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added optional parameter to skip subprompt weight normalization

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allows more control when fine-tuning
This commit is contained in:
xra 2022-08-23 00:03:32 +09:00
parent a3632f5b4f
commit e4eb775b63
2 changed files with 9 additions and 4 deletions
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12
ldm/simplet2i.py
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@ -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)

1
scripts/dream.py
View File

@ -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: