support for scripts

modules/img2img.py

@@ -7,8 +7,9 @@ import modules.shared as shared
 import modules.processing as processing
 from modules.ui import plaintext_to_html
 import modules.images as images
+import modules.scripts
 
-def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool):
+def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool, *args):
     is_inpaint = mode == 1
     is_loopback = mode == 2
     is_upscale = mode == 3
@@ -35,7 +36,6 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
         cfg_scale=cfg_scale,
         width=width,
         height=height,
-        prompt_matrix=prompt_matrix,
         use_GFPGAN=use_GFPGAN,
         init_images=[image],
         mask=mask,
@@ -128,6 +128,11 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
         processed = Processed(p, [combined_image], initial_seed, initial_info)
 
     else:
-        processed = process_images(p)
+
+        processed = modules.scripts.run(p, *args)
+
+        if processed is None:
+            processed = process_images(p)
+
 
     return processed.images, processed.js(), plaintext_to_html(processed.info)

modules/processing.py

@@ -28,11 +28,12 @@ def torch_gc():
 
 
 class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
         self.prompt: str = prompt
+        self.prompt_for_display: str = None
         self.negative_prompt: str = (negative_prompt or "")
         self.seed: int = seed
         self.sampler_index: int = sampler_index
@@ -42,7 +43,6 @@ class StableDiffusionProcessing:
         self.cfg_scale: float = cfg_scale
         self.width: int = width
         self.height: int = height
-        self.prompt_matrix: bool = prompt_matrix
         self.use_GFPGAN: bool = use_GFPGAN
         self.do_not_save_samples: bool = do_not_save_samples
         self.do_not_save_grid: bool = do_not_save_grid
@@ -71,8 +71,8 @@ class Processed:
 
     def js(self):
         obj = {
-            "prompt": self.prompt,
+            "prompt": self.prompt if type(self.prompt) != list else self.prompt[0],
-            "seed": int(self.seed),
+            "seed": int(self.seed if type(self.seed) != list else self.seed[0]),
             "width": self.width,
             "height": self.height,
             "sampler": self.sampler,
@@ -105,35 +105,22 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     assert p.prompt is not None
     torch_gc()
 
-    seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
+    seed = int(random.randrange(4294967294)) if p.seed == -1 else p.seed
 
     os.makedirs(p.outpath_samples, exist_ok=True)
     os.makedirs(p.outpath_grids, exist_ok=True)
 
     comments = []
 
-    prompt_matrix_parts = []
+    if type(prompt) == list:
-    if p.prompt_matrix:
+        all_prompts = prompt
-        all_prompts = []
-        prompt_matrix_parts = prompt.split("|")
-        combination_count = 2 ** (len(prompt_matrix_parts) - 1)
-        for combination_num in range(combination_count):
-            selected_prompts = [text.strip().strip(',') for n, text in enumerate(prompt_matrix_parts[1:]) if combination_num & (1 << n)]
-
-            if opts.prompt_matrix_add_to_start:
-                selected_prompts = selected_prompts + [prompt_matrix_parts[0]]
-            else:
-                selected_prompts = [prompt_matrix_parts[0]] + selected_prompts
-
-            all_prompts.append(", ".join(selected_prompts))
-
-        p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
-        all_seeds = len(all_prompts) * [seed]
-
-        print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
     else:
         all_prompts = p.batch_size * p.n_iter * [prompt]
-        all_seeds = [seed + x for x in range(len(all_prompts))]
+
+    if type(seed) == list:
+        all_seeds = seed
+    else:
+        all_seeds = [int(seed + x) for x in range(len(all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0):
         generation_params = {
@@ -149,7 +136,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
         generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
 
-        return f"{prompt}\n{generation_params_text}".strip() + "".join(["\n\n" + x for x in comments])
+        return f"{p.prompt_for_display or prompt}\n{generation_params_text}".strip() + "".join(["\n\n" + x for x in comments])
 
     if os.path.exists(cmd_opts.embeddings_dir):
         model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)
@@ -218,25 +205,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         if not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
             return_grid = opts.return_grid
 
-            if p.prompt_matrix:
+            grid = images.image_grid(output_images, p.batch_size)
-                grid = images.image_grid(output_images, p.batch_size, rows=1 << ((len(prompt_matrix_parts)-1)//2))
-
-                try:
-                    grid = images.draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts)
-                except Exception:
-                    import traceback
-                    print("Error creating prompt_matrix text:", file=sys.stderr)
-                    print(traceback.format_exc(), file=sys.stderr)
-
-                return_grid = True
-            else:
-                grid = images.image_grid(output_images, p.batch_size)
 
             if return_grid:
                 output_images.insert(0, grid)
 
             if opts.grid_save:
-                images.save_image(grid, p.outpath_grids, "grid", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
+                images.save_image(grid, p.outpath_grids, "grid", seed, all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
 
     torch_gc()
     return Processed(p, output_images, seed, infotext())

modules/scripts.py

@@ -2,19 +2,33 @@ import os
 import sys
 import traceback
 
+import modules.ui as ui
 import gradio as gr
 
+from modules.processing import StableDiffusionProcessing
+
 class Script:
     filename = None
+    args_from = None
+    args_to = None
 
     def title(self):
         raise NotImplementedError()
 
+    def ui(self, is_img2img):
+        pass
+
+    def run(self, *args):
+        raise NotImplementedError()
+
+    def describe(self):
+        return ""
+
 
 scripts = []
 
 
-def load_scripts(basedir, globs):
+def load_scripts(basedir):
     for filename in os.listdir(basedir):
         path = os.path.join(basedir, filename)
 
@@ -27,27 +41,73 @@ def load_scripts(basedir, globs):
         from types import ModuleType
         compiled = compile(text, path, 'exec')
         module = ModuleType(filename)
-        module.__dict__.update(globs)
         exec(compiled, module.__dict__)
 
-        for key, item in module.__dict__.items():
+        for key, script_class in module.__dict__.items():
-            if type(item) == type and issubclass(item, Script):
+            if type(script_class) == type and issubclass(script_class, Script):
-                item.filename = path
+                obj = script_class()
+                obj.filename = path
 
-                scripts.append(item)
+                scripts.append(obj)
 
 
 def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
     try:
-        res = func()
+        res = func(*args, **kwargs)
         return res
     except Exception:
-        print(f"Error calling: {filename/funcname}", file=sys.stderr)
+        print(f"Error calling: {filename}/{funcname}", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
 
     return default
 
-def setup_ui():
-    titles = [wrap_call(script.title, script.filename, "title") for script in scripts]
 
-    gr.Dropdown(options=[""] + titles, value="", type="index")
+def setup_ui(is_img2img):
+    titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in scripts]
+
+    dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index")
+
+    inputs = [dropdown]
+
+    for script in scripts:
+        script.args_from = len(inputs)
+        controls = script.ui(is_img2img)
+
+        for control in controls:
+            control.visible = False
+
+        inputs += controls
+        script.args_to = len(inputs)
+
+    def select_script(index):
+        if index > 0:
+            script = scripts[index-1]
+            args_from = script.args_from
+            args_to = script.args_to
+        else:
+            args_from = 0
+            args_to = 0
+
+        return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
+
+    dropdown.change(
+        fn=select_script,
+        inputs=[dropdown],
+        outputs=inputs
+    )
+
+    return inputs
+
+
+def run(p: StableDiffusionProcessing, *args):
+    script_index = args[0] - 1
+
+    if script_index < 0 or script_index >= len(scripts):
+        return None
+
+    script = scripts[script_index]
+
+    script_args = args[script.args_from:script.args_to]
+    processed = script.run(p, *script_args)
+
+    return processed

modules/sd_samplers.py

@@ -9,18 +9,28 @@ from ldm.models.diffusion.plms import PLMSSampler
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 
-SamplerData = namedtuple('SamplerData', ['name', 'constructor'])
+
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases'])
+
+samplers_k_diffusion = [
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a']),
+    ('Euler', 'sample_euler', ['k_euler']),
+    ('LMS', 'sample_lms', ['k_lms']),
+    ('Heun', 'sample_heun', ['k_heun']),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2']),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
+]
+
+samplers_data_k_diffusion = [
+    SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
+    for label, funcname, aliases in samplers_k_diffusion
+    if hasattr(k_diffusion.sampling, funcname)
+]
+
 samplers = [
-    *[SamplerData(x[0], lambda model, funcname=x[1]: KDiffusionSampler(funcname, model)) for x in [
+    *samplers_data_k_diffusion,
-        ('Euler a', 'sample_euler_ancestral'),
+    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(DDIMSampler, model), []),
-        ('Euler', 'sample_euler'),
+    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(PLMSSampler, model), []),
-        ('LMS', 'sample_lms'),
-        ('Heun', 'sample_heun'),
-        ('DPM2', 'sample_dpm_2'),
-        ('DPM2 a', 'sample_dpm_2_ancestral'),
-    ] if hasattr(k_diffusion.sampling, x[1])],
-    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(DDIMSample, model)),
-    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(PLMSSampler, model)),
 ]
 samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
 

modules/shared.py

@@ -76,7 +76,6 @@ class Options:
         "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
         "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
         "font": OptionInfo("arial.ttf", "Font for image grids  that have text"),
-        "prompt_matrix_add_to_start": OptionInfo(True, "In prompt matrix, add the variable combination of text to the start of the prompt, rather than the end"),
         "enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text text and [text] to make it pay less attention"),
         "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
 

modules/txt2img.py

@@ -1,4 +1,4 @@
-
+import modules.scripts
 from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
+def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -21,30 +21,13 @@ def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, u
         cfg_scale=cfg_scale,
         width=width,
         height=height,
-        prompt_matrix=prompt_matrix,
         use_GFPGAN=use_GFPGAN
     )
 
-    if code != '' and cmd_opts.allow_code:
+    processed = modules.scripts.run(p, *args)
-        p.do_not_save_grid = True
-        p.do_not_save_samples = True
 
-        display_result_data = [[], -1, ""]
+    if processed is not None:
-
+        pass
-        def display(imgs, s=display_result_data[1], i=display_result_data[2]):
-            display_result_data[0] = imgs
-            display_result_data[1] = s
-            display_result_data[2] = i
-
-        from types import ModuleType
-        compiled = compile(code, '', 'exec')
-        module = ModuleType("testmodule")
-        module.__dict__.update(globals())
-        module.p = p
-        module.display = display
-        exec(compiled, module.__dict__)
-
-        processed = Processed(p, *display_result_data)
     else:
         processed = process_images(p)
 

modules/ui.py

@@ -12,6 +12,7 @@ from PIL import Image
 
 import gradio as gr
 import gradio.utils
+import gradio.routes
 
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts
@@ -19,6 +20,7 @@ import modules.shared as shared
 from modules.sd_samplers import samplers, samplers_for_img2img
 import modules.gfpgan_model as gfpgan
 import modules.realesrgan_model as realesrgan
+import modules.scripts
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -131,7 +133,7 @@ def wrap_gradio_call(func):
     return f
 
 
-def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
+def create_ui(txt2img, img2img, run_extras, run_pnginfo):
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         with gr.Row():
@@ -145,8 +147,7 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
                 sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
 
                 with gr.Row():
-                    use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
+                    use_gfpgan = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
-                    prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
 
                 with gr.Row():
                     batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
@@ -160,7 +161,8 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
 
                 seed = gr.Number(label='Seed', value=-1)
 
-                code = gr.Textbox(label="Python script", visible=cmd_opts.allow_code, lines=1)
+                with gr.Group():
+                    custom_inputs = modules.scripts.setup_ui(is_img2img=False)
 
             with gr.Column(variant='panel'):
                 with gr.Group():
@@ -185,16 +187,14 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
                     negative_prompt,
                     steps,
                     sampler_index,
-                    use_GFPGAN,
+                    use_gfpgan,
-                    prompt_matrix,
                     batch_count,
                     batch_size,
                     cfg_scale,
                     seed,
                     height,
                     width,
-                    code
+                ] + custom_inputs,
-                ],
                 outputs=[
                     txt2img_gallery,
                     generation_info,
@@ -244,8 +244,7 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
                 inpainting_fill = gr.Radio(label='Msked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
 
                 with gr.Row():
-                    use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
+                    use_gfpgan = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
-                    prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
                     inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=True, visible=False)
 
                 with gr.Row():
@@ -266,6 +265,10 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
 
                 seed = gr.Number(label='Seed', value=-1)
 
+                with gr.Group():
+                    custom_inputs = modules.scripts.setup_ui(is_img2img=False)
+
+
             with gr.Column(variant='panel'):
                 with gr.Group():
                     img2img_gallery = gr.Gallery(label='Output', elem_id='img2img_gallery')
@@ -291,11 +294,10 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
                     init_img_with_mask: gr_show(is_inpaint),
                     mask_blur: gr_show(is_inpaint),
                     inpainting_fill: gr_show(is_inpaint),
-                    prompt_matrix: gr_show(is_classic),
                     batch_count: gr_show(not is_upscale),
                     batch_size: gr_show(not is_loopback),
                     sd_upscale_upscaler_name: gr_show(is_upscale),
-                    sd_upscale_overlap:gr_show(is_upscale),
+                    sd_upscale_overlap: gr_show(is_upscale),
                     inpaint_full_res: gr_show(is_inpaint),
                 }
 
@@ -307,7 +309,6 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
                     init_img_with_mask,
                     mask_blur,
                     inpainting_fill,
-                    prompt_matrix,
                     batch_count,
                     batch_size,
                     sd_upscale_upscaler_name,
@@ -326,8 +327,7 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
                     sampler_index,
                     mask_blur,
                     inpainting_fill,
-                    use_GFPGAN,
+                    use_gfpgan,
-                    prompt_matrix,
                     switch_mode,
                     batch_count,
                     batch_size,
@@ -340,7 +340,7 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
                     sd_upscale_upscaler_name,
                     sd_upscale_overlap,
                     inpaint_full_res,
-                ],
+                ] + custom_inputs,
                 outputs=[
                     img2img_gallery,
                     generation_info,
@@ -384,9 +384,6 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
                 outputs=[init_img_with_mask],
             )
 
-
-
-
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
@@ -420,7 +417,6 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
 
         submit.click(**extras_args)
 
-
         send_to_extras.click(
             fn=lambda x: image_from_url_text(x),
             _js="extract_image_from_gallery",
@@ -435,7 +431,6 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
             outputs=[image],
         )
 
-
     pnginfo_interface = gr.Interface(
         wrap_gradio_call(run_pnginfo),
         inputs=[
@@ -450,7 +445,6 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
         analytics_enabled=False,
     )
 
-
     def create_setting_component(key):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
@@ -520,20 +514,16 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
     return demo
 
 
-with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as file:
+with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
-    javascript = file.read()
+    javascript = jsfile.read()
-
-def inject_gradio_html(javascript):
-    import gradio.routes
-
-    def template_response(*args, **kwargs):
-        res = gradio_routes_templates_response(*args, **kwargs)
-        res.body = res.body.replace(b'</head>', f'<script>{javascript}</script></head>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
-    gradio.routes.templates.TemplateResponse = template_response
 
 
-inject_gradio_html(javascript)
+def template_response(*args, **kwargs):
+    res = gradio_routes_templates_response(*args, **kwargs)
+    res.body = res.body.replace(b'</head>', f'<script>{javascript}</script></head>'.encode("utf8"))
+    res.init_headers()
+    return res
+
+
+gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
+gradio.routes.templates.TemplateResponse = template_response

script.js

@@ -4,7 +4,7 @@ titles = {
 	"GFPGAN": "Restore low quality faces using GFPGAN neural network",
 	"Euler a": "Euler Ancestral - very creative, each can get acompletely different pictures depending on step count, setting seps tohigher than 30-40 does not help",
 	"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
-	"Prompt matrix": "Separate prompts into part using vertical pipe character (|) and the script will create a picture for every combination of them (except for first part, which will be present in all combinations)",
+
 	"Batch count": "How many batches of images to create",
 	"Batch size": "How many image to create in a single batch",
     "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
@@ -30,6 +30,16 @@ titles = {
 
     "Interrupt": "Stop processing images and return any results accumulated so far.",
     "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.",
+
+    "X values": "Separate values for X axis using commas.",
+    "Y values": "Separate values for Y axis using commas.",
+
+    "None": "Do not do anything special",
+    "Prompt matrix": "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)",
+    "X/Y Plot": "Create a grid where images will have different parameters. Use inputs below to specify which parameterswill be shared by columns and rows",
+    "Custom code": "Run python code. Advanced user only. Must run program with --allow-code for this to work",
+
+    "Prompt S/R": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others",
 }
 
 function gradioApp(){
@@ -37,12 +47,25 @@ function gradioApp(){
 }
 
 function addTitles(root){
-	root.querySelectorAll('span, button').forEach(function(span){
+	root.querySelectorAll('span, button, select').forEach(function(span){
 		tooltip = titles[span.textContent];
+
+		if(!tooltip){
+		    tooltip = titles[span.value];
+		}
+
 		if(tooltip){
 			span.title = tooltip;
 		}
 	})
+
+	root.querySelectorAll('select').forEach(function(select){
+	    if (select.onchange != null) return;
+
+	    select.onchange = function(){
+            select.title = titles[select.value] || "";
+	    }
+	})
 }
 
 document.addEventListener("DOMContentLoaded", function() {

style.css

@@ -22,22 +22,24 @@ button{
     overflow: visible !important;
 }
 
-fieldset span.text-gray-500, .gr-panel div.flex-col div.justify-between label, label.block span{
+#x_type, #y_type{
+    max-width: 10em;
+}
+
+fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block span{
     position: absolute;
     top: -0.6em;
     line-height: 1.2em;
     padding: 0 0.5em;
     margin: 0;
-}
 
-fieldset span.text-gray-500, .gr-panel div.flex-col div.justify-between label, label.block span{
     background-color: white;
     border-top: 1px solid #eee;
     border-left: 1px solid #eee;
     border-right: 1px solid #eee;
 }
 
-.dark fieldset span.text-gray-500, .dark .gr-panel div.flex-col div.justify-between label, .dark label.block span{
+.dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
     background-color: rgb(31, 41, 55);
     border-top: 1px solid rgb(55 65 81);
     border-left: 1px solid rgb(55 65 81);
@@ -63,4 +65,4 @@ input[type="range"]{
 #txt2img_sampling label{
     padding-left: 0.6em;
     padding-right: 0.6em;
-}
+}

webui.py

@@ -55,23 +55,6 @@ def load_model_from_config(config, ckpt, verbose=False):
     return model
 
 
-def draw_xy_grid(xs, ys, x_label, y_label, cell):
-    res = []
-
-    ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
-    hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
-
-    for y in ys:
-        for x in xs:
-            state.job = f"{x + y * len(xs)} out of {len(xs) * len(ys)}"
-            res.append(cell(x, y))
-
-    grid = images.image_grid(res, rows=len(ys))
-    grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
-
-    return grid
-
-
 def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_index):
     processing.torch_gc()
 
@@ -138,7 +121,6 @@ try:
 except Exception:
     pass
 
-
 sd_config = OmegaConf.load(cmd_opts.config)
 shared.sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
 shared.sd_model = (shared.sd_model if cmd_opts.no_half else shared.sd_model.half())
@@ -150,18 +132,18 @@ else:
 
 modules.sd_hijack.model_hijack.hijack(shared.sd_model)
 
+modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
+
 
 # make the program just exit at ctrl+c without waiting for anything
-def sigint_handler(signal, frame):
+def sigint_handler(sig, frame):
-    print('Interrupted')
+    print(f'Interrupted with singal {sig} in {frame}')
     os._exit(0)
 
 
 signal.signal(signal.SIGINT, sigint_handler)
 
 demo = modules.ui.create_ui(
-    opts=opts,
-    cmd_opts=cmd_opts,
     txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
     img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
     run_extras=wrap_gradio_gpu_call(run_extras),