Add D-OPSD as a distillation handeling option for MiniMax H3 ref2va

extensions_built_in/diffusion_models/minimax_h3/minimax_h3.py

@@ -1297,6 +1297,12 @@ class MinimaxH3Ref2VAModel(MinimaxH3Model):
         # control VIDEOS are cached like dataset items and consumed as
         # multi-frame reference blocks
         self.supports_video_control_images = True
+        # D-OPSD: a no-grad teacher pass with the target as its own reference
+        # becomes the training target for the reference-free student pass
+        self.dopsd = bool(self.model_config.model_kwargs.get("dopsd", False))
+        if self.dopsd:
+            self.dopsd_self_ref = True
+            self.require_pixel_tensor_cache = True
 
     def _dit_component(self) -> str:
         partition = str(
@@ -1319,6 +1325,10 @@ class MinimaxH3Ref2VAModel(MinimaxH3Model):
         # pixel area with its own aspect kept, then encoded on its own grid
         if batch is None:
             return None, None, (), ()
+        if getattr(batch, "dopsd_teacher_pass", False):
+            return self._build_dopsd_teacher_condition(
+                batch, latent_shape, device, dtype
+            )
         controls_per_item = None
         if batch.control_tensor_list is not None:
             controls_per_item = batch.control_tensor_list
@@ -1400,6 +1410,54 @@ class MinimaxH3Ref2VAModel(MinimaxH3Model):
         cond_audio = torch.cat(audio_rows, dim=1) if audio_rows else None
         return torch.cat(all_rows, dim=1), cond_audio, (), tuple(blocks)
 
+    def _build_dopsd_teacher_condition(self, batch, latent_shape, device, dtype):
+        """D-OPSD teacher: the target is its own (only) reference, built from
+        the batch's cached pixel tensors; its soundtrack rides as clean
+        reference audio rows."""
+        if batch.tensor is None:
+            raise ValueError(
+                "D-OPSD teacher pass needs pixel tensors on the batch; set "
+                "cache_tensors_to_disk: true on this dataset"
+            )
+        px = batch.tensor.detach().to(device, torch.float32)
+        if px.ndim == 4:
+            # image items: single-frame ref, or a static clip for image_refs_as_video
+            frames = px.unsqueeze(2)
+            as_video_frames = self._image_ref_video_frames()
+            if as_video_frames:
+                frames = frames.expand(-1, -1, as_video_frames, -1, -1).contiguous()
+        else:
+            # video items: (B, T, C, H, W) -> (B, C, T, H, W)
+            frames = px.permute(0, 2, 1, 3, 4).contiguous()
+        ref_latents = self.encode_keyframe_latents(frames)
+        ref_noise = torch.randn_like(ref_latents)
+        ref_latents = (
+            KEYFRAME_NOISE_AUG_T * ref_latents
+            + (1.0 - KEYFRAME_NOISE_AUG_T) * ref_noise
+        )
+        audio_rows = None
+        a_lat = 0
+        if (
+            batch.dataset_config is not None
+            and batch.dataset_config.do_audio
+            and batch.audio_latents is not None
+            and getattr(batch, "num_frames", 1) > 1
+        ):
+            a_lat = packing.audio_latent_num_frames(batch.num_frames)
+            audio_rows = torch.stack(
+                [
+                    self._fit_audio_rows(
+                        batch.audio_latents[b].detach().to(device, torch.float32),
+                        a_lat,
+                    )
+                    for b in range(batch.audio_latents.shape[0])
+                ]
+            ).to(dtype)
+        blocks = (
+            (ref_latents.shape[2], ref_latents.shape[3], ref_latents.shape[4], a_lat),
+        )
+        return patchify_video_latents(ref_latents).to(dtype), audio_rows, (), blocks
+
     @torch.no_grad()
     def _encode_ref_video_for_sampling(self, path: str, gen_config) -> torch.Tensor:
         """Decode a reference video with the SAME temporal treatment training

extensions_built_in/sd_trainer/SDTrainer.py

@@ -309,6 +309,9 @@ class SDTrainer(BaseSDTrainProcess):
         
         if self.train_config.do_prior_divergence:
             self.do_prior_prediction = True
+        if getattr(self.sd, 'dopsd_self_ref', False):
+            # D-OPSD: the teacher (prior) prediction is the training target
+            self.do_prior_prediction = True
         # move vae to device if we did not cache latents
         if not self.is_latents_cached:
             self.sd.vae.eval()
@@ -1995,6 +1998,19 @@ class SDTrainer(BaseSDTrainProcess):
                                 [blank_embeds] * noisy_latents.shape[0]
                             )
                         
+                        is_dopsd = getattr(self.sd, 'dopsd_self_ref', False)
+                        if is_dopsd:
+                            # teacher embeds: caption + vision block naming the item as its own reference
+                            if batch.dopsd_prompt_embeds is None:
+                                raise ValueError(
+                                    "D-OPSD requires cached text embeddings; enable "
+                                    "train.cache_text_embeddings"
+                                )
+                            prior_embeds_to_use = batch.dopsd_prompt_embeds.clone().detach().to(
+                                self.device_torch, dtype=dtype
+                            )
+                            batch.dopsd_teacher_pass = True
+
                         # joint audio models stash their audio pred on the batch.
                         # Give this pass its own slot so the preservation loss
                         # can pair it with the preservation pass below.
@@ -2012,6 +2028,11 @@ class SDTrainer(BaseSDTrainProcess):
                             conditioned_prompts=conditioned_prompts
                         )
                         batch.audio_pred_slot = None
+                        if is_dopsd:
+                            batch.dopsd_teacher_pass = False
+                            if batch.audio_pred_prior is not None:
+                                # the teacher's audio prediction is the audio target too
+                                batch.audio_target = batch.audio_pred_prior.detach()
                         if prior_pred is not None:
                             prior_pred = prior_pred.detach()
 

jobs/process/BaseSDTrainProcess.py

@@ -2247,6 +2247,22 @@ class BaseSDTrainProcess(BaseTrainProcess):
 
         ### HOOk ###
         self.before_dataset_load()
+        if getattr(self.sd, 'require_pixel_tensor_cache', False):
+            # model needs pixel tensors at train time even with cached latents;
+            # storing them changes the latent cache key (first run re-caches)
+            for ds_list in [self.datasets, self.datasets_reg]:
+                if ds_list is None:
+                    continue
+                for ds in ds_list:
+                    if not (ds.cache_latents or ds.cache_latents_to_disk):
+                        # live-loading datasets already have pixels on the batch
+                        continue
+                    if not ds.cache_tensors_to_disk:
+                        print_acc(
+                            f"Model requires cached pixel tensors: forcing "
+                            f"cache_tensors_to_disk on dataset {ds.folder_path}"
+                        )
+                        ds.cache_tensors_to_disk = True
         # load datasets if passed in the root process
         if self.datasets is not None:
             self.data_loader = get_dataloader_from_datasets(self.datasets, self.train_config.batch_size, self.sd)

toolkit/data_loader.py

@@ -539,6 +539,7 @@ class AiToolkitDataset(LatentCachingMixin, ControlCachingMixin, CLIPCachingMixin
                     dataset_root=dataset_folder,
                     encode_control_in_text_embeddings=self.sd.encode_control_in_text_embeddings if self.sd else False,
                     encode_first_frame_in_text_embeddings=getattr(self.sd, 'encode_first_frame_in_text_embeddings', False) if self.sd else False,
+                    dopsd_self_ref=getattr(self.sd, 'dopsd_self_ref', False) if self.sd else False,
                     text_embedding_space_version=self.sd.text_embedding_space_version if self.sd else "sd1",
                     te_padding_side=self.sd.te_padding_side if self.sd else "right",
                     latent_space_version=latent_space_version,

toolkit/data_transfer_object/data_loader.py

@@ -83,6 +83,8 @@ class FileItemDTO(
         self.encode_first_frame_in_text_embeddings = kwargs.get(
             "encode_first_frame_in_text_embeddings", False
         )
+        # D-OPSD: also cache teacher embeds with the item's own media as reference 1
+        self.dopsd_self_ref = kwargs.get("dopsd_self_ref", False)
         self.te_padding_side = kwargs.get("te_padding_side", "right")
         self.latent_space_version = kwargs.get("latent_space_version", "sd1")
         self.text_embedding_space_version = kwargs.get("text_embedding_space_version", "sd1")
@@ -265,6 +267,8 @@ class DataLoaderBatchDTO:
             # noisy/sigma bookkeeping) directly. The trainer sets this around
             # its prior / guidance-unconditional / preservation passes.
             self.audio_pred_slot: Union[str, None] = None
+            # set by the trainer around the D-OPSD teacher pass
+            self.dopsd_teacher_pass: bool = False
             # noisy audio rows and audio sigma of the primary pass, used to
             # rebuild the clean audio estimate for perceptual losses
             self.audio_noisy: Union[torch.Tensor, None] = None
@@ -325,6 +329,8 @@ class DataLoaderBatchDTO:
             self.prompt_embeds: Union[PromptEmbeds, None] = None
             # diff output preservation embeds (trigger word replaced with class)
             self.dop_prompt_embeds: Union[PromptEmbeds, None] = None
+            # D-OPSD teacher embeds (trigger word replaced with the self-reference token)
+            self.dopsd_prompt_embeds: Union[PromptEmbeds, None] = None
             # if self.file_items[0].control_tensor is not None:
             # if any have a control tensor, we concatenate them
             if any([x.control_tensor is not None for x in self.file_items]):
@@ -517,6 +523,24 @@ class DataLoaderBatchDTO:
 
                 self.dop_prompt_embeds = concat_prompt_embeds(dop_prompt_embeds_list, padding_side=padding_side)
 
+            if any([getattr(x, 'dopsd_prompt_embeds', None) is not None for x in self.file_items]):
+                # find one to use as a base
+                base_dopsd_prompt_embeds = None
+                for x in self.file_items:
+                    if x.dopsd_prompt_embeds is not None:
+                        base_dopsd_prompt_embeds = x.dopsd_prompt_embeds
+                        break
+                dopsd_prompt_embeds_list = []
+                for x in self.file_items:
+                    if x.dopsd_prompt_embeds is None:
+                        y = base_dopsd_prompt_embeds
+                    else:
+                        y = x.dopsd_prompt_embeds
+                    dopsd_prompt_embeds_list.append(y)
+                padding_side = self.file_items[0].te_padding_side
+
+                self.dopsd_prompt_embeds = concat_prompt_embeds(dopsd_prompt_embeds_list, padding_side=padding_side)
+
             if any([x.audio_tensor is not None for x in self.file_items]):
                 # find one to use as a base
                 base_audio_tensor = None

toolkit/dataloader_mixins.py

@@ -324,6 +324,8 @@ class CaptionProcessingDTOMixin:
             self.caption_short: str = None
             # caption with the trigger word replaced by the diff output preservation class
             self.caption_dop: str = None
+            # D-OPSD teacher caption (trigger word replaced by <Picture 1>/<Video 1>)
+            self.caption_dopsd: str = None
 
             dataset_config: DatasetConfig = kwargs.get('dataset_config', None)
             self.extra_values: List[float] = dataset_config.extra_values
@@ -378,6 +380,19 @@ class CaptionProcessingDTOMixin:
                 self.caption_dop = self.caption.replace(
                     self.trigger_word, self.dataset_config.diff_output_preservation_class
                 )
+        if getattr(self, 'dopsd_self_ref', False):
+            # trigger word -> the self-reference token, or the token prepended
+            # when there is no trigger word
+            if self.trigger_word is not None:
+                self.caption_dopsd = self.caption.replace(
+                    self.trigger_word, self.get_dopsd_ref_token()
+                )
+            else:
+                self.caption_dopsd = f"{self.get_dopsd_ref_token()} {self.caption}".strip()
+
+    def get_dopsd_ref_token(self: 'FileItemDTO') -> str:
+        # the item is always the only reference in D-OPSD mode
+        return "<Video 1>" if self.is_video else "<Picture 1>"
 
     def get_caption(
             self: 'FileItemDTO',
@@ -2111,13 +2126,18 @@ class TextEmbeddingFileItemDTOMixin:
         self._blank_text_embedding_path: Union[str, None] = None
         # DOP embeds for dropout steps (dropout caption with trigger replaced by class)
         self._dop_blank_text_embedding_path: Union[str, None] = None
+        # D-OPSD teacher embeds (caption with trigger replaced by the self-reference
+        # token, encoded WITH the item's own image/video as the vision reference)
+        self.dopsd_prompt_embeds: Union[PromptEmbeds, None] = None
+        self._dopsd_text_embedding_path: Union[str, None] = None
+        self._dopsd_blank_text_embedding_path: Union[str, None] = None
         self._loaded_text_embedding_path: Union[str, None] = None
         self._caption_was_dropped = False
         self.is_text_embedding_cached = False
         self.text_embedding_load_device = 'cpu'
         self.text_embedding_version = 1
 
-    def get_text_embedding_info_dict(self: 'FileItemDTO', caption_override=None, text_only=False):
+    def get_text_embedding_info_dict(self: 'FileItemDTO', caption_override=None, text_only=False, dopsd_self_ref=False):
         # make sure the caption is loaded here
         # TODO: we need a way to cache all the other features like trigger words, DOP, etc. For now, we need to throw an error if not compatible.
         if self.caption is None:
@@ -2127,6 +2147,10 @@ class TextEmbeddingFileItemDTOMixin:
             ("text_embedding_space_version", self.text_embedding_space_version),
             ("text_embedding_version", self.text_embedding_version),
         ])
+        if dopsd_self_ref:
+            # teacher embeds carry the item's own media as the vision reference
+            item["dopsd_self_ref"] = True
+            return item
         # dropout embeds are encoded as plain text, keep control conditioning
         # out of their cache key
         if text_only:
@@ -2150,11 +2174,11 @@ class TextEmbeddingFileItemDTOMixin:
             item["first_frame_in_te"] = True
         return item
 
-    def _build_text_embedding_path(self: 'FileItemDTO', caption_override=None, text_only=False):
+    def _build_text_embedding_path(self: 'FileItemDTO', caption_override=None, text_only=False, dopsd_self_ref=False):
         # we store text embeddings in a folder in same path as image called _text_embedding_cache
         img_dir = os.path.dirname(self.path)
         te_dir = os.path.join(img_dir, '_t_e_cache')
-        hash_dict = self.get_text_embedding_info_dict(caption_override=caption_override, text_only=text_only)
+        hash_dict = self.get_text_embedding_info_dict(caption_override=caption_override, text_only=text_only, dopsd_self_ref=dopsd_self_ref)
         filename_no_ext = os.path.splitext(os.path.basename(self.path))[0]
         # get base64 hash of md5 checksum of hash_dict
         hash_input = json.dumps(hash_dict, sort_keys=True).encode('utf-8')
@@ -2215,6 +2239,35 @@ class TextEmbeddingFileItemDTOMixin:
 
         return self._dop_blank_text_embedding_path
 
+    def get_dopsd_text_embedding_path(self: 'FileItemDTO', recalculate=False):
+        if self._dopsd_text_embedding_path is not None and not recalculate:
+            return self._dopsd_text_embedding_path
+        # make sure the caption is loaded so caption_dopsd is built
+        if self.caption is None:
+            self.load_caption()
+        self._dopsd_text_embedding_path = self._build_text_embedding_path(
+            caption_override=self.caption_dopsd, dopsd_self_ref=True
+        )
+        return self._dopsd_text_embedding_path
+
+    def get_dopsd_dropout_caption(self: 'FileItemDTO'):
+        # dropout caption with the trigger word swapped for the self-reference token
+        dropout_caption = self.get_dropout_caption()
+        if self.trigger_word is not None:
+            return dropout_caption.replace(
+                self.trigger_word, self.get_dopsd_ref_token()
+            )
+        return f"{self.get_dopsd_ref_token()} {dropout_caption}".strip()
+
+    def get_dopsd_blank_text_embedding_path(self: 'FileItemDTO', recalculate=False):
+        if self._dopsd_blank_text_embedding_path is not None and not recalculate:
+            return self._dopsd_blank_text_embedding_path
+        # unlike DOP, dropout embeds keep the vision reference
+        self._dopsd_blank_text_embedding_path = self._build_text_embedding_path(
+            caption_override=self.get_dopsd_dropout_caption(), dopsd_self_ref=True
+        )
+        return self._dopsd_blank_text_embedding_path
+
     def get_blank_text_embedding_path(self: 'FileItemDTO', recalculate=False):
         if self._blank_text_embedding_path is not None and not recalculate:
             return self._blank_text_embedding_path
@@ -2235,6 +2288,8 @@ class TextEmbeddingFileItemDTOMixin:
             self.prompt_embeds = None
         if self.dop_prompt_embeds is not None:
             self.dop_prompt_embeds = None
+        if self.dopsd_prompt_embeds is not None:
+            self.dopsd_prompt_embeds = None
 
     def load_prompt_embedding(self, device=None):
         if not self.is_text_embedding_cached:
@@ -2264,6 +2319,12 @@ class TextEmbeddingFileItemDTOMixin:
                 self.dop_prompt_embeds = self.prompt_embeds
             else:
                 self.dop_prompt_embeds = PromptEmbeds.load(dop_path)
+        if getattr(self, 'dopsd_self_ref', False) and self.dopsd_prompt_embeds is None:
+            if self._caption_was_dropped:
+                dopsd_path = self.get_dopsd_blank_text_embedding_path()
+            else:
+                dopsd_path = self.get_dopsd_text_embedding_path()
+            self.dopsd_prompt_embeds = PromptEmbeds.load(dopsd_path)
 
 class TextEmbeddingCachingMixin:
     def __init__(self: 'AiToolkitDataset', **kwargs):
@@ -2402,6 +2463,50 @@ class TextEmbeddingCachingMixin:
                             prompt_embeds: PromptEmbeds = self.sd.encode_prompt(caption)
                             prompt_embeds.save(path)
                             del prompt_embeds
+                if getattr(file_item, 'dopsd_self_ref', False):
+                    # D-OPSD teacher embeds encode with the item's own media as the reference
+                    control_video_paths = getattr(file_item, 'control_video_paths', None) or []
+                    if file_item.control_path is not None or len(control_video_paths) > 0:
+                        raise ValueError(
+                            "D-OPSD self-reference training cannot be combined with "
+                            "control images/videos: the item itself must be the only "
+                            f"reference. Offending item: {file_item.path}"
+                        )
+                    dopsd_targets = [(
+                        file_item.get_dopsd_text_embedding_path(recalculate=True),
+                        file_item.caption_dopsd,
+                    )]
+                    if self.dataset_config.caption_dropout_rate > 0:
+                        dopsd_blank_path = file_item.get_dopsd_blank_text_embedding_path(recalculate=True)
+                        if dopsd_blank_path != dopsd_targets[0][0]:
+                            dopsd_targets.append((dopsd_blank_path, file_item.get_dopsd_dropout_caption()))
+                    dopsd_targets = [t for t in dopsd_targets if not os.path.exists(t[0])]
+                    if len(dopsd_targets) > 0:
+                        if not did_move:
+                            self.sd.set_device_state_preset('cache_text_encoder')
+                            did_move = True
+                        if file_item.is_video:
+                            # own path rides through the video-ref presentation
+                            ctrl_img = [file_item.path]
+                            self.sd._ref_video_dataset_config = self.dataset_config
+                        else:
+                            # own bucketed pixels as the reference image
+                            file_item.load_and_process_image(self.transform, only_load_latents=True)
+                            img = file_item.tensor  # (C, H, W) in [-1, 1]
+                            ctrl_img = [
+                                ((img + 1.0) / 2.0)
+                                .clamp(0, 1)
+                                .unsqueeze(0)
+                                .to(self.sd.device_torch, dtype=self.sd.torch_dtype)
+                            ]
+                            # release the cached latent/pixels the load pulled in
+                            file_item.cleanup()
+                        if not self.sd.has_multiple_control_images:
+                            ctrl_img = ctrl_img[0]
+                        for path, caption in dopsd_targets:
+                            prompt_embeds: PromptEmbeds = self.sd.encode_prompt(caption, control_images=ctrl_img)
+                            prompt_embeds.save(path)
+                            del prompt_embeds
                 file_item.is_text_embedding_cached = True
                 i += 1
             # restore device state

toolkit/models/base_model.py

@@ -180,6 +180,10 @@ class BaseModel:
         # control files may be VIDEOS (cached like dataset items, exposed on
         # the batch as control_video_latents_list); see minimax_h3 ref2va
         self.supports_video_control_images = False
+        # D-OPSD: cache per-item teacher text embeds (item's own media as reference 1)
+        self.dopsd_self_ref = False
+        # forces cache_tensors_to_disk on latent-caching datasets (BaseSDTrainProcess)
+        self.require_pixel_tensor_cache = False
         # control images will come in as a list for encoding some things if true
         self.has_multiple_control_images = False
         # do not resize control images

toolkit/stable_diffusion_model.py

@@ -218,6 +218,10 @@ class StableDiffusion:
         # control files may be VIDEOS (paths exposed on the batch as
         # control_video_paths_list); see minimax_h3 ref2va
         self.supports_video_control_images = False
+        # D-OPSD: cache per-item teacher text embeds (item's own media as reference 1)
+        self.dopsd_self_ref = False
+        # forces cache_tensors_to_disk on latent-caching datasets (BaseSDTrainProcess)
+        self.require_pixel_tensor_cache = False
         # control images will come in as a list for encoding some things if true
         self.has_multiple_control_images = False
         # do not resize control images

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

@@ -786,6 +786,7 @@ export const modelArchs: ModelArch[] = [
           { value: 'cg', label: 'Contrastive Guidance' },
           { value: 'ta', label: 'Training Adapter' },
           { value: 'both', label: 'Contrastive Guidance + Training Adapter (default)' },
+          { value: 'none', label: 'None' },
         ],
         getValue: (config: JobConfig) => {
           const assistantLoraPath = config?.config?.process?.[0]?.model?.assistant_lora_path;
@@ -797,7 +798,10 @@ export const modelArchs: ModelArch[] = [
           if (hasAssistantLoraPath) {
             return 'ta';
           }
-          return 'cg';
+          if (hasContrastiveGuidance) {
+            return 'cg';
+          }
+          return 'none';
         },
         onChange: (value: string, config: JobConfig, setJobConfig: (value: any, key: string) => void) => {
           if (value === 'cg') {
@@ -822,6 +826,10 @@ export const modelArchs: ModelArch[] = [
             if (!config?.config?.process?.[0]?.train?.guidance_loss_target) {
               setJobConfig(3.5, 'config.process[0].train.guidance_loss_target');
             }
+          } else if (value === 'none') {
+            setJobConfig(undefined, 'config.process[0].train.do_guidance_loss');
+            setJobConfig(undefined, 'config.process[0].train.guidance_loss_target');
+            setJobConfig(undefined, 'config.process[0].model.assistant_lora_path');
           }
         },
         doc: {
@@ -934,8 +942,13 @@ export const modelArchs: ModelArch[] = [
           { value: 'cg', label: 'Contrastive Guidance' },
           { value: 'ta', label: 'Training Adapter' },
           { value: 'both', label: 'Contrastive Guidance + Training Adapter (default)' },
+          { value: 'dopsd', label: 'D-OPSD' },
+          { value: 'none', label: 'None' },
         ],
         getValue: (config: JobConfig) => {
+          if (config?.config?.process?.[0]?.model?.model_kwargs?.dopsd) {
+            return 'dopsd';
+          }
           const assistantLoraPath = config?.config?.process?.[0]?.model?.assistant_lora_path;
           const hasAssistantLoraPath = assistantLoraPath && assistantLoraPath.trim() !== '';
           const hasContrastiveGuidance = config?.config?.process?.[0]?.train?.do_guidance_loss;
@@ -945,9 +958,19 @@ export const modelArchs: ModelArch[] = [
           if (hasAssistantLoraPath) {
             return 'ta';
           }
-          return 'cg';
+          if (hasContrastiveGuidance) {
+            return 'cg';
+          }
+          return 'none';
         },
         onChange: (value: string, config: JobConfig, setJobConfig: (value: any, key: string) => void) => {
+          const kwargs = { ...(config?.config?.process?.[0]?.model?.model_kwargs ?? {}) };
+          if (value === 'dopsd') {
+            kwargs.dopsd = true;
+          } else {
+            delete kwargs.dopsd;
+          }
+          setJobConfig(kwargs, 'config.process[0].model.model_kwargs');
           if (value === 'cg') {
             setJobConfig(true, 'config.process[0].train.do_guidance_loss');
             setJobConfig(undefined, 'config.process[0].model.assistant_lora_path');
@@ -970,6 +993,10 @@ export const modelArchs: ModelArch[] = [
             if (!config?.config?.process?.[0]?.train?.guidance_loss_target) {
               setJobConfig(3.5, 'config.process[0].train.guidance_loss_target');
             }
+          } else if (value === 'dopsd' || value === 'none') {
+            setJobConfig(undefined, 'config.process[0].train.do_guidance_loss');
+            setJobConfig(undefined, 'config.process[0].train.guidance_loss_target');
+            setJobConfig(undefined, 'config.process[0].model.assistant_lora_path');
           }
         },
         doc: {
@@ -980,7 +1007,10 @@ export const modelArchs: ModelArch[] = [
               break down. There are two different ways to train on this model without breaking the guidance
               distillation: Contrastive Guidance and Training Adapter. Both have their pros and cons. The adapter is
               faster, but will still break down over a long run. Contrastive Guidance is slower, but is less likely to
-              break down.
+              break down. D-OPSD instead self-distills: a no-grad teacher pass sees the training target as its own
+              reference and its prediction becomes the training target for a reference-free pass, baking the reference
+              into your trigger word (or the caption itself when no trigger word is set). Re-caches latents with pixel
+              tensors.
             </div>
           ),
         },
@@ -1009,12 +1039,12 @@ export const modelArchs: ModelArch[] = [
           description: (
             <div className="space-y-2">
               <p>
-                How still-image references (dataset control images and sample ctrl images) are shown to the model.
-                Video references always use the video path.
+                How still-image references (dataset control images and sample ctrl images) are shown to the model. Video
+                references always use the video path.
               </p>
               <p>
-                <strong>Picture</strong>: the native ref2va recipe — a single-frame reference block, shown to Qwen3-VL as
-                a <code>&lt;Picture i&gt;</code> block, scaled down only.
+                <strong>Picture</strong>: the native ref2va recipe — a single-frame reference block, shown to Qwen3-VL
+                as a <code>&lt;Picture i&gt;</code> block, scaled down only.
               </p>
               <p>
                 <strong>Static video clip</strong>: the image is held for 5 frames (2 latent frames) and routed through
@@ -1035,10 +1065,10 @@ export const modelArchs: ModelArch[] = [
           Reference-to-video: control images and videos condition the output as subject/style references (never as a
           first frame). References keep their own aspect and are matched to the target's pixel area (images scale down
           only, never up; a same-aspect video reference is exactly the target size). Each rides into the packed sequence
-          as a reference block, and is also shown to the Qwen3-VL conditioner as a <code>&lt;Picture i&gt;</code> (image)
-          or timestamped <code>&lt;Video k&gt;</code> (video) vision block. Training references come from the dataset
-          control path(s); sampling uses the sample ctrl images — always as references. The Image Reference Presentation
-          option can route still images through the video-reference path as short static clips.
+          as a reference block, and is also shown to the Qwen3-VL conditioner as a <code>&lt;Picture i&gt;</code>{' '}
+          (image) or timestamped <code>&lt;Video k&gt;</code> (video) vision block. Training references come from the
+          dataset control path(s); sampling uses the sample ctrl images — always as references. The Image Reference
+          Presentation option can route still images through the video-reference path as short static clips.
         </p>
         <p>
           Weights load like MiniMax-H3 (see that arch's notes) from the{' '}

version.py

@@ -1 +1 @@
-VERSION = "0.12.26"
+VERSION = "0.12.27"