Merge branch 'master' into master

1 files changed, 32 insertions, 92 deletions

diff --git a/modules/ui.py b/modules/ui.py
index e6da1b2a..00809361 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -17,7 +17,7 @@ import gradio.routes
 import gradio.utils

 import numpy as np

 from PIL import Image, PngImagePlugin

-

+from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call

 

 from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru

 from modules.paths import script_path

@@ -157,84 +157,7 @@ def save_files(js_data, images, do_make_zip, index):
 

     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")

 

-def save_pil_to_file(pil_image, dir=None):

-    use_metadata = False

-    metadata = PngImagePlugin.PngInfo()

-    for key, value in pil_image.info.items():

-        if isinstance(key, str) and isinstance(value, str):

-            metadata.add_text(key, value)

-            use_metadata = True

-

-    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)

-    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))

-    return file_obj

-

-

-# override save to file function so that it also writes PNG info

-gr.processing_utils.save_pil_to_file = save_pil_to_file

-

-

-def wrap_gradio_call(func, extra_outputs=None, add_stats=False):

-    def f(*args, extra_outputs_array=extra_outputs, **kwargs):

-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats

-        if run_memmon:

-            shared.mem_mon.monitor()

-        t = time.perf_counter()

-

-        try:

-            res = list(func(*args, **kwargs))

-        except Exception as e:

-            # When printing out our debug argument list, do not print out more than a MB of text

-            max_debug_str_len = 131072 # (1024*1024)/8

-

-            print("Error completing request", file=sys.stderr)

-            argStr = f"Arguments: {str(args)} {str(kwargs)}"

-            print(argStr[:max_debug_str_len], file=sys.stderr)

-            if len(argStr) > max_debug_str_len:

-                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)

-

-            print(traceback.format_exc(), file=sys.stderr)

-

-            shared.state.job = ""

-            shared.state.job_count = 0

-

-            if extra_outputs_array is None:

-                extra_outputs_array = [None, '']

-

-            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]

-

-        shared.state.skipped = False

-        shared.state.interrupted = False

-        shared.state.job_count = 0

-

-        if not add_stats:

-            return tuple(res)

-

-        elapsed = time.perf_counter() - t

-        elapsed_m = int(elapsed // 60)

-        elapsed_s = elapsed % 60

-        elapsed_text = f"{elapsed_s:.2f}s"

-        if elapsed_m > 0:

-            elapsed_text = f"{elapsed_m}m "+elapsed_text

-

-        if run_memmon:

-            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}

-            active_peak = mem_stats['active_peak']

-            reserved_peak = mem_stats['reserved_peak']

-            sys_peak = mem_stats['system_peak']

-            sys_total = mem_stats['total']

-            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)

-

-            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"

-        else:

-            vram_html = ''

-

-        # last item is always HTML

-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"

 

-        return tuple(res)

-

-    return f

 

 

 def calc_time_left(progress, threshold, label, force_display):

@@ -478,9 +401,7 @@ def create_toprow(is_img2img):
         if is_img2img:

             with gr.Column(scale=1, elem_id="interrogate_col"):

                 button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")

-

-                if cmd_opts.deepdanbooru:

-                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")

+                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")

 

         with gr.Column(scale=1):

             with gr.Row():

@@ -684,7 +605,7 @@ Requested path was: {f}
                 return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info

 

 

-def create_ui(wrap_gradio_gpu_call):

+def create_ui():

     import modules.img2img

     import modules.txt2img

 

@@ -844,7 +765,7 @@ def create_ui(wrap_gradio_gpu_call):
                 height,

             ]

 

-            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])

+            token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])

 

     modules.scripts.scripts_current = modules.scripts.scripts_img2img

     modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)

@@ -1004,11 +925,10 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[img2img_prompt],

             )

 

-            if cmd_opts.deepdanbooru:

-                img2img_deepbooru.click(

-                    fn=interrogate_deepbooru,

-                    inputs=[init_img],

-                    outputs=[img2img_prompt],

+            img2img_deepbooru.click(

+                fn=interrogate_deepbooru,

+                inputs=[init_img],

+                outputs=[img2img_prompt],

             )

 

 

@@ -1063,6 +983,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (seed_resize_from_w, "Seed resize from-1"),

                 (seed_resize_from_h, "Seed resize from-2"),

                 (denoising_strength, "Denoising strength"),

+                (mask_blur, "Mask blur"),

                 *modules.scripts.scripts_img2img.infotext_fields

             ]

             parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)

@@ -1183,7 +1104,11 @@ def create_ui(wrap_gradio_gpu_call):
                 custom_name = gr.Textbox(label="Custom Name (Optional)")

                 interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)

                 interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")

-                save_as_half = gr.Checkbox(value=False, label="Save as float16")

+

+                with gr.Row():

+                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format")

+                    save_as_half = gr.Checkbox(value=False, label="Save as float16")

+

                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')

 

             with gr.Column(variant='panel'):

@@ -1213,7 +1138,7 @@ def create_ui(wrap_gradio_gpu_call):
 

                 with gr.Tab(label="Create hypernetwork"):

                     new_hypernetwork_name = gr.Textbox(label="Name")

-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])

+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"])

                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")

                     new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)

                     new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])

@@ -1240,7 +1165,7 @@ def create_ui(wrap_gradio_gpu_call):
                         process_split = gr.Checkbox(label='Split oversized images')

                         process_focal_crop = gr.Checkbox(label='Auto focal point crop')

                         process_caption = gr.Checkbox(label='Use BLIP for caption')

-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)

+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True)

 

                     with gr.Row(visible=False) as process_split_extra_row:

                         process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)

@@ -1259,7 +1184,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():

                             with gr.Row():

                                 interrupt_preprocessing = gr.Button("Interrupt")

-                                run_preprocess = gr.Button(value="Preprocess", variant='primary')

+                            run_preprocess = gr.Button(value="Preprocess", variant='primary')

 

                     process_split.change(

                         fn=lambda show: gr_show(show),

@@ -1286,6 +1211,7 @@ def create_ui(wrap_gradio_gpu_call):
                         hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")

 

                     batch_size = gr.Number(label='Batch size', value=1, precision=0)

+                    gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)

                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")

                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")

                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))

@@ -1296,6 +1222,11 @@ def create_ui(wrap_gradio_gpu_call):
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)

                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)

                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)

+                    with gr.Row():

+                        shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False)

+                        tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0)

+                    with gr.Row():

+                        latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])

 

                     with gr.Row():

                         interrupt_training = gr.Button(value="Interrupt")

@@ -1384,11 +1315,15 @@ def create_ui(wrap_gradio_gpu_call):
                 train_embedding_name,

                 embedding_learn_rate,

                 batch_size,

+                gradient_step,

                 dataset_directory,

                 log_directory,

                 training_width,

                 training_height,

                 steps,

+                shuffle_tags,

+                tag_drop_out,

+                latent_sampling_method,

                 create_image_every,

                 save_embedding_every,

                 template_file,

@@ -1409,11 +1344,15 @@ def create_ui(wrap_gradio_gpu_call):
                 train_hypernetwork_name,

                 hypernetwork_learn_rate,

                 batch_size,

+                gradient_step,

                 dataset_directory,

                 log_directory,

                 training_width,

                 training_height,

                 steps,

+                shuffle_tags,

+                tag_drop_out,

+                latent_sampling_method,

                 create_image_every,

                 save_embedding_every,

                 template_file,

@@ -1697,6 +1636,7 @@ def create_ui(wrap_gradio_gpu_call):
                 interp_amount,

                 save_as_half,

                 custom_name,

+                checkpoint_format,

             ],

             outputs=[

                 submit_result,