ def process_base(p: processing.StableDiffusionProcessing):
         modelstats.analyze()
     try:
         t0 = time.time()
+        p.prompts, p.network_data = extra_networks.parse_prompts(p.prompts, p.network_data)
         extra_networks.activate(p, exclude=['text_encoder', 'text_encoder_2', 'text_encoder_3'])
 
         if hasattr(shared.sd_model, 'tgate') and getattr(p, 'gate_step', -1) > 0:
@@ -297,10 +298,20 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
             p.denoising_strength = strength
             orig_image = p.task_args.pop('image', None) # remove image override from hires
             process_pre(p)
+
+            prompts = p.prompts
+            reset_prompts = False
+            if len(p.refiner_prompt) > 0:
+                prompts = len(output.images)* [p.refiner_prompt]
+                prompts, p.network_data = extra_networks.parse_prompts(prompts)
+                reset_prompts = True
+            if reset_prompts or ('base' in p.skip):
+                extra_networks.activate(p)
+
             hires_args = set_pipeline_args(
                 p=p,
                 model=shared.sd_model,
-                prompts=len(output.images)* [p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts,
+                prompts=prompts,
                 negative_prompts=len(output.images) * [p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts,
                 prompts_2=len(output.images) * [p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts,
                 negative_prompts_2=len(output.images) * [p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts,
@@ -314,11 +325,10 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
                 strength=strength,
                 desc='Hires',
             )
+
             hires_steps = hires_args.get('prior_num_inference_steps', None) or p.hr_second_pass_steps or hires_args.get('num_inference_steps', None)
             shared.state.update(get_job_name(p, shared.sd_model), hires_steps, 1)
             try:
-                if 'base' in p.skip:
-                    extra_networks.activate(p)
                 taskid = shared.state.begin('Inference')
                 output = shared.sd_model(**hires_args) # pylint: disable=not-callable
                 shared.state.end(taskid)