feat: add clip skip handling (#2999)

modules/async_worker.py

@@ -174,6 +174,7 @@ def worker():
         adm_scaler_negative = args.pop()
         adm_scaler_end = args.pop()
         adaptive_cfg = args.pop()
+        clip_skip = args.pop()
         sampler_name = args.pop()
         scheduler_name = args.pop()
         vae_name = args.pop()
@@ -297,6 +298,7 @@ def worker():
             adm_scaler_end = 0.0
 
         print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
+        print(f'[Parameters] CLIP Skip = {clip_skip}')
         print(f'[Parameters] Sharpness = {sharpness}')
         print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
         print(f'[Parameters] ADM Scale = '
@@ -466,6 +468,8 @@ def worker():
                                         loras=loras, base_model_additional_loras=base_model_additional_loras,
                                         use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
 
+            pipeline.set_clip_skip(clip_skip)
+
             progressbar(async_task, 3, 'Processing prompts ...')
             tasks = []
 
@@ -924,6 +928,8 @@ def worker():
                         d.append(
                             ('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
 
+                    if clip_skip > 1:
+                        d.append(('CLIP Skip', 'clip_skip', clip_skip))
                     d.append(('Sampler', 'sampler', sampler_name))
                     d.append(('Scheduler', 'scheduler', scheduler_name))
                     d.append(('VAE', 'vae', vae_name))

modules/config.py

@@ -434,6 +434,11 @@ default_cfg_tsnr = get_config_item_or_set_default(
     default_value=7.0,
     validator=lambda x: isinstance(x, numbers.Number)
 )
+default_clip_skip = get_config_item_or_set_default(
+    key='default_clip_skip',
+    default_value=1,
+    validator=lambda x: isinstance(x, numbers.Number)
+)
 default_overwrite_step = get_config_item_or_set_default(
     key='default_overwrite_step',
     default_value=-1,
@@ -488,6 +493,7 @@ possible_preset_keys = {
     "default_cfg_scale": "guidance_scale",
     "default_sample_sharpness": "sharpness",
     "default_cfg_tsnr": "adaptive_cfg",
+    "default_clip_skip": "clip_skip",
     "default_sampler": "sampler",
     "default_scheduler": "scheduler",
     "default_overwrite_step": "steps",

modules/default_pipeline.py

@@ -201,6 +201,17 @@ def clip_encode(texts, pool_top_k=1):
     return [[torch.cat(cond_list, dim=1), {"pooled_output": pooled_acc}]]
 
 
+@torch.no_grad()
+@torch.inference_mode()
+def set_clip_skip(clip_skip: int):
+    global final_clip
+
+    if final_clip is None:
+        return
+
+    final_clip.clip_layer(-abs(clip_skip))
+    return
+
 @torch.no_grad()
 @torch.inference_mode()
 def clear_all_caches():

modules/meta_parser.py

@@ -34,16 +34,17 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
     get_list('styles', 'Styles', loaded_parameter_dict, results)
     get_str('performance', 'Performance', loaded_parameter_dict, results)
     get_steps('steps', 'Steps', loaded_parameter_dict, results)
-    get_float('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
+    get_number('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
     get_resolution('resolution', 'Resolution', loaded_parameter_dict, results)
-    get_float('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results)
-    get_float('sharpness', 'Sharpness', loaded_parameter_dict, results)
+    get_number('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results)
+    get_number('sharpness', 'Sharpness', loaded_parameter_dict, results)
     get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results)
     get_str('refiner_swap_method', 'Refiner Swap Method', loaded_parameter_dict, results)
-    get_float('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results)
+    get_number('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results)
+    get_number('clip_skip', 'CLIP Skip', loaded_parameter_dict, results, cast_type=int)
     get_str('base_model', 'Base Model', loaded_parameter_dict, results)
     get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results)
-    get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)
+    get_number('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)
     get_str('sampler', 'Sampler', loaded_parameter_dict, results)
     get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)
     get_str('vae', 'VAE', loaded_parameter_dict, results)
@@ -83,11 +84,11 @@ def get_list(key: str, fallback: str | None, source_dict: dict, results: list, d
         results.append(gr.update())
 
 
-def get_float(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
+def get_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None, cast_type=float):
     try:
         h = source_dict.get(key, source_dict.get(fallback, default))
         assert h is not None
-        h = float(h)
+        h = cast_type(h)
         results.append(h)
     except:
         results.append(gr.update())
@@ -314,6 +315,7 @@ class A1111MetadataParser(MetadataParser):
         'adm_guidance': 'ADM Guidance',
         'refiner_swap_method': 'Refiner Swap Method',
         'adaptive_cfg': 'Adaptive CFG',
+        'clip_skip': 'Clip skip',
         'overwrite_switch': 'Overwrite Switch',
         'freeu': 'FreeU',
         'base_model': 'Model',
@@ -458,7 +460,7 @@ class A1111MetadataParser(MetadataParser):
                 self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash
             }
 
-        for key in ['adaptive_cfg', 'overwrite_switch', 'refiner_swap_method', 'freeu']:
+        for key in ['adaptive_cfg', 'clip_skip', 'overwrite_switch', 'refiner_swap_method', 'freeu']:
             if key in data:
                 generation_params[self.fooocus_to_a1111[key]] = data[key]