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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, def txt2img(self,prompt,outdir=None,batch_size=None,iterations=None,
steps=None,seed=None,grid=None,individual=None,width=None,height=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 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],...] 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) totalWeight = sum(weights)
# normalize each "sub prompt" and add it # normalize each "sub prompt" and add it
for i in range(0,len(subprompts)): 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) c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight)
else: # just standard 1 prompt else: # just standard 1 prompt
c = model.get_learned_conditioning(prompts) 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. # 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, 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, 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 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],...] 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) totalWeight = sum(weights)
# normalize each "sub prompt" and add it # normalize each "sub prompt" and add it
for i in range(0,len(subprompts)): 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) c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight)
else: # just standard 1 prompt else: # just standard 1 prompt
c = model.get_learned_conditioning(prompts) 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','--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('-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('-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 return parser
if readline_available: if readline_available: