Tons of bug fixes and improvements to special training. Fixed slider training.

extensions_built_in/sd_trainer/SDTrainer.py

@@ -2,13 +2,15 @@ from collections import OrderedDict
 from typing import Union, Literal, List
 from diffusers import T2IAdapter
 
+import torch.functional as F
 from toolkit import train_tools
 from toolkit.basic import value_map, adain, get_mean_std
 from toolkit.config_modules import GuidanceConfig
 from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO, FileItemDTO
+from toolkit.guidance import get_targeted_guidance_loss, get_guidance_loss
 from toolkit.image_utils import show_tensors, show_latents
 from toolkit.ip_adapter import IPAdapter
-from toolkit.prompt_utils import PromptEmbeds
+from toolkit.prompt_utils import PromptEmbeds, concat_prompt_embeds
 from toolkit.stable_diffusion_model import StableDiffusion, BlankNetwork
 from toolkit.train_tools import get_torch_dtype, apply_snr_weight, add_all_snr_to_noise_scheduler, \
     apply_learnable_snr_gos, LearnableSNRGamma
@@ -35,6 +37,7 @@ class SDTrainer(BaseSDTrainProcess):
         self.assistant_adapter: Union['T2IAdapter', None]
         self.do_prior_prediction = False
         self.do_long_prompts = False
+        self.do_guided_loss = False
         if self.train_config.inverted_mask_prior:
             self.do_prior_prediction = True
 
@@ -186,6 +189,33 @@ class SDTrainer(BaseSDTrainProcess):
             batch: 'DataLoaderBatchDTO',
             noise: torch.Tensor,
             **kwargs
+    ):
+        loss = get_guidance_loss(
+            noisy_latents=noisy_latents,
+            conditional_embeds=conditional_embeds,
+            match_adapter_assist=match_adapter_assist,
+            network_weight_list=network_weight_list,
+            timesteps=timesteps,
+            pred_kwargs=pred_kwargs,
+            batch=batch,
+            noise=noise,
+            sd=self.sd,
+            **kwargs
+        )
+
+        return loss
+
+    def get_guided_loss_targeted_polarity(
+            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,
+            **kwargs
     ):
         with torch.no_grad():
             # Perform targeted guidance (working title)
@@ -194,23 +224,28 @@ class SDTrainer(BaseSDTrainProcess):
             conditional_latents = batch.latents.to(self.device_torch, dtype=dtype).detach()
             unconditional_latents = batch.unconditional_latents.to(self.device_torch, dtype=dtype).detach()
 
-            unconditional_diff = unconditional_latents - conditional_latents
-            conditional_diff = conditional_latents - unconditional_latents
+            mean_latents = (conditional_latents + unconditional_latents) / 2.0
+
+            unconditional_diff = (unconditional_latents - mean_latents)
+            conditional_diff = (conditional_latents - mean_latents)
 
             # we need to determine the amount of signal and noise that would be present at the current timestep
-            conditional_signal = self.sd.add_noise(conditional_diff, torch.zeros_like(noise), timesteps)
-            unconditional_signal = self.sd.add_noise(torch.zeros_like(noise), unconditional_diff, timesteps)
+            # conditional_signal = self.sd.add_noise(conditional_diff, torch.zeros_like(noise), timesteps)
+            # unconditional_signal = self.sd.add_noise(torch.zeros_like(noise), unconditional_diff, timesteps)
+            # unconditional_signal = self.sd.add_noise(unconditional_diff, torch.zeros_like(noise), timesteps)
+            # conditional_blend = self.sd.add_noise(conditional_latents, unconditional_latents, timesteps)
+            # unconditional_blend = self.sd.add_noise(unconditional_latents, conditional_latents, timesteps)
 
-            target_noise = noise + unconditional_signal
+            # target_noise = noise + unconditional_signal
 
             conditional_noisy_latents = self.sd.add_noise(
-                unconditional_latents + conditional_signal,
-                target_noise,
+                mean_latents,
+                noise,
                 timesteps
             ).detach()
 
             unconditional_noisy_latents = self.sd.add_noise(
-                unconditional_latents,
+                mean_latents,
                 noise,
                 timesteps
             ).detach()
@@ -229,31 +264,210 @@ class SDTrainer(BaseSDTrainProcess):
                 **pred_kwargs  # adapter residuals in here
             ).detach()
 
-        # turn the LoRA network back on.
-        self.sd.unet.train()
-        self.network.is_active = True
-        self.network.multiplier = network_weight_list
+            # double up everything to run it through all at once
+            cat_embeds = concat_prompt_embeds([conditional_embeds, conditional_embeds])
+            cat_latents = torch.cat([conditional_noisy_latents, conditional_noisy_latents], dim=0)
+            cat_timesteps = torch.cat([timesteps, timesteps], dim=0)
+
+            # since we are dividing the polarity from the middle out, we need to double our network
+            # weights on training since the convergent point will be at half network strength
+
+            negative_network_weights = [weight * -2.0 for weight in network_weight_list]
+            positive_network_weights = [weight * 2.0 for weight in network_weight_list]
+            cat_network_weight_list = positive_network_weights + negative_network_weights
+
+            # turn the LoRA network back on.
+            self.sd.unet.train()
+            self.network.is_active = True
+
+            self.network.multiplier = cat_network_weight_list
 
         # do our prediction with LoRA active on the scaled guidance latents
         prediction = self.sd.predict_noise(
-            latents=conditional_noisy_latents.to(self.device_torch, dtype=dtype).detach(),
-            conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype).detach(),
-            timestep=timesteps,
+            latents=cat_latents.to(self.device_torch, dtype=dtype).detach(),
+            conditional_embeddings=cat_embeds.to(self.device_torch, dtype=dtype).detach(),
+            timestep=cat_timesteps,
             guidance_scale=1.0,
             **pred_kwargs  # adapter residuals in here
         )
 
-        # remove baseline from our prediction to extract our differential prediction
-        prediction = prediction - baseline_prediction
+        pred_pos, pred_neg = torch.chunk(prediction, 2, dim=0)
 
-        loss = torch.nn.functional.mse_loss(
-            prediction.float(),
-            unconditional_signal.float(),
+        pred_pos = pred_pos - baseline_prediction
+        pred_neg = pred_neg - baseline_prediction
+
+        pred_loss = torch.nn.functional.mse_loss(
+            pred_pos.float(),
+            unconditional_diff.float(),
             reduction="none"
         )
-        loss = loss.mean([1, 2, 3])
+        pred_loss = pred_loss.mean([1, 2, 3])
 
-        loss = self.apply_snr(loss, timesteps)
+        pred_neg_loss = torch.nn.functional.mse_loss(
+            pred_neg.float(),
+            conditional_diff.float(),
+            reduction="none"
+        )
+        pred_neg_loss = pred_neg_loss.mean([1, 2, 3])
+
+        loss = (pred_loss + pred_neg_loss) / 2.0
+
+        # loss = self.apply_snr(loss, timesteps)
+        loss = loss.mean()
+        loss.backward()
+
+        # detach it so parent class can run backward on no grads without throwing error
+        loss = loss.detach()
+        loss.requires_grad_(True)
+
+        return loss
+
+    def get_guided_loss_masked_polarity(
+            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,
+            **kwargs
+    ):
+        with torch.no_grad():
+            # Perform targeted guidance (working title)
+            dtype = get_torch_dtype(self.train_config.dtype)
+
+            conditional_latents = batch.latents.to(self.device_torch, dtype=dtype).detach()
+            unconditional_latents = batch.unconditional_latents.to(self.device_torch, dtype=dtype).detach()
+            inverse_latents = unconditional_latents - (conditional_latents - unconditional_latents)
+
+            mean_latents = (conditional_latents + unconditional_latents) / 2.0
+
+            # unconditional_diff = (unconditional_latents - mean_latents)
+            # conditional_diff = (conditional_latents - mean_latents)
+
+            # we need to determine the amount of signal and noise that would be present at the current timestep
+            # conditional_signal = self.sd.add_noise(conditional_diff, torch.zeros_like(noise), timesteps)
+            # unconditional_signal = self.sd.add_noise(torch.zeros_like(noise), unconditional_diff, timesteps)
+            # unconditional_signal = self.sd.add_noise(unconditional_diff, torch.zeros_like(noise), timesteps)
+            # conditional_blend = self.sd.add_noise(conditional_latents, unconditional_latents, timesteps)
+            # unconditional_blend = self.sd.add_noise(unconditional_latents, conditional_latents, timesteps)
+
+            # make a differential mask
+            differential_mask = torch.abs(conditional_latents - unconditional_latents)
+            max_differential = \
+                differential_mask.max(dim=1, keepdim=True)[0].max(dim=2, keepdim=True)[0].max(dim=3, keepdim=True)[0]
+            differential_scaler = 1.0 / max_differential
+            differential_mask = differential_mask * differential_scaler
+            spread_point = 0.1
+            # adjust mask to amplify the differential at 0.1
+            differential_mask = ((differential_mask - spread_point) * 10.0) + spread_point
+            # clip it
+            differential_mask = torch.clamp(differential_mask, 0.0, 1.0)
+
+            # target_noise = noise + unconditional_signal
+
+            conditional_noisy_latents = self.sd.add_noise(
+                conditional_latents,
+                noise,
+                timesteps
+            ).detach()
+
+            unconditional_noisy_latents = self.sd.add_noise(
+                unconditional_latents,
+                noise,
+                timesteps
+            ).detach()
+
+            inverse_noisy_latents = self.sd.add_noise(
+                inverse_latents,
+                noise,
+                timesteps
+            ).detach()
+
+            # Disable the LoRA network so we can predict parent network knowledge without it
+            self.network.is_active = False
+            self.sd.unet.eval()
+
+            # Predict noise to get a baseline of what the parent network wants to do with the latents + noise.
+            # This acts as our control to preserve the unaltered parts of the image.
+            # baseline_prediction = self.sd.predict_noise(
+            #     latents=unconditional_noisy_latents.to(self.device_torch, dtype=dtype).detach(),
+            #     conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype).detach(),
+            #     timestep=timesteps,
+            #     guidance_scale=1.0,
+            #     **pred_kwargs  # adapter residuals in here
+            # ).detach()
+
+            # double up everything to run it through all at once
+            cat_embeds = concat_prompt_embeds([conditional_embeds, conditional_embeds])
+            cat_latents = torch.cat([conditional_noisy_latents, unconditional_noisy_latents], dim=0)
+            cat_timesteps = torch.cat([timesteps, timesteps], dim=0)
+
+            # since we are dividing the polarity from the middle out, we need to double our network
+            # weights on training since the convergent point will be at half network strength
+
+            negative_network_weights = [weight * -1.0 for weight in network_weight_list]
+            positive_network_weights = [weight * 1.0 for weight in network_weight_list]
+            cat_network_weight_list = positive_network_weights + negative_network_weights
+
+            # turn the LoRA network back on.
+            self.sd.unet.train()
+            self.network.is_active = True
+
+            self.network.multiplier = cat_network_weight_list
+
+        # do our prediction with LoRA active on the scaled guidance latents
+        prediction = self.sd.predict_noise(
+            latents=cat_latents.to(self.device_torch, dtype=dtype).detach(),
+            conditional_embeddings=cat_embeds.to(self.device_torch, dtype=dtype).detach(),
+            timestep=cat_timesteps,
+            guidance_scale=1.0,
+            **pred_kwargs  # adapter residuals in here
+        )
+
+        pred_pos, pred_neg = torch.chunk(prediction, 2, dim=0)
+
+        # create a loss to balance the mean to 0 between the two predictions
+        differential_mean_pred_loss = torch.abs(pred_pos - pred_neg).mean([1, 2, 3]) ** 2.0
+
+        # pred_pos = pred_pos - baseline_prediction
+        # pred_neg = pred_neg - baseline_prediction
+
+        pred_loss = torch.nn.functional.mse_loss(
+            pred_pos.float(),
+            noise.float(),
+            reduction="none"
+        )
+        # apply mask
+        pred_loss = pred_loss * (1.0 + differential_mask)
+        pred_loss = pred_loss.mean([1, 2, 3])
+
+        pred_neg_loss = torch.nn.functional.mse_loss(
+            pred_neg.float(),
+            noise.float(),
+            reduction="none"
+        )
+        # apply inverse mask
+        pred_neg_loss = pred_neg_loss * (1.0 - differential_mask)
+        pred_neg_loss = pred_neg_loss.mean([1, 2, 3])
+
+        # make a loss to balance to losses of the pos and neg so they are equal
+        # differential_mean_loss_loss = torch.abs(pred_loss - pred_neg_loss)
+        #
+        # differential_mean_loss = differential_mean_pred_loss + differential_mean_loss_loss
+        #
+        # # add a multiplier to balancing losses to make them the top priority
+        # differential_mean_loss = differential_mean_loss
+
+        # remove the grads from the negative as it is only a balancing loss
+        # pred_neg_loss = pred_neg_loss.detach()
+
+        # loss = pred_loss + pred_neg_loss + differential_mean_loss
+        loss = pred_loss + pred_neg_loss
+
+        # loss = self.apply_snr(loss, timesteps)
         loss = loss.mean()
         loss.backward()
 
@@ -556,7 +770,7 @@ class SDTrainer(BaseSDTrainProcess):
 
                 self.before_unet_predict()
                 # do a prior pred if we have an unconditional image, we will swap out the giadance later
-                if batch.unconditional_latents is not None:
+                if batch.unconditional_latents is not None or self.do_guided_loss:
                     # do guided loss
                     loss = self.get_guided_loss(
                         noisy_latents=noisy_latents,

jobs/process/BaseSDTrainProcess.py

@@ -293,6 +293,10 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 # will end in safetensors or pt
                 embed_files = [f for f in embed_items if f.endswith('.safetensors') or f.endswith('.pt')]
 
+            # check for critic files
+            critic_pattern = f"CRITIC_{self.job.name}_*"
+            critic_items = glob.glob(os.path.join(self.save_root, critic_pattern))
+
             # Sort the lists by creation time if they are not empty
             if safetensors_files:
                 safetensors_files.sort(key=os.path.getctime)
@@ -302,6 +306,8 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 directories.sort(key=os.path.getctime)
             if embed_files:
                 embed_files.sort(key=os.path.getctime)
+            if critic_items:
+                critic_items.sort(key=os.path.getctime)
 
             # Combine and sort the lists
             combined_items = safetensors_files + directories + pt_files
@@ -313,8 +319,9 @@ class BaseSDTrainProcess(BaseTrainProcess):
             pt_files_to_remove = pt_files[:-self.save_config.max_step_saves_to_keep] if pt_files else []
             directories_to_remove = directories[:-self.save_config.max_step_saves_to_keep] if directories else []
             embeddings_to_remove = embed_files[:-self.save_config.max_step_saves_to_keep] if embed_files else []
+            critic_to_remove = critic_items[:-self.save_config.max_step_saves_to_keep] if critic_items else []
 
-            items_to_remove = safetensors_to_remove + pt_files_to_remove + directories_to_remove + embeddings_to_remove
+            items_to_remove = safetensors_to_remove + pt_files_to_remove + directories_to_remove + embeddings_to_remove + critic_to_remove
 
             # remove all but the latest max_step_saves_to_keep
             # items_to_remove = combined_items[:-self.save_config.max_step_saves_to_keep]
@@ -1041,8 +1048,9 @@ class BaseSDTrainProcess(BaseTrainProcess):
                     train_text_encoder=self.train_config.train_text_encoder,
                     conv_lora_dim=self.network_config.conv,
                     conv_alpha=self.network_config.conv_alpha,
-                    is_sdxl=self.model_config.is_xl,
+                    is_sdxl=self.model_config.is_xl or self.model_config.is_ssd,
                     is_v2=self.model_config.is_v2,
+                    is_ssd=self.model_config.is_ssd,
                     dropout=self.network_config.dropout,
                     use_text_encoder_1=self.model_config.use_text_encoder_1,
                     use_text_encoder_2=self.model_config.use_text_encoder_2,

jobs/process/TrainSliderProcess.py

@@ -371,7 +371,7 @@ class TrainSliderProcess(BaseSDTrainProcess):
 
                 # ger a random number of steps
                 timesteps_to = torch.randint(
-                    1, self.train_config.max_denoising_steps, (1,)
+                    1, self.train_config.max_denoising_steps - 1, (1,)
                 ).item()
 
                 # get noise
@@ -389,7 +389,8 @@ class TrainSliderProcess(BaseSDTrainProcess):
                 assert not self.network.is_active
                 self.sd.unet.eval()
                 # pass the multiplier list to the network
-                self.network.multiplier = prompt_pair.multiplier_list
+                # double up since we are doing cfg
+                self.network.multiplier = prompt_pair.multiplier_list + prompt_pair.multiplier_list
                 denoised_latents = self.sd.diffuse_some_steps(
                     latents,  # pass simple noise latents
                     train_tools.concat_prompt_embeddings(
@@ -507,7 +508,7 @@ class TrainSliderProcess(BaseSDTrainProcess):
                 for anchor_chunk, denoised_latent_chunk, anchor_target_noise_chunk in zip(
                         anchor_chunks, denoised_latent_chunks, anchor_target_noise_chunks
                 ):
-                    self.network.multiplier = anchor_chunk.multiplier_list
+                    self.network.multiplier = anchor_chunk.multiplier_list + anchor_chunk.multiplier_list
 
                     anchor_pred_noise = get_noise_pred(
                         anchor_chunk.neg_prompt, anchor_chunk.prompt, 1, current_timestep, denoised_latent_chunk
@@ -582,7 +583,7 @@ class TrainSliderProcess(BaseSDTrainProcess):
                 mask_multiplier_chunks,
                 unmasked_target_chunks
             ):
-                self.network.multiplier = prompt_pair_chunk.multiplier_list
+                self.network.multiplier = prompt_pair_chunk.multiplier_list + prompt_pair_chunk.multiplier_list
                 target_latents = get_noise_pred(
                     prompt_pair_chunk.positive_target,
                     prompt_pair_chunk.target_class,
@@ -611,6 +612,7 @@ class TrainSliderProcess(BaseSDTrainProcess):
                 offset_neutral = neutral_latents_chunk
                 # offsets are already adjusted on a per-batch basis
                 offset_neutral += offset
+                offset_neutral = offset_neutral.detach().requires_grad_(False)
 
                 # 16.15 GB RAM for 512x512 -> 4.20GB RAM for 512x512 with new grad_checkpointing
                 loss = torch.nn.functional.mse_loss(target_latents.float(), offset_neutral.float(), reduction="none")

scripts/generate_sampler_step_scales.py

@@ -0,0 +1,20 @@
+import argparse
+import torch
+import os
+from diffusers import StableDiffusionPipeline
+import sys
+
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+# add project root to path
+sys.path.append(PROJECT_ROOT)
+
+SAMPLER_SCALES_ROOT = os.path.join(PROJECT_ROOT, 'toolkit', 'samplers_scales')
+
+
+parser = argparse.ArgumentParser(description='Process some images.')
+add_arg = parser.add_argument
+add_arg('--model', type=str, required=True, help='Path to model')
+add_arg('--sampler', type=str, required=True, help='Name of sampler')
+
+args = parser.parse_args()
+

toolkit/config_modules.py

@@ -1,6 +1,6 @@
 import os
 import time
-from typing import List, Optional, Literal, Union
+from typing import List, Optional, Literal, Union, TYPE_CHECKING
 import random
 
 import torch
@@ -11,6 +11,8 @@ ImgExt = Literal['jpg', 'png', 'webp']
 
 SaveFormat = Literal['safetensors', 'diffusers']
 
+if TYPE_CHECKING:
+    from toolkit.guidance import GuidanceType
 
 class SaveConfig:
     def __init__(self, **kwargs):
@@ -400,6 +402,7 @@ class DatasetConfig:
         if legacy_caption_type:
             self.caption_ext = legacy_caption_type
         self.caption_type = self.caption_ext
+        self.guidance_type: GuidanceType = kwargs.get('guidance_type', 'targeted_polarity')
 
 
 def preprocess_dataset_raw_config(raw_config: List[dict]) -> List[dict]:

toolkit/guidance.py

@@ -1,5 +1,7 @@
 import torch
 from typing import Literal
+
+from toolkit.basic import value_map
 from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
 from toolkit.prompt_utils import PromptEmbeds, concat_prompt_embeds
 from toolkit.stable_diffusion_model import StableDiffusion
@@ -8,13 +10,16 @@ from toolkit.train_tools import get_torch_dtype
 GuidanceType = Literal["targeted", "polarity", "targeted_polarity"]
 
 DIFFERENTIAL_SCALER = 0.2
+
+
 # DIFFERENTIAL_SCALER = 0.25
 
 
 def get_differential_mask(
         conditional_latents: torch.Tensor,
         unconditional_latents: torch.Tensor,
-        threshold: float = 0.2
+        threshold: float = 0.2,
+        gradient: bool = False,
 ):
     # make a differential mask
     differential_mask = torch.abs(conditional_latents - unconditional_latents)
@@ -23,12 +28,27 @@ def get_differential_mask(
     differential_scaler = 1.0 / max_differential
     differential_mask = differential_mask * differential_scaler
 
-    # make everything less than 0.2 be 0.0 and everything else be 1.0
-    differential_mask = torch.where(
-        differential_mask < threshold,
-        torch.zeros_like(differential_mask),
-        torch.ones_like(differential_mask)
-    )
+    if gradient:
+        # wew need to scale it to 0-1
+        # differential_mask = differential_mask - differential_mask.min()
+        # differential_mask = differential_mask / differential_mask.max()
+        # add 0.2 threshold to both sides and clip
+        differential_mask = value_map(
+            differential_mask,
+            differential_mask.min(),
+            differential_mask.max(),
+            0 - threshold,
+            1 + threshold
+        )
+        differential_mask = torch.clamp(differential_mask, 0.0, 1.0)
+    else:
+
+        # make everything less than 0.2 be 0.0 and everything else be 1.0
+        differential_mask = torch.where(
+            differential_mask < threshold,
+            torch.zeros_like(differential_mask),
+            torch.ones_like(differential_mask)
+        )
     return differential_mask
 
 
@@ -47,7 +67,6 @@ def get_targeted_polarity_loss(
     dtype = get_torch_dtype(sd.torch_dtype)
     device = sd.device_torch
     with torch.no_grad():
-
         conditional_latents = batch.latents.to(device, dtype=dtype).detach()
         unconditional_latents = batch.unconditional_latents.to(device, dtype=dtype).detach()
 
@@ -164,7 +183,7 @@ def get_targeted_polarity_loss(
 
     return loss
 
-# This targets only the positive differential
+
 # targeted
 def get_targeted_guidance_loss(
         noisy_latents: torch.Tensor,
@@ -183,35 +202,71 @@ def get_targeted_guidance_loss(
         dtype = get_torch_dtype(sd.torch_dtype)
         device = sd.device_torch
 
+
         conditional_latents = batch.latents.to(device, dtype=dtype).detach()
         unconditional_latents = batch.unconditional_latents.to(device, dtype=dtype).detach()
 
-        unconditional_diff = (unconditional_latents - conditional_latents)
+        # apply random offset to both latents
+        offset = torch.randn((conditional_latents.shape[0], 1, 1, 1), device=device, dtype=dtype)
+        offset = offset * 0.1
+        conditional_latents = conditional_latents + offset
+        unconditional_latents = unconditional_latents + offset
+
+        # get random scale 0f 0.8 to 1.2
+        scale = torch.rand((conditional_latents.shape[0], 1, 1, 1), device=device, dtype=dtype)
+        scale = scale * 0.4
+        scale = scale + 0.8
+        conditional_latents = conditional_latents * scale
+        unconditional_latents = unconditional_latents * scale
 
         diff_mask = get_differential_mask(
             conditional_latents,
             unconditional_latents,
-            threshold=0.1
+            threshold=0.2,
+            gradient=True
         )
 
-        # this is a magic number I spent weeks deducing. It works and I have no idea why.
-        # unconditional_diff_noise = unconditional_diff * DIFFERENTIAL_SCALER
+        # standardize inpute to std of 1
+        # combo_std = torch.cat([conditional_latents, unconditional_latents], dim=1).std(dim=[1, 2, 3], keepdim=True)
+        #
+        # # scale the latents to std of 1
+        # conditional_latents = conditional_latents / combo_std
+        # unconditional_latents = unconditional_latents / combo_std
 
-        inputs_abs_mean = torch.abs(conditional_latents).mean(dim=[1, 2, 3], keepdim=True)
-        noise_abs_mean = torch.abs(noise).mean(dim=[1, 2, 3], keepdim=True)
-        diff_noise_scaler = noise_abs_mean / inputs_abs_mean
-        unconditional_diff_noise = unconditional_diff * diff_noise_scaler
+        unconditional_diff = (unconditional_latents - conditional_latents)
+
+
+
+        # get a -0.5 to 0.5 multiplier for the diff noise
+        # noise_multiplier = torch.rand((unconditional_diff.shape[0], 1, 1, 1), device=device, dtype=dtype)
+        # noise_multiplier = noise_multiplier - 0.5
+        noise_multiplier = 1.0
+
+        # unconditional_diff_noise = unconditional_diff * noise_multiplier
+        unconditional_diff_noise = unconditional_diff * noise_multiplier
+
+        # scale it to the timestep
+        unconditional_diff_noise = sd.add_noise(
+            torch.zeros_like(unconditional_latents),
+            unconditional_diff_noise,
+            timesteps
+        )
+
+        unconditional_diff_noise = unconditional_diff_noise * 0.2
+
+        unconditional_diff_noise = unconditional_diff_noise.detach().requires_grad_(False)
 
         baseline_noisy_latents = sd.add_noise(
-            conditional_latents,
+            unconditional_latents,
             noise,
             timesteps
         ).detach()
 
+        target_noise = noise + unconditional_diff_noise
         noisy_latents = sd.add_noise(
             conditional_latents,
-            # noise + unconditional_diff_noise,
-            noise,
+            target_noise,
+            # noise,
             timesteps
         ).detach()
         # Disable the LoRA network so we can predict parent network knowledge without it
@@ -226,15 +281,20 @@ def get_targeted_guidance_loss(
             timestep=timesteps,
             guidance_scale=1.0,
             **pred_kwargs  # adapter residuals in here
-        ).detach()
+        ).detach().requires_grad_(False)
+
+        # determine the error for the baseline prediction
+        baseline_prediction_error = baseline_prediction - noise
+
+
         # turn the LoRA network back on.
         sd.unet.train()
         sd.network.is_active = True
 
         sd.network.multiplier = network_weight_list
 
-        unmasked_baseline_prediction = baseline_prediction * (1.0 - diff_mask)
-        masked_noise = noise * diff_mask
+        # unmasked_baseline_prediction = baseline_prediction * (1.0 - diff_mask)
+        # masked_noise = noise * diff_mask
         # pred_target = unmasked_noise + unconditional_diff_noise
 
     # do our prediction with LoRA active on the scaled guidance latents
@@ -246,30 +306,32 @@ def get_targeted_guidance_loss(
         **pred_kwargs  # adapter residuals in here
     )
 
-    prediction = prediction - unmasked_baseline_prediction
-    # prediction = prediction - baseline_prediction
 
-    baseline_loss = torch.nn.functional.mse_loss(
-        baseline_prediction.float(),
-        noise.float(),
+
+    baselined_prediction = prediction - baseline_prediction
+
+    guidance_loss = torch.nn.functional.mse_loss(
+        baselined_prediction.float(),
+        # unconditional_diff_noise.float(),
+        unconditional_diff_noise.float(),
         reduction="none"
     )
-    baseline_loss = baseline_loss * (1.0 - diff_mask)
-    baseline_loss = baseline_loss.mean([1, 2, 3])
+    guidance_loss = guidance_loss.mean([1, 2, 3])
 
-    # loss = torch.nn.functional.l1_loss(
-    loss = torch.nn.functional.mse_loss(
+    guidance_loss = guidance_loss.mean()
+
+
+    # do the masked noise prediction
+    masked_noise_loss = torch.nn.functional.mse_loss(
         prediction.float(),
-        masked_noise.float(),
+        target_noise.float(),
         reduction="none"
-    )
-    loss = loss * diff_mask
-    loss = loss.mean([1, 2, 3])
-    primary_loss_scaler = 1.0
+    ) * diff_mask
+    masked_noise_loss = masked_noise_loss.mean([1, 2, 3])
+    masked_noise_loss = masked_noise_loss.mean()
 
-    loss = (loss * primary_loss_scaler) + baseline_loss
 
-    loss = loss.mean()
+    loss = guidance_loss + masked_noise_loss
 
     loss.backward()
 
@@ -280,8 +342,158 @@ def get_targeted_guidance_loss(
     return loss
 
 
+def get_targeted_guidance_loss_WIP(
+        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,
+        sd: 'StableDiffusion',
+        **kwargs
+):
+    with torch.no_grad():
+        # Perform targeted guidance (working title)
+        dtype = get_torch_dtype(sd.torch_dtype)
+        device = sd.device_torch
 
 
+        conditional_latents = batch.latents.to(device, dtype=dtype).detach()
+        unconditional_latents = batch.unconditional_latents.to(device, dtype=dtype).detach()
+
+        # apply random offset to both latents
+        offset = torch.randn((conditional_latents.shape[0], 1, 1, 1), device=device, dtype=dtype)
+        offset = offset * 0.1
+        conditional_latents = conditional_latents + offset
+        unconditional_latents = unconditional_latents + offset
+
+        # get random scale 0f 0.8 to 1.2
+        scale = torch.rand((conditional_latents.shape[0], 1, 1, 1), device=device, dtype=dtype)
+        scale = scale * 0.4
+        scale = scale + 0.8
+        conditional_latents = conditional_latents * scale
+        unconditional_latents = unconditional_latents * scale
+
+        diff_mask = get_differential_mask(
+            conditional_latents,
+            unconditional_latents,
+            threshold=0.2,
+            gradient=True
+        )
+
+        # standardize inpute to std of 1
+        # combo_std = torch.cat([conditional_latents, unconditional_latents], dim=1).std(dim=[1, 2, 3], keepdim=True)
+        #
+        # # scale the latents to std of 1
+        # conditional_latents = conditional_latents / combo_std
+        # unconditional_latents = unconditional_latents / combo_std
+
+        unconditional_diff = (unconditional_latents - conditional_latents)
+
+
+
+        # get a -0.5 to 0.5 multiplier for the diff noise
+        # noise_multiplier = torch.rand((unconditional_diff.shape[0], 1, 1, 1), device=device, dtype=dtype)
+        # noise_multiplier = noise_multiplier - 0.5
+        noise_multiplier = 1.0
+
+        # unconditional_diff_noise = unconditional_diff * noise_multiplier
+        unconditional_diff_noise = unconditional_diff * noise_multiplier
+
+        # scale it to the timestep
+        unconditional_diff_noise = sd.add_noise(
+            torch.zeros_like(unconditional_latents),
+            unconditional_diff_noise,
+            timesteps
+        )
+
+        unconditional_diff_noise = unconditional_diff_noise * 0.2
+
+        unconditional_diff_noise = unconditional_diff_noise.detach().requires_grad_(False)
+
+        baseline_noisy_latents = sd.add_noise(
+            unconditional_latents,
+            noise,
+            timesteps
+        ).detach()
+
+        target_noise = noise + unconditional_diff_noise
+        noisy_latents = sd.add_noise(
+            conditional_latents,
+            target_noise,
+            # noise,
+            timesteps
+        ).detach()
+        # Disable the LoRA network so we can predict parent network knowledge without it
+        sd.network.is_active = False
+        sd.unet.eval()
+
+        # Predict noise to get a baseline of what the parent network wants to do with the latents + noise.
+        # This acts as our control to preserve the unaltered parts of the image.
+        baseline_prediction = sd.predict_noise(
+            latents=baseline_noisy_latents.to(device, dtype=dtype).detach(),
+            conditional_embeddings=conditional_embeds.to(device, dtype=dtype).detach(),
+            timestep=timesteps,
+            guidance_scale=1.0,
+            **pred_kwargs  # adapter residuals in here
+        ).detach().requires_grad_(False)
+
+        # turn the LoRA network back on.
+        sd.unet.train()
+        sd.network.is_active = True
+
+        sd.network.multiplier = network_weight_list
+
+        # unmasked_baseline_prediction = baseline_prediction * (1.0 - diff_mask)
+        # masked_noise = noise * diff_mask
+        # pred_target = unmasked_noise + unconditional_diff_noise
+
+    # do our prediction with LoRA active on the scaled guidance latents
+    prediction = sd.predict_noise(
+        latents=noisy_latents.to(device, dtype=dtype).detach(),
+        conditional_embeddings=conditional_embeds.to(device, dtype=dtype).detach(),
+        timestep=timesteps,
+        guidance_scale=1.0,
+        **pred_kwargs  # adapter residuals in here
+    )
+
+
+
+    baselined_prediction = prediction - baseline_prediction
+
+    guidance_loss = torch.nn.functional.mse_loss(
+        baselined_prediction.float(),
+        # unconditional_diff_noise.float(),
+        unconditional_diff_noise.float(),
+        reduction="none"
+    )
+    guidance_loss = guidance_loss.mean([1, 2, 3])
+
+    guidance_loss = guidance_loss.mean()
+
+
+    # do the masked noise prediction
+    masked_noise_loss = torch.nn.functional.mse_loss(
+        prediction.float(),
+        target_noise.float(),
+        reduction="none"
+    ) * diff_mask
+    masked_noise_loss = masked_noise_loss.mean([1, 2, 3])
+    masked_noise_loss = masked_noise_loss.mean()
+
+
+    loss = guidance_loss + masked_noise_loss
+
+    loss.backward()
+
+    # detach it so parent class can run backward on no grads without throwing error
+    loss = loss.detach()
+    loss.requires_grad_(True)
+
+    return loss
+
 def get_guided_loss_polarity(
         noisy_latents: torch.Tensor,
         conditional_embeds: PromptEmbeds,

toolkit/network_mixins.py

@@ -13,6 +13,7 @@ from toolkit.config_modules import NetworkConfig
 from toolkit.lorm import extract_conv, extract_linear, count_parameters
 from toolkit.metadata import add_model_hash_to_meta
 from toolkit.paths import KEYMAPS_ROOT
+from toolkit.saving import get_lora_keymap_from_model_keymap
 
 if TYPE_CHECKING:
     from toolkit.lycoris_special import LycorisSpecialNetwork, LoConSpecialModule
@@ -338,6 +339,7 @@ class ToolkitNetworkMixin:
             train_unet: Optional[bool] = True,
             is_sdxl=False,
             is_v2=False,
+            is_ssd=False,
             network_config: Optional[NetworkConfig] = None,
             is_lorm=False,
             **kwargs
@@ -348,6 +350,7 @@ class ToolkitNetworkMixin:
         self._multiplier: float = 1.0
         self.is_active: bool = False
         self.is_sdxl = is_sdxl
+        self.is_ssd = is_ssd
         self.is_v2 = is_v2
         self.is_merged_in = False
         self.is_lorm = is_lorm
@@ -357,14 +360,25 @@ class ToolkitNetworkMixin:
         self.can_merge_in = not is_lorm
 
     def get_keymap(self: Network):
-        if self.is_sdxl:
+        use_weight_mapping = False
+
+        if self.is_ssd:
+            keymap_tail = 'ssd'
+            use_weight_mapping = True
+        elif self.is_sdxl:
             keymap_tail = 'sdxl'
         elif self.is_v2:
             keymap_tail = 'sd2'
         else:
             keymap_tail = 'sd1'
+            # todo double check this
+            use_weight_mapping = True
+
         # load keymap
         keymap_name = f"stable_diffusion_locon_{keymap_tail}.json"
+        if use_weight_mapping:
+            keymap_name = f"stable_diffusion_{keymap_tail}.json"
+
         keymap_path = os.path.join(KEYMAPS_ROOT, keymap_name)
 
         keymap = None
@@ -373,6 +387,10 @@ class ToolkitNetworkMixin:
             with open(keymap_path, 'r') as f:
                 keymap = json.load(f)['ldm_diffusers_keymap']
 
+        if use_weight_mapping and keymap is not None:
+            # get keymap from weights
+            keymap = get_lora_keymap_from_model_keymap(keymap)
+
         return keymap
 
     def save_weights(

toolkit/saving.py

@@ -206,6 +206,7 @@ def load_t2i_model(
 
 IP_ADAPTER_MODULES = ['image_proj', 'ip_adapter']
 
+
 def save_ip_adapter_from_diffusers(
         combined_state_dict: 'OrderedDict',
         output_file: str,
@@ -241,3 +242,58 @@ def load_ip_adapter_model(
         return combined_state_dict
     else:
         return torch.load(path_to_file, map_location=device)
+
+
+def get_lora_keymap_from_model_keymap(model_keymap: 'OrderedDict') -> 'OrderedDict':
+    lora_keymap = OrderedDict()
+
+    # see if we have dual text encoders " a key that starts with conditioner.embedders.1
+    has_dual_text_encoders = False
+    for key in model_keymap:
+        if key.startswith('conditioner.embedders.1'):
+            has_dual_text_encoders = True
+            break
+
+    # map through the keys and values
+    for key, value in model_keymap.items():
+        # ignore bias weights
+        if key.endswith('bias'):
+            continue
+        if key.endswith('.weight'):
+            # remove the .weight
+            key = key[:-7]
+        if value.endswith(".weight"):
+            # remove the .weight
+            value = value[:-7]
+
+        # unet for all
+        key = key.replace('model.diffusion_model', 'lora_unet')
+        if value.startswith('unet'):
+            value = f"lora_{value}"
+
+        # text encoder
+        if has_dual_text_encoders:
+            key = key.replace('conditioner.embedders.0', 'lora_te1')
+            key = key.replace('conditioner.embedders.1', 'lora_te2')
+            if value.startswith('te0') or value.startswith('te1'):
+                value = f"lora_{value}"
+            value.replace('lora_te1', 'lora_te2')
+            value.replace('lora_te0', 'lora_te1')
+
+        key = key.replace('cond_stage_model.transformer', 'lora_te')
+
+        if value.startswith('te_'):
+            value = f"lora_{value}"
+
+        # replace periods with underscores
+        key = key.replace('.', '_')
+        value = value.replace('.', '_')
+
+        # add all the weights
+        lora_keymap[f"{key}.lora_down.weight"] = f"{value}.lora_down.weight"
+        lora_keymap[f"{key}.lora_down.bias"] = f"{value}.lora_down.bias"
+        lora_keymap[f"{key}.lora_up.weight"] = f"{value}.lora_up.weight"
+        lora_keymap[f"{key}.lora_up.bias"] = f"{value}.lora_up.bias"
+        lora_keymap[f"{key}.alpha"] = f"{value}.alpha"
+
+    return lora_keymap

toolkit/stable_diffusion_model.py

@@ -35,7 +35,7 @@ from toolkit.pipelines import CustomStableDiffusionXLPipeline, CustomStableDiffu
     StableDiffusionKDiffusionXLPipeline, StableDiffusionXLRefinerPipeline
 from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, T2IAdapter, DDPMScheduler, \
     StableDiffusionXLAdapterPipeline, StableDiffusionAdapterPipeline, DiffusionPipeline, \
-    StableDiffusionXLImg2ImgPipeline
+    StableDiffusionXLImg2ImgPipeline, LCMScheduler
 import diffusers
 from diffusers import \
     AutoencoderKL, \
@@ -279,6 +279,20 @@ class StableDiffusion:
         self.load_refiner()
         self.is_loaded = True
 
+    def te_train(self):
+        if isinstance(self.text_encoder, list):
+            for te in self.text_encoder:
+                te.train()
+        else:
+            self.text_encoder.train()
+
+    def te_eval(self):
+        if isinstance(self.text_encoder, list):
+            for te in self.text_encoder:
+                te.eval()
+        else:
+            self.text_encoder.eval()
+
     def load_refiner(self):
         # for now, we are just going to rely on the TE from the base model
         # which is TE2 for SDXL and TE for SD (no refiner currently)
@@ -721,6 +735,7 @@ class StableDiffusion:
             add_time_ids=None,
             conditional_embeddings: Union[PromptEmbeds, None] = None,
             unconditional_embeddings: Union[PromptEmbeds, None] = None,
+            is_input_scaled=True,
             **kwargs,
     ):
         with torch.no_grad():
@@ -764,6 +779,8 @@ class StableDiffusion:
 
 
         def scale_model_input(model_input, timestep_tensor):
+            if is_input_scaled:
+                return model_input
             mi_chunks = torch.chunk(model_input, model_input.shape[0], dim=0)
             timestep_chunks = torch.chunk(timestep_tensor, timestep_tensor.shape[0], dim=0)
             out_chunks = []
@@ -859,7 +876,7 @@ class StableDiffusion:
 
                 # predict the noise residual
                 noise_pred = self.unet(
-                    latent_model_input,
+                    latent_model_input.to(self.device_torch, self.torch_dtype),
                     timestep,
                     encoder_hidden_states=text_embeddings.text_embeds,
                     added_cond_kwargs=added_cond_kwargs,
@@ -903,7 +920,7 @@ class StableDiffusion:
 
             # predict the noise residual
             noise_pred = self.unet(
-                latent_model_input,
+                latent_model_input.to(self.device_torch, self.torch_dtype),
                 timestep,
                 encoder_hidden_states=text_embeddings.text_embeds,
                 **kwargs,
@@ -924,6 +941,15 @@ class StableDiffusion:
         return noise_pred
 
     def step_scheduler(self, model_input, latent_input, timestep_tensor):
+        # // sometimes they are on the wrong device, no idea why
+        if isinstance(self.noise_scheduler, DDPMScheduler) or isinstance(self.noise_scheduler, LCMScheduler):
+            try:
+                self.noise_scheduler.betas = self.noise_scheduler.betas.to(self.device_torch)
+                self.noise_scheduler.alphas = self.noise_scheduler.alphas.to(self.device_torch)
+                self.noise_scheduler.alphas_cumprod = self.noise_scheduler.alphas_cumprod.to(self.device_torch)
+            except Exception as e:
+                pass
+
         mi_chunks = torch.chunk(model_input, model_input.shape[0], dim=0)
         latent_chunks = torch.chunk(latent_input, latent_input.shape[0], dim=0)
         timestep_chunks = torch.chunk(timestep_tensor, timestep_tensor.shape[0], dim=0)
@@ -955,10 +981,12 @@ class StableDiffusion:
             add_time_ids=None,
             bleed_ratio: float = 0.5,
             bleed_latents: torch.FloatTensor = None,
+            is_input_scaled=False,
             **kwargs,
     ):
         timesteps_to_run = self.noise_scheduler.timesteps[start_timesteps:total_timesteps]
 
+
         for timestep in tqdm(timesteps_to_run, leave=False):
             timestep = timestep.unsqueeze_(0)
             noise_pred = self.predict_noise(
@@ -967,6 +995,7 @@ class StableDiffusion:
                 timestep,
                 guidance_scale=guidance_scale,
                 add_time_ids=add_time_ids,
+                is_input_scaled=is_input_scaled,
                 **kwargs,
             )
             # some schedulers need to run separately, so do that. (euler for example)
@@ -977,6 +1006,9 @@ class StableDiffusion:
             if bleed_latents is not None and timestep != self.noise_scheduler.timesteps[-1]:
                 latents = (latents * (1 - bleed_ratio)) + (bleed_latents * bleed_ratio)
 
+            # only skip first scaling
+            is_input_scaled = False
+
         # return latents_steps
         return latents