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Merge branch 'more-refactor' of https://github.com/bakkot/stable-diffusion into bakkot-more-refactor

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Lincoln Stein 2022-08-25 21:55:08 -04:00
commit e202441f0c
1 changed files with 112 additions and 155 deletions
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267
ldm/simplet2i.py
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@ -52,7 +52,7 @@ t2i = T2I(model = <path> // models/ldm/stable-diffusion-v1/model.ck
# do the slow model initialization
t2i.load_model()
# Do the fast inference & image generation. Any options passed here
# Do the fast inference & image generation. Any options passed here
# override the default values assigned during class initialization
# Will call load_model() if the model was not previously loaded and so
# may be slow at first.
@ -70,7 +70,7 @@ results = t2i.prompt2png(prompt = "an astronaut riding a horse",
outdir = "./outputs/,
iterations = 3,
init_img = "./sketches/horse+rider.png")
for row in results:
print(f'filename={row[0]}')
print(f'seed ={row[1]}')
@ -181,7 +181,7 @@ The vast majority of these arguments default to reasonable values.
outdir = kwargs.get('outdir','outputs/img-samples')
assert 'init_img' in kwargs,'call to img2img() must include the init_img argument'
return self.prompt2png(prompt,outdir,**kwargs)
def prompt2image(self,
# these are common
prompt,
@ -216,10 +216,10 @@ The vast majority of these arguments default to reasonable values.
strength // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely
ddim_eta // image randomness (eta=0.0 means the same seed always produces the same image)
variants // if >0, the 1st generated image will be passed back to img2img to generate the requested number of variants
callback // a function or method that will be called each time an image is generated
image_callback // a function or method that will be called each time an image is generated
To use the callback, define a function of method that receives two arguments, an Image object
and the seed. You can then do whatever you like with the image, including converting it to
and the seed. You can then do whatever you like with the image, including converting it to
different formats and manipulating it. For example:
def process_image(image,seed):
@ -249,116 +249,85 @@ The vast majority of these arguments default to reasonable values.
height = h
width = w
data = [batch_size * [prompt]]
scope = autocast if self.precision=="autocast" else nullcontext
tic = time.time()
if init_img:
assert os.path.exists(init_img),f'{init_img}: File not found'
results = self._img2img(prompt,
data=data,precision_scope=scope,
batch_size=batch_size,iterations=iterations,
steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
init_img=init_img,strength=strength,variants=variants,
callback=image_callback)
else:
results = self._txt2img(prompt,
data=data,precision_scope=scope,
batch_size=batch_size,iterations=iterations,
steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
width=width,height=height,
callback=image_callback)
toc = time.time()
print(f'{len(results)} images generated in',"%4.2fs"% (toc-tic))
return results
@torch.no_grad()
def _txt2img(self,prompt,
data,precision_scope,
batch_size,iterations,
steps,seed,cfg_scale,ddim_eta,
skip_normalize,
width,height,
callback): # the callback is called each time a new Image is generated
"""
Generate an image from the prompt, writing iteration images into the outdir
The output is a list of lists in the format: [[image1,seed1], [image2,seed2],...]
"""
tic = time.time()
results = list()
sampler = self.sampler
images = list()
image_count = 0
# Gawd. Too many levels of indent here. Need to refactor into smaller routines!
try:
with precision_scope(self.device.type), self.model.ema_scope():
all_samples = list()
if init_img:
assert os.path.exists(init_img),f'{init_img}: File not found'
images_iterator = self._img2img(prompt,
precision_scope=scope,
batch_size=batch_size,
steps=steps,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
init_img=init_img,strength=strength)
else:
images_iterator = self._txt2img(prompt,
precision_scope=scope,
batch_size=batch_size,
steps=steps,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
width=width,height=height)
with scope(self.device.type), self.model.ema_scope():
for n in trange(iterations, desc="Sampling"):
seed_everything(seed)
for prompts in tqdm(data, desc="data", dynamic_ncols=True):
uc = None
if cfg_scale != 1.0:
uc = self.model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(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,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
else: # just standard 1 prompt
c = self.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,
batch_size=batch_size,
shape=shape,
verbose=False,
unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,
eta=ddim_eta)
x_samples_ddim = self.model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
image = Image.fromarray(x_sample.astype(np.uint8))
images.append([image,seed])
if callback is not None:
callback(image,seed)
iter_images = next(images_iterator)
for image in iter_images:
results.append([image, seed])
if image_callback is not None:
image_callback(image,seed)
seed = self._new_seed()
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))
return images
toc = time.time()
print(f'{len(results)} images generated in',"%4.2fs"% (toc-tic))
return results
@torch.no_grad()
def _img2img(self,prompt,
data,precision_scope,
batch_size,iterations,
steps,seed,cfg_scale,ddim_eta,
def _txt2img(self,
prompt,
precision_scope,
batch_size,
steps,cfg_scale,ddim_eta,
skip_normalize,
init_img,strength,variants,
callback):
width,height):
"""
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: [[image,seed1], [image,seed2],...]
An infinite iterator of images from the prompt.
"""
sampler = self.sampler
while True:
uc, c = self._get_uc_and_c(prompt, batch_size, skip_normalize)
shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
samples, _ = sampler.sample(S=steps,
conditioning=c,
batch_size=batch_size,
shape=shape,
verbose=False,
unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,
eta=ddim_eta)
yield self._samples_to_images(samples)
@torch.no_grad()
def _img2img(self,
prompt,
precision_scope,
batch_size,
steps,cfg_scale,ddim_eta,
skip_normalize,
init_img,strength):
"""
An infinite iterator of images from the prompt and the initial image
"""
# PLMS sampler not supported yet, so ignore previous sampler
@ -374,62 +343,50 @@ The vast majority of these arguments default to reasonable values.
init_latent = self.model.get_first_stage_encoding(self.model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
t_enc = int(strength * steps)
# print(f"target t_enc is {t_enc} steps")
while True:
uc, c = self._get_uc_and_c(prompt, batch_size, skip_normalize)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
# decode it
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,)
yield self._samples_to_images(samples)
# TODO: does this actually need to run every loop? does anything in it vary by random seed?
def _get_uc_and_c(self, prompt, batch_size, skip_normalize):
uc = self.model.get_learned_conditioning(batch_size * [""])
# weighted sub-prompts
subprompts,weights = T2I._split_weighted_subprompts(prompt)
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, self.model.get_learned_conditioning(batch_size * [subprompts[i]]), alpha=weight)
else: # just standard 1 prompt
c = self.model.get_learned_conditioning(batch_size * [prompt])
return (uc, c)
def _samples_to_images(self, samples):
x_samples = self.model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
images = list()
try:
with precision_scope(self.device.type), self.model.ema_scope():
all_samples = list()
for n in trange(iterations, desc="Sampling"):
seed_everything(seed)
for prompts in tqdm(data, desc="data", dynamic_ncols=True):
uc = None
if cfg_scale != 1.0:
uc = self.model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(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,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
else: # just standard 1 prompt
c = self.model.get_learned_conditioning(prompts)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
# decode it
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,)
x_samples = self.model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
for x_sample in x_samples:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
image = Image.fromarray(x_sample.astype(np.uint8))
images.append([image,seed])
if callback is not None:
callback(image,seed)
seed = self._new_seed()
except KeyboardInterrupt:
print('*interrupted*')
print('Partial results will be returned; if --grid was requested, nothing will be returned.')
except RuntimeError as e:
print("Oops! A runtime error has occurred. If this is unexpected, please copy-and-paste this stack trace and post it as an Issue to http://github.com/lstein/stable-diffusion")
traceback.print_exc()
for x_sample in x_samples:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
image = Image.fromarray(x_sample.astype(np.uint8))
images.append(image)
return images
def _new_seed(self):
@ -476,7 +433,7 @@ The vast majority of these arguments default to reasonable values.
print(msg)
return self.model
def _load_model_from_config(self, config, ckpt):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
@ -507,7 +464,7 @@ The vast majority of these arguments default to reasonable values.
def _split_weighted_subprompts(text):
"""
grabs all text up to the first occurrence of ':'
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
@ -523,7 +480,7 @@ The vast majority of these arguments default to reasonable values.
remaining -= idx
# remove from main text
text = text[idx+1:]
# find value for weight
# find value for weight
if " " in text:
idx = text.index(" ") # first occurence
else: # no space, read to end