Minor fixes

testing/merge_in_text_encoder_adapter.py

@@ -7,16 +7,21 @@ from safetensors.torch import load_file, save_file
 from collections import OrderedDict
 import json
 
-model_path = "/mnt/Models/stable-diffusion/models/stable-diffusion/Ostris/objective_reality_v2.safetensors"
+model_path = "/home/jaret/Dev/models/hf/kl-f16-d42_sd15_v01_000527000"
 te_path = "google/flan-t5-xl"
 te_aug_path = "/mnt/Train/out/ip_adapter/t5xx_sd15_v1/t5xx_sd15_v1_000032000.safetensors"
-output_path = "/home/jaret/Dev/models/hf/t5xl_sd15_v1"
+output_path = "/home/jaret/Dev/models/hf/kl-f16-d42_sd15_t5xl_raw"
 
 print("Loading te adapter")
 te_aug_sd = load_file(te_aug_path)
 
 print("Loading model")
-sd = StableDiffusionPipeline.from_single_file(model_path, torch_dtype=torch.float16)
+is_diffusers = (not os.path.exists(model_path)) or os.path.isdir(model_path)
+
+if is_diffusers:
+    sd = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
+else:
+    sd = StableDiffusionPipeline.from_single_file(model_path, torch_dtype=torch.float16)
 
 print("Loading Text Encoder")
 # Load the text encoder
@@ -74,6 +79,7 @@ for name in sd.unet.attn_processors.keys():
 
 
 print("Saving unmodified model")
+sd = sd.to("cpu", torch.float16)
 sd.save_pretrained(
     output_path,
     safe_serialization=True,

toolkit/config_modules.py

@@ -339,7 +339,7 @@ class ModelConfig:
         self.is_v2: bool = kwargs.get('is_v2', False)
         self.is_xl: bool = kwargs.get('is_xl', False)
         self.is_pixart: bool = kwargs.get('is_pixart', False)
-        self.is_pixart_sigma: bool = kwargs.get('is_pixart', False)
+        self.is_pixart_sigma: bool = kwargs.get('is_pixart_sigma', False)
         self.is_v3: bool = kwargs.get('is_v3', False)
         if self.is_pixart_sigma:
             self.is_pixart = True

toolkit/guidance.py

@@ -240,7 +240,7 @@ def get_direct_guidance_loss(
 
     noise_pred_uncond, noise_pred_cond = torch.chunk(prediction, 2, dim=0)
 
-    guidance_scale = 1.25
+    guidance_scale = 1.1
     guidance_pred = noise_pred_uncond + guidance_scale * (
             noise_pred_cond - noise_pred_uncond
     )
@@ -552,39 +552,17 @@ def get_guided_tnt(
         reduction="none"
     )
 
-    with torch.no_grad():
-        tnt_loss = this_loss - that_loss
-
-        # create a mask by scaling loss from 0 to mean to 1 to 0
-        # this will act to regularize unchanged areas to prior prediction
-        loss_min = tnt_loss.min(dim=1, keepdim=True)[0].min(dim=2, keepdim=True)[0].min(dim=3, keepdim=True)[0]
-        loss_mean = tnt_loss.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True)
-        mask = value_map(
-            torch.abs(tnt_loss),
-            loss_min,
-            loss_mean,
-            0.0,
-            1.0
-        ).clamp(0.0, 1.0).detach()
-
-        prior_mask = 1.0 - mask
-
-
-    this_loss = this_loss * mask
-    that_loss = that_loss * prior_mask
-
     this_loss = this_loss.mean([1, 2, 3])
-    that_loss = that_loss.mean([1, 2, 3])
+    # negative loss on that
+    that_loss = -that_loss.mean([1, 2, 3])
 
-    prior_loss = torch.nn.functional.mse_loss(
+    with torch.no_grad():
-        this_prediction.float(),
+        # match that loss with this loss so it is not a negative value and same scale
-        prior_pred.detach().float(),
+        that_loss_scaler = torch.abs(this_loss) / torch.abs(that_loss)
-        reduction="none"
-    )
-    prior_loss = prior_loss * prior_mask
-    prior_loss = prior_loss.mean([1, 2, 3])
 
-    loss = prior_loss + this_loss - that_loss
+    that_loss = that_loss * that_loss_scaler * 0.01
+
+    loss = this_loss + that_loss
 
     loss = loss.mean()