Merge pull request #6264 from vladmandic/add-state-info

add missing state info

modules/extras.py

@@ -58,6 +58,9 @@ cached_images: LruCache = LruCache(max_size=5)
 def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
     devices.torch_gc()
 
+    shared.state.begin()
+    shared.state.job = 'extras'
+
     imageArr = []
     # Also keep track of original file names
     imageNameArr = []
@@ -94,6 +97,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     # Extra operation definitions
 
     def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        shared.state.job = 'extras-gfpgan'
         restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
         res = Image.fromarray(restored_img)
 
@@ -104,6 +108,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         return (res, info)
 
     def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        shared.state.job = 'extras-codeformer'
         restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
         res = Image.fromarray(restored_img)
 
@@ -114,6 +119,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         return (res, info)
 
     def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
+        shared.state.job = 'extras-upscale'
         upscaler = shared.sd_upscalers[scaler_index]
         res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
         if mode == 1 and crop:
@@ -180,6 +186,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
+
+        shared.state.textinfo = f'Processing image {image_name}'
+        
         existing_pnginfo = image.info or {}
 
         image = image.convert("RGB")
@@ -193,6 +202,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         else:
             basename = ''
 
+        if opts.enable_pnginfo: # append info before save
+            image.info = existing_pnginfo
+            image.info["extras"] = info
+
         if save_output:
             # Add upscaler name as a suffix.
             suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
@@ -203,10 +216,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
                             no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
 
-        if opts.enable_pnginfo:
-            image.info = existing_pnginfo
-            image.info["extras"] = info
-
         if extras_mode != 2 or show_extras_results :
             outputs.append(image)
 
@@ -242,6 +251,9 @@ def run_pnginfo(image):
 
 
 def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
+    shared.state.begin()
+    shared.state.job = 'model-merge'
+
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
@@ -263,8 +275,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
     theta_func1, theta_func2 = theta_funcs[interp_method]
 
     if theta_func1 and not tertiary_model_info:
+        shared.state.textinfo = "Failed: Interpolation method requires a tertiary model."
+        shared.state.end()
         return ["Failed: Interpolation method requires a tertiary model."] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
 
+    shared.state.textinfo = f"Loading {secondary_model_info.filename}..."
     print(f"Loading {secondary_model_info.filename}...")
     theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
 
@@ -281,6 +296,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
                     theta_1[key] = torch.zeros_like(theta_1[key])
         del theta_2
 
+    shared.state.textinfo = f"Loading {primary_model_info.filename}..."
     print(f"Loading {primary_model_info.filename}...")
     theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
 
@@ -291,6 +307,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
             a = theta_0[key]
             b = theta_1[key]
 
+            shared.state.textinfo = f'Merging layer {key}'
             # this enables merging an inpainting model (A) with another one (B);
             # where normal model would have 4 channels, for latenst space, inpainting model would
             # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
@@ -303,8 +320,6 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
                 theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
                 result_is_inpainting_model = True
             else:
-                assert a.shape == b.shape, f'Incompatible shapes for layer {key}: A is {a.shape}, and B is {b.shape}'
-
                 theta_0[key] = theta_func2(a, b, multiplier)
 
             if save_as_half:
@@ -332,6 +347,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
 
     output_modelname = os.path.join(ckpt_dir, filename)
 
+    shared.state.textinfo = f"Saving to {output_modelname}..."
     print(f"Saving to {output_modelname}...")
 
     _, extension = os.path.splitext(output_modelname)
@@ -343,4 +359,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
     sd_models.list_models()
 
     print("Checkpoint saved.")
+    shared.state.textinfo = "Checkpoint saved to " + output_modelname
+    shared.state.end()
+
     return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]

modules/hypernetworks/hypernetwork.py

@@ -417,6 +417,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
     shared.loaded_hypernetwork = Hypernetwork()
     shared.loaded_hypernetwork.load(path)
 
+    shared.state.job = "train-hypernetwork"
     shared.state.textinfo = "Initializing hypernetwork training..."
     shared.state.job_count = steps
 

modules/interrogate.py

@@ -136,7 +136,8 @@ class InterrogateModels:
 
     def interrogate(self, pil_image):
         res = ""
-
+        shared.state.begin()
+        shared.state.job = 'interrogate'
         try:
 
             if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -177,5 +178,6 @@ class InterrogateModels:
             res += "<error>"
 
         self.unload()
+        shared.state.end()
 
         return res

modules/textual_inversion/preprocess.py

@@ -124,6 +124,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
 
     files = listfiles(src)
 
+    shared.state.job = "preprocess"
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
 

modules/textual_inversion/textual_inversion.py

@@ -245,6 +245,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     create_image_every = create_image_every or 0
     validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
 
+    shared.state.job = "train-embedding"
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps