Merge remote-tracking branch 'upstream/master' into ModelLoader

.gitignore

@@ -8,6 +8,8 @@ __pycache__
 /tmp
 /model.ckpt
 /models/**/*
+/GFPGANv1.3.pth
+/gfpgan/weights/*.pth
 /ui-config.json
 /outputs
 /config.json

javascript/hints.js

@@ -15,6 +15,7 @@ titles = {
     "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
     "\u{1f3a8}": "Add a random artist to the prompt.",
     "\u2199\ufe0f": "Read generation parameters from prompt into user interface.",
+    "\uD83D\uDCC2": "Open images output directory",
 
     "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
     "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",

javascript/ui.js

@@ -182,4 +182,23 @@ onUiUpdate(function(){
     });
 
     json_elem.parentElement.style.display="none"
+
+	if (!txt2img_textarea) {
+		txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
+		txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button"));
+	}
+	if (!img2img_textarea) {
+		img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
+		img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
+	}
 })
+
+let txt2img_textarea, img2img_textarea = undefined;
+let wait_time = 800
+let token_timeout;
+
+function update_token_counter(button_id) {
+	if (token_timeout)
+		clearTimeout(token_timeout);
+	token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
+}

launch.py

@@ -15,11 +15,11 @@ torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113
 requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
 commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
 
-k_diffusion_package = os.environ.get('K_DIFFUSION_PACKAGE', "git+https://github.com/crowsonkb/k-diffusion.git@1a0703dfb7d24d8806267c3e7ccc4caf67fd1331")
 gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
 
 stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
 taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
+k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "a7ec1974d4ccb394c2dca275f42cd97490618924")
 codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
 blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
@@ -107,10 +107,7 @@ if not is_installed("torch") or not is_installed("torchvision"):
     run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
 
 if not skip_torch_cuda_test:
-    run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDINE_ARGS variable to disable this check'")
+    run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
-
-if not is_installed("k_diffusion.sampling"):
-    run_pip(f"install {k_diffusion_package}", "k-diffusion")
 
 if not is_installed("gfpgan"):
     run_pip(f"install {gfpgan_package}", "gfpgan")
@@ -119,6 +116,7 @@ os.makedirs(dir_repos, exist_ok=True)
 
 git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
 git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
+git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
 git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
 git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
 if os.path.isdir(repo_dir('latent-diffusion')):
@@ -133,6 +131,9 @@ run_pip(f"install -r {requirements_file}", "requirements for Web UI")
 
 sys.argv += args
 
+if "--exit" in args:
+    print("Exiting because of --exit argument")
+    exit(0)
 
 def start_webui():
     print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")

modules/extras.py

@@ -6,13 +6,14 @@ from PIL import Image
 import torch
 import tqdm
 
-from modules import processing, shared, images, devices
+from modules import processing, shared, images, devices, sd_models
 from modules.shared import opts
 import modules.gfpgan_model
 from modules.ui import plaintext_to_html
 import modules.codeformer_model
 import piexif
 import piexif.helper
+import gradio as gr
 
 
 cached_images = {}
@@ -141,7 +142,7 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(modelname_0, modelname_1, interp_method, interp_amount):
+def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half, custom_name):
     # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
@@ -151,45 +152,52 @@ def run_modelmerger(modelname_0, modelname_1, interp_method, interp_amount):
         alpha = alpha * alpha * (3 - (2 * alpha))
         return theta0 + ((theta1 - theta0) * alpha)
 
-    if os.path.exists(modelname_0):
+    # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-        model0_filename = modelname_0
+    def inv_sigmoid(theta0, theta1, alpha):
-        modelname_0 = os.path.splitext(os.path.basename(modelname_0))[0]
+        import math
-    else:
+        alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
-        model0_filename = 'models/' + modelname_0 + '.ckpt'
+        return theta0 + ((theta1 - theta0) * alpha)
 
-    if os.path.exists(modelname_1):
+    primary_model_info = sd_models.checkpoints_list[primary_model_name]
-        model1_filename = modelname_1
+    secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
-        modelname_1 = os.path.splitext(os.path.basename(modelname_1))[0]
-    else:
-        model1_filename = 'models/' + modelname_1 + '.ckpt'
 
-    print(f"Loading {model0_filename}...")
+    print(f"Loading {primary_model_info.filename}...")
-    model_0 = torch.load(model0_filename, map_location='cpu')
+    primary_model = torch.load(primary_model_info.filename, map_location='cpu')
 
-    print(f"Loading {model1_filename}...")
+    print(f"Loading {secondary_model_info.filename}...")
-    model_1 = torch.load(model1_filename, map_location='cpu')
+    secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
-    
+   
-    theta_0 = model_0['state_dict']
+    theta_0 = primary_model['state_dict']
-    theta_1 = model_1['state_dict']
+    theta_1 = secondary_model['state_dict']
 
     theta_funcs = {
         "Weighted Sum": weighted_sum,
         "Sigmoid": sigmoid,
+        "Inverse Sigmoid": inv_sigmoid,
     }
     theta_func = theta_funcs[interp_method]
 
     print(f"Merging...")
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], interp_amount)
+            theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+            if save_as_half:
+                theta_0[key] = theta_0[key].half()
     
     for key in theta_1.keys():
         if 'model' in key and key not in theta_0:
             theta_0[key] = theta_1[key]
+            if save_as_half:
+                theta_0[key] = theta_0[key].half()
+
+    filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
+    filename = filename if custom_name == '' else (custom_name + '.ckpt')
+    output_modelname = os.path.join(shared.cmd_opts.ckpt_dir, filename)
 
-    output_modelname = 'models/' + modelname_0 + '-' + modelname_1 + '-merged.ckpt'
     print(f"Saving to {output_modelname}...")
-    torch.save(model_0, output_modelname)
+    torch.save(primary_model, output_modelname)
+
+    sd_models.list_models()
 
     print(f"Checkpoint saved.")
-    return "Checkpoint saved to " + output_modelname
+    return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(3)]

modules/img2img.py

@@ -124,4 +124,4 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     if opts.samples_log_stdout:
         print(generation_info_js)
 
-    return processed.images, generation_info_js, plaintext_to_html(processed.info)
+    return processed.images, generation_info_js, plaintext_to_html(processed.info)

modules/paths.py

@@ -21,6 +21,7 @@ path_dirs = [
     (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer'),
     (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP'),
     (os.path.join(sd_path, '../latent-diffusion'), 'LDSR.py', 'LDSR'),
+    (os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion'),
 ]
 
 paths = {}

modules/processing.py

@@ -49,7 +49,7 @@ def apply_color_correction(correction, image):
 
 
 class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=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="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -75,15 +75,15 @@ class StableDiffusionProcessing:
         self.do_not_save_grid: bool = do_not_save_grid
         self.extra_generation_params: dict = extra_generation_params or {}
         self.overlay_images = overlay_images
+        self.eta = eta
         self.paste_to = None
         self.color_corrections = None
         self.denoising_strength: float = 0
-        
+
-        self.ddim_eta = opts.ddim_eta
         self.ddim_discretize = opts.ddim_discretize
         self.s_churn = opts.s_churn
         self.s_tmin = opts.s_tmin
-        self.s_tmax = float('inf') # not representable as a standard ui option
+        self.s_tmax = float('inf')  # not representable as a standard ui option
         self.s_noise = opts.s_noise
         
         if not seed_enable_extras:
@@ -100,7 +100,7 @@ class StableDiffusionProcessing:
 
 
 class Processed:
-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0):
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
         self.images = images_list
         self.prompt = p.prompt
         self.negative_prompt = p.negative_prompt
@@ -124,7 +124,7 @@ class Processed:
         self.extra_generation_params = p.extra_generation_params
         self.index_of_first_image = index_of_first_image
 
-        self.ddim_eta = p.ddim_eta
+        self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
         self.s_churn = p.s_churn
         self.s_tmin = p.s_tmin
@@ -139,6 +139,7 @@ class Processed:
         self.all_prompts = all_prompts or [self.prompt]
         self.all_seeds = all_seeds or [self.seed]
         self.all_subseeds = all_subseeds or [self.subseed]
+        self.infotexts = infotexts or [info]
 
     def js(self):
         obj = {
@@ -165,6 +166,7 @@ class Processed:
             "denoising_strength": self.denoising_strength,
             "extra_generation_params": self.extra_generation_params,
             "index_of_first_image": self.index_of_first_image,
+            "infotexts": self.infotexts,
         }
 
         return json.dumps(obj)
@@ -269,6 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
+        "Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
     }
 
     generation_params.update(p.extra_generation_params)
@@ -277,7 +280,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
 
-    return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip() + "".join(["\n\n" + x for x in comments])
+    return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
 
 def process_images(p: StableDiffusionProcessing) -> Processed:
@@ -322,6 +325,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     if os.path.exists(cmd_opts.embeddings_dir):
         model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)
 
+    infotexts = []
     output_images = []
     precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
     ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)
@@ -404,6 +408,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 if opts.samples_save and not p.do_not_save_samples:
                     images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
 
+                infotexts.append(infotext(n, i))
                 output_images.append(image)
 
             state.nextjob()
@@ -416,6 +421,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             grid = images.image_grid(output_images, p.batch_size)
 
             if opts.return_grid:
+                infotexts.insert(0, infotext())
                 output_images.insert(0, grid)
                 index_of_first_image = 1
 
@@ -423,7 +429,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, all_seeds[0], infotext(), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image)
+    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

modules/prompt_parser.py

@@ -126,5 +126,93 @@ def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
     return res
 
 
+re_attention = re.compile(r"""
+\\\(|
+\\\)|
+\\\[|
+\\]|
+\\\\|
+\\|
+\(|
+\[|
+:([+-]?[.\d]+)\)|
+\)|
+]|
+[^\\()\[\]:]+|
+:
+""", re.X)
 
-#get_learned_conditioning_prompt_schedules(["fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"], 100)
+
+def parse_prompt_attention(text):
+    """
+    Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
+    Accepted tokens are:
+      (abc) - increases attention to abc by a multiplier of 1.1
+      (abc:3.12) - increases attention to abc by a multiplier of 3.12
+      [abc] - decreases attention to abc by a multiplier of 1.1
+      \( - literal character '('
+      \[ - literal character '['
+      \) - literal character ')'
+      \] - literal character ']'
+      \\ - literal character '\'
+      anything else - just text
+
+    Example:
+
+        'a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).'
+
+    produces:
+
+    [
+        ['a ', 1.0],
+        ['house', 1.5730000000000004],
+        [' ', 1.1],
+        ['on', 1.0],
+        [' a ', 1.1],
+        ['hill', 0.55],
+        [', sun, ', 1.1],
+        ['sky', 1.4641000000000006],
+        ['.', 1.1]
+    ]
+    """
+
+    res = []
+    round_brackets = []
+    square_brackets = []
+
+    round_bracket_multiplier = 1.1
+    square_bracket_multiplier = 1 / 1.1
+
+    def multiply_range(start_position, multiplier):
+        for p in range(start_position, len(res)):
+            res[p][1] *= multiplier
+
+    for m in re_attention.finditer(text):
+        text = m.group(0)
+        weight = m.group(1)
+
+        if text.startswith('\\'):
+            res.append([text[1:], 1.0])
+        elif text == '(':
+            round_brackets.append(len(res))
+        elif text == '[':
+            square_brackets.append(len(res))
+        elif weight is not None and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), float(weight))
+        elif text == ')' and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), round_bracket_multiplier)
+        elif text == ']' and len(square_brackets) > 0:
+            multiply_range(square_brackets.pop(), square_bracket_multiplier)
+        else:
+            res.append([text, 1.0])
+
+    for pos in round_brackets:
+        multiply_range(pos, round_bracket_multiplier)
+
+    for pos in square_brackets:
+        multiply_range(pos, square_bracket_multiplier)
+
+    if len(res) == 0:
+        res = [["", 1.0]]
+
+    return res

modules/scripts.py

@@ -55,7 +55,7 @@ def load_scripts(basedir):
     if not os.path.exists(basedir):
         return
 
-    for filename in os.listdir(basedir):
+    for filename in sorted(os.listdir(basedir)):
         path = os.path.join(basedir, filename)
 
         if not os.path.isfile(path):

modules/sd_hijack.py

@@ -6,6 +6,7 @@ import torch
 import numpy as np
 from torch import einsum
 
+from modules import prompt_parser
 from modules.shared import opts, device, cmd_opts
 
 from ldm.util import default
@@ -180,6 +181,7 @@ class StableDiffusionModelHijack:
     dir_mtime = None
     layers = None
     circular_enabled = False
+    clip = None
 
     def load_textual_inversion_embeddings(self, dirname, model):
         mt = os.path.getmtime(dirname)
@@ -210,6 +212,7 @@ class StableDiffusionModelHijack:
                     param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11
                 assert len(param_dict) == 1, 'embedding file has multiple terms in it'
                 emb = next(iter(param_dict.items()))[1]
+            # diffuser concepts
             elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
                 assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
 
@@ -235,7 +238,7 @@ class StableDiffusionModelHijack:
                 print(traceback.format_exc(), file=sys.stderr)
                 continue
 
-        print(f"Loaded a total of {len(self.word_embeddings)} text inversion embeddings.")
+        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
 
     def hijack(self, m):
         model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
@@ -243,6 +246,8 @@ class StableDiffusionModelHijack:
         model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
         m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
 
+        self.clip = m.cond_stage_model
+
         if cmd_opts.opt_split_attention_v1:
             ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
         elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
@@ -259,6 +264,14 @@ class StableDiffusionModelHijack:
 
         self.layers = flatten(m)
 
+    def undo_hijack(self, m):
+        if type(m.cond_stage_model) == FrozenCLIPEmbedderWithCustomWords:
+            m.cond_stage_model = m.cond_stage_model.wrapped
+
+        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+        if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
+            model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
+
     def apply_circular(self, enable):
         if self.circular_enabled == enable:
             return
@@ -268,6 +281,11 @@ class StableDiffusionModelHijack:
         for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
             layer.padding_mode = 'circular' if enable else 'zeros'
 
+    def tokenize(self, text):
+        max_length = self.clip.max_length - 2
+        _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
+        return remade_batch_tokens[0], token_count, max_length
+
 
 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
@@ -294,14 +312,101 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-    def forward(self, text):
+
-        self.hijack.fixes = []
+    def tokenize_line(self, line, used_custom_terms, hijack_comments):
-        self.hijack.comments = []
+        id_start = self.wrapped.tokenizer.bos_token_id
+        id_end = self.wrapped.tokenizer.eos_token_id
+        maxlen = self.wrapped.max_length
+
+        if opts.enable_emphasis:
+            parsed = prompt_parser.parse_prompt_attention(line)
+        else:
+            parsed = [[line, 1.0]]
+
+        tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]
+
+        fixes = []
+        remade_tokens = []
+        multipliers = []
+
+        for tokens, (text, weight) in zip(tokenized, parsed):
+            i = 0
+            while i < len(tokens):
+                token = tokens[i]
+
+                possible_matches = self.hijack.ids_lookup.get(token, None)
+
+                if possible_matches is None:
+                    remade_tokens.append(token)
+                    multipliers.append(weight)
+                else:
+                    found = False
+                    for ids, word in possible_matches:
+                        if tokens[i:i + len(ids)] == ids:
+                            emb_len = int(self.hijack.word_embeddings[word].shape[0])
+                            fixes.append((len(remade_tokens), word))
+                            remade_tokens += [0] * emb_len
+                            multipliers += [weight] * emb_len
+                            i += len(ids) - 1
+                            found = True
+                            used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
+                            break
+
+                    if not found:
+                        remade_tokens.append(token)
+                        multipliers.append(weight)
+                i += 1
+
+        if len(remade_tokens) > maxlen - 2:
+            vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
+            ovf = remade_tokens[maxlen - 2:]
+            overflowing_words = [vocab.get(int(x), "") for x in ovf]
+            overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
+            hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+
+        token_count = len(remade_tokens)
+        remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
+        remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
+
+        multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
+        multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
+
+        return remade_tokens, fixes, multipliers, token_count
+
+    def process_text(self, texts):
+        used_custom_terms = []
         remade_batch_tokens = []
+        hijack_comments = []
+        hijack_fixes = []
+        token_count = 0
+
+        cache = {}
+        batch_multipliers = []
+        for line in texts:
+            if line in cache:
+                remade_tokens, fixes, multipliers = cache[line]
+            else:
+                remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+
+                cache[line] = (remade_tokens, fixes, multipliers)
+
+            remade_batch_tokens.append(remade_tokens)
+            hijack_fixes.append(fixes)
+            batch_multipliers.append(multipliers)
+
+        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
+
+
+    def process_text_old(self, text):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
         maxlen = self.wrapped.max_length
         used_custom_terms = []
+        remade_batch_tokens = []
+        overflowing_words = []
+        hijack_comments = []
+        hijack_fixes = []
+        token_count = 0
 
         cache = {}
         batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
@@ -353,9 +458,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     ovf = remade_tokens[maxlen - 2:]
                     overflowing_words = [vocab.get(int(x), "") for x in ovf]
                     overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
-
+                    hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-                    self.hijack.comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+                token_count = len(remade_tokens)
-
                 remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
                 remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
                 cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
@@ -364,11 +468,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
 
             remade_batch_tokens.append(remade_tokens)
-            self.hijack.fixes.append(fixes)
+            hijack_fixes.append(fixes)
             batch_multipliers.append(multipliers)
+        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
+
+    def forward(self, text):
+
+        if opts.use_old_emphasis_implementation:
+            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
+        else:
+            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
+
+
+        self.hijack.fixes = hijack_fixes
+        self.hijack.comments = hijack_comments
 
         if len(used_custom_terms) > 0:
-            self.hijack.comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
+            self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
 
         tokens = torch.asarray(remade_batch_tokens).to(device)
         outputs = self.wrapped.transformer(input_ids=tokens)

modules/sd_models.py

@@ -15,8 +15,9 @@ model_dir = "Stable-diffusion"
 model_path = os.path.join(models_path, model_dir)
 model_name = "sd-v1-4.ckpt"
 model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1"
+user_dir = None
 
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
 checkpoints_list = {}
 
 try:
@@ -47,23 +48,56 @@ def setup_model(dirname):
     global model_path
     global model_name
     global model_url
+    global user_dir
+    global model_list
+    user_dir = dirname
     if not os.path.exists(model_path):
         os.makedirs(model_path)
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=dirname, download_name=model_name, ext_filter=".ckpt")
+    list_models()
+
+
+def checkpoint_tiles():
+    return sorted([x.title for x in checkpoints_list.values()])
+
+
+def list_models():
+    global model_path
+    global model_url
+    global model_name
+    global user_dir
+    checkpoints_list.clear()
+    model_list = modelloader.load_models(model_path=model_path,model_url=model_url,command_path= user_dir,
+                                         ext_filter=[".ckpt"], download_name=model_name)
+    print(f"Model list: {model_list}")
+    model_dir = os.path.abspath(model_path)
+
+    def modeltitle(path, h):
+        abspath = os.path.abspath(path)
+
+        if abspath.startswith(model_dir):
+            name = abspath.replace(model_dir, '')
+        else:
+            name = os.path.basename(path)
+
+        if name.startswith("\\") or name.startswith("/"):
+            name = name[1:]
+
+        shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
+
+        return f'{name} [{h}]', shortname
 
     cmd_ckpt = shared.cmd_opts.ckpt
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
-        title = modeltitle(cmd_ckpt, h)
+        title, model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, model_name)
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
-
     for filename in model_list:
         h = model_hash(filename)
         title = modeltitle(filename, h)
-        checkpoints_list[title] = CheckpointInfo(filename, title, h)
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, model_name)
 
 
 def model_hash(filename):
@@ -89,7 +123,7 @@ def select_checkpoint():
     if len(checkpoints_list) == 0:
         print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
         print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
-        print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
+        print(f" - directory {os.path.abspath(shared.cmd_opts.stablediffusion_models_path)}", file=sys.stderr)
         print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
         exit(1)
 
@@ -142,7 +176,7 @@ def load_model():
 
 
 def reload_model_weights(sd_model, info=None):
-    from modules import lowvram, devices
+    from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
 
     if sd_model.sd_model_checkpint == checkpoint_info.filename:
@@ -153,8 +187,12 @@ def reload_model_weights(sd_model, info=None):
     else:
         sd_model.to(devices.cpu)
 
+    sd_hijack.model_hijack.undo_hijack(sd_model)
+
     load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
 
+    sd_hijack.model_hijack.hijack(sd_model)
+
     if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
         sd_model.to(devices.device)
 

modules/sd_samplers.py

@@ -3,6 +3,7 @@ import numpy as np
 import torch
 import tqdm
 from PIL import Image
+import inspect
 
 import k_diffusion.sampling
 import ldm.models.diffusion.ddim
@@ -22,6 +23,8 @@ samplers_k_diffusion = [
     ('Heun', 'sample_heun', ['k_heun']),
     ('DPM2', 'sample_dpm_2', ['k_dpm_2']),
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
+    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),
+    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
 ]
 
 samplers_data_k_diffusion = [
@@ -35,12 +38,12 @@ samplers = [
     SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
     SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
 ]
-samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
+samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']]
 
 sampler_extra_params = {
-    'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'],
+    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'],
+    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'],
+    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
 }
 
 def setup_img2img_steps(p, steps=None):
@@ -98,6 +101,8 @@ class VanillaStableDiffusionSampler:
         self.init_latent = None
         self.sampler_noises = None
         self.step = 0
+        self.eta = None
+        self.default_eta = 0.0
 
     def number_of_needed_noises(self, p):
         return 0
@@ -120,20 +125,29 @@ class VanillaStableDiffusionSampler:
         self.step += 1
         return res
 
+    def initialize(self, p):
+        self.eta = p.eta or opts.eta_ddim
+
+        for fieldname in ['p_sample_ddim', 'p_sample_plms']:
+            if hasattr(self.sampler, fieldname):
+                setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
+
+        self.mask = p.mask if hasattr(p, 'mask') else None
+        self.nmask = p.nmask if hasattr(p, 'nmask') else None
+
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
         steps, t_enc = setup_img2img_steps(p, steps)
 
+        self.initialize(p)
+
         # existing code fails with cetain step counts, like 9
         try:
-            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
+            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
         except Exception:
-            self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
+            self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
 
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
 
-        self.sampler.p_sample_ddim = self.p_sample_ddim_hook
-        self.mask = p.mask if hasattr(p, 'mask') else None
-        self.nmask = p.nmask if hasattr(p, 'nmask') else None
         self.init_latent = x
         self.step = 0
 
@@ -142,11 +156,8 @@ class VanillaStableDiffusionSampler:
         return samples
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
-        for fieldname in ['p_sample_ddim', 'p_sample_plms']:
+        self.initialize(p)
-            if hasattr(self.sampler, fieldname):
+
-                setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
-        self.mask = None
-        self.nmask = None
         self.init_latent = None
         self.step = 0
 
@@ -154,9 +165,9 @@ class VanillaStableDiffusionSampler:
 
         # existing code fails with cetin step counts, like 9
         try:
-            samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_t=x, eta=p.ddim_eta)
+            samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
         except Exception:
-            samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_t=x, eta=p.ddim_eta)
+            samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
 
         return samples_ddim
 
@@ -229,11 +240,13 @@ class KDiffusionSampler:
         self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
         self.funcname = funcname
         self.func = getattr(k_diffusion.sampling, self.funcname)
-        self.extra_params = sampler_extra_params.get(funcname,[]) 
+        self.extra_params = sampler_extra_params.get(funcname, [])
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
         self.sampler_noises = None
         self.sampler_noise_index = 0
         self.stop_at = None
+        self.eta = None
+        self.default_eta = 1.0
 
     def callback_state(self, d):
         store_latent(d["denoised"])
@@ -252,22 +265,12 @@ class KDiffusionSampler:
         self.sampler_noise_index += 1
         return res
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
+    def initialize(self, p):
-        steps, t_enc = setup_img2img_steps(p, steps)
-
-        sigmas = self.model_wrap.get_sigmas(steps)
-
-        noise = noise * sigmas[steps - t_enc - 1]
-
-        xi = x + noise
-
-        sigma_sched = sigmas[steps - t_enc - 1:]
-
         self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
         self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
-        self.model_wrap_cfg.init_latent = x
         self.model_wrap.step = 0
         self.sampler_noise_index = 0
+        self.eta = p.eta or opts.eta_ancestral
 
         if hasattr(k_diffusion.sampling, 'trange'):
             k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
@@ -276,9 +279,28 @@ class KDiffusionSampler:
             k_diffusion.sampling.torch = TorchHijack(self)
 
         extra_params_kwargs = {}
-        for val in self.extra_params:
+        for param_name in self.extra_params:
-          if hasattr(p,val):
+            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
-            extra_params_kwargs[val] = getattr(p,val)
+                extra_params_kwargs[param_name] = getattr(p, param_name)
+
+        if 'eta' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['eta'] = self.eta
+
+        return extra_params_kwargs
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
+        steps, t_enc = setup_img2img_steps(p, steps)
+
+        sigmas = self.model_wrap.get_sigmas(steps)
+
+        noise = noise * sigmas[steps - t_enc - 1]
+        xi = x + noise
+
+        extra_params_kwargs = self.initialize(p)
+
+        sigma_sched = sigmas[steps - t_enc - 1:]
+
+        self.model_wrap_cfg.init_latent = x
 
         return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
 
@@ -288,21 +310,14 @@ class KDiffusionSampler:
         sigmas = self.model_wrap.get_sigmas(steps)
         x = x * sigmas[0]
 
-        self.model_wrap_cfg.step = 0
+        extra_params_kwargs = self.initialize(p)
-        self.sampler_noise_index = 0
+        if 'sigma_min' in inspect.signature(self.func).parameters:
-
+            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-        if hasattr(k_diffusion.sampling, 'trange'):
+            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
-            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
+            if 'n' in inspect.signature(self.func).parameters:
-
+                extra_params_kwargs['n'] = steps
-        if self.sampler_noises is not None:
+        else:
-            k_diffusion.sampling.torch = TorchHijack(self)
+            extra_params_kwargs['sigmas'] = sigmas
-
+        samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
-        extra_params_kwargs = {}
-        for val in self.extra_params:
-          if hasattr(p,val):
-            extra_params_kwargs[val] = getattr(p,val)
-
-        samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
-
         return samples
 

modules/shared.py

@@ -155,6 +155,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "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"),
+    "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
 }))
 
 options_templates.update(options_section(('saving-paths', "Paths for saving"), {
@@ -182,7 +183,6 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
-    "ldsr_pre_down": OptionInfo(1, "LDSR Pre-process downssample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
 }))
 
@@ -190,7 +190,6 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
     "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
     "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
-    "save_selected_only": OptionInfo(False, "When using 'Save' button, only save a single selected image"),
 }))
 
 options_templates.update(options_section(('system', "System"), {
@@ -200,12 +199,13 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-    "enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+    "enable_emphasis": OptionInfo(True, "Eemphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
@@ -231,8 +231,9 @@ options_templates.update(options_section(('ui', "User interface"), {
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-  "ddim_eta": OptionInfo(0.0, "DDIM eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+  "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-  "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}),
+  "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+  "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
   's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
   's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
   's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

modules/ui.py

@@ -9,10 +9,13 @@ import random
 import sys
 import time
 import traceback
+import platform
+import subprocess as sp
 
 import numpy as np
 import torch
-from PIL import Image
+from PIL import Image, PngImagePlugin
+import piexif
 
 import gradio as gr
 import gradio.utils
@@ -22,6 +25,7 @@ from modules.paths import script_path
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
 from modules.sd_samplers import samplers, samplers_for_img2img
+from modules.sd_hijack import model_hijack
 import modules.ldsr_model
 import modules.scripts
 import modules.gfpgan_model
@@ -60,7 +64,7 @@ random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
 reuse_symbol = '\u267b\ufe0f'  # ♻️
 art_symbol = '\U0001f3a8'  # 🎨
 paste_symbol = '\u2199\ufe0f'  # ↙
-
+folder_symbol = '\uD83D\uDCC2'
 
 def plaintext_to_html(text):
     text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
@@ -97,10 +101,11 @@ def save_files(js_data, images, index):
     filenames = []
 
     data = json.loads(js_data)
-    
+    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-    if index > -1 and opts.save_selected_only and (index > 0 or not opts.return_grid): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
         images = [images[index]]
-        data["seed"] += (index - 1 if opts.return_grid else index)
+        infotexts = [data["infotexts"][index]]
+    else:
+        infotexts = data["infotexts"]
 
     with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
         at_start = file.tell() == 0
@@ -109,15 +114,26 @@ def save_files(js_data, images, index):
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
 
         filename_base = str(int(time.time() * 1000))
+        extension = opts.samples_format.lower()
         for i, filedata in enumerate(images):
-            filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + ".png"
+            filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + f".{extension}"
             filepath = os.path.join(opts.outdir_save, filename)
 
             if filedata.startswith("data:image/png;base64,"):
                 filedata = filedata[len("data:image/png;base64,"):]
 
-            with open(filepath, "wb") as imgfile:
+            image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))
-                imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
+            if opts.enable_pnginfo and extension == 'png':
+                pnginfo = PngImagePlugin.PngInfo()
+                pnginfo.add_text('parameters', infotexts[i])
+                image.save(filepath, pnginfo=pnginfo)
+            else:
+                image.save(filepath, quality=opts.jpeg_quality)
+
+            if opts.enable_pnginfo and extension in ("jpg", "jpeg", "webp"):
+                piexif.insert(piexif.dump({"Exif": {
+                    piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(infotexts[i], encoding="unicode")
+                }}), filepath)
 
             filenames.append(filename)
 
@@ -329,6 +345,10 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
         outputs=[seed, dummy_component]
     )
 
+def update_token_counter(text):
+    tokens, token_count, max_length = model_hijack.tokenize(text)
+    style_class = ' class="red"' if (token_count > max_length) else ""
+    return f"<span {style_class}>{token_count}/{max_length}</span>"
 
 def create_toprow(is_img2img):
     id_part = "img2img" if is_img2img else "txt2img"
@@ -338,11 +358,14 @@ def create_toprow(is_img2img):
             with gr.Row():
                 with gr.Column(scale=80):
                     with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id="prompt", show_label=False, placeholder="Prompt", lines=2)
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)
 
                 with gr.Column(scale=1, elem_id="roll_col"):
                     roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
                     paste = gr.Button(value=paste_symbol, elem_id="paste")
+                    token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+                    hidden_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+                    hidden_button.click(fn=update_token_counter, inputs=[prompt], outputs=[token_counter])
 
                 with gr.Column(scale=10, elem_id="style_pos_col"):
                     prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
@@ -449,6 +472,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                         send_to_img2img = gr.Button('Send to img2img')
                         send_to_inpaint = gr.Button('Send to inpaint')
                         send_to_extras = gr.Button('Send to extras')
+                        button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+                        open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
 
                 with gr.Group():
                     html_info = gr.HTML()
@@ -625,6 +650,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                         img2img_send_to_img2img = gr.Button('Send to img2img')
                         img2img_send_to_inpaint = gr.Button('Send to inpaint')
                         img2img_send_to_extras = gr.Button('Send to extras')
+                        button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+                        open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
 
                 with gr.Group():
                     html_info = gr.HTML()
@@ -797,6 +824,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 html_info = gr.HTML()
                 extras_send_to_img2img = gr.Button('Send to img2img')
                 extras_send_to_inpaint = gr.Button('Send to inpaint')
+                button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''
+                open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
 
         submit.click(
             fn=run_extras,
@@ -857,30 +886,20 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
-                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>/models</b> directory.</p>")
+                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
                 
-                modelname_0 = gr.Textbox(elem_id="modelmerger_modelname_0", label="Model Name (to)")
+                with gr.Row():
-                modelname_1 = gr.Textbox(elem_id="modelmerger_modelname_1", label="Model Name (from)")
+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")
-                interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid"], value="Weighted Sum", label="Interpolation Method")
+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
+                custom_name = gr.Textbox(label="Custom Name (Optional)")
                 interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
-                submit = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
+                interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
+                save_as_half = gr.Checkbox(value=False, label="Safe as float16")
+                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
             
             with gr.Column(variant='panel'):
                 submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
 
-            submit.click(
-                fn=run_modelmerger,
-                inputs=[
-                    modelname_0,
-                    modelname_1,
-                    interp_method,
-                    interp_amount
-                ],
-                outputs=[
-                    submit_result,
-                ]
-            )
-
     def create_setting_component(key):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
@@ -904,6 +923,17 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         return comp(label=info.label, value=fun, **(args or {}))
 
     components = []
+    component_dict = {}
+
+    def open_folder(f):
+        if not shared.cmd_opts.hide_ui_dir_config:
+            path = os.path.normpath(f)
+            if platform.system() == "Windows":
+                os.startfile(path)
+            elif platform.system() == "Darwin":
+                sp.Popen(["open", path])
+            else:
+                sp.Popen(["xdg-open", path])
 
     def run_settings(*args):
         changed = 0
@@ -959,7 +989,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
                     gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
 
-                components.append(create_setting_component(k))
+                component = create_setting_component(k)
+                component_dict[k] = component
+                components.append(component)
                 items_displayed += 1
 
         request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
@@ -1009,7 +1041,34 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             inputs=components,
             outputs=[result, text_settings],
         )
+        
+        def modelmerger(*args):
+            try:
+                results = run_modelmerger(*args)
+            except Exception as e:
+                print("Error loading/saving model file:", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+                modules.sd_models.list_models() #To remove the potentially missing models from the list
+                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
+            return results
 
+        modelmerger_merge.click(
+            fn=modelmerger,
+            inputs=[
+                primary_model_name,
+                secondary_model_name,
+                interp_method,
+                interp_amount,
+                save_as_half,
+                custom_name,
+            ],
+            outputs=[
+                submit_result,
+                primary_model_name,
+                secondary_model_name,
+                component_dict['sd_model_checkpoint'],
+            ]
+        )
         paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2']
         txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names]
         img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names]
@@ -1048,6 +1107,24 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             outputs=[extras_image],
         )
 
+        open_txt2img_folder.click(
+            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_txt2img_samples),
+            inputs=[],
+            outputs=[],
+        )
+
+        open_img2img_folder.click(
+            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_img2img_samples),
+            inputs=[],
+            outputs=[],
+        )
+
+        open_extras_folder.click(
+            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_extras_samples),
+            inputs=[],
+            outputs=[],
+        )
+
         img2img_send_to_extras.click(
             fn=lambda x: image_from_url_text(x),
             _js="extract_image_from_gallery_extras",

requirements.txt

@@ -4,9 +4,8 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio
+gradio==3.4b3
 invisible-watermark
-git+https://github.com/crowsonkb/k-diffusion.git
 numpy
 omegaconf
 piexif
@@ -16,5 +15,12 @@ realesrgan
 scikit-image>=0.19
 git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379
 timm==0.4.12
-transformers
+transformers==4.19.2
 torch
+einops
+jsonmerge
+clean-fid
+git+https://github.com/openai/CLIP@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
+resize-right
+torchdiffeq
+kornia

requirements_versions.txt

@@ -14,4 +14,11 @@ fonts
 font-roboto
 timm==0.6.7
 fairscale==0.4.9
-piexif==1.1.3
+piexif==1.1.3
+einops==0.4.1
+jsonmerge==1.8.0
+clean-fid==0.1.29
+git+https://github.com/openai/CLIP@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
+resize-right==0.0.2
+torchdiffeq==0.2.3
+kornia==0.6.7

scripts/xy_grid.py

@@ -87,12 +87,12 @@ axis_options = [
     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 Churn", float, apply_field("s_churn"), format_value_add_label),
-    AxisOption("Sigma min",   float, apply_field("s_tmin"),   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 max", float, apply_field("s_tmax"), format_value_add_label),
-    AxisOption("Sigma noise", float, apply_field("s_noise"),  format_value_add_label),
+    AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
-    AxisOption("DDIM Eta",    float, apply_field("ddim_eta"), 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
+    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
 ]
 
 
@@ -159,6 +159,9 @@ class Script(scripts.Script):
         p.batch_size = 1
 
         def process_axis(opt, vals):
+            if opt.label == 'Nothing':
+                return [0]
+
             valslist = [x.strip() for x in vals.split(",")]
 
             if opt.type == int:

style.css

@@ -1,5 +1,11 @@
 .output-html p {margin: 0 0.5em;}
 
+.row > *,
+.row > .gr-form > * {
+    min-width: min(120px, 100%);
+    flex: 1 1 0%;
+}
+
 .performance {
     font-size: 0.85em;
     color: #444;
@@ -43,13 +49,17 @@
     margin-right: auto;
 }
 
-#random_seed, #random_subseed, #reuse_seed, #reuse_subseed{
+#random_seed, #random_subseed, #reuse_seed, #reuse_subseed, #open_folder{
     min-width: auto;
     flex-grow: 0;
     padding-left: 0.25em;
     padding-right: 0.25em;
 }
 
+#hidden_element{
+    display: none;
+}
+
 #seed_row, #subseed_row{
     gap: 0.5rem;
 }
@@ -389,3 +399,7 @@ input[type="range"]{
   border-radius: 8px;
   display: none;
 }
+
+.red {
+	color: red;
+}

webui.py

@@ -1,4 +1,8 @@
 import os
+import threading
+
+from modules import devices
+from modules.paths import script_path
 import signal
 import threading
 import modules.paths
@@ -44,6 +48,8 @@ def wrap_queued_call(func):
 
 def wrap_gradio_gpu_call(func):
     def f(*args, **kwargs):
+        devices.torch_gc()
+
         shared.state.sampling_step = 0
         shared.state.job_count = -1
         shared.state.job_no = 0
@@ -59,6 +65,8 @@ def wrap_gradio_gpu_call(func):
         shared.state.job = ""
         shared.state.job_count = 0
 
+        devices.torch_gc()
+
         return res
 
     return modules.ui.wrap_gradio_call(f)