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prompt weighting not working

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This commit is contained in:
Lincoln Stein 2022-08-23 01:23:14 -04:00
commit 11c0df07b7
2 changed files with 90 additions and 14 deletions
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103
ldm/simplet2i.py
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@ -143,7 +143,7 @@ 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],...]
@ -189,6 +189,7 @@ The vast majority of these arguments default to reasonable values.
image_count = 0
tic = time.time()
# Gawd. Too many levels of indent here. Need to refactor into smaller routines!
try:
with torch.no_grad():
with precision_scope("cuda"):
@ -202,7 +203,23 @@ The vast majority of these arguments default to reasonable values.
uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
# weighted sub-prompts
subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
if len(subprompts) > 1:
# i dont know if this is correct.. but it works
c = torch.zeros_like(uc)
# get total weight for normalizing
totalWeight = sum(weights)
# normalize each "sub prompt" and add it
for i in range(0,len(subprompts)):
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)
shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
samples_ddim, _ = sampler.sample(S=steps,
conditioning=c,
@ -220,24 +237,22 @@ The vast majority of these arguments default to reasonable values.
if not grid:
for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
filename = self._unique_filename(outdir,previousname=filename,
seed=seed,isbatch=(batch_size>1))
assert not os.path.exists(filename)
filename = os.path.join(outdir, f"{base_count:05}.png")
Image.fromarray(x_sample.astype(np.uint8)).save(filename)
images.append([filename,seed])
base_count += 1
else:
all_samples.append(x_samples_ddim)
seeds.append(seed)
image_count += 1
seed = self._new_seed()
if grid:
images = self._make_grid(samples=all_samples,
seeds=seeds,
batch_size=batch_size,
iterations=iterations,
outdir=outdir)
except KeyboardInterrupt:
print('*interrupted*')
print('Partial results will be returned; if --grid was requested, nothing will be returned.')
@ -252,7 +267,7 @@ 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],...]
@ -314,9 +329,8 @@ The vast majority of these arguments default to reasonable values.
seeds = list()
filename = None
image_count = 0 # actual number of iterations performed
tic = time.time()
# Gawd. Too many levels of indent here. Need to refactor into smaller routines!
try:
with torch.no_grad():
with precision_scope("cuda"):
@ -330,7 +344,22 @@ The vast majority of these arguments default to reasonable values.
uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
c = model.get_learned_conditioning(prompts)
# weighted sub-prompts
subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
if len(subprompts) > 1:
# i dont know if this is correct.. but it works
c = torch.zeros_like(uc)
# get total weight for normalizing
totalWeight = sum(weights)
# normalize each "sub prompt" and add it
for i in range(0,len(subprompts)):
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)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
@ -344,14 +373,14 @@ The vast majority of these arguments default to reasonable values.
if not grid:
for x_sample in x_samples:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
filename = self._unique_filename(outdir,filename,seed=seed,isbatch=(batch_size>1))
assert not os.path.exists(filename)
filename = os.path.join(outdir, f"{base_count:05}.png")
Image.fromarray(x_sample.astype(np.uint8)).save(filename)
images.append([filename,seed])
base_count += 1
else:
all_samples.append(x_samples)
seeds.append(seed)
image_count += 1
image_count +=1
seed = self._new_seed()
if grid:
images = self._make_grid(samples=all_samples,
@ -361,6 +390,7 @@ The vast majority of these arguments default to reasonable values.
outdir=outdir)
except KeyboardInterrupt:
print('*interrupted*')
print('Partial results will be returned; if --grid was requested, nothing will be returned.')
except RuntimeError as e:
print(str(e))
@ -481,3 +511,48 @@ The vast majority of these arguments default to reasonable values.
filename = f'{basecount:06}.{seed}.{series:02}.png'
finished = not os.path.exists(os.path.join(outdir,filename))
return os.path.join(outdir,filename)
def _split_weighted_subprompts(text):
"""
grabs all text up to the first occurrence of ':'
uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
if ':' has no value defined, defaults to 1.0
repeats until no text remaining
"""
remaining = len(text)
prompts = []
weights = []
while remaining > 0:
if ":" in text:
idx = text.index(":") # first occurrence from start
# grab up to index as sub-prompt
prompt = text[:idx]
remaining -= idx
# remove from main text
text = text[idx+1:]
# find value for weight
if " " in text:
idx = text.index(" ") # first occurence
else: # no space, read to end
idx = len(text)
if idx != 0:
try:
weight = float(text[:idx])
except: # couldn't treat as float
print(f"Warning: '{text[:idx]}' is not a value, are you missing a space?")
weight = 1.0
else: # no value found
weight = 1.0
# remove from main text
remaining -= idx
text = text[idx+1:]
# append the sub-prompt and its weight
prompts.append(prompt)
weights.append(weight)
else: # no : found
if len(text) > 0: # there is still text though
# take remainder as weight 1
prompts.append(text)
weights.append(1.0)
remaining = 0
return prompts, weights

1
scripts/dream.py
View File

@ -285,6 +285,7 @@ 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: