add option to show/hide warnings

removed hiding warnings from LDSR
fixed/reworked few places that produced warnings

extensions-builtin/LDSR/ldsr_model_arch.py

@@ -1,7 +1,6 @@
 import os
 import gc
 import time
-import warnings
 
 import numpy as np
 import torch
@@ -15,8 +14,6 @@ from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.util import instantiate_from_config, ismap
 from modules import shared, sd_hijack
 
-warnings.filterwarnings("ignore", category=UserWarning)
-
 cached_ldsr_model: torch.nn.Module = None
 
 

javascript/localization.js

@@ -11,7 +11,7 @@ ignore_ids_for_localization={
     train_embedding: 'OPTION',
     train_hypernetwork: 'OPTION',
     txt2img_styles: 'OPTION',
-    img2img_styles 'OPTION',
+    img2img_styles: 'OPTION',
     setting_random_artist_categories: 'SPAN',
     setting_face_restoration_model: 'SPAN',
     setting_realesrgan_enabled_models: 'SPAN',

modules/hypernetworks/hypernetwork.py

@@ -12,7 +12,7 @@ import torch
 import tqdm
 from einops import rearrange, repeat
 from ldm.util import default
-from modules import devices, processing, sd_models, shared, sd_samplers, hashes
+from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint
 from modules.textual_inversion import textual_inversion, logging
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
@@ -575,6 +575,8 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 
     pbar = tqdm.tqdm(total=steps - initial_step)
     try:
+        sd_hijack_checkpoint.add()
+
         for i in range((steps-initial_step) * gradient_step):
             if scheduler.finished:
                 break
@@ -724,6 +726,9 @@ Last saved image: {html.escape(last_saved_image)}<br/>
         pbar.close()
         hypernetwork.eval()
         #report_statistics(loss_dict)
+        sd_hijack_checkpoint.remove()
+
+
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
     hypernetwork.optimizer_name = optimizer_name

modules/sd_hijack.py

@@ -69,12 +69,6 @@ def undo_optimizations():
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
 
-def fix_checkpoint():
-    ldm.modules.attention.BasicTransformerBlock.forward = sd_hijack_checkpoint.BasicTransformerBlock_forward
-    ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = sd_hijack_checkpoint.ResBlock_forward
-    ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = sd_hijack_checkpoint.AttentionBlock_forward
-
-
 class StableDiffusionModelHijack:
     fixes = None
     comments = []
@@ -106,8 +100,6 @@ class StableDiffusionModelHijack:
         self.optimization_method = apply_optimizations()
 
         self.clip = m.cond_stage_model
-        
-        fix_checkpoint()
 
         def flatten(el):
             flattened = [flatten(children) for children in el.children()]

modules/sd_hijack_checkpoint.py

@@ -1,10 +1,46 @@
 from torch.utils.checkpoint import checkpoint
 
+import ldm.modules.attention
+import ldm.modules.diffusionmodules.openaimodel
+
+
 def BasicTransformerBlock_forward(self, x, context=None):
     return checkpoint(self._forward, x, context)
 
+
 def AttentionBlock_forward(self, x):
     return checkpoint(self._forward, x)
 
+
 def ResBlock_forward(self, x, emb):
-    return checkpoint(self._forward, x, emb)
+    return checkpoint(self._forward, x, emb)
+
+
+stored = []
+
+
+def add():
+    if len(stored) != 0:
+        return
+
+    stored.extend([
+        ldm.modules.attention.BasicTransformerBlock.forward,
+        ldm.modules.diffusionmodules.openaimodel.ResBlock.forward,
+        ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward
+    ])
+
+    ldm.modules.attention.BasicTransformerBlock.forward = BasicTransformerBlock_forward
+    ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = ResBlock_forward
+    ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = AttentionBlock_forward
+
+
+def remove():
+    if len(stored) == 0:
+        return
+
+    ldm.modules.attention.BasicTransformerBlock.forward = stored[0]
+    ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = stored[1]
+    ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = stored[2]
+
+    stored.clear()
+

modules/shared.py

@@ -369,6 +369,7 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
 }))
 
 options_templates.update(options_section(('system', "System"), {
+    "show_warnings": OptionInfo(False, "Show warnings in console."),
     "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
     "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
     "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),

modules/textual_inversion/textual_inversion.py

@@ -15,7 +15,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 from torch.utils.tensorboard import SummaryWriter
 
-from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers
+from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -452,6 +452,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
 
     pbar = tqdm.tqdm(total=steps - initial_step)
     try:
+        sd_hijack_checkpoint.add()
+
         for i in range((steps-initial_step) * gradient_step):
             if scheduler.finished:
                 break
@@ -617,9 +619,11 @@ Last saved image: {html.escape(last_saved_image)}<br/>
         pbar.close()
         shared.sd_model.first_stage_model.to(devices.device)
         shared.parallel_processing_allowed = old_parallel_processing_allowed
+        sd_hijack_checkpoint.remove()
 
     return embedding, filename
 
+
 def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True):
     old_embedding_name = embedding.name
     old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None

modules/ui.py

@@ -11,6 +11,7 @@ import tempfile
 import time
 import traceback
 from functools import partial, reduce
+import warnings
 
 import gradio as gr
 import gradio.routes
@@ -41,6 +42,8 @@ from modules.textual_inversion import textual_inversion
 import modules.hypernetworks.ui
 from modules.generation_parameters_copypaste import image_from_url_text
 
+warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
+
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
@@ -417,17 +420,16 @@ def apply_setting(key, value):
     return value
 
 
-def update_generation_info(args):
-    generation_info, html_info, img_index = args
+def update_generation_info(generation_info, html_info, img_index):
     try:
         generation_info = json.loads(generation_info)
         if img_index < 0 or img_index >= len(generation_info["infotexts"]):
-            return html_info
-        return plaintext_to_html(generation_info["infotexts"][img_index])
+            return html_info, gr.update()
+        return plaintext_to_html(generation_info["infotexts"][img_index]), gr.update()
     except Exception:
         pass
     # if the json parse or anything else fails, just return the old html_info
-    return html_info
+    return html_info, gr.update()
 
 
 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
@@ -508,10 +510,9 @@ Requested path was: {f}
                             generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
                             generation_info_button.click(
                                 fn=update_generation_info,
-                                _js="(x, y) => [x, y, selected_gallery_index()]",
-                                inputs=[generation_info, html_info],
-                                outputs=[html_info],
-                                preprocess=False
+                                _js="function(x, y, z){ console.log(x, y, z); return [x, y, selected_gallery_index()] }",
+                                inputs=[generation_info, html_info, html_info],
+                                outputs=[html_info, html_info],
                             )
 
                         save.click(
@@ -526,7 +527,8 @@ Requested path was: {f}
                             outputs=[
                                 download_files,
                                 html_log,
-                            ]
+                            ],
+                            show_progress=False,
                         )
 
                         save_zip.click(
@@ -588,7 +590,7 @@ def create_ui():
         txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
-        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
 
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='compact', elem_id="txt2img_settings"):
@@ -768,7 +770,7 @@ def create_ui():
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True)
 
-        img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
+        img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
 
         with FormRow().style(equal_height=False):
             with gr.Column(variant='compact', elem_id="img2img_settings"):
@@ -1768,7 +1770,10 @@ def create_ui():
             if saved_value is None:
                 ui_settings[key] = getattr(obj, field)
             elif condition and not condition(saved_value):
-                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
+                pass
+
+                # this warning is generally not useful;
+                # print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
             else:
                 setattr(obj, field, saved_value)
                 if init_field is not None:

scripts/prompts_from_file.py

@@ -116,7 +116,7 @@ class Script(scripts.Script):
         checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False, elem_id=self.elem_id("checkbox_iterate_batch"))
 
         prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1, elem_id=self.elem_id("prompt_txt"))
-        file = gr.File(label="Upload prompt inputs", type='bytes', elem_id=self.elem_id("file"))
+        file = gr.File(label="Upload prompt inputs", type='binary', elem_id=self.elem_id("file"))
 
         file.change(fn=load_prompt_file, inputs=[file], outputs=[file, prompt_txt, prompt_txt])
 

style.css

@@ -299,9 +299,8 @@ input[type="range"]{
 }
 
 /* more gradio's garbage cleanup */
-.min-h-\[4rem\] {
-  min-height: unset !important;
-}
+.min-h-\[4rem\] { min-height: unset !important; }
+.min-h-\[6rem\] { min-height: unset !important; }
 
 .progressDiv{
     position: absolute;