Merge branch 'master' of github.com:AUTOMATIC1111/stable-diffusion-webui into gamepad

javascript/hints.js

@@ -17,7 +17,7 @@ titles = {
     "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "\u{1f4c2}": "Open images output directory",
     "\u{1f4be}": "Save style",
-    "\U0001F5D1": "Clear prompt",
+    "\u{1f5d1}": "Clear prompt",
     "\u{1f4cb}": "Apply selected styles to current prompt",
     "\u{1f4d2}": "Paste available values into the field",
     "\u{1f3b4}": "Show extra networks",

modules/devices.py

@@ -2,6 +2,7 @@ import sys, os, shlex
 import contextlib
 import torch
 from modules import errors
+from modules.sd_hijack_utils import CondFunc
 from packaging import version
 
 
@@ -156,36 +157,7 @@ def test_for_nans(x, where):
     raise NansException(message)
 
 
-# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
-orig_tensor_to = torch.Tensor.to
-def tensor_to_fix(self, *args, **kwargs):
-    if self.device.type != 'mps' and \
-       ((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \
-       (isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):
-        self = self.contiguous()
-    return orig_tensor_to(self, *args, **kwargs)
-
-
-# MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
-orig_layer_norm = torch.nn.functional.layer_norm
-def layer_norm_fix(*args, **kwargs):
-    if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':
-        args = list(args)
-        args[0] = args[0].contiguous()
-    return orig_layer_norm(*args, **kwargs)
-
-
-# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
-orig_tensor_numpy = torch.Tensor.numpy
-def numpy_fix(self, *args, **kwargs):
-    if self.requires_grad:
-        self = self.detach()
-    return orig_tensor_numpy(self, *args, **kwargs)
-
-
 # MPS workaround for https://github.com/pytorch/pytorch/issues/89784
-orig_cumsum = torch.cumsum
-orig_Tensor_cumsum = torch.Tensor.cumsum
 def cumsum_fix(input, cumsum_func, *args, **kwargs):
     if input.device.type == 'mps':
         output_dtype = kwargs.get('dtype', input.dtype)
@@ -199,11 +171,20 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs):
 if has_mps():
     if version.parse(torch.__version__) < version.parse("1.13"):
         # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
-        torch.Tensor.to = tensor_to_fix
-        torch.nn.functional.layer_norm = layer_norm_fix
-        torch.Tensor.numpy = numpy_fix
+
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+        CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
+                                                          lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
+        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
+                                                                                        lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/90532
+        CondFunc('torch.Tensor.numpy', lambda orig_func, self, *args, **kwargs: orig_func(self.detach(), *args, **kwargs), lambda _, self, *args, **kwargs: self.requires_grad)
     elif version.parse(torch.__version__) > version.parse("1.13.1"):
         cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))
         cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0))
-        torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) )
-        torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )
+        cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs)
+        CondFunc('torch.cumsum', cumsum_fix_func, None)
+        CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
+        CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
+

modules/shared.py

@@ -327,7 +327,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "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"),
 
-    "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
+    "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
     "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
     "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),

modules/ui.py

@@ -479,8 +479,8 @@ def create_ui():
                                 width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
                                 height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
 
-                            if opts.dimensions_and_batch_together:
                             res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
+                            if opts.dimensions_and_batch_together:
                                 with gr.Column(elem_id="txt2img_column_batch"):
                                     batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
                                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
@@ -757,8 +757,8 @@ def create_ui():
                                 width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
                                 height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
 
-                            if opts.dimensions_and_batch_together:
                             res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+                            if opts.dimensions_and_batch_together:
                                 with gr.Column(elem_id="img2img_column_batch"):
                                     batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
                                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")

scripts/prompt_matrix.py

@@ -45,15 +45,39 @@ class Script(scripts.Script):
         return "Prompt matrix"
 
     def ui(self, is_img2img):
-        put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False, elem_id=self.elem_id("put_at_start"))
-        different_seeds = gr.Checkbox(label='Use different seed for each picture', value=False, elem_id=self.elem_id("different_seeds"))
-
-        return [put_at_start, different_seeds]
-
-    def run(self, p, put_at_start, different_seeds):
+        gr.HTML('<br />')
+        with gr.Row():
+            with gr.Column():
+                put_at_start = gr.Checkbox(label='Put variable parts at start of prompt',
+                                           value=False, elem_id=self.elem_id("put_at_start"))
+            with gr.Column():
+                # Radio buttons for selecting the prompt between positive and negative
+                prompt_type = gr.Radio(["positive", "negative"], label="Select prompt",
+                                       elem_id=self.elem_id("prompt_type"), value="positive")
+        with gr.Row():
+            with gr.Column():
+                different_seeds = gr.Checkbox(
+                    label='Use different seed for each picture', value=False, elem_id=self.elem_id("different_seeds"))
+            with gr.Column():
+                # Radio buttons for selecting the delimiter to use in the resulting prompt
+                variations_delimiter = gr.Radio(["comma", "space"], label="Select delimiter", elem_id=self.elem_id(
+                    "variations_delimiter"), value="comma")
+        return [put_at_start, different_seeds, prompt_type, variations_delimiter]
+
+    def run(self, p, put_at_start, different_seeds, prompt_type, variations_delimiter):
         modules.processing.fix_seed(p)
+        # Raise error if promp type is not positive or negative
+        if prompt_type not in ["positive", "negative"]:
+            raise ValueError(f"Unknown prompt type {prompt_type}")
+        # Raise error if variations delimiter is not comma or space
+        if variations_delimiter not in ["comma", "space"]:
+            raise ValueError(f"Unknown variations delimiter {variations_delimiter}")
 
-        original_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
+        prompt = p.prompt if prompt_type == "positive" else p.negative_prompt
+        original_prompt = prompt[0] if type(prompt) == list else prompt
+        positive_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
+
+        delimiter = ", " if variations_delimiter == "comma" else " "
 
         all_prompts = []
         prompt_matrix_parts = original_prompt.split("|")
@@ -66,16 +90,19 @@ class Script(scripts.Script):
             else:
                 selected_prompts = [prompt_matrix_parts[0]] + selected_prompts
 
-            all_prompts.append(", ".join(selected_prompts))
+            all_prompts.append(delimiter.join(selected_prompts))
 
         p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
         p.do_not_save_grid = True
 
         print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
 
+        if prompt_type == "positive":
             p.prompt = all_prompts
+        else:
+            p.negative_prompt = all_prompts
         p.seed = [p.seed + (i if different_seeds else 0) for i in range(len(all_prompts))]
-        p.prompt_for_display = original_prompt
+        p.prompt_for_display = positive_prompt
         processed = process_images(p)
 
         grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))

scripts/xyz_grid.py

@@ -286,23 +286,24 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
         print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
         return Processed(p, [])
 
-    grids = [None] * len(zs)
+    sub_grids = [None] * len(zs)
     for i in range(len(zs)):
         start_index = i * len(xs) * len(ys)
         end_index = start_index + len(xs) * len(ys)
         grid = images.image_grid(image_cache[start_index:end_index], rows=len(ys))
         if draw_legend:
             grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts)
-        
-        grids[i] = grid        
+        sub_grids[i] = grid
         if include_sub_grids and len(zs) > 1:
             processed_result.images.insert(i+1, grid)
 
-    original_grid_size = grids[0].size
-    grids = images.image_grid(grids, rows=1)
-    processed_result.images[0] = images.draw_grid_annotations(grids, original_grid_size[0], original_grid_size[1], title_texts, [[images.GridAnnotation()]])
+    sub_grid_size = sub_grids[0].size
+    z_grid = images.image_grid(sub_grids, rows=1)
+    if draw_legend:
+        z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
+    processed_result.images[0] = z_grid
 
-    return processed_result
+    return processed_result, sub_grids
 
 
 class SharedSettingsStackHelper(object):
@@ -576,7 +577,7 @@ class Script(scripts.Script):
             return res
 
         with SharedSettingsStackHelper():
-            processed = draw_xyz_grid(
+            processed, sub_grids = draw_xyz_grid(
                 p,
                 xs=xs,
                 ys=ys,
@@ -592,6 +593,10 @@ class Script(scripts.Script):
                 second_axes_processed=second_axes_processed
             )
 
+        if opts.grid_save and len(sub_grids) > 1:
+            for sub_grid in sub_grids:
+                images.save_image(sub_grid, p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
+
         if opts.grid_save:
             images.save_image(processed.images[0], p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)