diff --git a/README.md b/README.md
index 38cb46f681..6adce59e9d 100644
--- a/README.md
+++ b/README.md
@@ -23,6 +23,7 @@ text-to-image generator. This fork supports:
3. A basic Web interface that allows you to run a local web server for
generating images in your browser.
+
4. A notebook for running the code on Google Colab.
5. Upscaling and face fixing using the optional ESRGAN and GFPGAN
@@ -30,7 +31,11 @@ text-to-image generator. This fork supports:
6. Weighted subprompts for prompt tuning.
-7. Textual inversion for customization of the prompt language and images.
+7. [Image variations](Variations.md) which allow you to systematically
+generate variations of an image you like and combine two or more
+images together to combine the best features of both.
+
+8. Textual inversion for customization of the prompt language and images.
8. ...and more!
diff --git a/VARIATIONS.md b/VARIATIONS.md
new file mode 100644
index 0000000000..c0699909a1
--- /dev/null
+++ b/VARIATIONS.md
@@ -0,0 +1,113 @@
+# Cheat Sheat for Generating Variations
+
+Release 1.13 of SD-Dream adds support for image variations. There are two things that you can do:
+
+1. Generate a series of systematic variations of an image, given a
+prompt. The amount of variation from one image to the next can be
+controlled.
+
+2. Given two or more variations that you like, you can combine them in
+a weighted fashion
+
+This cheat sheet provides a quick guide for how this works in
+practice, using variations to create the desired image of Xena,
+Warrior Princess.
+
+## Step 1 -- find a base image that you like
+
+The prompt we will use throughout is "lucy lawless as xena, warrior
+princess, character portrait, high resolution." This will be indicated
+as "prompt" in the examples below.
+
+First we let SD create a series of images in the usual way, in this case
+requesting six iterations:
+
+~~~
+dream> lucy lawless as xena, warrior princess, character portrait, high resolution -n6
+...
+Outputs:
+./outputs/Xena/000001.1579445059.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1579445059
+./outputs/Xena/000001.1880768722.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1880768722
+./outputs/Xena/000001.332057179.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S332057179
+./outputs/Xena/000001.2224800325.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S2224800325
+./outputs/Xena/000001.465250761.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S465250761
+./outputs/Xena/000001.3357757885.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S3357757885
+~~~
+
+The one with seed 3357757885 looks nice:
+
+
+
+Let's try to generate some variations. Using the same seed, we pass
+the argument -v0.1 (or --variant_amount), which generates a series of
+variations each differing by a variation amount of 0.2. This number
+ranges from 0 to 1.0, with higher numbers being larger amounts of
+variation.
+
+~~~
+dream> "prompt" -n6 -S3357757885 -v0.2
+...
+Outputs:
+./outputs/Xena/000002.784039624.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 784039624,0.2 -S3357757885
+./outputs/Xena/000002.3647897225.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225,0.2 -S3357757885
+./outputs/Xena/000002.917731034.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 917731034,0.2 -S3357757885
+./outputs/Xena/000002.4116285959.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 4116285959,0.2 -S3357757885
+./outputs/Xena/000002.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1614299449,0.2 -S3357757885
+./outputs/Xena/000002.1335553075.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1335553075,0.2 -S3357757885
+~~~
+
+Note that the output for each image has a -V option giving the
+"variant subseed" for that image, consisting of a seed followed by the
+variation amount used to generate it.
+
+This gives us a series of closely-related variations, including the
+two shown here.
+
+
+
+
+
+I like the expression on Xena's face in the first one (subseed
+3647897225), and the armor on her shoulder in the second one (subseed
+1614299449). Can we combine them to get the best of both worlds?
+
+We combine the two variations using -V (--with_variations). Again, we
+must provide the seed for the originally-chosen image in order for
+this to work.
+
+~~~
+dream> "prompt" -S3357757885 -V3647897225,0.1;1614299449,0.1
+Outputs:
+./outputs/Xena/000003.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225,0.1;1614299449,0.1 -S3357757885
+~~~
+
+Here we are providing equal weights (0.1 and 0.1) for both the
+subseeds. The resulting image is close, but not exactly what I
+wanted:
+
+
+
+We could either try combining the images with different weights, or we
+can generate more variations around the almost-but-not-quite image. We
+do the latter, using both the -V (combining) and -v (variation
+strength) options. Note that we use -n6 to generate 6 variations:
+
+~~~~
+dream> "prompt" -S3357757885 -V3647897225,0.1;1614299449,0.1 -v0.05 -n6
+Outputs:
+./outputs/Xena/000004.3279757577.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225,0.1;1614299449,0.1;3279757577,0.05 -S3357757885
+./outputs/Xena/000004.2853129515.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225,0.1;1614299449,0.1;2853129515,0.05 -S3357757885
+./outputs/Xena/000004.3747154981.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225,0.1;1614299449,0.1;3747154981,0.05 -S3357757885
+./outputs/Xena/000004.2664260391.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225,0.1;1614299449,0.1;2664260391,0.05 -S3357757885
+./outputs/Xena/000004.1642517170.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225,0.1;1614299449,0.1;1642517170,0.05 -S3357757885
+./outputs/Xena/000004.2183375608.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225,0.1;1614299449,0.1;2183375608,0.05 -S3357757885
+~~~
+
+This produces six images, all slight variations on the combination of
+the chosen two images. Here's the one I like best:
+
+
+
+As you can see, this is a very powerful too, which when combined with
+subprompt weighting, gives you great control over the content and
+quality of your generated images.
\ No newline at end of file
diff --git a/ldm/dream/pngwriter.py b/ldm/dream/pngwriter.py
index 8dfc09236a..1e86eb8fbc 100644
--- a/ldm/dream/pngwriter.py
+++ b/ldm/dream/pngwriter.py
@@ -69,6 +69,11 @@ class PromptFormatter:
switches.append(f'-G{opt.gfpgan_strength}')
if opt.upscale:
switches.append(f'-U {" ".join([str(u) for u in opt.upscale])}')
+ if opt.variation_amount > 0:
+ switches.append(f'-v {opt.variation_amount}')
+ if opt.with_variations:
+ formatted_variations = ';'.join(f'{seed},{weight}' for seed, weight in opt.with_variations)
+ switches.append(f'-V {formatted_variations}')
if t2i.full_precision:
switches.append('-F')
return ' '.join(switches)
diff --git a/ldm/models/diffusion/ksampler.py b/ldm/models/diffusion/ksampler.py
index 7e3f40883d..0f6814940e 100644
--- a/ldm/models/diffusion/ksampler.py
+++ b/ldm/models/diffusion/ksampler.py
@@ -66,8 +66,8 @@ class KSampler(object):
img_callback(k_callback_values['x'], k_callback_values['i'])
sigmas = self.model.get_sigmas(S)
- if x_T:
- x = x_T
+ if x_T is not None:
+ x = x_T * sigmas[0]
else:
x = (
torch.randn([batch_size, *shape], device=self.device)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 10720a7483..bfe2c99cc4 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -226,6 +226,8 @@ class T2I:
upscale = None,
sampler_name = None,
log_tokenization= False,
+ with_variations = None,
+ variation_amount = 0.0,
**args,
): # eat up additional cruft
"""
@@ -244,6 +246,8 @@ class T2I:
ddim_eta // image randomness (eta=0.0 means the same seed always produces the same image)
step_callback // a function or method that will be called each step
image_callback // a function or method that will be called each time an image is generated
+ with_variations // a weighted list [(seed_1, weight_1), (seed_2, weight_2), ...] of variations which should be applied before doing any generation
+ variation_amount // optional 0-1 value to slerp from -S noise to random noise (allows variations on an image)
To use the step callback, define a function that receives two arguments:
- Image GPU data
@@ -262,7 +266,6 @@ class T2I:
"""
# TODO: convert this into a getattr() loop
steps = steps or self.steps
- seed = seed or self.seed
width = width or self.width
height = height or self.height
cfg_scale = cfg_scale or self.cfg_scale
@@ -270,6 +273,7 @@ class T2I:
iterations = iterations or self.iterations
strength = strength or self.strength
self.log_tokenization = log_tokenization
+ with_variations = [] if with_variations is None else with_variations
model = (
self.load_model()
@@ -278,7 +282,20 @@ class T2I:
assert (
0.0 <= strength <= 1.0
), 'can only work with strength in [0.0, 1.0]'
+ assert (
+ 0.0 <= variation_amount <= 1.0
+ ), '-v --variation_amount must be in [0.0, 1.0]'
+ if len(with_variations) > 0:
+ assert seed is not None,\
+ 'seed must be specified when using with_variations'
+ if variation_amount == 0.0:
+ assert iterations == 1,\
+ 'when using --with_variations, multiple iterations are only possible when using --variation_amount'
+ assert all(0 <= weight <= 1 for _, weight in with_variations),\
+ f'variation weights must be in [0.0, 1.0]: got {[weight for _, weight in with_variations]}'
+
+ seed = seed or self.seed
width, height, _ = self._resolution_check(width, height, log=True)
# TODO: - Check if this is still necessary to run on M1 devices.
@@ -301,24 +318,25 @@ class T2I:
try:
if init_img:
assert os.path.exists(init_img), f'{init_img}: File not found'
- images_iterator = self._img2img(
+ init_image = self._load_img(init_img, width, height, fit).to(self.device)
+ with scope(self.device.type):
+ init_latent = self.model.get_first_stage_encoding(
+ self.model.encode_first_stage(init_image)
+ ) # move to latent space
+
+ make_image = self._img2img(
prompt,
- precision_scope=scope,
steps=steps,
cfg_scale=cfg_scale,
ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
- init_img=init_img,
- width=width,
- height=height,
- fit=fit,
+ init_latent=init_latent,
strength=strength,
callback=step_callback,
)
else:
- images_iterator = self._txt2img(
+ make_image = self._txt2img(
prompt,
- precision_scope=scope,
steps=steps,
cfg_scale=cfg_scale,
ddim_eta=ddim_eta,
@@ -328,11 +346,45 @@ class T2I:
callback=step_callback,
)
+ def get_noise():
+ if init_img:
+ return torch.randn_like(init_latent, device=self.device)
+ else:
+ return torch.randn([1,
+ self.latent_channels,
+ height // self.downsampling_factor,
+ width // self.downsampling_factor],
+ device=self.device)
+
+ initial_noise = None
+ if variation_amount > 0 or len(with_variations) > 0:
+ # use fixed initial noise plus random noise per iteration
+ seed_everything(seed)
+ initial_noise = get_noise()
+ for v_seed, v_weight in with_variations:
+ seed = v_seed
+ seed_everything(seed)
+ next_noise = get_noise()
+ initial_noise = self.slerp(v_weight, initial_noise, next_noise)
+ if variation_amount > 0:
+ random.seed() # reset RNG to an actually random state, so we can get a random seed for variations
+ seed = random.randrange(0,np.iinfo(np.uint32).max)
+
device_type = choose_autocast_device(self.device)
with scope(device_type), self.model.ema_scope():
for n in trange(iterations, desc='Generating'):
- seed_everything(seed)
- image = next(images_iterator)
+ x_T = None
+ if variation_amount > 0:
+ seed_everything(seed)
+ target_noise = get_noise()
+ x_T = self.slerp(variation_amount, initial_noise, target_noise)
+ elif initial_noise is not None:
+ # i.e. we specified particular variations
+ x_T = initial_noise
+ else:
+ seed_everything(seed)
+ # make_image will do the equivalent of get_noise itself
+ image = make_image(x_T)
results.append([image, seed])
if image_callback is not None:
image_callback(image, seed)
@@ -406,7 +458,6 @@ class T2I:
def _txt2img(
self,
prompt,
- precision_scope,
steps,
cfg_scale,
ddim_eta,
@@ -416,12 +467,13 @@ class T2I:
callback,
):
"""
- An infinite iterator of images from the prompt.
+ Returns a function returning an image derived from the prompt and the initial image
+ Return value depends on the seed at the time you call it
"""
sampler = self.sampler
- while True:
+ def make_image(x_T):
uc, c = self._get_uc_and_c(prompt, skip_normalize)
shape = [
self.latent_channels,
@@ -431,6 +483,7 @@ class T2I:
samples, _ = sampler.sample(
batch_size=1,
S=steps,
+ x_T=x_T,
conditioning=c,
shape=shape,
verbose=False,
@@ -439,26 +492,24 @@ class T2I:
eta=ddim_eta,
img_callback=callback
)
- yield self._sample_to_image(samples)
+ return self._sample_to_image(samples)
+ return make_image
@torch.no_grad()
def _img2img(
self,
prompt,
- precision_scope,
steps,
cfg_scale,
ddim_eta,
skip_normalize,
- init_img,
- width,
- height,
- fit,
+ init_latent,
strength,
callback, # Currently not implemented for img2img
):
"""
- An infinite iterator of images from the prompt and the initial image
+ Returns a function returning an image derived from the prompt and the initial image
+ Return value depends on the seed at the time you call it
"""
# PLMS sampler not supported yet, so ignore previous sampler
@@ -470,24 +521,20 @@ class T2I:
else:
sampler = self.sampler
- init_image = self._load_img(init_img, width, height,fit).to(self.device)
- with precision_scope(self.device.type):
- 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)
- while True:
+ def make_image(x_T):
uc, c = self._get_uc_and_c(prompt, skip_normalize)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(
- init_latent, torch.tensor([t_enc]).to(self.device)
+ init_latent,
+ torch.tensor([t_enc]).to(self.device),
+ noise=x_T
)
# decode it
samples = sampler.decode(
@@ -498,7 +545,8 @@ class T2I:
unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,
)
- yield self._sample_to_image(samples)
+ return self._sample_to_image(samples)
+ return make_image
# TODO: does this actually need to run every loop? does anything in it vary by random seed?
def _get_uc_and_c(self, prompt, skip_normalize):
@@ -513,8 +561,7 @@ class T2I:
# i dont know if this is correct.. but it works
c = torch.zeros_like(uc)
# normalize each "sub prompt" and add it
- for i in range(0, len(weighted_subprompts)):
- subprompt, weight = weighted_subprompts[i]
+ for subprompt, weight in weighted_subprompts:
self._log_tokenization(subprompt)
c = torch.add(
c,
@@ -619,7 +666,7 @@ class T2I:
print(
f'>> loaded input image of size {image.width}x{image.height} from {path}'
)
-
+
# The logic here is:
# 1. If "fit" is true, then the image will be fit into the bounding box defined
# by width and height. It will do this in a way that preserves the init image's
@@ -644,7 +691,7 @@ class T2I:
if resize_needed:
return InitImageResizer(image).resize(x,y)
return image
-
+
def _fit_image(self,image,max_dimensions):
w,h = max_dimensions
@@ -677,10 +724,10 @@ class T2I:
(?:\\\:|[^:])+ # match one or more non ':' characters or escaped colons '\:'
) # end 'prompt'
(?: # non-capture group
- :+ # match one or more ':' characters
+ :+ # match one or more ':' characters
(?P # capture group for 'weight'
-?\d+(?:\.\d+)? # match positive or negative integer or decimal number
- )? # end weight capture group, make optional
+ )? # end weight capture group, make optional
\s* # strip spaces after weight
| # OR
$ # else, if no ':' then match end of line
@@ -741,3 +788,41 @@ class T2I:
print(">> This input is larger than your defaults. If you run out of memory, please use a smaller image.")
return width, height, resize_needed
+
+
+ def slerp(self, t, v0, v1, DOT_THRESHOLD=0.9995):
+ '''
+ Spherical linear interpolation
+ Args:
+ t (float/np.ndarray): Float value between 0.0 and 1.0
+ v0 (np.ndarray): Starting vector
+ v1 (np.ndarray): Final vector
+ DOT_THRESHOLD (float): Threshold for considering the two vectors as
+ colineal. Not recommended to alter this.
+ Returns:
+ v2 (np.ndarray): Interpolation vector between v0 and v1
+ '''
+ inputs_are_torch = False
+ if not isinstance(v0, np.ndarray):
+ inputs_are_torch = True
+ v0 = v0.detach().cpu().numpy()
+ if not isinstance(v1, np.ndarray):
+ inputs_are_torch = True
+ v1 = v1.detach().cpu().numpy()
+
+ dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+ if np.abs(dot) > DOT_THRESHOLD:
+ v2 = (1 - t) * v0 + t * v1
+ else:
+ theta_0 = np.arccos(dot)
+ sin_theta_0 = np.sin(theta_0)
+ theta_t = theta_0 * t
+ sin_theta_t = np.sin(theta_t)
+ s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+ s1 = sin_theta_t / sin_theta_0
+ v2 = s0 * v0 + s1 * v1
+
+ if inputs_are_torch:
+ v2 = torch.from_numpy(v2).to(self.device)
+
+ return v2
diff --git a/scripts/dream.py b/scripts/dream.py
index 1535ac386c..54d6f86e77 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -181,9 +181,32 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
print(f'No previous seed at position {opt.seed} found')
opt.seed = None
- normalized_prompt = PromptFormatter(t2i, opt).normalize_prompt()
do_grid = opt.grid or t2i.grid
- individual_images = not do_grid
+
+ if opt.with_variations is not None:
+ # shotgun parsing, woo
+ parts = []
+ broken = False # python doesn't have labeled loops...
+ for part in opt.with_variations.split(';'):
+ seed_and_weight = part.split(',')
+ if len(seed_and_weight) != 2:
+ print(f'could not parse with_variation part "{part}"')
+ broken = True
+ break
+ try:
+ seed = int(seed_and_weight[0])
+ weight = float(seed_and_weight[1])
+ except ValueError:
+ print(f'could not parse with_variation part "{part}"')
+ broken = True
+ break
+ parts.append([seed, weight])
+ if broken:
+ continue
+ if len(parts) > 0:
+ opt.with_variations = parts
+ else:
+ opt.with_variations = None
if opt.outdir:
if not os.path.exists(opt.outdir):
@@ -211,7 +234,7 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
file_writer = PngWriter(current_outdir)
prefix = file_writer.unique_prefix()
seeds = set()
- results = []
+ results = [] # list of filename, prompt pairs
grid_images = dict() # seed -> Image, only used if `do_grid`
def image_writer(image, seed, upscaled=False):
if do_grid:
@@ -221,10 +244,26 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
filename = f'{prefix}.{seed}.postprocessed.png'
else:
filename = f'{prefix}.{seed}.png'
- path = file_writer.save_image_and_prompt_to_png(image, f'{normalized_prompt} -S{seed}', filename)
+ if opt.variation_amount > 0:
+ iter_opt = argparse.Namespace(**vars(opt)) # copy
+ this_variation = [[seed, opt.variation_amount]]
+ if opt.with_variations is None:
+ iter_opt.with_variations = this_variation
+ else:
+ iter_opt.with_variations = opt.with_variations + this_variation
+ iter_opt.variation_amount = 0
+ normalized_prompt = PromptFormatter(t2i, iter_opt).normalize_prompt()
+ metadata_prompt = f'{normalized_prompt} -S{iter_opt.seed}'
+ elif opt.with_variations is not None:
+ normalized_prompt = PromptFormatter(t2i, opt).normalize_prompt()
+ metadata_prompt = f'{normalized_prompt} -S{opt.seed}' # use the original seed - the per-iteration value is the last variation-seed
+ else:
+ normalized_prompt = PromptFormatter(t2i, opt).normalize_prompt()
+ metadata_prompt = f'{normalized_prompt} -S{seed}'
+ path = file_writer.save_image_and_prompt_to_png(image, metadata_prompt, filename)
if (not upscaled) or opt.save_original:
# only append to results if we didn't overwrite an earlier output
- results.append([path, seed])
+ results.append([path, metadata_prompt])
seeds.add(seed)
@@ -235,11 +274,12 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
first_seed = next(iter(seeds))
filename = f'{prefix}.{first_seed}.png'
# TODO better metadata for grid images
- metadata_prompt = f'{normalized_prompt} -S{first_seed}'
+ normalized_prompt = PromptFormatter(t2i, opt).normalize_prompt()
+ metadata_prompt = f'{normalized_prompt} -S{first_seed} --grid -N{len(grid_images)}'
path = file_writer.save_image_and_prompt_to_png(
grid_img, metadata_prompt, filename
)
- results = [[path, seeds]]
+ results = [[path, metadata_prompt]]
last_seeds = list(seeds)
@@ -253,7 +293,7 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
print('Outputs:')
log_path = os.path.join(current_outdir, 'dream_log.txt')
- write_log_message(normalized_prompt, results, log_path)
+ write_log_message(results, log_path)
print('goodbye!')
@@ -291,9 +331,9 @@ def dream_server_loop(t2i):
dream_server.server_close()
-def write_log_message(prompt, results, log_path):
+def write_log_message(results, log_path):
"""logs the name of the output image, prompt, and prompt args to the terminal and log file"""
- log_lines = [f'{r[0]}: {prompt} -S{r[1]}\n' for r in results]
+ log_lines = [f'{path}: {prompt}\n' for path, prompt in results]
print(*log_lines, sep='')
with open(log_path, 'a', encoding='utf-8') as file:
@@ -546,6 +586,20 @@ def create_cmd_parser():
action='store_true',
help='shows how the prompt is split into tokens'
)
+ parser.add_argument(
+ '-v',
+ '--variation_amount',
+ default=0.0,
+ type=float,
+ help='If > 0, generates variations on the initial seed instead of random seeds per iteration. Must be between 0 and 1. Higher values will be more different.'
+ )
+ parser.add_argument(
+ '-V',
+ '--with_variations',
+ default=None,
+ type=str,
+ help='list of variations to apply, in the format `seed,weight;seed,weight;...'
+ )
return parser
diff --git a/static/variation_walkthru/000001.3357757885.png b/static/variation_walkthru/000001.3357757885.png
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diff --git a/static/variation_walkthru/000002.1614299449.png b/static/variation_walkthru/000002.1614299449.png
new file mode 100644
index 0000000000..0db167ae6c
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diff --git a/static/variation_walkthru/000002.3647897225.png b/static/variation_walkthru/000002.3647897225.png
new file mode 100644
index 0000000000..7fe1f29227
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diff --git a/static/variation_walkthru/000003.1614299449.png b/static/variation_walkthru/000003.1614299449.png
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diff --git a/static/variation_walkthru/000004.3747154981.png b/static/variation_walkthru/000004.3747154981.png
new file mode 100644
index 0000000000..e6ac5f3bc9
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