Merge branch 'master' into master

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
@@ -121,17 +120,45 @@ 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_index = [sampler.name for sampler in sd_samplers.samplers].index("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):
@@ -155,11 +182,11 @@ 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_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
 
             sigmas = sampler.model_wrap.get_sigmas(p.steps)
             

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):

scripts/prompts_from_file.py

@@ -10,7 +10,6 @@ from modules.processing import Processed, process_images
 from PIL import Image
 from modules.shared import opts, cmd_opts, state
 
-
 class Script(scripts.Script):
     def title(self):
         return "Prompts from file or textbox"
@@ -67,6 +66,9 @@ class Script(scripts.Script):
         "do_not_save_grid": process_boolean_tag
     }
 
+    def on_show(self, checkbox_txt, file, prompt_txt):
+        return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
+
     def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
         if (checkbox_txt):
             lines = [x.strip() for x in prompt_txt.splitlines()]

scripts/sd_upscale.py

@@ -34,7 +34,11 @@ class Script(scripts.Script):
         seed = p.seed
 
         init_img = p.init_images[0]
-        img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)
+        
+        if(upscaler.name != "None"): 
+            img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)
+        else:
+            img = init_img
 
         devices.torch_gc()
 

scripts/xy_grid.py

@@ -1,7 +1,9 @@
 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
 
@@ -9,7 +11,8 @@ import modules.scripts as scripts
 import gradio as gr
 
 from modules import images
-from modules.processing import process_images, Processed
+from modules.hypernetworks import hypernetwork
+from modules.processing import process_images, Processed, get_correct_sampler, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.sd_samplers
@@ -25,31 +28,101 @@ 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 build_samplers_dict(p):
+    samplers_dict = {}
+    for i, sampler in enumerate(get_correct_sampler(p)):
+        samplers_dict[sampler.name.lower()] = i
+        for alias in sampler.aliases:
+            samplers_dict[alias.lower()] = i
+    return samplers_dict
 
 
 def apply_sampler(p, x, xs):
-    sampler_index = samplers_dict.get(x.lower(), None)
+    sampler_index = build_samplers_dict(p).get(x.lower(), None)
     if sampler_index is None:
         raise RuntimeError(f"Unknown sampler: {x}")
 
     p.sampler_index = sampler_index
 
 
+def confirm_samplers(p, xs):
+    samplers_dict = build_samplers_dict(p)
+    for x in xs:
+        if x.lower() not in samplers_dict.keys():
+            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)
 
 
+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 format_value_add_label(p, opt, x):
     if type(x) == float:
         x = round(x, 8)
@@ -60,46 +133,64 @@ 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),
 ]
 
 
-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 +198,39 @@ 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
 
 
 re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
@@ -143,23 +251,30 @@ class Script(scripts.Script):
             x_values = gr.Textbox(label="X values", visible=False, 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_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, visible=False, type="index", elem_id="y_type")
             y_values = gr.Textbox(label="Y values", visible=False, 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
+
+
+        CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
 
         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 +283,6 @@ 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 +320,15 @@ 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 +338,7 @@ class Script(scripts.Script):
         ys = process_axis(y_opt, y_values)
 
         def fix_axis_seeds(axis_opt, axis_list):
-            if axis_opt.label == '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 +354,9 @@ 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)
 
@@ -251,7 +374,8 @@ class Script(scripts.Script):
             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
+            draw_legend=draw_legend,
+            include_lone_images=include_lone_images
         )
 
         if opts.grid_save:
@@ -260,4 +384,10 @@ class Script(scripts.Script):
         # restore checkpoint in case it was changed by axes
         modules.sd_models.reload_model_weights(shared.sd_model)
 
+        hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
+        hypernetwork.apply_strength()
+
+
+        opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers
+
         return processed