Added support for new concept slider training script to CLI and UI

extensions_built_in/concept_slider/ConceptSliderTrainer.py

@@ -0,0 +1,277 @@
+from collections import OrderedDict
+from typing import Optional
+
+import torch
+
+from extensions_built_in.sd_trainer.DiffusionTrainer import DiffusionTrainer
+from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
+from toolkit.prompt_utils import PromptEmbeds, concat_prompt_embeds
+from toolkit.train_tools import get_torch_dtype
+
+
+class ConceptSliderTrainerConfig:
+    def __init__(self, **kwargs):
+        self.guidance_strength: float = kwargs.get("guidance_strength", 3.0)
+        self.anchor_strength: float = kwargs.get("anchor_strength", 1.0)
+        self.positive_prompt: str = kwargs.get("positive_prompt", "")
+        self.negative_prompt: str = kwargs.get("negative_prompt", "")
+        self.target_class: str = kwargs.get("target_class", "")
+        self.anchor_class: Optional[str] = kwargs.get("anchor_class", None)
+
+
+class ConceptSliderTrainer(DiffusionTrainer):
+    def __init__(self, process_id: int, job, config: OrderedDict, **kwargs):
+        super().__init__(process_id, job, config, **kwargs)
+        self.do_guided_loss = True
+
+        self.slider: ConceptSliderTrainerConfig = ConceptSliderTrainerConfig(
+            **self.config.get("slider", {})
+        )
+
+        self.positive_prompt = self.slider.positive_prompt
+        self.positive_prompt_embeds: Optional[PromptEmbeds] = None
+        self.negative_prompt = self.slider.negative_prompt
+        self.negative_prompt_embeds: Optional[PromptEmbeds] = None
+        self.target_class = self.slider.target_class
+        self.target_class_embeds: Optional[PromptEmbeds] = None
+        self.anchor_class = self.slider.anchor_class
+        self.anchor_class_embeds: Optional[PromptEmbeds] = None
+
+    def hook_before_train_loop(self):
+        # do this before calling parent as it unloads the text encoder if requested
+        if self.is_caching_text_embeddings:
+            # make sure model is on cpu for this part so we don't oom.
+            self.sd.unet.to("cpu")
+
+        # cache unconditional embeds (blank prompt)
+        with torch.no_grad():
+            self.positive_prompt_embeds = (
+                self.sd.encode_prompt(
+                    [self.positive_prompt],
+                )
+                .to(self.device_torch, dtype=self.sd.torch_dtype)
+                .detach()
+            )
+
+            self.target_class_embeds = (
+                self.sd.encode_prompt(
+                    [self.target_class],
+                )
+                .to(self.device_torch, dtype=self.sd.torch_dtype)
+                .detach()
+            )
+
+            self.negative_prompt_embeds = (
+                self.sd.encode_prompt(
+                    [self.negative_prompt],
+                )
+                .to(self.device_torch, dtype=self.sd.torch_dtype)
+                .detach()
+            )
+
+            if self.anchor_class is not None:
+                self.anchor_class_embeds = (
+                    self.sd.encode_prompt(
+                        [self.anchor_class],
+                    )
+                    .to(self.device_torch, dtype=self.sd.torch_dtype)
+                    .detach()
+                )
+
+        # call parent
+        super().hook_before_train_loop()
+
+    def get_guided_loss(
+        self,
+        noisy_latents: torch.Tensor,
+        conditional_embeds: PromptEmbeds,
+        match_adapter_assist: bool,
+        network_weight_list: list,
+        timesteps: torch.Tensor,
+        pred_kwargs: dict,
+        batch: "DataLoaderBatchDTO",
+        noise: torch.Tensor,
+        unconditional_embeds: Optional[PromptEmbeds] = None,
+        **kwargs,
+    ):
+        # todo for embeddings, we need to run without trigger words
+        was_unet_training = self.sd.unet.training
+        was_network_active = False
+        if self.network is not None:
+            was_network_active = self.network.is_active
+            self.network.is_active = False
+        
+
+        # do out prior preds first
+        with torch.no_grad():
+            dtype = get_torch_dtype(self.train_config.dtype)
+            self.sd.unet.eval()
+            noisy_latents = noisy_latents.to(self.device_torch, dtype=dtype).detach()
+
+            batch_size = noisy_latents.shape[0]
+
+            positive_embeds = concat_prompt_embeds(
+                [self.positive_prompt_embeds] * batch_size
+            ).to(self.device_torch, dtype=dtype)
+            target_class_embeds = concat_prompt_embeds(
+                [self.target_class_embeds] * batch_size
+            ).to(self.device_torch, dtype=dtype)
+            negative_embeds = concat_prompt_embeds(
+                [self.negative_prompt_embeds] * batch_size
+            ).to(self.device_torch, dtype=dtype)
+
+            if self.anchor_class_embeds is not None:
+                anchor_embeds = concat_prompt_embeds(
+                    [self.anchor_class_embeds] * batch_size
+                ).to(self.device_torch, dtype=dtype)
+
+            if self.anchor_class_embeds is not None:
+                # if we have an anchor, do it
+                combo_embeds = concat_prompt_embeds(
+                    [
+                        positive_embeds,
+                        target_class_embeds,
+                        negative_embeds,
+                        anchor_embeds,
+                    ]
+                )
+                num_embeds = 4
+            else:
+                combo_embeds = concat_prompt_embeds(
+                    [positive_embeds, target_class_embeds, negative_embeds]
+                )
+                num_embeds = 3
+
+            # do them in one batch, VRAM should handle it since we are no grad
+            combo_pred = self.sd.predict_noise(
+                latents=torch.cat([noisy_latents] * num_embeds, dim=0),
+                conditional_embeddings=combo_embeds,
+                timestep=torch.cat([timesteps] * num_embeds, dim=0),
+                guidance_scale=1.0,
+                guidance_embedding_scale=1.0,
+                batch=batch,
+            )
+
+            if self.anchor_class_embeds is not None:
+                positive_pred, neutral_pred, negative_pred, anchor_target = (
+                    combo_pred.chunk(4, dim=0)
+                )
+            else:
+                anchor_target = None
+                positive_pred, neutral_pred, negative_pred = combo_pred.chunk(3, dim=0)
+
+            # calculate the targets
+            guidance_scale = self.slider.guidance_strength
+
+            # enhance_positive_target = neutral_pred + guidance_scale * (
+            #     positive_pred - negative_pred
+            # )
+            # enhance_negative_target = neutral_pred + guidance_scale * (
+            #     negative_pred - positive_pred
+            # )
+            # erase_negative_target = neutral_pred - guidance_scale * (
+            #     negative_pred - positive_pred
+            # )
+            # erase_positive_target = neutral_pred - guidance_scale * (
+            #     positive_pred - negative_pred
+            # )
+            
+            positive = (positive_pred - neutral_pred) - (negative_pred - neutral_pred)
+            negative = (negative_pred - neutral_pred) - (positive_pred - neutral_pred)
+
+            enhance_positive_target = neutral_pred + guidance_scale * positive
+            enhance_negative_target = neutral_pred + guidance_scale * negative
+            erase_negative_target = neutral_pred - guidance_scale * negative
+            erase_positive_target = neutral_pred - guidance_scale * positive
+
+            if was_unet_training:
+                self.sd.unet.train()
+
+            # restore network
+            if self.network is not None:
+                self.network.is_active = was_network_active
+
+            if self.anchor_class_embeds is not None:
+                # do a grad inference with our target prompt
+                embeds = concat_prompt_embeds([target_class_embeds, anchor_embeds]).to(
+                    self.device_torch, dtype=dtype
+                )
+
+                noisy_latents = torch.cat([noisy_latents, noisy_latents], dim=0).to(
+                    self.device_torch, dtype=dtype
+                )
+                timesteps = torch.cat([timesteps, timesteps], dim=0)
+            else:
+                embeds = target_class_embeds.to(self.device_torch, dtype=dtype)
+
+        # do positive first
+        self.network.set_multiplier(1.0)
+        pred = self.sd.predict_noise(
+            latents=noisy_latents,
+            conditional_embeddings=embeds,
+            timestep=timesteps,
+            guidance_scale=1.0,
+            guidance_embedding_scale=1.0,
+            batch=batch,
+        )
+
+        if self.anchor_class_embeds is not None:
+            class_pred, anchor_pred = pred.chunk(2, dim=0)
+        else:
+            class_pred = pred
+            anchor_pred = None
+
+        # enhance positive loss
+        enhance_loss = torch.nn.functional.mse_loss(class_pred, enhance_positive_target)
+
+        erase_loss = torch.nn.functional.mse_loss(class_pred, erase_negative_target)
+
+        if anchor_target is None:
+            anchor_loss = torch.zeros_like(erase_loss)
+        else:
+            anchor_loss = torch.nn.functional.mse_loss(anchor_pred, anchor_target)
+        
+        anchor_loss = anchor_loss * self.slider.anchor_strength
+
+        # send backward now because gradient checkpointing needs network polarity intact
+        total_pos_loss = (enhance_loss + erase_loss + anchor_loss) / 3.0
+        total_pos_loss.backward()
+        total_pos_loss = total_pos_loss.detach()
+
+        # now do negative
+        self.network.set_multiplier(-1.0)
+        pred = self.sd.predict_noise(
+            latents=noisy_latents,
+            conditional_embeddings=embeds,
+            timestep=timesteps,
+            guidance_scale=1.0,
+            guidance_embedding_scale=1.0,
+            batch=batch,
+        )
+
+        if self.anchor_class_embeds is not None:
+            class_pred, anchor_pred = pred.chunk(2, dim=0)
+        else:
+            class_pred = pred
+            anchor_pred = None
+
+        # enhance negative loss
+        enhance_loss = torch.nn.functional.mse_loss(class_pred, enhance_negative_target)
+        erase_loss = torch.nn.functional.mse_loss(class_pred, erase_positive_target)
+
+        if anchor_target is None:
+            anchor_loss = torch.zeros_like(erase_loss)
+        else:
+            anchor_loss = torch.nn.functional.mse_loss(anchor_pred, anchor_target)
+        anchor_loss = anchor_loss * self.slider.anchor_strength
+        total_neg_loss = (enhance_loss + erase_loss + anchor_loss) / 3.0
+        total_neg_loss.backward()
+        total_neg_loss = total_neg_loss.detach()
+
+        self.network.set_multiplier(1.0)
+
+        total_loss = (total_pos_loss + total_neg_loss) / 2.0
+
+        # add a grad so backward works right
+        total_loss.requires_grad_(True)
+        return total_loss

extensions_built_in/concept_slider/__init__.py

@@ -0,0 +1,26 @@
+# This is an example extension for custom training. It is great for experimenting with new ideas.
+from toolkit.extension import Extension
+
+
+# This is for generic training (LoRA, Dreambooth, FineTuning)
+class ConceptSliderTrainerTrainer(Extension):
+    # uid must be unique, it is how the extension is identified
+    uid = "concept_slider"
+
+    # name is the name of the extension for printing
+    name = "Concept Slider Trainer"
+
+    # This is where your process class is loaded
+    # keep your imports in here so they don't slow down the rest of the program
+    @classmethod
+    def get_process(cls):
+        # import your process class here so it is only loaded when needed and return it
+        from .ConceptSliderTrainer import ConceptSliderTrainer
+
+        return ConceptSliderTrainer
+
+
+AI_TOOLKIT_EXTENSIONS = [
+    # you can put a list of extensions here
+    ConceptSliderTrainerTrainer
+]

extensions_built_in/sd_trainer/DiffusionTrainer.py

@@ -0,0 +1,297 @@
+from collections import OrderedDict
+import os
+import sqlite3
+import asyncio
+import concurrent.futures
+from extensions_built_in.sd_trainer.SDTrainer import SDTrainer
+from typing import Literal, Optional
+import threading
+import time
+import signal
+
+AITK_Status = Literal["running", "stopped", "error", "completed"]
+
+
+class DiffusionTrainer(SDTrainer):
+    def __init__(self, process_id: int, job, config: OrderedDict, **kwargs):
+        super(DiffusionTrainer, self).__init__(process_id, job, config, **kwargs)
+        self.sqlite_db_path = self.config.get("sqlite_db_path", "./aitk_db.db")
+        self.job_id = os.environ.get("AITK_JOB_ID", None)
+        self.job_id = self.job_id.strip() if self.job_id is not None else None
+        self.is_ui_trainer = True
+        if not os.path.exists(self.sqlite_db_path):
+            self.is_ui_trainer = False
+        else:
+            print(f"Using SQLite database at {self.sqlite_db_path}")
+        if self.job_id is None:
+            self.is_ui_trainer = False
+        else:
+            print(f"Job ID: \"{self.job_id}\"")
+        
+        if self.is_ui_trainer:
+            self.is_stopping = False
+            # Create a thread pool for database operations
+            self.thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
+            # Track all async tasks
+            self._async_tasks = []
+            # Initialize the status
+            self._run_async_operation(self._update_status("running", "Starting"))
+            self._stop_watcher_started = False
+            # self.start_stop_watcher(interval_sec=2.0)
+    
+    def start_stop_watcher(self, interval_sec: float = 5.0):
+        """
+        Start a daemon thread that periodically checks should_stop()
+        and terminates the process immediately when triggered.
+        """
+        if not self.is_ui_trainer:
+            return
+        if getattr(self, "_stop_watcher_started", False):
+            return
+        self._stop_watcher_started = True
+        t = threading.Thread(
+            target=self._stop_watcher_thread, args=(interval_sec,), daemon=True
+        )
+        t.start()
+
+    def _stop_watcher_thread(self, interval_sec: float):
+        while True:
+            try:
+                if self.should_stop():
+                    # Mark and update status (non-blocking; uses existing infra)
+                    self.is_stopping = True
+                    self._run_async_operation(
+                        self._update_status("stopped", "Job stopped (remote)")
+                    )
+                    # Best-effort flush pending async ops
+                    try:
+                        asyncio.run(self.wait_for_all_async())
+                    except RuntimeError:
+                        pass
+                    # Try to stop DB thread pool quickly
+                    try:
+                        self.thread_pool.shutdown(wait=False, cancel_futures=True)
+                    except TypeError:
+                        self.thread_pool.shutdown(wait=False)
+                    print("")
+                    print("****************************************************")
+                    print("    Stop signal received; terminating process.      ")
+                    print("****************************************************")
+                    os.kill(os.getpid(), signal.SIGINT)
+                time.sleep(interval_sec)
+            except Exception:
+                time.sleep(interval_sec)
+
+    def _run_async_operation(self, coro):
+        """Helper method to run an async coroutine and track the task."""
+        try:
+            loop = asyncio.get_event_loop()
+        except RuntimeError:
+            # No event loop exists, create a new one
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+
+        # Create a task and track it
+        if loop.is_running():
+            task = asyncio.run_coroutine_threadsafe(coro, loop)
+            self._async_tasks.append(asyncio.wrap_future(task))
+        else:
+            task = loop.create_task(coro)
+            self._async_tasks.append(task)
+            loop.run_until_complete(task)
+
+    async def _execute_db_operation(self, operation_func):
+        """Execute a database operation in a separate thread to avoid blocking."""
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(self.thread_pool, operation_func)
+
+    def _db_connect(self):
+        """Create a new connection for each operation to avoid locking."""
+        conn = sqlite3.connect(self.sqlite_db_path, timeout=10.0)
+        conn.isolation_level = None  # Enable autocommit mode
+        return conn
+
+    def should_stop(self):
+        if not self.is_ui_trainer:
+            return False
+        def _check_stop():
+            with self._db_connect() as conn:
+                cursor = conn.cursor()
+                cursor.execute(
+                    "SELECT stop FROM Job WHERE id = ?", (self.job_id,))
+                stop = cursor.fetchone()
+                return False if stop is None else stop[0] == 1
+
+        return _check_stop()
+
+    def maybe_stop(self):
+        if not self.is_ui_trainer:
+            return
+        if self.should_stop():
+            self._run_async_operation(
+                self._update_status("stopped", "Job stopped"))
+            self.is_stopping = True
+            raise Exception("Job stopped")
+
+    async def _update_key(self, key, value):
+        if not self.accelerator.is_main_process:
+            return
+
+        def _do_update():
+            with self._db_connect() as conn:
+                cursor = conn.cursor()
+                cursor.execute("BEGIN IMMEDIATE")
+                try:
+                    # Convert the value to string if it's not already
+                    if isinstance(value, str):
+                        value_to_insert = value
+                    else:
+                        value_to_insert = str(value)
+
+                    # Use parameterized query for both the column name and value
+                    update_query = f"UPDATE Job SET {key} = ? WHERE id = ?"
+                    cursor.execute(
+                        update_query, (value_to_insert, self.job_id))
+                finally:
+                    cursor.execute("COMMIT")
+
+        await self._execute_db_operation(_do_update)
+
+    def update_step(self):
+        """Non-blocking update of the step count."""
+        if self.accelerator.is_main_process and self.is_ui_trainer:
+            self._run_async_operation(self._update_key("step", self.step_num))
+
+    def update_db_key(self, key, value):
+        """Non-blocking update a key in the database."""
+        if self.accelerator.is_main_process and self.is_ui_trainer:
+            self._run_async_operation(self._update_key(key, value))
+
+    async def _update_status(self, status: AITK_Status, info: Optional[str] = None):
+        if not self.accelerator.is_main_process or not self.is_ui_trainer:
+            return
+
+        def _do_update():
+            with self._db_connect() as conn:
+                cursor = conn.cursor()
+                cursor.execute("BEGIN IMMEDIATE")
+                try:
+                    if info is not None:
+                        cursor.execute(
+                            "UPDATE Job SET status = ?, info = ? WHERE id = ?",
+                            (status, info, self.job_id)
+                        )
+                    else:
+                        cursor.execute(
+                            "UPDATE Job SET status = ? WHERE id = ?",
+                            (status, self.job_id)
+                        )
+                finally:
+                    cursor.execute("COMMIT")
+
+        await self._execute_db_operation(_do_update)
+
+    def update_status(self, status: AITK_Status, info: Optional[str] = None):
+        """Non-blocking update of status."""
+        if self.accelerator.is_main_process and self.is_ui_trainer:
+            self._run_async_operation(self._update_status(status, info))
+
+    async def wait_for_all_async(self):
+        """Wait for all tracked async operations to complete."""
+        if not self._async_tasks:
+            return
+
+        try:
+            await asyncio.gather(*self._async_tasks)
+        except Exception as e:
+            pass
+        finally:
+            # Clear the task list after completion
+            self._async_tasks.clear()
+
+    def on_error(self, e: Exception):
+        super(DiffusionTrainer, self).on_error(e)
+        if self.is_ui_trainer:
+            if self.accelerator.is_main_process and not self.is_stopping:
+                self.update_status("error", str(e))
+            self.update_db_key("step", self.last_save_step)
+            asyncio.run(self.wait_for_all_async())
+            self.thread_pool.shutdown(wait=True)
+
+    def handle_timing_print_hook(self, timing_dict):
+        if "train_loop" not in timing_dict:
+            print("train_loop not found in timing_dict", timing_dict)
+            return
+        seconds_per_iter = timing_dict["train_loop"]
+        # determine iter/sec or sec/iter
+        if seconds_per_iter < 1:
+            iters_per_sec = 1 / seconds_per_iter
+            self.update_db_key("speed_string", f"{iters_per_sec:.2f} iter/sec")
+        else:
+            self.update_db_key(
+                "speed_string", f"{seconds_per_iter:.2f} sec/iter")
+
+    def done_hook(self):
+        super(DiffusionTrainer, self).done_hook()
+        if self.is_ui_trainer:
+            self.update_status("completed", "Training completed")
+            # Wait for all async operations to finish before shutting down
+            asyncio.run(self.wait_for_all_async())
+            self.thread_pool.shutdown(wait=True)
+
+    def end_step_hook(self):
+        super(DiffusionTrainer, self).end_step_hook()
+        if self.is_ui_trainer:
+            self.update_step()
+            self.maybe_stop()
+
+    def hook_before_model_load(self):
+        super().hook_before_model_load()
+        if self.is_ui_trainer:
+            self.maybe_stop()
+            self.update_status("running", "Loading model")
+
+    def before_dataset_load(self):
+        super().before_dataset_load()
+        if self.is_ui_trainer:
+            self.maybe_stop()
+            self.update_status("running", "Loading dataset")
+
+    def hook_before_train_loop(self):
+        super().hook_before_train_loop()
+        if self.is_ui_trainer:
+            self.maybe_stop()
+            self.update_step()
+            self.update_status("running", "Training")
+            self.timer.add_after_print_hook(self.handle_timing_print_hook)
+
+    def status_update_hook_func(self, string):
+        self.update_status("running", string)
+
+    def hook_after_sd_init_before_load(self):
+        super().hook_after_sd_init_before_load()
+        if self.is_ui_trainer:
+            self.maybe_stop()
+            self.sd.add_status_update_hook(self.status_update_hook_func)
+
+    def sample_step_hook(self, img_num, total_imgs):
+        super().sample_step_hook(img_num, total_imgs)
+        if self.is_ui_trainer:
+            self.maybe_stop()
+            self.update_status(
+                "running", f"Generating images - {img_num + 1}/{total_imgs}")
+
+    def sample(self, step=None, is_first=False):
+        self.maybe_stop()
+        total_imgs = len(self.sample_config.prompts)
+        self.update_status("running", f"Generating images - 0/{total_imgs}")
+        super().sample(step, is_first)
+        self.maybe_stop()
+        self.update_status("running", "Training")
+
+    def save(self, step=None):
+        self.maybe_stop()
+        self.update_status("running", "Saving model")
+        super().save(step)
+        self.maybe_stop()
+        self.update_status("running", "Training")

extensions_built_in/sd_trainer/__init__.py

@@ -16,8 +16,10 @@ class SDTrainerExtension(Extension):
     def get_process(cls):
         # import your process class here so it is only loaded when needed and return it
         from .SDTrainer import SDTrainer
+
         return SDTrainer
 
+
 # This is for generic training (LoRA, Dreambooth, FineTuning)
 class UITrainerExtension(Extension):
     # uid must be unique, it is how the extension is identified
@@ -32,9 +34,28 @@ class UITrainerExtension(Extension):
     def get_process(cls):
         # import your process class here so it is only loaded when needed and return it
         from .UITrainer import UITrainer
+
         return UITrainer
 
 
+# This is a universal trainer that can be from ui or api
+class DiffusionTrainerExtension(Extension):
+    # uid must be unique, it is how the extension is identified
+    uid = "diffusion_trainer"
+
+    # name is the name of the extension for printing
+    name = "Diffusion Trainer"
+
+    # This is where your process class is loaded
+    # keep your imports in here so they don't slow down the rest of the program
+    @classmethod
+    def get_process(cls):
+        # import your process class here so it is only loaded when needed and return it
+        from .DiffusionTrainer import DiffusionTrainer
+
+        return DiffusionTrainer
+
+
 # for backwards compatability
 class TextualInversionTrainer(SDTrainerExtension):
     uid = "textual_inversion_trainer"
@@ -42,5 +63,8 @@ class TextualInversionTrainer(SDTrainerExtension):
 
 AI_TOOLKIT_EXTENSIONS = [
     # you can put a list of extensions here
-    SDTrainerExtension, TextualInversionTrainer, UITrainerExtension
+    SDTrainerExtension,
+    TextualInversionTrainer,
+    UITrainerExtension,
+    DiffusionTrainerExtension,
 ]

toolkit/config_modules.py

@@ -57,6 +57,13 @@ class SampleItem:
         self.ctrl_img: Optional[str] = kwargs.get('ctrl_img', None)
         self.ctrl_idx: int = kwargs.get('ctrl_idx', 0)
         self.network_multiplier: float = kwargs.get('network_multiplier', sample_config.network_multiplier)
+        # convert to a number if it is a string
+        if isinstance(self.network_multiplier, str):
+            try:
+                self.network_multiplier = float(self.network_multiplier)
+            except:
+                print(f"Invalid network_multiplier {self.network_multiplier}, defaulting to 1.0")
+                self.network_multiplier = 1.0
         
 
 class SampleConfig:

ui/src/app/jobs/new/AdvancedJob.tsx

@@ -108,7 +108,7 @@ export default function AdvancedJob({ jobConfig, setJobConfig, settings }: Props
 
         // We have to ensure certain things are always set
         try {
-          parsed.config.process[0].type = 'ui_trainer';
+          // parsed.config.process[0].type = 'ui_trainer';
           parsed.config.process[0].sqlite_db_path = './aitk_db.db';
           parsed.config.process[0].training_folder = settings.TRAINING_FOLDER;
           parsed.config.process[0].device = 'cuda';

ui/src/app/jobs/new/SimpleJob.tsx

@@ -1,6 +1,13 @@
 'use client';
 import { useMemo } from 'react';
-import { modelArchs, ModelArch, groupedModelOptions, quantizationOptions, defaultQtype } from './options';
+import {
+  modelArchs,
+  ModelArch,
+  groupedModelOptions,
+  quantizationOptions,
+  defaultQtype,
+  jobTypeOptions,
+} from './options';
 import { defaultDatasetConfig } from './jobConfig';
 import { GroupedSelectOption, JobConfig, SelectOption } from '@/types';
 import { objectCopy } from '@/utils/basic';
@@ -8,7 +15,7 @@ import { TextInput, SelectInput, Checkbox, FormGroup, NumberInput } from '@/comp
 import Card from '@/components/Card';
 import { X } from 'lucide-react';
 import AddSingleImageModal, { openAddImageModal } from '@/components/AddSingleImageModal';
-import {FlipHorizontal2, FlipVertical2} from "lucide-react"
+import { FlipHorizontal2, FlipVertical2 } from 'lucide-react';
 
 type Props = {
   jobConfig: JobConfig;
@@ -39,6 +46,21 @@ export default function SimpleJob({
     return modelArchs.find(a => a.name === jobConfig.config.process[0].model.arch) as ModelArch;
   }, [jobConfig.config.process[0].model.arch]);
 
+  const jobType = useMemo(() => {
+    return jobTypeOptions.find(j => j.value === jobConfig.config.process[0].type);
+  }, [jobConfig.config.process[0].type]);
+
+  const disableSections = useMemo(() => {
+    let sections: string[] = [];
+    if (modelArch?.disableSections) {
+      sections = sections.concat(modelArch.disableSections);
+    }
+    if (jobType?.disableSections) {
+      sections = sections.concat(jobType.disableSections);
+    }
+    return sections;
+  }, [modelArch, jobType]);
+
   const isVideoModel = !!(modelArch?.group === 'video');
 
   const numTopCards = useMemo(() => {
@@ -46,12 +68,14 @@ export default function SimpleJob({
     if (modelArch?.additionalSections?.includes('model.multistage')) {
       count += 1; // add multistage card
     }
-    if (!modelArch?.disableSections?.includes('model.quantize')) {
+    if (!disableSections.includes('model.quantize')) {
       count += 1; // add quantization card
     }
+    if (!disableSections.includes('slider')) {
+      count += 1; // add slider card
+    }
     return count;
-    
-  }, [modelArch]);
+  }, [modelArch, disableSections]);
 
   let topBarClass = 'grid grid-cols-1 md:grid-cols-2 lg:grid-cols-4 xl:grid-cols-4 gap-6';
 
@@ -62,6 +86,20 @@ export default function SimpleJob({
     topBarClass = 'grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 xl:grid-cols-3 2xl:grid-cols-6 gap-6';
   }
 
+  const numTrainingCols = useMemo(() => {
+    let count = 4;
+    if (!disableSections.includes('train.diff_output_preservation')) {
+      count += 1;
+    }
+    return count;
+  }, [disableSections]);
+
+  let trainingBarClass = 'grid grid-cols-1 md:grid-cols-2 lg:grid-cols-4 gap-6';
+
+  if (numTrainingCols == 5) {
+    trainingBarClass = 'grid grid-cols-1 md:grid-cols-3 lg:grid-cols-5 gap-6';
+  }
+
   const transformerQuantizationOptions: GroupedSelectOption[] | SelectOption[] = useMemo(() => {
     const hasARA = modelArch?.accuracyRecoveryAdapters && Object.keys(modelArch.accuracyRecoveryAdapters).length > 0;
     if (!hasARA) {
@@ -78,7 +116,7 @@ export default function SimpleJob({
     let ARAs: SelectOption[] = [];
     if (modelArch.accuracyRecoveryAdapters) {
       for (const [label, value] of Object.entries(modelArch.accuracyRecoveryAdapters)) {
-         ARAs.push({ value, label });
+        ARAs.push({ value, label });
       }
     }
     if (ARAs.length > 0) {
@@ -124,19 +162,21 @@ export default function SimpleJob({
               onChange={value => setGpuIDs(value)}
               options={gpuList.map((gpu: any) => ({ value: `${gpu.index}`, label: `GPU #${gpu.index}` }))}
             />
-            <TextInput
-              label="Trigger Word"
-              value={jobConfig.config.process[0].trigger_word || ''}
-              docKey="config.process[0].trigger_word"
-              onChange={(value: string | null) => {
-                if (value?.trim() === '') {
-                  value = null;
-                }
-                setJobConfig(value, 'config.process[0].trigger_word');
-              }}
-              placeholder=""
-              required
-            />
+            {disableSections.includes('trigger_word') ? null : (
+              <TextInput
+                label="Trigger Word"
+                value={jobConfig.config.process[0].trigger_word || ''}
+                docKey="config.process[0].trigger_word"
+                onChange={(value: string | null) => {
+                  if (value?.trim() === '') {
+                    value = null;
+                  }
+                  setJobConfig(value, 'config.process[0].trigger_word');
+                }}
+                placeholder=""
+                required
+              />
+            )}
           </Card>
 
           {/* Model Configuration Section */}
@@ -223,7 +263,7 @@ export default function SimpleJob({
               </FormGroup>
             )}
           </Card>
-          {modelArch?.disableSections?.includes('model.quantize') ? null : (
+          {disableSections.includes('model.quantize') ? null : (
             <Card title="Quantization">
               <SelectInput
                 label="Transformer"
@@ -270,14 +310,14 @@ export default function SimpleJob({
                 />
               </FormGroup>
               <NumberInput
-                  label="Switch Every"
-                  value={jobConfig.config.process[0].train.switch_boundary_every}
-                  onChange={value => setJobConfig(value, 'config.process[0].train.switch_boundary_every')}
-                  placeholder="eg. 1"
-                  docKey={'train.switch_boundary_every'}
-                  min={1}
-                  required
-                />
+                label="Switch Every"
+                value={jobConfig.config.process[0].train.switch_boundary_every}
+                onChange={value => setJobConfig(value, 'config.process[0].train.switch_boundary_every')}
+                placeholder="eg. 1"
+                docKey={'train.switch_boundary_every'}
+                min={1}
+                required
+              />
             </Card>
           )}
           <Card title="Target">
@@ -319,7 +359,7 @@ export default function SimpleJob({
                   max={1024}
                   required
                 />
-                {modelArch?.disableSections?.includes('network.conv') ? null : (
+                {disableSections.includes('network.conv') ? null : (
                   <NumberInput
                     label="Conv Rank"
                     value={jobConfig.config.process[0].network.conv}
@@ -336,6 +376,38 @@ export default function SimpleJob({
               </>
             )}
           </Card>
+          {!disableSections.includes('slider') && (
+            <Card title="Slider">
+              <TextInput
+                label="Target Class"
+                className=""
+                value={jobConfig.config.process[0].slider?.target_class ?? ''}
+                onChange={value => setJobConfig(value, 'config.process[0].slider.target_class')}
+                placeholder="eg. person"
+              />
+              <TextInput
+                label="Positive Prompt"
+                className=""
+                value={jobConfig.config.process[0].slider?.positive_prompt ?? ''}
+                onChange={value => setJobConfig(value, 'config.process[0].slider.positive_prompt')}
+                placeholder="eg. person who is happy"
+              />
+              <TextInput
+                label="Negative Prompt"
+                className=""
+                value={jobConfig.config.process[0].slider?.negative_prompt ?? ''}
+                onChange={value => setJobConfig(value, 'config.process[0].slider.negative_prompt')}
+                placeholder="eg. person who is sad"
+              />
+              <TextInput
+                label="Anchor Class"
+                className=""
+                value={jobConfig.config.process[0].slider?.anchor_class ?? ''}
+                onChange={value => setJobConfig(value, 'config.process[0].slider.anchor_class')}
+                placeholder=""
+              />
+            </Card>
+          )}
           <Card title="Save">
             <SelectInput
               label="Data Type"
@@ -367,7 +439,7 @@ export default function SimpleJob({
         </div>
         <div>
           <Card title="Training">
-            <div className="grid grid-cols-1 md:grid-cols-3 lg:grid-cols-5 gap-6">
+            <div className={trainingBarClass}>
               <div>
                 <NumberInput
                   label="Batch Size"
@@ -426,11 +498,11 @@ export default function SimpleJob({
                 />
               </div>
               <div>
-                {modelArch?.disableSections?.includes('train.timestep_type') ? null : (
+                {disableSections.includes('train.timestep_type') ? null : (
                   <SelectInput
                     label="Timestep Type"
                     value={jobConfig.config.process[0].train.timestep_type}
-                    disabled={modelArch?.disableSections?.includes('train.timestep_type') || false}
+                    disabled={disableSections.includes('train.timestep_type') || false}
                     onChange={value => setJobConfig(value, 'config.process[0].train.timestep_type')}
                     options={[
                       { value: 'sigmoid', label: 'Sigmoid' },
@@ -508,33 +580,39 @@ export default function SimpleJob({
                 </FormGroup>
               </div>
               <div>
-                <FormGroup label="Regularization">
-                  <Checkbox
-                    label="Differtial Output Preservation"
-                    className="pt-1"
-                    checked={jobConfig.config.process[0].train.diff_output_preservation || false}
-                    onChange={value => setJobConfig(value, 'config.process[0].train.diff_output_preservation')}
-                  />
-                </FormGroup>
-                {jobConfig.config.process[0].train.diff_output_preservation && (
+                {disableSections.includes('train.diff_output_preservation') ? null : (
                   <>
-                    <NumberInput
-                      label="DOP Loss Multiplier"
-                      className="pt-2"
-                      value={jobConfig.config.process[0].train.diff_output_preservation_multiplier as number}
-                      onChange={value =>
-                        setJobConfig(value, 'config.process[0].train.diff_output_preservation_multiplier')
-                      }
-                      placeholder="eg. 1.0"
-                      min={0}
-                    />
-                    <TextInput
-                      label="DOP Preservation Class"
-                      className="pt-2"
-                      value={jobConfig.config.process[0].train.diff_output_preservation_class as string}
-                      onChange={value => setJobConfig(value, 'config.process[0].train.diff_output_preservation_class')}
-                      placeholder="eg. woman"
-                    />
+                    <FormGroup label="Regularization">
+                      <Checkbox
+                        label="Differential Output Preservation"
+                        className="pt-1"
+                        checked={jobConfig.config.process[0].train.diff_output_preservation || false}
+                        onChange={value => setJobConfig(value, 'config.process[0].train.diff_output_preservation')}
+                      />
+                    </FormGroup>
+                    {jobConfig.config.process[0].train.diff_output_preservation && (
+                      <>
+                        <NumberInput
+                          label="DOP Loss Multiplier"
+                          className="pt-2"
+                          value={jobConfig.config.process[0].train.diff_output_preservation_multiplier as number}
+                          onChange={value =>
+                            setJobConfig(value, 'config.process[0].train.diff_output_preservation_multiplier')
+                          }
+                          placeholder="eg. 1.0"
+                          min={0}
+                        />
+                        <TextInput
+                          label="DOP Preservation Class"
+                          className="pt-2"
+                          value={jobConfig.config.process[0].train.diff_output_preservation_class as string}
+                          onChange={value =>
+                            setJobConfig(value, 'config.process[0].train.diff_output_preservation_class')
+                          }
+                          placeholder="eg. woman"
+                        />
+                      </>
+                    )}
                   </>
                 )}
               </div>
@@ -641,12 +719,20 @@ export default function SimpleJob({
                       </FormGroup>
                       <FormGroup label="Flipping" docKey={'datasets.flip'} className="mt-2">
                         <Checkbox
-                          label={<>Flip X <FlipHorizontal2 className="inline-block w-4 h-4 ml-1" /></>}
+                          label={
+                            <>
+                              Flip X <FlipHorizontal2 className="inline-block w-4 h-4 ml-1" />
+                            </>
+                          }
                           checked={dataset.flip_x || false}
                           onChange={value => setJobConfig(value, `config.process[0].datasets[${i}].flip_x`)}
                         />
                         <Checkbox
-                          label={<>Flip Y <FlipVertical2 className="inline-block w-4 h-4 ml-1" /></>}
+                          label={
+                            <>
+                              Flip Y <FlipVertical2 className="inline-block w-4 h-4 ml-1" />
+                            </>
+                          }
                           checked={dataset.flip_y || false}
                           onChange={value => setJobConfig(value, `config.process[0].datasets[${i}].flip_y`)}
                         />
@@ -812,7 +898,7 @@ export default function SimpleJob({
                       onChange={value => {
                         setJobConfig(value, 'config.process[0].train.skip_first_sample');
                         // cannot do both, so disable the other
-                        if (value){
+                        if (value) {
                           setJobConfig(false, 'config.process[0].train.force_first_sample');
                         }
                       }}
@@ -827,7 +913,7 @@ export default function SimpleJob({
                       onChange={value => {
                         setJobConfig(value, 'config.process[0].train.force_first_sample');
                         // cannot do both, so disable the other
-                        if (value){
+                        if (value) {
                           setJobConfig(false, 'config.process[0].train.skip_first_sample');
                         }
                       }}
@@ -841,7 +927,7 @@ export default function SimpleJob({
                       onChange={value => {
                         setJobConfig(value, 'config.process[0].train.disable_sampling');
                         // cannot do both, so disable the other
-                        if (value){
+                        if (value) {
                           setJobConfig(false, 'config.process[0].train.force_first_sample');
                         }
                       }}
@@ -866,6 +952,113 @@ export default function SimpleJob({
                           placeholder="Enter prompt"
                           required
                         />
+                        <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-4 gap-4 mt-2">
+                          <TextInput
+                            label={`Width`}
+                            value={sample.width ? `${sample.width}` : ''}
+                            onChange={value => {
+                              // remove any non-numeric characters
+                              value = value.replace(/\D/g, '');
+                              if (value === '') {
+                                // remove the key from the config if empty
+                                let newConfig = objectCopy(jobConfig);
+                                if (newConfig.config.process[0].sample.samples[i]) {
+                                  delete newConfig.config.process[0].sample.samples[i].width;
+                                  setJobConfig(
+                                    newConfig.config.process[0].sample.samples,
+                                    'config.process[0].sample.samples',
+                                  );
+                                }
+                              } else {
+                                const intValue = parseInt(value);
+                                if (!isNaN(intValue)) {
+                                  setJobConfig(intValue, `config.process[0].sample.samples[${i}].width`);
+                                } else {
+                                  console.warn('Invalid width value:', value);
+                                }
+                              }
+                            }}
+                            placeholder={`${jobConfig.config.process[0].sample.width} (default)`}
+                          />
+                          <TextInput
+                            label={`Height`}
+                            value={sample.height ? `${sample.height}` : ''}
+                            onChange={value => {
+                              // remove any non-numeric characters
+                              value = value.replace(/\D/g, '');
+                              if (value === '') {
+                                // remove the key from the config if empty
+                                let newConfig = objectCopy(jobConfig);
+                                if (newConfig.config.process[0].sample.samples[i]) {
+                                  delete newConfig.config.process[0].sample.samples[i].height;
+                                  setJobConfig(
+                                    newConfig.config.process[0].sample.samples,
+                                    'config.process[0].sample.samples',
+                                  );
+                                }
+                              } else {
+                                const intValue = parseInt(value);
+                                if (!isNaN(intValue)) {
+                                  setJobConfig(intValue, `config.process[0].sample.samples[${i}].height`);
+                                } else {
+                                  console.warn('Invalid height value:', value);
+                                }
+                              }
+                            }}
+                            placeholder={`${jobConfig.config.process[0].sample.height} (default)`}
+                          />
+                          <TextInput
+                            label={`Seed`}
+                            value={sample.seed ? `${sample.seed}` : ''}
+                            onChange={value => {
+                              // remove any non-numeric characters
+                              value = value.replace(/\D/g, '');
+                              if (value === '') {
+                                // remove the key from the config if empty
+                                let newConfig = objectCopy(jobConfig);
+                                if (newConfig.config.process[0].sample.samples[i]) {
+                                  delete newConfig.config.process[0].sample.samples[i].seed;
+                                  setJobConfig(
+                                    newConfig.config.process[0].sample.samples,
+                                    'config.process[0].sample.samples',
+                                  );
+                                }
+                              } else {
+                                const intValue = parseInt(value);
+                                if (!isNaN(intValue)) {
+                                  setJobConfig(intValue, `config.process[0].sample.samples[${i}].seed`);
+                                } else {
+                                  console.warn('Invalid seed value:', value);
+                                }
+                              }
+                            }}
+                            placeholder={`${jobConfig.config.process[0].sample.walk_seed ? jobConfig.config.process[0].sample.seed + i : jobConfig.config.process[0].sample.seed} (default)`}
+                          />
+                          <TextInput
+                            label={`LoRA Scale`}
+                            value={sample.network_multiplier ? `${sample.network_multiplier}` : ''}
+                            onChange={value => {
+                              // remove any non-numeric, - or . characters
+                              value = value.replace(/[^0-9.-]/g, '');
+                              if (value === '') {
+                                // remove the key from the config if empty
+                                let newConfig = objectCopy(jobConfig);
+                                if (newConfig.config.process[0].sample.samples[i]) {
+                                  delete newConfig.config.process[0].sample.samples[i].network_multiplier;
+                                  setJobConfig(
+                                    newConfig.config.process[0].sample.samples,
+                                    'config.process[0].sample.samples',
+                                  );
+                                }
+                              } else {
+                                // set it as a string
+                                setJobConfig(value, `config.process[0].sample.samples[${i}].network_multiplier`);
+                                return;
+                              }
+                            }}
+                            placeholder={`1.0 (default)`}
+                          />
+                        </div>
                       </div>
 
                       {modelArch?.additionalSections?.includes('sample.ctrl_img') && (

ui/src/app/jobs/new/jobConfig.ts

@@ -1,4 +1,4 @@
-import { JobConfig, DatasetConfig } from '@/types';
+import { JobConfig, DatasetConfig, SliderConfig } from '@/types';
 
 export const defaultDatasetConfig: DatasetConfig = {
   folder_path: '/path/to/images/folder',
@@ -20,13 +20,22 @@ export const defaultDatasetConfig: DatasetConfig = {
   flip_y: false,
 };
 
+export const defaultSliderConfig: SliderConfig = {
+  guidance_strength: 3.0,
+  anchor_strength: 1.0,
+  positive_prompt: 'person who is happy',
+  negative_prompt: 'person who is sad',
+  target_class: 'person',
+  anchor_class: "",
+};
+
 export const defaultJobConfig: JobConfig = {
   job: 'extension',
   config: {
     name: 'my_first_lora_v1',
     process: [
       {
-        type: 'ui_trainer',
+        type: 'diffusion_trainer',
         training_folder: 'output',
         sqlite_db_path: './aitk_db.db',
         device: 'cuda',
@@ -100,7 +109,7 @@ export const defaultJobConfig: JobConfig = {
           height: 1024,
           samples: [
             {
-              prompt: 'woman with red hair, playing chess at the park, bomb going off in the background'
+              prompt: 'woman with red hair, playing chess at the park, bomb going off in the background',
             },
             {
               prompt: 'a woman holding a coffee cup, in a beanie, sitting at a cafe',
@@ -109,7 +118,8 @@ export const defaultJobConfig: JobConfig = {
               prompt: 'a horse is a DJ at a night club, fish eye lens, smoke machine, lazer lights, holding a martini',
             },
             {
-              prompt: 'a man showing off his cool new t shirt at the beach, a shark is jumping out of the water in the background',
+              prompt:
+                'a man showing off his cool new t shirt at the beach, a shark is jumping out of the water in the background',
             },
             {
               prompt: 'a bear building a log cabin in the snow covered mountains',
@@ -121,13 +131,15 @@ export const defaultJobConfig: JobConfig = {
               prompt: 'hipster man with a beard, building a chair, in a wood shop',
             },
             {
-              prompt: 'photo of a man, white background, medium shot, modeling clothing, studio lighting, white backdrop',
+              prompt:
+                'photo of a man, white background, medium shot, modeling clothing, studio lighting, white backdrop',
             },
             {
               prompt: "a man holding a sign that says, 'this is a sign'",
             },
             {
-              prompt: 'a bulldog, in a post apocalyptic world, with a shotgun, in a leather jacket, in a desert, with a motorcycle',
+              prompt:
+                'a bulldog, in a post apocalyptic world, with a shotgun, in a leather jacket, in a desert, with a motorcycle',
             },
           ],
           neg: '',

ui/src/app/jobs/new/options.ts

@@ -1,8 +1,16 @@
-import { GroupedSelectOption, SelectOption } from '@/types';
+import { GroupedSelectOption, SelectOption, JobConfig } from '@/types';
+import { defaultSliderConfig } from './jobConfig';
 
 type Control = 'depth' | 'line' | 'pose' | 'inpaint';
 
-type DisableableSections = 'model.quantize' | 'train.timestep_type' | 'network.conv';
+type DisableableSections =
+  | 'model.quantize'
+  | 'train.timestep_type'
+  | 'network.conv'
+  | 'trigger_word'
+  | 'train.diff_output_preservation'
+  | 'slider';
+
 type AdditionalSections =
   | 'datasets.control_path'
   | 'datasets.do_i2v'
@@ -439,3 +447,33 @@ export const quantizationOptions: SelectOption[] = [
 ];
 
 export const defaultQtype = 'qfloat8';
+
+interface JobTypeOption extends SelectOption {
+  disableSections?: DisableableSections[];
+  processSections?: string[];
+  onActivate?: (config: JobConfig) => JobConfig;
+  onDeactivate?: (config: JobConfig) => JobConfig;
+}
+
+export const jobTypeOptions: JobTypeOption[] = [
+  {
+    value: 'diffusion_trainer',
+    label: 'LoRA Trainer',
+    disableSections: ['slider'],
+  },
+  {
+    value: 'concept_slider',
+    label: 'Concept Slider',
+    disableSections: ['trigger_word', 'train.diff_output_preservation'],
+    onActivate: (config: JobConfig) => {
+      // add default slider config
+      config.config.process[0].slider = { ...defaultSliderConfig };
+      return config;
+    },
+    onDeactivate: (config: JobConfig) => {
+      // remove slider config
+      delete config.config.process[0].slider;
+      return config;
+    },
+  },
+];

ui/src/app/jobs/new/page.tsx

@@ -3,6 +3,7 @@
 import { useEffect, useState } from 'react';
 import { useSearchParams, useRouter } from 'next/navigation';
 import { defaultJobConfig, defaultDatasetConfig, migrateJobConfig } from './jobConfig';
+import { jobTypeOptions } from './options';
 import { JobConfig } from '@/types';
 import { objectCopy } from '@/utils/basic';
 import { useNestedState } from '@/utils/hooks';
@@ -144,6 +145,38 @@ export default function TrainingForm() {
             <div className="mx-4 bg-gray-200 dark:bg-gray-800 w-1 h-6"></div>
           </>
         )}
+        {!showAdvancedView && (
+          <>
+            <div>
+              <SelectInput
+                value={`${jobConfig?.config.process[0].type}`}
+                onChange={value => {
+                  // undo current job type changes
+                  const currentOption = jobTypeOptions.find(
+                    option => option.value === jobConfig?.config.process[0].type,
+                  );
+                  if (currentOption && currentOption.onDeactivate) {
+                    setJobConfig(currentOption.onDeactivate(objectCopy(jobConfig)));
+                  }
+                  const option = jobTypeOptions.find(option => option.value === value);
+                  if (option) {
+                    if (option.onActivate) {
+                      setJobConfig(option.onActivate(objectCopy(jobConfig)));
+                    }
+                    jobTypeOptions.forEach(opt => {
+                      if (opt.value !== option.value && opt.onDeactivate) {
+                        setJobConfig(opt.onDeactivate(objectCopy(jobConfig)));
+                      }
+                    });
+                  }
+                  setJobConfig(value, 'config.process[0].type');
+                }}
+                options={jobTypeOptions}
+              />
+            </div>
+            <div className="mx-4 bg-gray-200 dark:bg-gray-800 w-1 h-6"></div>
+          </>
+        )}
 
         <div className="pr-2">
           <Button

ui/src/components/formInputs.tsx

@@ -68,8 +68,8 @@ export const TextInput = forwardRef<HTMLInputElement, TextInputProps>((props: Te
 TextInput.displayName = 'TextInput';
 
 export interface NumberInputProps extends InputProps {
-  value: number;
-  onChange: (value: number) => void;
+  value: number | null;
+  onChange: (value: number | null) => void;
   min?: number;
   max?: number;
 }

ui/src/types.ts

@@ -143,7 +143,7 @@ export interface ModelConfig {
 
 export interface SampleItem {
   prompt: string;
-  width?: number
+  width?: number;
   height?: number;
   neg?: string;
   seed?: number;
@@ -153,6 +153,7 @@ export interface SampleItem {
   num_frames?: number;
   ctrl_img?: string | null;
   ctrl_idx?: number;
+  network_multiplier?: number;
 }
 
 export interface SampleConfig {
@@ -171,14 +172,24 @@ export interface SampleConfig {
   fps: number;
 }
 
+export interface SliderConfig {
+  guidance_strength?: number;
+  anchor_strength?: number;
+  positive_prompt?: string;
+  negative_prompt?: string;
+  target_class?: string;
+  anchor_class?: string | null;
+}
+
 export interface ProcessConfig {
-  type: 'ui_trainer';
+  type: string;
   sqlite_db_path?: string;
   training_folder: string;
   performance_log_every: number;
   trigger_word: string | null;
   device: string;
   network?: NetworkConfig;
+  slider?: SliderConfig;
   save: SaveConfig;
   datasets: DatasetConfig[];
   train: TrainConfig;

version.py

@@ -1 +1 @@
-VERSION = "0.5.8"
+VERSION = "0.5.9"