add more job state updates and update history tab

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

modules/civitai/download_civitai.py

@@ -62,7 +62,7 @@ def download_civit_preview(model_path: str, preview_url: str):
     block_size = 16384 # 16KB blocks
     written = 0
     img = None
-    jobid = shared.state.begin('Download')
+    jobid = shared.state.begin('Download CivitAI')
     if pbar is None:
         pbar = p.Progress(p.TextColumn('[cyan]Download'), p.DownloadColumn(), p.BarColumn(), p.TaskProgressColumn(), p.TimeRemainingColumn(), p.TimeElapsedColumn(), p.TransferSpeedColumn(), p.TextColumn('[yellow]{task.description}'), console=shared.console)
     try:
@@ -139,7 +139,7 @@ def download_civit_model_thread(model_name: str, model_url: str, model_path: str
 
     res += f' size={round((starting_pos + total_size)/1024/1024, 2)}Mb'
     shared.log.info(res)
-    jobid = shared.state.begin('Download')
+    jobid = shared.state.begin('Download CivitAI')
     block_size = 16384 # 16KB blocks
     written = starting_pos
     global pbar # pylint: disable=global-statement

modules/control/processor.py

@@ -51,6 +51,7 @@ def preprocess_image(
         has_models:bool = False,
     ):
     t0 = time.time()
+    jobid = shared.state.begin('Preprocess')
 
     # run resize before
     if p.resize_mode_before != 0 and p.resize_name_before != 'None':
@@ -246,4 +247,5 @@ def preprocess_image(
 
     t1 = time.time()
     process_timer.add('proc', t1-t0)
+    shared.state.end(jobid)
     return processed_image, blended_image

modules/control/processors.py

@@ -160,6 +160,7 @@ class Processor():
                 self.load_config[k] = v
 
     def load(self, processor_id: str = None, force: bool = True) -> str:
+        from modules.shared import state
         try:
             t0 = time.time()
             processor_id = processor_id or self.processor_id
@@ -179,6 +180,7 @@ class Processor():
             cls = config[processor_id]['class']
             # log.debug(f'Control Processor loading: id="{processor_id}" class={cls.__name__}')
             debug(f'Control Processor config={self.load_config}')
+            jobid = state.begin('Load processor')
             if 'DWPose' in processor_id:
                 det_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth'
                 if 'Tiny' == config['DWPose']['model']:
@@ -209,6 +211,7 @@ class Processor():
             else:
                 self.model = cls() # class instance only
             t1 = time.time()
+            state.end(jobid)
             self.processor_id = processor_id
             log.debug(f'Control Processor loaded: id="{processor_id}" class={self.model.__class__.__name__} time={t1-t0:.2f}')
             return f'Processor loaded: {processor_id}'

modules/control/run.py

@@ -597,12 +597,9 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
                                 if processed_image is not None and isinstance(processed_image, Image.Image):
                                     output_images.append(processed_image)
 
-                            if is_generator:
+                            if is_generator and frame is not None and video is not None:
                                 image_txt = f'{output_image.width}x{output_image.height}' if output_image is not None else 'None'
-                                if video is not None:
-                                    msg = f'Control output | {index} of {frames} skip {video_skip_frames} | Frame {image_txt}'
-                                else:
-                                    msg = f'Control output | {index} of {len(inputs)} | Image {image_txt}'
+                                msg = f'Control output | {index} of {frames} skip {video_skip_frames} | Frame {image_txt}'
                                 yield (output_image, blended_image, msg) # result is control_output, proces_output
 
                 if video is not None and frame is not None:
@@ -643,5 +640,7 @@ def control_run(state: str = '', # pylint: disable=keyword-arg-before-vararg
     if len(info_txt) > 0:
         html_txt = html_txt + infotext_to_html(info_txt[0])
     if is_generator:
+        jobid = shared.state.begin('UI')
         yield (output_images, blended_image, html_txt, output_filename)
+        shared.state.end(jobid)
     return (output_images, blended_image, html_txt, output_filename)

modules/control/units/controlnet.py

@@ -4,7 +4,7 @@ import threading
 from typing import Union
 from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, FluxPipeline, StableDiffusion3Pipeline, ControlNetModel
 from modules.control.units import detect
-from modules.shared import log, opts, cmd_opts, listdir
+from modules.shared import log, opts, cmd_opts, state, listdir
 from modules import errors, sd_models, devices, model_quant
 from modules.processing import StableDiffusionProcessingControl
 
@@ -308,6 +308,7 @@ class ControlNet():
                     log.error(f'Control {what} model load: id="{model_id}" unknown base model')
                     return
                 self.reset()
+                jobid = state.begin(f'Load {what}')
                 if model_path.endswith('.safetensors'):
                     self.load_safetensors(model_id, model_path, cls, config)
                 else:
@@ -365,6 +366,7 @@ class ControlNet():
                 t1 = time.time()
                 self.model_id = model_id
                 log.info(f'Control {what} model loaded: id="{model_id}" path="{model_path}" cls={cls.__name__} time={t1-t0:.2f}')
+                state.end(jobid)
                 return f'{what} loaded model: {model_id}'
             except Exception as e:
                 log.error(f'Control {what} model load: id="{model_id}" {e}')

modules/framepack/framepack_load.py

@@ -61,7 +61,7 @@ def reset_model():
 
 
 def load_model(variant:str=None, pipeline:str=None, text_encoder:str=None, text_encoder_2:str=None, feature_extractor:str=None, image_encoder:str=None, transformer:str=None):
-    shared.state.begin('Load')
+    shared.state.begin('Load FramePack')
     if variant is not None:
         if variant not in models.keys():
             raise ValueError(f'FramePack: variant="{variant}" invalid')

modules/framepack/framepack_worker.py

@@ -84,6 +84,7 @@ def worker(
         prompts = list(reversed(prompts))
 
     def text_encode(prompt, i:int=None):
+        jobid = shared.state.begin('TE Encode')
         pbar.update(task, description=f'text encode section={i}')
         t0 = time.time()
         torch.manual_seed(seed)
@@ -102,9 +103,11 @@ def worker(
         llama_vec_n, llama_attention_mask_n = utils.crop_or_pad_yield_mask(llama_vec_n, length=512)
         sd_models.apply_balanced_offload(shared.sd_model)
         timer.process.add('prompt', time.time()-t0)
+        shared.state.end(jobid)
         return llama_vec, llama_vec_n, llama_attention_mask, llama_attention_mask_n, clip_l_pooler, clip_l_pooler_n
 
     def latents_encode(input_image, end_image):
+        jobid = shared.state.begin('VAE Encode')
         pbar.update(task, description='image encode')
         # shared.log.debug(f'FramePack: image encode init={input_image.shape} end={end_image.shape if end_image is not None else None}')
         t0 = time.time()
@@ -126,6 +129,7 @@ def worker(
             end_latent = None
         sd_models.apply_balanced_offload(shared.sd_model)
         timer.process.add('encode', time.time()-t0)
+        shared.state.end(jobid)
         return start_latent, end_latent
 
     def vision_encode(input_image, end_image):

modules/images.py

@@ -46,7 +46,7 @@ def atomically_save_image():
     Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
     while True:
         image, filename, extension, params, exifinfo, filename_txt, is_grid = save_queue.get()
-        jobid = shared.state.begin('Save')
+        jobid = shared.state.begin('Save image')
         shared.state.image_history += 1
         if len(exifinfo) > 2:
             with open(paths.params_path, "w", encoding="utf8") as file:

modules/interrogate/openclip.py

@@ -135,7 +135,7 @@ def interrogate(image, mode, caption=None):
 
 
 def interrogate_image(image, clip_model, blip_model, mode):
-    jobid = shared.state.begin('Interrogate')
+    jobid = shared.state.begin('Interrogate CLiP')
     try:
         if shared.sd_loaded:
             from modules.sd_models import apply_balanced_offload # prevent circular import

modules/interrogate/vqa.py

@@ -605,7 +605,7 @@ def sa2(question: str, image: Image.Image, repo: str = None):
 
 def interrogate(question:str='', system_prompt:str=None, prompt:str=None, image:Image.Image=None, model_name:str=None, quiet:bool=False):
     global quant_args # pylint: disable=global-statement
-    jobid = shared.state.begin('Interrogate')
+    jobid = shared.state.begin('Interrogate LLM')
     t0 = time.time()
     quant_args = model_quant.create_config(module='LLM')
     model_name = model_name or shared.opts.interrogate_vlm_model

modules/ipadapter.py

@@ -188,6 +188,7 @@ def load_image_encoder(pipe: diffusers.DiffusionPipeline, adapter_names: list[st
 
     # load image encoder used by ip adapter
     if pipe.image_encoder is None or clip_loaded != f'{clip_repo}/{clip_subfolder}':
+        jobid = shared.state.begin('Load encoder')
         try:
             if shared.sd_model_type == 'sd3':
                 image_encoder = transformers.SiglipVisionModel.from_pretrained(clip_repo, torch_dtype=devices.dtype, cache_dir=shared.opts.hfcache_dir)
@@ -209,6 +210,7 @@ def load_image_encoder(pipe: diffusers.DiffusionPipeline, adapter_names: list[st
             shared.log.error(f'IP adapter load: encoder="{clip_repo}/{clip_subfolder}" {e}')
             errors.display(e, 'IP adapter: type=encoder')
             return False
+        shared.state.end(jobid)
     sd_models.move_model(pipe.image_encoder, devices.device)
     return True
 
@@ -217,6 +219,7 @@ def load_feature_extractor(pipe):
     # load feature extractor used by ip adapter
     if pipe.feature_extractor is None:
         try:
+            jobid = shared.state.begin('Load extractor')
             if shared.sd_model_type == 'sd3':
                 feature_extractor = transformers.SiglipImageProcessor.from_pretrained(SIGLIP_ID, torch_dtype=devices.dtype, cache_dir=shared.opts.hfcache_dir)
             else:
@@ -231,6 +234,7 @@ def load_feature_extractor(pipe):
             shared.log.error(f'IP adapter load: extractor {e}')
             errors.display(e, 'IP adapter: type=extractor')
             return False
+        shared.state.end(jobid)
     return True
 
 

modules/processing_args.py

@@ -137,6 +137,7 @@ def task_specific_kwargs(p, model):
 def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:typing.Optional[list]=None, negative_prompts_2:typing.Optional[list]=None, prompt_attention:typing.Optional[str]=None, desc:typing.Optional[str]='', **kwargs):
     t0 = time.time()
     shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
+    argsid = shared.state.begin('Params')
     apply_circular(p.tiling, model)
     args = {}
     has_vae = hasattr(model, 'vae') or (hasattr(model, 'pipe') and hasattr(model.pipe, 'vae'))
@@ -445,4 +446,5 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
         else:
             _args[k] = v
 
+    shared.state.end(argsid)
     return _args

modules/processing_vae.py

@@ -316,6 +316,7 @@ def vae_decode(latents, model, output_type='np', vae_type='Full', width=None, he
 
 
 def vae_encode(image, model, vae_type='Full'): # pylint: disable=unused-variable
+    jobid = shared.state.begin('VAE Encode')
     import torchvision.transforms.functional as f
     if shared.state.interrupted or shared.state.skipped:
         return []
@@ -331,6 +332,7 @@ def vae_encode(image, model, vae_type='Full'): # pylint: disable=unused-variable
     else:
         latents = taesd_vae_encode(image=tensor)
     devices.torch_gc()
+    shared.state.end(jobid)
     return latents
 
 

modules/sd_models.py

@@ -1122,7 +1122,7 @@ def reload_model_weights(sd_model=None, info=None, op='model', force=False, revi
     if checkpoint_info is None:
         unload_model_weights(op=op)
         return None
-    jobid = shared.state.begin('Load')
+    jobid = shared.state.begin('Load model')
     if sd_model is None:
         sd_model = model_data.sd_model if op == 'model' or op == 'dict' else model_data.sd_refiner
     if sd_model is None:  # previous model load failed

modules/styles.py

@@ -319,6 +319,7 @@ class StyleDatabase:
         if seeds is None or not isinstance(prompts, list):
             shared.log.error(f'Styles invalid seeds: {seeds}')
             return prompts, negatives
+        jobid = shared.state.begin('Styles')
         parsed_positive = []
         parsed_negative = []
         for i in range(len(prompts)):
@@ -331,6 +332,7 @@ class StyleDatabase:
             prompt = apply_styles_to_prompt(prompt, [self.find_style(x).negative_prompt for x in styles])
             prompt = apply_wildcards_to_prompt(prompt, [self.find_style(x).wildcards for x in styles], seeds[i])
             parsed_negative.append(prompt)
+        shared.state.end(jobid)
         return parsed_positive, parsed_negative
 
     def apply_styles_to_prompt(self, prompt, styles, wildcards:bool=True):

modules/video.py

@@ -29,6 +29,7 @@ def save_video_atomic(images, filename, video_type: str = 'none', duration: floa
     except Exception as e:
         shared.log.error(f'Save video: cv2: {e}')
         return
+    jobid = shared.state.begin('Save video')
     os.makedirs(os.path.dirname(filename), exist_ok=True)
     if video_type.lower() in ['gif', 'png']:
         append = images.copy()
@@ -56,6 +57,7 @@ def save_video_atomic(images, filename, video_type: str = 'none', duration: floa
             video_writer.write(img)
         size = os.path.getsize(filename)
         shared.log.info(f'Save video: file="{filename}" frames={len(frames)} duration={duration} fourcc={fourcc} size={size}')
+    shared.state.end(jobid)
 
 
 def save_video(p, images, filename = None, video_type: str = 'none', duration: float = 2.0, loop: bool = False, interpolate: int = 0, scale: float = 1.0, pad: int = 1, change: float = 0.3, sync: bool = False):

modules/video_models/video_load.py

@@ -15,7 +15,7 @@ def load_model(selected: models_def.Model):
         return ''
     sd_models.unload_model_weights()
     t0 = time.time()
-    jobid = shared.state.begin('Load')
+    jobid = shared.state.begin('Load model')
 
     video_cache.apply_teacache_patch(selected.dit_cls)
 

modules/video_models/video_save.py

@@ -78,6 +78,7 @@ def save_video(
     size = pixels.element_size() * pixels.numel()
     shared.log.debug(f'Video: video={mp4_video} export={mp4_frames} safetensors={mp4_sf} interpolate={mp4_interpolate}')
     shared.log.debug(f'Video: encode={t} raw={size} latent={pixels.shape} fps={mp4_fps} codec={mp4_codec} ext={mp4_ext} options="{mp4_opt}"')
+    jobid = shared.state.begin('Save video')
     try:
         if stream is not None:
             stream.output_queue.push(('progress', (None, 'Saving video...')))
@@ -124,4 +125,5 @@ def save_video(
         shared.log.error(f'Video save: raw={size} {e}')
         errors.display(e, 'video')
     timer.process.add('save', time.time()-t_save)
+    shared.state.end(jobid)
     return t, output_video