fix to tokens lenght, addend embs generator, add new features to edit the embedding before the generation using text

modules/aesthetic_clip.py

@@ -0,0 +1,78 @@
+import itertools
+import os
+from pathlib import Path
+import html
+import gc
+
+import gradio as gr
+import torch
+from PIL import Image
+from modules import shared
+from modules.shared import device, aesthetic_embeddings
+from transformers import CLIPModel, CLIPProcessor
+
+from tqdm.auto import tqdm
+
+
+def get_all_images_in_folder(folder):
+    return [os.path.join(folder, f) for f in os.listdir(folder) if
+            os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)]
+
+
+def check_is_valid_image_file(filename):
+    return filename.lower().endswith(('.png', '.jpg', '.jpeg'))
+
+
+def batched(dataset, total, n=1):
+    for ndx in range(0, total, n):
+        yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
+
+
+def iter_to_batched(iterable, n=1):
+    it = iter(iterable)
+    while True:
+        chunk = tuple(itertools.islice(it, n))
+        if not chunk:
+            return
+        yield chunk
+
+
+def generate_imgs_embd(name, folder, batch_size):
+    # clipModel = CLIPModel.from_pretrained(
+    #     shared.sd_model.cond_stage_model.clipModel.name_or_path
+    # )
+    model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path).to(device)
+    processor = CLIPProcessor.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path)
+
+    with torch.no_grad():
+        embs = []
+        for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size),
+                          desc=f"Generating embeddings for {name}"):
+            if shared.state.interrupted:
+                break
+            inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device)
+            outputs = model.get_image_features(**inputs).cpu()
+            embs.append(torch.clone(outputs))
+            inputs.to("cpu")
+            del inputs, outputs
+
+        embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True)
+
+        # The generated embedding will be located here
+        path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
+        torch.save(embs, path)
+
+        model = model.cpu()
+        del model
+        del processor
+        del embs
+        gc.collect()
+        torch.cuda.empty_cache()
+        res = f"""
+        Done generating embedding for {name}!
+        Hypernetwork saved to {html.escape(path)}
+        """
+        shared.update_aesthetic_embeddings()
+        return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding",
+                           value=sorted(aesthetic_embeddings.keys())[0] if len(
+                               aesthetic_embeddings) > 0 else None), res, ""

modules/processing.py

@@ -20,7 +20,6 @@ import modules.images as images
 import modules.styles
 import logging
 
-
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
 opt_f = 8
@@ -52,8 +51,13 @@ def get_correct_sampler(p):
     elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
         return sd_samplers.samplers_for_img2img
 
+
 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, eta=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
@@ -104,7 +108,8 @@ 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, infotexts=None):
+    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
@@ -141,7 +146,8 @@ class Processed:
         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
         self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
-        self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
+        self.subseed = int(
+            self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
 
         self.all_prompts = all_prompts or [self.prompt]
         self.all_seeds = all_seeds or [self.seed]
@@ -181,39 +187,43 @@ class Processed:
 
         return json.dumps(obj)
 
-    def infotext(self,  p: StableDiffusionProcessing, index):
-        return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
+    def infotext(self, p: StableDiffusionProcessing, index):
+        return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[],
+                               position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
 
 
 # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
 def slerp(val, low, high):
-    low_norm = low/torch.norm(low, dim=1, keepdim=True)
-    high_norm = high/torch.norm(high, dim=1, keepdim=True)
-    dot = (low_norm*high_norm).sum(1)
+    low_norm = low / torch.norm(low, dim=1, keepdim=True)
+    high_norm = high / torch.norm(high, dim=1, keepdim=True)
+    dot = (low_norm * high_norm).sum(1)
 
     if dot.mean() > 0.9995:
         return low * val + high * (1 - val)
 
     omega = torch.acos(dot)
     so = torch.sin(omega)
-    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
     return res
 
 
-def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
+def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0,
+                          p=None):
     xs = []
 
     # if we have multiple seeds, this means we are working with batch size>1; this then
     # enables the generation of additional tensors with noise that the sampler will use during its processing.
     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
     # produce the same images as with two batches [100], [101].
-    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
+    if p is not None and p.sampler is not None and (
+            len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
     else:
         sampler_noises = None
 
     for i, seed in enumerate(seeds):
-        noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
+        noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (
+            shape[0], seed_resize_from_h // 8, seed_resize_from_w // 8)
 
         subnoise = None
         if subseeds is not None:
@@ -241,7 +251,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
             dx = max(-dx, 0)
             dy = max(-dy, 0)
 
-            x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
+            x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w]
             noise = x
 
         if sampler_noises is not None:
@@ -293,14 +303,20 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed": all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
-        "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
+        "Model hash": getattr(p, 'sd_model_hash',
+                              None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
+        "Model": (
+            None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(
+                ',', '').replace(':', '')),
+        "Hypernet": (
+            None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(
+                ':', '')),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
         "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}"),
+        "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 is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
@@ -309,7 +325,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params.update(p.extra_generation_params)
 
-    generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
+    generation_params_text = ", ".join(
+        [k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
 
     negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
 
@@ -317,7 +334,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
 
 def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0,
-                   aesthetic_imgs=None,aesthetic_slerp=False) -> Processed:
+                   aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="",
+                   aesthetic_slerp_angle=0.15,
+                   aesthetic_text_negative=False) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
     aesthetic_lr = float(aesthetic_lr)
@@ -385,7 +404,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
         for n in range(p.n_iter):
             if state.skipped:
                 state.skipped = False
-            
+
             if state.interrupted:
                 break
 
@@ -396,16 +415,19 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
             if (len(prompts) == 0):
                 break
 
-            #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
-            #c = p.sd_model.get_learned_conditioning(prompts)
+            # uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
+            # c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
                 if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
-                    shared.sd_model.cond_stage_model.set_aesthetic_params(0, 0, 0)
+                    shared.sd_model.cond_stage_model.set_aesthetic_params()
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt],
                                                             p.steps)
                 if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
                     shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight,
-                                                                          aesthetic_steps, aesthetic_imgs,aesthetic_slerp)
+                                                                          aesthetic_steps, aesthetic_imgs,
+                                                                          aesthetic_slerp, aesthetic_imgs_text,
+                                                                          aesthetic_slerp_angle,
+                                                                          aesthetic_text_negative)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
@@ -413,13 +435,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                     comments[comment] = 1
 
             if p.n_iter > 1:
-                shared.state.job = f"Batch {n+1} out of {p.n_iter}"
+                shared.state.job = f"Batch {n + 1} out of {p.n_iter}"
 
             with devices.autocast():
-                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
+                                        subseed_strength=p.subseed_strength)
 
             if state.interrupted or state.skipped:
-
                 # if we are interrupted, sample returns just noise
                 # use the image collected previously in sampler loop
                 samples_ddim = shared.state.current_latent
@@ -445,7 +467,9 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
 
                 if p.restore_faces:
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
-                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
+                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i],
+                                          opts.samples_format, info=infotext(n, i), p=p,
+                                          suffix="-before-face-restoration")
 
                     devices.torch_gc()
 
@@ -456,7 +480,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
 
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
-                        images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format,
+                                          info=infotext(n, i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
                 if p.overlay_images is not None and i < len(p.overlay_images):
@@ -474,7 +499,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                     image = image.convert('RGB')
 
                 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)
+                    images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format,
+                                      info=infotext(n, i), p=p)
 
                 text = infotext(n, i)
                 infotexts.append(text)
@@ -482,7 +508,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                     image.info["parameters"] = text
                 output_images.append(image)
 
-            del x_samples_ddim 
+            del x_samples_ddim
 
             devices.torch_gc()
 
@@ -504,10 +530,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                 index_of_first_image = 1
 
             if opts.grid_save:
-                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)
+                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() + "".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)
+    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):
@@ -543,25 +572,34 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
-            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds,
+                                      subseeds=subseeds, subseed_strength=self.subseed_strength,
+                                      seed_resize_from_h=self.seed_resize_from_h,
+                                      seed_resize_from_w=self.seed_resize_from_w, p=self)
             samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
             return samples
 
-        x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds,
+                                  subseeds=subseeds, subseed_strength=self.subseed_strength,
+                                  seed_resize_from_h=self.seed_resize_from_h,
+                                  seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
         truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
         truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
 
-        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
+        samples = samples[:, :, truncate_y // 2:samples.shape[2] - truncate_y // 2,
+                  truncate_x // 2:samples.shape[3] - truncate_x // 2]
 
         if self.scale_latent:
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f),
+                                                      mode="bilinear")
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
 
             if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
-                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
+                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width),
+                                                                  mode="bilinear")
             else:
                 lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
 
@@ -585,13 +623,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
-        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds,
+                                      subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h,
+                                      seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning,
+                                              steps=self.steps)
 
         return samples
 
@@ -599,7 +640,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs):
+    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4,
+                 inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0,
+                 **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
@@ -607,7 +650,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.denoising_strength: float = denoising_strength
         self.init_latent = None
         self.image_mask = mask
-        #self.image_unblurred_mask = None
+        # self.image_unblurred_mask = None
         self.latent_mask = None
         self.mask_for_overlay = None
         self.mask_blur = mask_blur
@@ -619,7 +662,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.nmask = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index,
+                                                             self.sd_model)
         crop_region = None
 
         if self.image_mask is not None:
@@ -628,7 +672,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if self.inpainting_mask_invert:
                 self.image_mask = ImageOps.invert(self.image_mask)
 
-            #self.image_unblurred_mask = self.image_mask
+            # self.image_unblurred_mask = self.image_mask
 
             if self.mask_blur > 0:
                 self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
@@ -642,7 +686,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
                 mask = mask.crop(crop_region)
                 self.image_mask = images.resize_image(2, mask, self.width, self.height)
-                self.paste_to = (x1, y1, x2-x1, y2-y1)
+                self.paste_to = (x1, y1, x2 - x1, y2 - y1)
             else:
                 self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
                 np_mask = np.array(self.image_mask)
@@ -665,7 +709,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
             if self.image_mask is not None:
                 image_masked = Image.new('RGBa', (image.width, image.height))
-                image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
+                image_masked.paste(image.convert("RGBA").convert("RGBa"),
+                                   mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
 
                 self.overlay_images.append(image_masked.convert('RGBA'))
 
@@ -714,12 +759,17 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
             # this needs to be fixed to be done in sample() using actual seeds for batches
             if self.inpainting_fill == 2:
-                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
+                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:],
+                                                                                        all_seeds[
+                                                                                        0:self.init_latent.shape[
+                                                                                            0]]) * self.nmask
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds,
+                                  subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h,
+                                  seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
 

modules/sd_hijack.py

@@ -14,7 +14,8 @@ from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
-from transformers import CLIPVisionModel, CLIPModel
+from tqdm import trange
+from transformers import CLIPVisionModel, CLIPModel, CLIPTokenizer
 import torch.optim as optim
 import copy
 
@@ -22,21 +23,25 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
 
+
 def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
-    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (
+            6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
+    elif not cmd_opts.disable_opt_split_attention and (
+            cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
         if not invokeAI_mps_available and shared.device.type == 'mps':
-            print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
+            print(
+                "The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
             print("Applying v1 cross attention optimization.")
             ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
         else:
@@ -112,14 +117,16 @@ class StableDiffusionModelHijack:
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
 
+
 def slerp(low, high, val):
-    low_norm = low/torch.norm(low, dim=1, keepdim=True)
-    high_norm = high/torch.norm(high, dim=1, keepdim=True)
-    omega = torch.acos((low_norm*high_norm).sum(1))
+    low_norm = low / torch.norm(low, dim=1, keepdim=True)
+    high_norm = high / torch.norm(high, dim=1, keepdim=True)
+    omega = torch.acos((low_norm * high_norm).sum(1))
     so = torch.sin(omega)
-    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
     return res
 
+
 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
         super().__init__()
@@ -128,6 +135,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             self.wrapped.transformer.name_or_path
         )
         del self.clipModel.vision_model
+        self.tokenizer = CLIPTokenizer.from_pretrained(self.wrapped.transformer.name_or_path)
         self.hijack: StableDiffusionModelHijack = hijack
         self.tokenizer = wrapped.tokenizer
         # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval()
@@ -139,7 +147,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
 
-        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
+        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if
+                              '(' in k or ')' in k or '[' in k or ']' in k]
         for text, ident in tokens_with_parens:
             mult = 1.0
             for c in text:
@@ -155,8 +164,13 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-    def set_aesthetic_params(self, aesthetic_lr, aesthetic_weight, aesthetic_steps, image_embs_name=None,
-                             aesthetic_slerp=True):
+    def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
+                             aesthetic_slerp=True, aesthetic_imgs_text="",
+                             aesthetic_slerp_angle=0.15,
+                             aesthetic_text_negative=False):
+        self.aesthetic_imgs_text = aesthetic_imgs_text
+        self.aesthetic_slerp_angle = aesthetic_slerp_angle
+        self.aesthetic_text_negative = aesthetic_text_negative
         self.slerp = aesthetic_slerp
         self.aesthetic_lr = aesthetic_lr
         self.aesthetic_weight = aesthetic_weight
@@ -180,7 +194,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         else:
             parsed = [[line, 1.0]]
 
-        tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]
+        tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)[
+            "input_ids"]
 
         fixes = []
         remade_tokens = []
@@ -196,18 +211,20 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
                 if token == self.comma_token:
                     last_comma = len(remade_tokens)
-                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
+                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens),
+                                                               1) % 75 == 0 and last_comma != -1 and len(
+                    remade_tokens) - last_comma <= opts.comma_padding_backtrack:
                     last_comma += 1
                     reloc_tokens = remade_tokens[last_comma:]
                     reloc_mults = multipliers[last_comma:]
 
                     remade_tokens = remade_tokens[:last_comma]
                     length = len(remade_tokens)
-                    
+
                     rem = int(math.ceil(length / 75)) * 75 - length
                     remade_tokens += [id_end] * rem + reloc_tokens
                     multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
-                
+
                 if embedding is None:
                     remade_tokens.append(token)
                     multipliers.append(weight)
@@ -248,7 +265,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if line in cache:
                 remade_tokens, fixes, multipliers = cache[line]
             else:
-                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms,
+                                                                                            hijack_comments)
                 token_count = max(current_token_count, token_count)
 
                 cache[line] = (remade_tokens, fixes, multipliers)
@@ -259,7 +277,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         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
@@ -289,7 +306,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 while i < len(tokens):
                     token = tokens[i]
 
-                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens,
+                                                                                                                i)
 
                     mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
                     if mult_change is not None:
@@ -312,11 +330,12 @@ 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")
+                    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]
+                remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
                 cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
 
             multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
@@ -326,23 +345,26 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             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):
         use_old = opts.use_old_emphasis_implementation
         if use_old:
-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
+            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)
+            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(
+                text)
 
         self.hijack.comments += hijack_comments
 
         if len(used_custom_terms) > 0:
-            self.hijack.comments.append("Used embeddings: " + ", ".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]))
+
         if use_old:
             self.hijack.fixes = hijack_fixes
             return self.process_tokens(remade_batch_tokens, batch_multipliers)
-        
+
         z = None
         i = 0
         while max(map(len, remade_batch_tokens)) != 0:
@@ -356,7 +378,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     if fix[0] == i:
                         fixes.append(fix[1])
                 self.hijack.fixes.append(fixes)
-            
+
             tokens = []
             multipliers = []
             for j in range(len(remade_batch_tokens)):
@@ -378,19 +400,30 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                         remade_batch_tokens]
 
                 tokens = torch.asarray(remade_batch_tokens).to(device)
+
+                model = copy.deepcopy(self.clipModel).to(device)
+                model.requires_grad_(True)
+                if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
+                    text_embs_2 = model.get_text_features(
+                        **self.tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
+                    if self.aesthetic_text_negative:
+                        text_embs_2 = self.image_embs - text_embs_2
+                        text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
+                    img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
+                else:
+                    img_embs = self.image_embs
+
                 with torch.enable_grad():
-                    model = copy.deepcopy(self.clipModel).to(device)
-                    model.requires_grad_(True)
 
                     # We optimize the model to maximize the similarity
                     optimizer = optim.Adam(
                         model.text_model.parameters(), lr=self.aesthetic_lr
                     )
 
-                    for i in range(self.aesthetic_steps):
+                    for i in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
                         text_embs = model.get_text_features(input_ids=tokens)
                         text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
-                        sim = text_embs @ self.image_embs.T
+                        sim = text_embs @ img_embs.T
                         loss = -sim
                         optimizer.zero_grad()
                         loss.mean().backward()
@@ -405,6 +438,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     model.cpu()
                     del model
 
+                zn = torch.concat([zn for i in range(z.shape[1] // 77)], 1)
                 if self.slerp:
                     z = slerp(z, zn, self.aesthetic_weight)
                 else:
@@ -413,15 +447,16 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             remade_batch_tokens = rem_tokens
             batch_multipliers = rem_multipliers
             i += 1
-        
+
         return z
-        
-    
+
     def process_tokens(self, remade_batch_tokens, batch_multipliers):
         if not opts.use_old_emphasis_implementation:
-            remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
+            remade_batch_tokens = [
+                [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in
+                remade_batch_tokens]
             batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
-        
+
         tokens = torch.asarray(remade_batch_tokens).to(device)
         outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
 
@@ -461,8 +496,8 @@ class EmbeddingsWithFixes(torch.nn.Module):
         for fixes, tensor in zip(batch_fixes, inputs_embeds):
             for offset, embedding in fixes:
                 emb = embedding.vec
-                emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
-                tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
+                emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
+                tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
 
             vecs.append(tensor)
 

modules/shared.py

@@ -95,6 +95,10 @@ loaded_hypernetwork = None
 aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
                         os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
 
+def update_aesthetic_embeddings():
+    global aesthetic_embeddings
+    aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
+                            os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
 
 def reload_hypernetworks():
     global hypernetworks

modules/txt2img.py

@@ -13,7 +13,11 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
             aesthetic_lr=0,
             aesthetic_weight=0, aesthetic_steps=0,
             aesthetic_imgs=None,
-            aesthetic_slerp=False, *args):
+            aesthetic_slerp=False,
+            aesthetic_imgs_text="",
+            aesthetic_slerp_angle=0.15,
+            aesthetic_text_negative=False,
+            *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -47,7 +51,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     processed = modules.scripts.scripts_txt2img.run(p, *args)
 
     if processed is None:
-        processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp)
+        processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp,aesthetic_imgs_text,
+                           aesthetic_slerp_angle,
+                           aesthetic_text_negative)
 
     shared.total_tqdm.clear()
 

modules/ui.py

@@ -41,6 +41,7 @@ from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
+import modules.aesthetic_clip
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -449,7 +450,7 @@ def create_toprow(is_img2img):
                     with gr.Row():
                         negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
                 with gr.Column(scale=1, elem_id="roll_col"):
-                    sh = gr.Button(elem_id="sh", visible=True)                           
+                    sh = gr.Button(elem_id="sh", visible=True)
 
                 with gr.Column(scale=1, elem_id="style_neg_col"):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_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)
@@ -536,9 +537,13 @@ def create_ui(wrap_gradio_gpu_call):
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
                 with gr.Group():
-                    aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
-                    aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.7)
-                    aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=50)
+                    aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001")
+                    aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
+                    aesthetic_steps = gr.Slider(minimum=0, maximum=256, step=1, label="Aesthetic steps", value=5)
+                    with gr.Row():
+                        aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
+                        aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
+                        aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
 
                     aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
                     aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
@@ -617,7 +622,10 @@ def create_ui(wrap_gradio_gpu_call):
                            aesthetic_weight,
                            aesthetic_steps,
                            aesthetic_imgs,
-                           aesthetic_slerp
+                           aesthetic_slerp,
+                           aesthetic_imgs_text,
+                           aesthetic_slerp_angle,
+                           aesthetic_text_negative
                        ] + custom_inputs,
                 outputs=[
                     txt2img_gallery,
@@ -721,7 +729,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                         with gr.Row():
                             inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=1024, step=4, value=32)
 
                     with gr.TabItem('Batch img2img', id='batch'):
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
@@ -1071,6 +1079,17 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create embedding", variant='primary')
 
+                with gr.Tab(label="Create images embedding"):
+                    new_embedding_name_ae = gr.Textbox(label="Name")
+                    process_src_ae = gr.Textbox(label='Source directory')
+                    batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256)
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding_ae = gr.Button(value="Create images embedding", variant='primary')
+
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
@@ -1139,7 +1158,7 @@ def create_ui(wrap_gradio_gpu_call):
             fn=modules.textual_inversion.ui.create_embedding,
             inputs=[
                 new_embedding_name,
-                initialization_text,
+                process_src,
                 nvpt,
             ],
             outputs=[
@@ -1149,6 +1168,20 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
+        create_embedding_ae.click(
+            fn=modules.aesthetic_clip.generate_imgs_embd,
+            inputs=[
+                new_embedding_name_ae,
+                process_src_ae,
+                batch_ae
+            ],
+            outputs=[
+                aesthetic_imgs,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
         create_hypernetwork.click(
             fn=modules.hypernetworks.ui.create_hypernetwork,
             inputs=[