validate and fix all refiner workflows

CHANGELOG.md

@@ -49,17 +49,21 @@
 - **Internal**
   - remove obsolete textual inversion training code
   - remove obsolete hypernetworks training code
+- **Refiner** validated workflows:
+  - Fully functional: SD15 + SD15, SDXL + SDXL, SDXL + SDXL-R
+  - Functional, but result is not as good: SD15 + SDXL, SDXL + SD15, SD15 + SDXL-R
 - **Fixes**
-  - improve model cpu offload compatibility
-  - improve model sequential offload compatibility
-  - improve bfloat16 compatibility
-  - improve xformers installer to match cuda version and install triton
+  - improve *model cpu offload* compatibility
+  - improve *model sequential offload* compatibility
+  - improve *bfloat16* compatibility
+  - improve *xformers* installer to match cuda version and install triton
   - fix extra networks refresh
-  - fix sdp memory attention in backend original
+  - fix *sdp memory attention* in backend original
   - fix autodetect sd21 models
   - fix api info endpoint
-  - fix sampler eta in xyz grid, thanks @AI-Casanova
-  - use diffusers lora load override for lcm/tcd/turbo loras
+  - fix *sampler eta* in xyz grid, thanks @AI-Casanova
+  - fix *requires_aesthetics_score* errors
+  - use diffusers lora load override for *lcm/tcd/turbo loras*
   - exception handler around vram memory stats gather
   - improve ZLUDA installer with `--use-zluda` cli param, thanks @lshqqytiger
 

modules/processing_diffusers.py

@@ -561,8 +561,8 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
             )
             shared.state.sampling_steps = refiner_args['num_inference_steps']
             try:
-                if 'requires_aesthetics_score' in shared.sd_refiner.config:
-                    shared.sd_refiner.register_to_config(requires_aesthetics_score=shared.opts.diffusers_aesthetics_score)
+                if 'requires_aesthetics_score' in shared.sd_refiner.config: # sdxl-model needs false and sdxl-refiner needs true
+                    shared.sd_refiner.register_to_config(requires_aesthetics_score = getattr(shared.sd_refiner, 'tokenizer', None) is None)
                 refiner_output = shared.sd_refiner(**refiner_args) # pylint: disable=not-callable
                 if isinstance(refiner_output, dict):
                     refiner_output = SimpleNamespace(**refiner_output)

modules/prompt_parser_diffusers.py

@@ -151,13 +151,11 @@ def prepare_embedding_providers(pipe, clip_skip):
         embedding_type = -(clip_skip + 1)
     else:
         embedding_type = clip_skip
-    if hasattr(pipe, "tokenizer") and hasattr(pipe, "text_encoder"):
-        provider = EmbeddingsProvider(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder, truncate=False,
-                                      returned_embeddings_type=embedding_type, device=device)
+    if getattr(pipe, "tokenizer", None) is not None and getattr(pipe, "text_encoder", None) is not None:
+        provider = EmbeddingsProvider(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder, truncate=False, returned_embeddings_type=embedding_type, device=device)
         embeddings_providers.append(provider)
-    if hasattr(pipe, "tokenizer_2") and hasattr(pipe, "text_encoder_2"):
-        provider = EmbeddingsProvider(tokenizer=pipe.tokenizer_2, text_encoder=pipe.text_encoder_2, truncate=False,
-                                      returned_embeddings_type=embedding_type, device=device)
+    if getattr(pipe, "tokenizer_2", None) is not None and getattr(pipe, "text_encoder_2", None) is not None:
+        provider = EmbeddingsProvider(tokenizer=pipe.tokenizer_2, text_encoder=pipe.text_encoder_2, truncate=False, returned_embeddings_type=embedding_type, device=device)
         embeddings_providers.append(provider)
     return embeddings_providers
 

modules/sd_models.py

@@ -998,9 +998,6 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
                 if model_type.startswith('Stable Diffusion'):
                     if shared.opts.diffusers_force_zeros:
                         diffusers_load_config['force_zeros_for_empty_prompt '] = shared.opts.diffusers_force_zeros
-                    if shared.opts.diffusers_aesthetics_score:
-                        diffusers_load_config['requires_aesthetics_score'] = shared.opts.diffusers_aesthetics_score
-                    # diffusers_load_config['config_files'] = get_load_config(checkpoint_info.path.lower())
                     diffusers_load_config['original_config_file'] = get_load_config(checkpoint_info.path, model_type)
                 if hasattr(pipeline, 'from_single_file'):
                     diffusers_load_config['use_safetensors'] = True
@@ -1263,7 +1260,7 @@ def set_diffuser_pipe(pipe, new_pipe_type):
     new_pipe.is_sdxl = getattr(pipe, 'is_sdxl', False) # a1111 compatibility item
     new_pipe.is_sd2 = getattr(pipe, 'is_sd2', False)
     new_pipe.is_sd1 = getattr(pipe, 'is_sd1', True)
-    shared.log.debug(f"Pipeline class change: original={pipe.__class__.__name__} target={new_pipe.__class__.__name__} fn={sys._getframe().f_back.f_code.co_name}")
+    shared.log.debug(f"Pipeline class change: original={pipe.__class__.__name__} target={new_pipe.__class__.__name__} fn={sys._getframe().f_back.f_code.co_name}") # pylint: disable=protected-access
     pipe = new_pipe
     return pipe
 

modules/shared.py

@@ -458,7 +458,6 @@ options_templates.update(options_section(('diffusers', "Diffusers Settings"), {
     "diffusers_to_gpu": OptionInfo(False, "Load model directly to GPU"),
     "disable_accelerate": OptionInfo(False, "Disable accelerate"),
     "diffusers_force_zeros": OptionInfo(False, "Force zeros for prompts when empty", gr.Checkbox, {"visible": False}),
-    "diffusers_aesthetics_score": OptionInfo(False, "Require aesthetics score"),
     "diffusers_pooled": OptionInfo("default", "Diffusers SDXL pooled embeds", gr.Radio, {"choices": ['default', 'weighted']}),
     "huggingface_token": OptionInfo('', 'HuggingFace token'),