Merge branch 'master' into xygrid_infotext_improvements

scripts/custom_code.py

@@ -14,7 +14,7 @@ class Script(scripts.Script):
         return cmd_opts.allow_code
 
     def ui(self, is_img2img):
-        code = gr.Textbox(label="Python code", visible=False, lines=1)
+        code = gr.Textbox(label="Python code", lines=1)
 
         return [code]
 

scripts/img2imgalt.py

@@ -8,7 +8,6 @@ import gradio as gr
 
 from modules import processing, shared, sd_samplers, prompt_parser
 from modules.processing import Processed
-from modules.sd_samplers import samplers
 from modules.shared import opts, cmd_opts, state
 
 import torch
@@ -35,6 +34,9 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
         sigma_in = torch.cat([sigmas[i] * s_in] * 2)
         cond_in = torch.cat([uncond, cond])
 
+        image_conditioning = torch.cat([p.image_conditioning] * 2)
+        cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
+
         c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
         t = dnw.sigma_to_t(sigma_in)
 
@@ -79,6 +81,9 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
         sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
         cond_in = torch.cat([uncond, cond])
 
+        image_conditioning = torch.cat([p.image_conditioning] * 2)
+        cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
+
         c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
 
         if i == 1:
@@ -121,20 +126,47 @@ class Script(scripts.Script):
         return is_img2img
 
     def ui(self, is_img2img):
+        info = gr.Markdown('''
+        * `CFG Scale` should be 2 or lower.
+        ''')
+
+        override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True)
+
+        override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True)
         original_prompt = gr.Textbox(label="Original prompt", lines=1)
         original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1)
-        cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
+
+        override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True)
         st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
+
+        override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True)
+
+        cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
         randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
         sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False)
-        return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
 
-    def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
-        p.batch_size = 1
-        p.batch_count = 1
+        return [
+            info, 
+            override_sampler,
+            override_prompt, original_prompt, original_negative_prompt, 
+            override_steps, st,
+            override_strength,
+            cfg, randomness, sigma_adjustment,
+        ]
 
+    def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
+        # Override
+        if override_sampler:
+            p.sampler_name = "Euler"
+        if override_prompt:
+            p.prompt = original_prompt
+            p.negative_prompt = original_negative_prompt
+        if override_steps:
+            p.steps = st
+        if override_strength:
+            p.denoising_strength = 1.0
 
-        def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+        def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
             lat = (p.init_latent.cpu().numpy() * 10).astype(int)
 
             same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \
@@ -155,11 +187,11 @@ class Script(scripts.Script):
                     rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
                 self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
 
-            rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
+            rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
             
             combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
             
-            sampler = samplers[p.sampler_index].constructor(p.sd_model)
+            sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
 
             sigmas = sampler.model_wrap.get_sigmas(p.steps)
             
@@ -167,7 +199,7 @@ class Script(scripts.Script):
             
             p.seed = p.seed + 1
             
-            return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning)
+            return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
 
         p.sample = sample_extra
 

scripts/loopback.py

@@ -38,6 +38,7 @@ class Script(scripts.Script):
 
         grids = []
         all_images = []
+        original_init_image = p.init_images
         state.job_count = loops * batch_count
 
         initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
@@ -45,6 +46,9 @@ class Script(scripts.Script):
         for n in range(batch_count):
             history = []
 
+            # Reset to original init image at the start of each batch
+            p.init_images = original_init_image
+
             for i in range(loops):
                 p.n_iter = 1
                 p.batch_size = 1

scripts/outpainting_mk_2.py

@@ -85,8 +85,11 @@ def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.0
     src_dist = np.absolute(src_fft)
     src_phase = src_fft / src_dist
 
+    # create a generator with a static seed to make outpainting deterministic / only follow global seed
+    rng = np.random.default_rng(0)
+
     noise_window = _get_gaussian_window(width, height, mode=1)  # start with simple gaussian noise
-    noise_rgb = np.random.random_sample((width, height, num_channels))
+    noise_rgb = rng.random((width, height, num_channels))
     noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
     noise_rgb *= color_variation  # the colorfulness of the starting noise is blended to greyscale with a parameter
     for c in range(num_channels):
@@ -129,7 +132,7 @@ class Script(scripts.Script):
         info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>")
 
         pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128)
-        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, visible=False)
+        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8)
         direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'])
         noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0)
         color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05)
@@ -169,54 +172,54 @@ class Script(scripts.Script):
         if down > 0:
             down = target_h - init_img.height - up
 
-        init_image = p.init_images[0]
-
-        state.job_count = (1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0)
-
-        def expand(init, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
+        def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
             is_horiz = is_left or is_right
             is_vert = is_top or is_bottom
             pixels_horiz = expand_pixels if is_horiz else 0
             pixels_vert = expand_pixels if is_vert else 0
 
-            res_w = init.width + pixels_horiz
-            res_h = init.height + pixels_vert
-            process_res_w = math.ceil(res_w / 64) * 64
-            process_res_h = math.ceil(res_h / 64) * 64
+            images_to_process = []
+            output_images = []
+            for n in range(count):
+                res_w = init[n].width + pixels_horiz
+                res_h = init[n].height + pixels_vert
+                process_res_w = math.ceil(res_w / 64) * 64
+                process_res_h = math.ceil(res_h / 64) * 64
 
-            img = Image.new("RGB", (process_res_w, process_res_h))
-            img.paste(init, (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
-            mask = Image.new("RGB", (process_res_w, process_res_h), "white")
-            draw = ImageDraw.Draw(mask)
-            draw.rectangle((
-                expand_pixels + mask_blur if is_left else 0,
-                expand_pixels + mask_blur if is_top else 0,
-                mask.width - expand_pixels - mask_blur if is_right else res_w,
-                mask.height - expand_pixels - mask_blur if is_bottom else res_h,
-            ), fill="black")
+                img = Image.new("RGB", (process_res_w, process_res_h))
+                img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
+                mask = Image.new("RGB", (process_res_w, process_res_h), "white")
+                draw = ImageDraw.Draw(mask)
+                draw.rectangle((
+                    expand_pixels + mask_blur if is_left else 0,
+                    expand_pixels + mask_blur if is_top else 0,
+                    mask.width - expand_pixels - mask_blur if is_right else res_w,
+                    mask.height - expand_pixels - mask_blur if is_bottom else res_h,
+                ), fill="black")
 
-            np_image = (np.asarray(img) / 255.0).astype(np.float64)
-            np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
-            noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
-            out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
+                np_image = (np.asarray(img) / 255.0).astype(np.float64)
+                np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
+                noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
+                output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB"))
 
-            target_width = min(process_width, init.width + pixels_horiz) if is_horiz else img.width
-            target_height = min(process_height, init.height + pixels_vert) if is_vert else img.height
+                target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width
+                target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height
+                p.width = target_width if is_horiz else img.width
+                p.height = target_height if is_vert else img.height
 
-            crop_region = (
-                0 if is_left else out.width - target_width,
-                0 if is_top else out.height - target_height,
-                target_width if is_left else out.width,
-                target_height if is_top else out.height,
-            )
+                crop_region = (
+                    0 if is_left else output_images[n].width - target_width,
+                    0 if is_top else output_images[n].height - target_height,
+                    target_width if is_left else output_images[n].width,
+                    target_height if is_top else output_images[n].height,
+                )
+                mask = mask.crop(crop_region)
+                p.image_mask = mask
 
-            image_to_process = out.crop(crop_region)
-            mask = mask.crop(crop_region)
+                image_to_process = output_images[n].crop(crop_region)
+                images_to_process.append(image_to_process)
 
-            p.width = target_width if is_horiz else img.width
-            p.height = target_height if is_vert else img.height
-            p.init_images = [image_to_process]
-            p.image_mask = mask
+            p.init_images = images_to_process
 
             latent_mask = Image.new("RGB", (p.width, p.height), "white")
             draw = ImageDraw.Draw(latent_mask)
@@ -229,31 +232,52 @@ class Script(scripts.Script):
             p.latent_mask = latent_mask
 
             proc = process_images(p)
-            proc_img = proc.images[0]
 
             if initial_seed_and_info[0] is None:
                 initial_seed_and_info[0] = proc.seed
                 initial_seed_and_info[1] = proc.info
 
-            out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height))
-            out = out.crop((0, 0, res_w, res_h))
-            return out
+            for n in range(count):
+                output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height))
+                output_images[n] = output_images[n].crop((0, 0, res_w, res_h))
 
-        img = init_image
+            return output_images
 
-        if left > 0:
-            img = expand(img, left, is_left=True)
-        if right > 0:
-            img = expand(img, right, is_right=True)
-        if up > 0:
-            img = expand(img, up, is_top=True)
-        if down > 0:
-            img = expand(img, down, is_bottom=True)
+        batch_count = p.n_iter
+        batch_size = p.batch_size
+        p.n_iter = 1
+        state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0))
+        all_processed_images = []
 
-        res = Processed(p, [img], initial_seed_and_info[0], initial_seed_and_info[1])
+        for i in range(batch_count):
+            imgs = [init_img] * batch_size
+            state.job = f"Batch {i + 1} out of {batch_count}"
+
+            if left > 0:
+                imgs = expand(imgs, batch_size, left, is_left=True)
+            if right > 0:
+                imgs = expand(imgs, batch_size, right, is_right=True)
+            if up > 0:
+                imgs = expand(imgs, batch_size, up, is_top=True)
+            if down > 0:
+                imgs = expand(imgs, batch_size, down, is_bottom=True)
+
+            all_processed_images += imgs
+
+        all_images = all_processed_images
+
+        combined_grid_image = images.image_grid(all_processed_images)
+        unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple
+        if opts.return_grid and not unwanted_grid_because_of_img_count:
+            all_images = [combined_grid_image] + all_processed_images
+
+        res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])
 
         if opts.samples_save:
-            images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
+            for img in all_processed_images:
+                images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
+
+        if opts.grid_save and not unwanted_grid_because_of_img_count:
+            images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
 
         return res
-

scripts/poor_mans_outpainting.py

@@ -22,8 +22,8 @@ class Script(scripts.Script):
             return None
 
         pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128)
-        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
-        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
+        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index")
         direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'])
 
         return [pixels, mask_blur, inpainting_fill, direction]

scripts/prompt_matrix.py

@@ -18,7 +18,7 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
     ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
     hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
 
-    first_pocessed = None
+    first_processed = None
 
     state.job_count = len(xs) * len(ys)
 
@@ -27,17 +27,17 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
             state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
 
             processed = cell(x, y)
-            if first_pocessed is None:
-                first_pocessed = processed
+            if first_processed is None:
+                first_processed = processed
 
             res.append(processed.images[0])
 
     grid = images.image_grid(res, rows=len(ys))
     grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
 
-    first_pocessed.images = [grid]
+    first_processed.images = [grid]
 
-    return first_pocessed
+    return first_processed
 
 
 class Script(scripts.Script):
@@ -46,10 +46,11 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):
         put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False)
+        different_seeds = gr.Checkbox(label='Use different seed for each picture', value=False)
 
-        return [put_at_start]
+        return [put_at_start, different_seeds]
 
-    def run(self, p, put_at_start):
+    def run(self, p, put_at_start, different_seeds):
         modules.processing.fix_seed(p)
 
         original_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
@@ -73,15 +74,17 @@ class Script(scripts.Script):
         print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
 
         p.prompt = all_prompts
-        p.seed = [p.seed for _ in 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
         processed = process_images(p)
 
         grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))
         grid = images.draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts)
         processed.images.insert(0, grid)
+        processed.index_of_first_image = 1
+        processed.infotexts.insert(0, processed.infotexts[0])
 
         if opts.grid_save:
-            images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", prompt=original_prompt, seed=processed.seed, grid=True, p=p)
+            images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", extension=opts.grid_format, prompt=original_prompt, seed=processed.seed, grid=True, p=p)
 
         return processed

scripts/prompts_from_file.py

@@ -1,55 +1,181 @@
+import copy
 import math
 import os
+import random
 import sys
 import traceback
+import shlex
 
 import modules.scripts as scripts
 import gradio as gr
 
+from modules import sd_samplers
 from modules.processing import Processed, process_images
 from PIL import Image
 from modules.shared import opts, cmd_opts, state
 
 
+def process_string_tag(tag):
+    return tag
+
+
+def process_int_tag(tag):
+    return int(tag)
+
+
+def process_float_tag(tag):
+    return float(tag)
+
+
+def process_boolean_tag(tag):
+    return True if (tag == "true") else False
+
+
+prompt_tags = {
+    "sd_model": None,
+    "outpath_samples": process_string_tag,
+    "outpath_grids": process_string_tag,
+    "prompt_for_display": process_string_tag,
+    "prompt": process_string_tag,
+    "negative_prompt": process_string_tag,
+    "styles": process_string_tag,
+    "seed": process_int_tag,
+    "subseed_strength": process_float_tag,
+    "subseed": process_int_tag,
+    "seed_resize_from_h": process_int_tag,
+    "seed_resize_from_w": process_int_tag,
+    "sampler_index": process_int_tag,
+    "sampler_name": process_string_tag,
+    "batch_size": process_int_tag,
+    "n_iter": process_int_tag,
+    "steps": process_int_tag,
+    "cfg_scale": process_float_tag,
+    "width": process_int_tag,
+    "height": process_int_tag,
+    "restore_faces": process_boolean_tag,
+    "tiling": process_boolean_tag,
+    "do_not_save_samples": process_boolean_tag,
+    "do_not_save_grid": process_boolean_tag
+}
+
+
+def cmdargs(line):
+    args = shlex.split(line)
+    pos = 0
+    res = {}
+
+    while pos < len(args):
+        arg = args[pos]
+
+        assert arg.startswith("--"), f'must start with "--": {arg}'
+        assert pos+1 < len(args), f'missing argument for command line option {arg}'
+
+        tag = arg[2:]
+
+        if tag == "prompt" or tag == "negative_prompt":
+            pos += 1
+            prompt = args[pos]
+            pos += 1
+            while pos < len(args) and not args[pos].startswith("--"):
+                prompt += " "
+                prompt += args[pos]
+                pos += 1
+            res[tag] = prompt
+            continue
+
+
+        func = prompt_tags.get(tag, None)
+        assert func, f'unknown commandline option: {arg}'
+
+        val = args[pos+1]
+        if tag == "sampler_name":
+            val = sd_samplers.samplers_map.get(val.lower(), None)
+
+        res[tag] = func(val)
+
+        pos += 2
+
+    return res
+
+
+def load_prompt_file(file):
+    if file is None:
+        lines = []
+    else:
+        lines = [x.strip() for x in file.decode('utf8', errors='ignore').split("\n")]
+
+    return None, "\n".join(lines), gr.update(lines=7)
+
+
 class Script(scripts.Script):
     def title(self):
         return "Prompts from file or textbox"
 
     def ui(self, is_img2img):
-        # This checkbox would look nicer as two tabs, but there are two problems:
-        # 1) There is a bug in Gradio 3.3 that prevents visibility from working on Tabs
-        # 2) Even with Gradio 3.3.1, returning a control (like Tabs) that can't be used as input
-        #    causes a AttributeError: 'Tabs' object has no attribute 'preprocess' assert,
-        #    due to the way Script assumes all controls returned can be used as inputs.
-        # Therefore, there's no good way to use grouping components right now,
-        # so we will use a checkbox! :)
-        checkbox_txt = gr.Checkbox(label="Show Textbox", value=False)
-        file = gr.File(label="File with inputs", type='bytes')
-        prompt_txt = gr.TextArea(label="Prompts")
-        checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
-        return [checkbox_txt, file, prompt_txt]
+        checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False)
+        checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False)
 
-    def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
-        if (checkbox_txt):
-            lines = [x.strip() for x in prompt_txt.splitlines()]
-        else:
-            lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
+        prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1)
+        file = gr.File(label="Upload prompt inputs", type='bytes')
+
+        file.change(fn=load_prompt_file, inputs=[file], outputs=[file, prompt_txt, prompt_txt])
+
+        # We start at one line. When the text changes, we jump to seven lines, or two lines if no \n.
+        # We don't shrink back to 1, because that causes the control to ignore [enter], and it may
+        # be unclear to the user that shift-enter is needed.
+        prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt])
+        return [checkbox_iterate, checkbox_iterate_batch, prompt_txt]
+
+    def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_txt: str):
+        lines = [x.strip() for x in prompt_txt.splitlines()]
         lines = [x for x in lines if len(x) > 0]
 
-        img_count = len(lines) * p.n_iter
-        batch_count = math.ceil(img_count / p.batch_size)
-        loop_count = math.ceil(batch_count / p.n_iter)
-        print(f"Will process {img_count} images in {batch_count} batches.")
-
         p.do_not_save_grid = True
 
-        state.job_count = batch_count
+        job_count = 0
+        jobs = []
+
+        for line in lines:
+            if "--" in line:
+                try:
+                    args = cmdargs(line)
+                except Exception:
+                    print(f"Error parsing line {line} as commandline:", file=sys.stderr)
+                    print(traceback.format_exc(), file=sys.stderr)
+                    args = {"prompt": line}
+            else:
+                args = {"prompt": line}
+
+            n_iter = args.get("n_iter", 1)
+            if n_iter != 1:
+                job_count += n_iter
+            else:
+                job_count += 1
+
+            jobs.append(args)
+
+        print(f"Will process {len(lines)} lines in {job_count} jobs.")
+        if (checkbox_iterate or checkbox_iterate_batch) and p.seed == -1:
+            p.seed = int(random.randrange(4294967294))
+
+        state.job_count = job_count
 
         images = []
-        for loop_no in range(loop_count):
-            state.job = f"{loop_no + 1} out of {loop_count}"
-            p.prompt = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
-            proc = process_images(p)
-            images += proc.images
+        all_prompts = []
+        infotexts = []
+        for n, args in enumerate(jobs):
+            state.job = f"{state.job_no + 1} out of {state.job_count}"
 
-        return Processed(p, images, p.seed, "")
+            copy_p = copy.copy(p)
+            for k, v in args.items():
+                setattr(copy_p, k, v)
+
+            proc = process_images(copy_p)
+            images += proc.images
+            
+            if checkbox_iterate:
+                p.seed = p.seed + (p.batch_size * p.n_iter)
+            all_prompts += proc.all_prompts
+            infotexts += proc.infotexts
+
+        return Processed(p, images, p.seed, "", all_prompts=all_prompts, infotexts=infotexts)

scripts/sd_upscale.py

@@ -17,13 +17,14 @@ class Script(scripts.Script):
         return is_img2img
 
     def ui(self, is_img2img):
-        info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image to twice the dimensions; use width and height sliders to set tile size</p>")
-        overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, visible=False)
-        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", visible=False)
+        info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
+        overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
+        scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0)
+        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
-        return [info, overlap, upscaler_index]
+        return [info, overlap, upscaler_index, scale_factor]
 
-    def run(self, p, _, overlap, upscaler_index):
+    def run(self, p, _, overlap, upscaler_index, scale_factor):
         processing.fix_seed(p)
         upscaler = shared.sd_upscalers[upscaler_index]
 
@@ -34,7 +35,12 @@ class Script(scripts.Script):
         seed = p.seed
 
         init_img = p.init_images[0]
-        img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)
+        init_img = images.flatten(init_img, opts.img2img_background_color)
+
+        if upscaler.name != "None":
+            img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)
+        else:
+            img = init_img
 
         devices.torch_gc()
 
@@ -65,7 +71,7 @@ class Script(scripts.Script):
             work_results = []
             for i in range(batch_count):
                 p.batch_size = batch_size
-                p.init_images = work[i*batch_size:(i+1)*batch_size]
+                p.init_images = work[i * batch_size:(i + 1) * batch_size]
 
                 state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"
                 processed = processing.process_images(p)

scripts/xy_grid.py

@@ -1,19 +1,25 @@
 from collections import namedtuple
 from copy import copy
+from itertools import permutations, chain
 import random
-
+import csv
+from io import StringIO
 from PIL import Image
 import numpy as np
 
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images
-from modules.processing import process_images, Processed
+from modules import images, paths, sd_samplers
+from modules.hypernetworks import hypernetwork
+from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.sd_samplers
 import modules.sd_models
+import modules.sd_vae
+import glob
+import os
 import re
 
 
@@ -25,29 +31,122 @@ def apply_field(field):
 
 
 def apply_prompt(p, x, xs):
+    if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
+        raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
+
     p.prompt = p.prompt.replace(xs[0], x)
     p.negative_prompt = p.negative_prompt.replace(xs[0], x)
 
 
-samplers_dict = {}
-for i, sampler in enumerate(modules.sd_samplers.samplers):
-    samplers_dict[sampler.name.lower()] = i
-    for alias in sampler.aliases:
-        samplers_dict[alias.lower()] = i
+def apply_order(p, x, xs):
+    token_order = []
+
+    # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
+    for token in x:
+        token_order.append((p.prompt.find(token), token))
+
+    token_order.sort(key=lambda t: t[0])
+
+    prompt_parts = []
+
+    # Split the prompt up, taking out the tokens
+    for _, token in token_order:
+        n = p.prompt.find(token)
+        prompt_parts.append(p.prompt[0:n])
+        p.prompt = p.prompt[n + len(token):]
+
+    # Rebuild the prompt with the tokens in the order we want
+    prompt_tmp = ""
+    for idx, part in enumerate(prompt_parts):
+        prompt_tmp += part
+        prompt_tmp += x[idx]
+    p.prompt = prompt_tmp + p.prompt
 
 
 def apply_sampler(p, x, xs):
-    sampler_index = samplers_dict.get(x.lower(), None)
-    if sampler_index is None:
+    sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
+    if sampler_name is None:
         raise RuntimeError(f"Unknown sampler: {x}")
 
-    p.sampler_index = sampler_index
+    p.sampler_name = sampler_name
+
+
+def confirm_samplers(p, xs):
+    for x in xs:
+        if x.lower() not in sd_samplers.samplers_map:
+            raise RuntimeError(f"Unknown sampler: {x}")
 
 
 def apply_checkpoint(p, x, xs):
     info = modules.sd_models.get_closet_checkpoint_match(x)
-    assert info is not None, f'Checkpoint for {x} not found'
+    if info is None:
+        raise RuntimeError(f"Unknown checkpoint: {x}")
     modules.sd_models.reload_model_weights(shared.sd_model, info)
+    p.sd_model = shared.sd_model
+
+
+def confirm_checkpoints(p, xs):
+    for x in xs:
+        if modules.sd_models.get_closet_checkpoint_match(x) is None:
+            raise RuntimeError(f"Unknown checkpoint: {x}")
+
+
+def apply_hypernetwork(p, x, xs):
+    if x.lower() in ["", "none"]:
+        name = None
+    else:
+        name = hypernetwork.find_closest_hypernetwork_name(x)
+        if not name:
+            raise RuntimeError(f"Unknown hypernetwork: {x}")
+    hypernetwork.load_hypernetwork(name)
+
+
+def apply_hypernetwork_strength(p, x, xs):
+    hypernetwork.apply_strength(x)
+
+
+def confirm_hypernetworks(p, xs):
+    for x in xs:
+        if x.lower() in ["", "none"]:
+            continue
+        if not hypernetwork.find_closest_hypernetwork_name(x):
+            raise RuntimeError(f"Unknown hypernetwork: {x}")
+
+
+def apply_clip_skip(p, x, xs):
+    opts.data["CLIP_stop_at_last_layers"] = x
+
+
+def apply_upscale_latent_space(p, x, xs):
+    if x.lower().strip() != '0':
+        opts.data["use_scale_latent_for_hires_fix"] = True
+    else:
+        opts.data["use_scale_latent_for_hires_fix"] = False
+
+
+def find_vae(name: str):
+    if name.lower() in ['auto', 'none']:
+        return name
+    else:
+        vae_path = os.path.abspath(os.path.join(paths.models_path, 'VAE'))
+        found = glob.glob(os.path.join(vae_path, f'**/{name}.*pt'), recursive=True)
+        if found:
+            return found[0]
+        else:
+            return 'auto'
+
+
+def apply_vae(p, x, xs):
+    if x.lower().strip() == 'none':
+        modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file='None')
+    else:
+        found = find_vae(x)
+        if found:
+            v = modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=found)
+
+
+def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
+    p.styles = x.split(',')
 
 
 def format_value_add_label(p, opt, x):
@@ -60,46 +159,67 @@ def format_value_add_label(p, opt, x):
 def format_value(p, opt, x):
     if type(x) == float:
         x = round(x, 8)
-
     return x
 
+
+def format_value_join_list(p, opt, x):
+    return ", ".join(x)
+
+
 def do_nothing(p, x, xs):
     pass
 
+
 def format_nothing(p, opt, x):
     return ""
 
 
-AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])
-AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])
+def str_permutations(x):
+    """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
+    return x
+
+AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])
+AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])
 
 
 axis_options = [
-    AxisOption("Nothing", str, do_nothing, format_nothing),
-    AxisOption("Seed", int, apply_field("seed"), format_value_add_label),
-    AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label),
-    AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label),
-    AxisOption("Steps", int, apply_field("steps"), format_value_add_label),
-    AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
-    AxisOption("Prompt S/R", str, apply_prompt, format_value),
-    AxisOption("Sampler", str, apply_sampler, format_value),
-    AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
-    AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
-    AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
-    AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
-    AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
-    AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
-    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
+    AxisOption("Nothing", str, do_nothing, format_nothing, None),
+    AxisOption("Seed", int, apply_field("seed"), format_value_add_label, None),
+    AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label, None),
+    AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label, None),
+    AxisOption("Steps", int, apply_field("steps"), format_value_add_label, None),
+    AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label, None),
+    AxisOption("Prompt S/R", str, apply_prompt, format_value, None),
+    AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list, None),
+    AxisOption("Sampler", str, apply_sampler, format_value, confirm_samplers),
+    AxisOption("Checkpoint name", str, apply_checkpoint, format_value, confirm_checkpoints),
+    AxisOption("Hypernetwork", str, apply_hypernetwork, format_value, confirm_hypernetworks),
+    AxisOption("Hypernet str.", float, apply_hypernetwork_strength, format_value_add_label, None),
+    AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label, None),
+    AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label, None),
+    AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label, None),
+    AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None),
+    AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
+    AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
+    AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
+    AxisOption("Hires upscaler", str, apply_field("hr_upscaler"), format_value_add_label, None),
+    AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),
+    AxisOption("VAE", str, apply_vae, format_value_add_label, None),
+    AxisOption("Styles", str, apply_styles, format_value_add_label, None),
 ]
 
 
-def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
-    res = []
-
+def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_images):
     ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
     hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
 
-    first_pocessed = None
+    # Temporary list of all the images that are generated to be populated into the grid.
+    # Will be filled with empty images for any individual step that fails to process properly
+    image_cache = []
+
+    processed_result = None
+    cell_mode = "P"
+    cell_size = (1,1)
 
     state.job_count = len(xs) * len(ys) * p.n_iter
 
@@ -107,22 +227,56 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
         for ix, x in enumerate(xs):
             state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
 
-            processed = cell(x, y)
-            if first_pocessed is None:
-                first_pocessed = processed
-
+            processed:Processed = cell(x, y)
             try:
-              res.append(processed.images[0])
+                # this dereference will throw an exception if the image was not processed
+                # (this happens in cases such as if the user stops the process from the UI)
+                processed_image = processed.images[0]
+                
+                if processed_result is None:
+                    # Use our first valid processed result as a template container to hold our full results
+                    processed_result = copy(processed)
+                    cell_mode = processed_image.mode
+                    cell_size = processed_image.size
+                    processed_result.images = [Image.new(cell_mode, cell_size)]
+
+                image_cache.append(processed_image)
+                if include_lone_images:
+                    processed_result.images.append(processed_image)
+                    processed_result.all_prompts.append(processed.prompt)
+                    processed_result.all_seeds.append(processed.seed)
+                    processed_result.infotexts.append(processed.infotexts[0])
             except:
-              res.append(Image.new(res[0].mode, res[0].size))
+                image_cache.append(Image.new(cell_mode, cell_size))
 
-    grid = images.image_grid(res, rows=len(ys))
+    if not processed_result:
+        print("Unexpected error: draw_xy_grid failed to return even a single processed image")
+        return Processed()
+
+    grid = images.image_grid(image_cache, rows=len(ys))
     if draw_legend:
-        grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
+        grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts)
 
-    first_pocessed.images = [grid]
+    processed_result.images[0] = grid
 
-    return first_pocessed
+    return processed_result
+
+
+class SharedSettingsStackHelper(object):
+    def __enter__(self):
+        self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
+        self.hypernetwork = opts.sd_hypernetwork
+        self.model = shared.sd_model
+        self.vae = opts.sd_vae
+  
+    def __exit__(self, exc_type, exc_value, tb):
+        modules.sd_models.reload_model_weights(self.model)
+        modules.sd_vae.reload_vae_weights(self.model, vae_file=find_vae(self.vae))
+
+        hypernetwork.load_hypernetwork(self.hypernetwork)
+        hypernetwork.apply_strength()
+
+        opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
 
 
 re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
@@ -139,27 +293,31 @@ class Script(scripts.Script):
         current_axis_options = [x for x in axis_options if type(x) == AxisOption or type(x) == AxisOptionImg2Img and is_img2img]
 
         with gr.Row():
-            x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, visible=False, type="index", elem_id="x_type")
-            x_values = gr.Textbox(label="X values", visible=False, lines=1)
+            x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, type="index", elem_id="x_type")
+            x_values = gr.Textbox(label="X values", lines=1)
 
         with gr.Row():
-            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[4].label, visible=False, type="index", elem_id="y_type")
-            y_values = gr.Textbox(label="Y values", visible=False, lines=1)
+            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, type="index", elem_id="y_type")
+            y_values = gr.Textbox(label="Y values", lines=1)
         
         draw_legend = gr.Checkbox(label='Draw legend', value=True)
+        include_lone_images = gr.Checkbox(label='Include Separate Images', value=False)
         no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
 
-        return [x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds]
+        return [x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds]
 
-    def run(self, p, x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds):
-        modules.processing.fix_seed(p)
-        p.batch_size = 1
+    def run(self, p, x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds):
+        if not no_fixed_seeds:
+            modules.processing.fix_seed(p)
+
+        if not opts.return_grid:
+            p.batch_size = 1
 
         def process_axis(opt, vals):
             if opt.label == 'Nothing':
                 return [0]
 
-            valslist = [x.strip() for x in vals.split(",")]
+            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]
 
             if opt.type == int:
                 valslist_ext = []
@@ -168,7 +326,6 @@ class Script(scripts.Script):
                     m = re_range.fullmatch(val)
                     mc = re_range_count.fullmatch(val)
                     if m is not None:
-
                         start = int(m.group(1))
                         end = int(m.group(2))+1
                         step = int(m.group(3)) if m.group(3) is not None else 1
@@ -206,9 +363,15 @@ class Script(scripts.Script):
                         valslist_ext.append(val)
 
                 valslist = valslist_ext
+            elif opt.type == str_permutations:
+                valslist = list(permutations(valslist))
 
             valslist = [opt.type(x) for x in valslist]
 
+            # Confirm options are valid before starting
+            if opt.confirm:
+                opt.confirm(p, valslist)
+
             return valslist
 
         x_opt = axis_options[x_type]
@@ -218,7 +381,7 @@ class Script(scripts.Script):
         ys = process_axis(y_opt, y_values)
 
         def fix_axis_seeds(axis_opt, axis_list):
-            if axis_opt.label in ["Seed","Var. seed"]:
+            if axis_opt.label in ['Seed','Var. seed']:
                 return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
             else:
                 return axis_list
@@ -234,6 +397,9 @@ class Script(scripts.Script):
         else:
             total_steps = p.steps * len(xs) * len(ys)
 
+        if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
+            total_steps *= 2
+
         print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")
         shared.total_tqdm.updateTotal(total_steps * p.n_iter)
 
@@ -256,20 +422,19 @@ class Script(scripts.Script):
             if y_opt.label in ["Seed","Var. seed"] and not no_fixed_seeds:
                 p.extra_generation_params["XY Plot Fixed Y Values"] = '{' + ", ".join([str(y) for y in ys])+ '}'
 
-        processed = draw_xy_grid(
-            p,
-            xs=xs,
-            ys=ys,
-            x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
-            y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
-            cell=cell,
-            draw_legend=draw_legend
-        )
+        with SharedSettingsStackHelper():
+            processed = draw_xy_grid(
+                p,
+                xs=xs,
+                ys=ys,
+                x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
+                y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
+                cell=cell,
+                draw_legend=draw_legend,
+                include_lone_images=include_lone_images
+            )
 
         if opts.grid_save:
-            images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p)
-
-        # restore checkpoint in case it was changed by axes
-        modules.sd_models.reload_model_weights(shared.sd_model)
+            images.save_image(processed.images[0], p.outpath_grids, "xy_grid", extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
 
         return processed