prompt paste improvements

javascript/sdnext.css

@@ -100,8 +100,8 @@ button.custom-button{ border-radius: var(--button-large-radius); padding: var(--
 #control_generate_box { min-width: unset; width: 100%; }
 #txt2img_actions_column, #img2img_actions_column, #control_actions { flex-flow: wrap; justify-content: space-between; }
 #txt2img_enqueue_wrapper, #img2img_enqueue_wrapper, #control_enqueue_wrapper { min-width: unset !important; width: 32%; }
-.interrogate-clip { position: absolute; right: 3em; top: -2.5em; max-width: fit-content; background: none !important; }
-.interrogate-blip { position: absolute; right: 1em; top: -2.5em; max-width: fit-content; background: none !important; }
+.interrogate-clip { position: absolute; right: 6em; top: 8px; max-width: fit-content; background: none !important; z-index: 50; }
+.interrogate-blip { position: absolute; right: 4em; top: 8px; max-width: fit-content; background: none !important; z-index: 50; }
 .interrogate-col { min-width: 0 !important; max-width: fit-content; margin-right: var(--spacing-xxl); }
 .interrogate-col>button { flex: 1; width: 7em; max-height: 84px; }
 #sampler_selection_img2img { margin-top: 1em; }

modules/control/run.py

@@ -132,6 +132,10 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
     if p.enable_hr and (p.hr_resize_x == 0 or p.hr_resize_y == 0):
         p.hr_upscale_to_x, p.hr_upscale_to_y = 8 * int(p.width * p.hr_scale / 8), 8 * int(p.height * p.hr_scale / 8)
 
+    if shared.sd_model is None:
+        shared.log.warning('Model not loaded')
+        return [], '', '', 'Error: model not loaded'
+
     t0 = time.time()
     num_units = 0
     for u in units:

modules/deepbooru.py

@@ -47,56 +47,40 @@ class DeepDanbooru:
         return res
 
     def tag_multi(self, pil_image, force_disable_ranks=False):
-        threshold = shared.opts.interrogate_deepbooru_score_threshold
-        use_spaces = shared.opts.deepbooru_use_spaces
-        use_escape = shared.opts.deepbooru_escape
-        alpha_sort = shared.opts.deepbooru_sort_alpha
-        include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks
-
         if isinstance(pil_image, list):
-            pil_image = pil_image[0]
+            pil_image = pil_image[0] if len(pil_image) > 0 else None
         if isinstance(pil_image, dict) and 'name' in pil_image:
             pil_image = Image.open(pil_image['name'])
-
+        if pil_image is None:
+            return ''
         pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512)
         a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
-
         with devices.inference_context(), devices.autocast():
             x = torch.from_numpy(a).to(devices.device)
             y = self.model(x)[0].detach().float().cpu().numpy()
-
         probability_dict = {}
-
         for tag, probability in zip(self.model.tags, y):
-            if probability < threshold:
+            if probability < shared.opts.interrogate_deepbooru_score_threshold:
                 continue
-
             if tag.startswith("rating:"):
                 continue
-
             probability_dict[tag] = probability
-
-        if alpha_sort:
+        if shared.opts.deepbooru_sort_alpha:
             tags = sorted(probability_dict)
         else:
             tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])]
-
         res = []
-
         filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")}
-
         for tag in [x for x in tags if x not in filtertags]:
             probability = probability_dict[tag]
             tag_outformat = tag
-            if use_spaces:
+            if shared.opts.deepbooru_use_spaces:
                 tag_outformat = tag_outformat.replace('_', ' ')
-            if use_escape:
+            if shared.opts.deepbooru_escape:
                 tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
-            if include_ranks:
+            if shared.opts.interrogate_return_ranks and not force_disable_ranks:
                 tag_outformat = f"({tag_outformat}:{probability:.3f})"
-
             res.append(tag_outformat)
-
         return ", ".join(res)
 
 

modules/face/instantid.py

@@ -21,7 +21,7 @@ def instant_id(p: processing.StableDiffusionProcessing, app, source_images, stre
         shared.log.warning('InstantID: no input images')
         return None
 
-    c = shared.sd_model.__class__.__name__ if shared.sd_model is not None else ''
+    c = shared.sd_model.__class__.__name__ if shared.sd_loaded else ''
     if c != 'StableDiffusionXLPipeline':
         shared.log.warning(f'InstantID invalid base model: current={c} required=StableDiffusionXLPipeline')
         return None

modules/face/photomaker.py

@@ -11,7 +11,7 @@ def photo_maker(p: processing.StableDiffusionProcessing, input_images, trigger,
         shared.log.warning('PhotoMaker: no input images')
         return None
 
-    c = shared.sd_model.__class__.__name__ if shared.sd_model is not None else ''
+    c = shared.sd_model.__class__.__name__ if shared.sd_loaded else ''
     if c != 'StableDiffusionXLPipeline':
         shared.log.warning(f'PhotoMaker invalid base model: current={c} required=StableDiffusionXLPipeline')
         return None

modules/generation_parameters_copypaste.py

@@ -187,7 +187,7 @@ def send_image_and_dimensions(x):
     return img, w, h
 
 
-def parse_generation_parameters(infotext):
+def parse_generation_parameters(infotext, no_prompt=False):
     if not isinstance(infotext, str):
         return {}
     debug(f'Parse infotext: {infotext}')
@@ -236,8 +236,9 @@ def parse_generation_parameters(infotext):
             params["Full quality"] = False
         else:
             params[k] = v
-    params["Prompt"] = prompt.replace('Prompt:', '').strip()
-    params["Negative prompt"] = negative.replace('Negative prompt:', '').strip()
+    if not no_prompt:
+        params["Prompt"] = prompt.replace('Prompt:', '').strip()
+        params["Negative prompt"] = negative.replace('Negative prompt:', '').strip()
     debug(f"Parse: {params}")
     return params
 
@@ -314,7 +315,7 @@ def connect_paste(button, local_paste_fields, input_comp, override_settings_comp
                 prompt = ''
         else:
             shared.log.debug(f'Paste prompt: type="current" prompt="{prompt}"')
-        params = parse_generation_parameters(prompt)
+        params = parse_generation_parameters(prompt, no_prompt=False)
         script_callbacks.infotext_pasted_callback(prompt, params)
         res = []
         applied = {}
@@ -324,7 +325,7 @@ def connect_paste(button, local_paste_fields, input_comp, override_settings_comp
             else:
                 v = params.get(key, None)
             if v is None:
-                res.append(gr.update())
+                res.append(gr.update()) # triggers update for each gradio component even if there are no updates
             elif isinstance(v, type_of_gr_update):
                 res.append(v)
                 applied[key] = v
@@ -344,6 +345,10 @@ def connect_paste(button, local_paste_fields, input_comp, override_settings_comp
 
     if override_settings_component is not None:
         def paste_settings(params):
+            params.pop('Prompt', None)
+            params.pop('Negative prompt', None)
+            if not params:
+                gr.Dropdown.update(value=[], choices=[], visible=False)
             vals = {}
             for param_name, setting_name in infotext_to_setting_name_mapping:
                 v = params.get(param_name, None)
@@ -360,8 +365,10 @@ def connect_paste(button, local_paste_fields, input_comp, override_settings_comp
                     continue
                 vals[param_name] = v
             vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
-            shared.log.debug(f'Settings overrides: {vals_pairs}')
+            if len(vals_pairs) > 0:
+                shared.log.debug(f'Settings overrides: {vals_pairs}')
             return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=len(vals_pairs) > 0)
+
         local_paste_fields = local_paste_fields + [(override_settings_component, paste_settings)]
 
     button.click(

modules/interrogate.py

@@ -170,9 +170,11 @@ class InterrogateModels:
                 devices.torch_gc()
             self.load()
             if isinstance(pil_image, list):
-                pil_image = pil_image[0]
+                pil_image = pil_image[0] if len(pil_image) > 0 else None
             if isinstance(pil_image, dict) and 'name' in pil_image:
                 pil_image = Image.open(pil_image['name'])
+            if pil_image is None:
+                return ''
             pil_image = pil_image.convert("RGB")
             caption = self.generate_caption(pil_image)
             self.send_blip_to_ram()

modules/modeldata.py

@@ -47,6 +47,11 @@ class ModelData:
 
 # provides shared.sd_model field as a property
 class Shared(sys.modules[__name__].__class__):
+    @property
+    def sd_loaded(self):
+        import modules.sd_models # pylint: disable=W0621
+        return modules.sd_models.model_data.sd_model is not None
+
     @property
     def sd_model(self):
         import modules.sd_models # pylint: disable=W0621

modules/sd_samplers_kdiffusion.py

@@ -103,7 +103,7 @@ class CFGDenoiser(torch.nn.Module):
                 time.sleep(0.1)
         # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
         # so is_edit_model is set to False to support AND composition.
-        is_edit_model = (shared.sd_model is not None) and hasattr(shared.sd_model, 'cond_stage_key') and (shared.sd_model.cond_stage_key == "edit") and (self.image_cfg_scale is not None) and (self.image_cfg_scale != 1.0)
+        is_edit_model = shared.sd_loaded and hasattr(shared.sd_model, 'cond_stage_key') and (shared.sd_model.cond_stage_key == "edit") and (self.image_cfg_scale is not None) and (self.image_cfg_scale != 1.0)
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
         assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"

modules/shared.py

@@ -1078,6 +1078,7 @@ sd_model: diffusers.DiffusionPipeline = None # dummy and overwritten by class
 sd_refiner: diffusers.DiffusionPipeline = None # dummy and overwritten by class
 sd_model_type: str = '' # dummy and overwritten by class
 sd_refiner_type: str = '' # dummy and overwritten by class
+sd_loaded: bool = False # dummy and overwritten by class
 compiled_model_state = None
 listfiles = listdir
 

modules/textual_inversion/textual_inversion.py

@@ -140,7 +140,7 @@ class EmbeddingDatabase:
     def get_expected_shape(self):
         if shared.backend == shared.Backend.DIFFUSERS:
             return 0
-        if shared.sd_model is None:
+        if shared.sd_loaded:
             shared.log.error('Model not loaded')
             return 0
         vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
@@ -151,7 +151,7 @@ class EmbeddingDatabase:
         embeddings_to_load = []
         loaded_embeddings = {}
         skipped_embeddings = []
-        if shared.sd_model is None:
+        if not shared.sd_loaded:
             return 0
         tokenizer   = getattr(shared.sd_model, 'tokenizer',   None)
         tokenizer_2 = getattr(shared.sd_model, 'tokenizer_2', None)
@@ -336,7 +336,7 @@ class EmbeddingDatabase:
                     continue
 
     def load_textual_inversion_embeddings(self, force_reload=False):
-        if shared.sd_model is None:
+        if not shared.sd_loaded:
             return
         t0 = time.time()
         if not force_reload:

modules/ui_common.py

@@ -286,10 +286,14 @@ def create_output_panel(tabname, preview=True, prompt=None, height=None):
             debug(f'Paste field: tab={tabname} fields={paste_field_names}')
             for paste_tabname, paste_button in buttons.items():
                 debug(f'Create output panel: source={tabname} target={paste_tabname} button={paste_button}')
-                bindings = generation_parameters_copypaste.ParamBinding(paste_button=paste_button, tabname=paste_tabname, source_tabname=tabname, source_image_component=result_gallery, paste_field_names=paste_field_names, source_text_component=generation_info)
-
-                # txt2img_bindings = generation_parameters_copypaste.ParamBinding(paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None)
-
+                bindings = generation_parameters_copypaste.ParamBinding(
+                    paste_button=paste_button,
+                    tabname=paste_tabname,
+                    source_tabname=tabname,
+                    source_image_component=result_gallery,
+                    paste_field_names=paste_field_names,
+                    source_text_component=prompt or generation_info
+                )
                 generation_parameters_copypaste.register_paste_params_button(bindings)
             return result_gallery, generation_info, html_info, html_info_formatted, html_log
 
@@ -373,7 +377,7 @@ def update_token_counter(text, steps):
         from modules import sd_hijack
         token_count, max_length = max([sd_hijack.model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
     elif shared.backend == shared.Backend.DIFFUSERS:
-        if shared.sd_model is not None and hasattr(shared.sd_model, 'tokenizer') and shared.sd_model.tokenizer is not None:
+        if shared.sd_loaded and hasattr(shared.sd_model, 'tokenizer') and shared.sd_model.tokenizer is not None:
             has_bos_token = shared.sd_model.tokenizer.bos_token_id is not None
             has_eos_token = shared.sd_model.tokenizer.eos_token_id is not None
             ids = [shared.sd_model.tokenizer(prompt) for prompt in prompts]

modules/ui_img2img.py

@@ -155,7 +155,7 @@ def create_ui():
                 with gr.Group(elem_id="img2img_script_container"):
                     img2img_script_inputs = modules.scripts.scripts_img2img.setup_ui(parent='img2img', accordion=True)
 
-            img2img_gallery, img2img_generation_info, img2img_html_info, _img2img_html_info_formatted, img2img_html_log = ui_common.create_output_panel("img2img", prompt=None)
+            img2img_gallery, img2img_generation_info, img2img_html_info, _img2img_html_info_formatted, img2img_html_log = ui_common.create_output_panel("img2img", prompt=img2img_prompt)
 
             ui_common.connect_reuse_seed(seed, reuse_seed, img2img_generation_info, is_subseed=False)
             ui_common.connect_reuse_seed(subseed, reuse_subseed, img2img_generation_info, is_subseed=True)

modules/ui_txt2img.py

@@ -50,7 +50,7 @@ def create_ui():
                 with gr.Group(elem_id="txt2img_script_container"):
                     txt2img_script_inputs = modules.scripts.scripts_txt2img.setup_ui(parent='txt2img', accordion=True)
 
-            txt2img_gallery, txt2img_generation_info, txt2img_html_info, _txt2img_html_info_formatted, txt2img_html_log = ui_common.create_output_panel("txt2img", preview=True, prompt=None)
+            txt2img_gallery, txt2img_generation_info, txt2img_html_info, _txt2img_html_info_formatted, txt2img_html_log = ui_common.create_output_panel("txt2img", preview=True, prompt=txt2img_prompt)
             ui_common.connect_reuse_seed(seed, reuse_seed, txt2img_generation_info, is_subseed=False)
             ui_common.connect_reuse_seed(subseed, reuse_subseed, txt2img_generation_info, is_subseed=True)
 

scripts/animatediff.py

@@ -48,14 +48,14 @@ orig_pipe = None # original sd_model pipeline
 
 
 def set_adapter(adapter_name: str = 'None'):
-    if shared.sd_model is None:
+    if not shared.sd_loaded:
         return
     if shared.backend != shared.Backend.DIFFUSERS:
         shared.log.warning('AnimateDiff: not in diffusers mode')
         return
     global motion_adapter, loaded_adapter, orig_pipe # pylint: disable=global-statement
     # adapter_name = name if name is not None and isinstance(name, str) else loaded_adapter
-    if adapter_name is None or adapter_name == 'None' or shared.sd_model is None:
+    if adapter_name is None or adapter_name == 'None' or not shared.sd_loaded:
         motion_adapter = None
         loaded_adapter = None
         if orig_pipe is not None:

scripts/blipdiffusion.py

@@ -24,7 +24,7 @@ class Script(scripts.Script):
         return [source_subject, target_subject, prompt_strength]
 
     def run(self, p: processing.StableDiffusionProcessing, source_subject, target_subject, prompt_strength): # pylint: disable=arguments-differ, unused-argument
-        c = shared.sd_model.__class__.__name__ if shared.sd_model is not None else ''
+        c = shared.sd_model.__class__.__name__ if shared.sd_loaded else ''
         if c != 'BlipDiffusionPipeline':
             shared.log.error(f'{title}: model selected={c} required=BLIPDiffusion')
             return None

scripts/demofusion.py

@@ -1244,7 +1244,7 @@ class Script(scripts.Script):
         return [cosine_scale_1, cosine_scale_2, cosine_scale_3, sigma, view_batch_size, stride, multi_decoder]
 
     def run(self, p: processing.StableDiffusionProcessing, cosine_scale_1, cosine_scale_2, cosine_scale_3, sigma, view_batch_size, stride, multi_decoder): # pylint: disable=arguments-differ
-        c = shared.sd_model.__class__.__name__ if shared.sd_model is not None else ''
+        c = shared.sd_model.__class__.__name__ if shared.sd_loaded else ''
         if c != 'StableDiffusionXLPipeline':
             shared.log.warning(f'DemoFusion: pipeline={c} required=StableDiffusionXLPipeline')
             return None

scripts/example.py

@@ -88,7 +88,7 @@ class Script(scripts.Script):
     # Run pipeline
     def run(self, p: processing.StableDiffusionProcessing, *args): # pylint: disable=arguments-differ
         # prepare pipeline
-        c = shared.sd_model.__class__.__name__ if shared.sd_model is not None else ''
+        c = shared.sd_model.__class__.__name__ if shared.sd_loaded else ''
         if c != pipeline_base:
             shared.log.warning(f'{title}: pipeline={c} required={pipeline_base}')
             return None

scripts/stablevideodiffusion.py

@@ -71,7 +71,7 @@ class Script(scripts.Script):
             shared.log.error(f'SVD: no checkpoint for {model_name}')
             modelloader.load_reference(model_path, variant='fp16')
         c = shared.sd_model.__class__.__name__
-        model_loaded = shared.sd_model.sd_checkpoint_info.model_name if shared.sd_model is not None else None
+        model_loaded = shared.sd_model.sd_checkpoint_info.model_name if shared.sd_loaded else None
         if model_name != model_loaded or c != 'StableVideoDiffusionPipeline':
             shared.opts.sd_model_checkpoint = model_path
             sd_models.reload_model_weights()