From e4eb775b6388c284ab307fa6a74f17aac93c0853 Mon Sep 17 00:00:00 2001
From: xra <mail@xra.dev>
Date: Tue, 23 Aug 2022 00:03:32 +0900
Subject: [PATCH] added optional parameter to skip subprompt weight
 normalization

allows more control when fine-tuning
---
 ldm/simplet2i.py | 12 ++++++++----
 scripts/dream.py |  1 +
 2 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 78b70000df..ab3195284a 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -142,7 +142,7 @@ The vast majority of these arguments default to reasonable values.
 
     def txt2img(self,prompt,outdir=None,batch_size=None,iterations=None,
                 steps=None,seed=None,grid=None,individual=None,width=None,height=None,
-                cfg_scale=None,ddim_eta=None,strength=None,init_img=None):
+                cfg_scale=None,ddim_eta=None,strength=None,init_img=None,skip_normalize=False):
         """
         Generate an image from the prompt, writing iteration images into the outdir
         The output is a list of lists in the format: [[filename1,seed1], [filename2,seed2],...]
@@ -210,7 +210,9 @@ The vast majority of these arguments default to reasonable values.
                                 totalWeight = sum(weights)
                                 # normalize each "sub prompt" and add it
                                 for i in range(0,len(subprompts)):
-                                    weight = weights[i] / totalWeight
+                                    weight = weights[i]
+                                    if not skip_normalize:
+                                        weight = weight / totalWeight
                                     c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight)
                             else: # just standard 1 prompt
                                 c = model.get_learned_conditioning(prompts)
@@ -257,7 +259,7 @@ The vast majority of these arguments default to reasonable values.
     # There is lots of shared code between this and txt2img and should be refactored.
     def img2img(self,prompt,outdir=None,init_img=None,batch_size=None,iterations=None,
                 steps=None,seed=None,grid=None,individual=None,width=None,height=None,
-                cfg_scale=None,ddim_eta=None,strength=None):
+                cfg_scale=None,ddim_eta=None,strength=None,skip_normalize=False):
         """
         Generate an image from the prompt and the initial image, writing iteration images into the outdir
         The output is a list of lists in the format: [[filename1,seed1], [filename2,seed2],...]
@@ -343,7 +345,9 @@ The vast majority of these arguments default to reasonable values.
                                 totalWeight = sum(weights)
                                 # normalize each "sub prompt" and add it
                                 for i in range(0,len(subprompts)):
-                                    weight = weights[i] / totalWeight
+                                    weight = weights[i]
+                                    if not skip_normalize:
+                                        weight = weight / totalWeight
                                     c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight)
                             else: # just standard 1 prompt
                                 c = model.get_learned_conditioning(prompts)
diff --git a/scripts/dream.py b/scripts/dream.py
index 4e18492b3d..2625b6027f 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -233,6 +233,7 @@ def create_cmd_parser():
     parser.add_argument('-i','--individual',action='store_true',help="generate individual files (default)")
     parser.add_argument('-I','--init_img',type=str,help="path to input image (supersedes width and height)")
     parser.add_argument('-f','--strength',default=0.75,type=float,help="strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely")
+    parser.add_argument('-x','--skip_normalize',action='store_true',help="skip subprompt weight normalization")
     return parser
 
 if readline_available: