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Fix batch handling, attempt to deal with non-4 dimensional latents#6

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Fix batch handling, attempt to deal with non-4 dimensional latents #6

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22 changes: 17 additions & 5 deletions custom_samplers.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -11,16 +11,28 @@
def matrix_batch_slerp(t, tn, w):
dots = torch.mul(tn.unsqueeze(0), tn.unsqueeze(1)).sum(dim=[-1,-2], keepdim=True).clamp(min=-1.0 + EPSILON, max=1.0 - EPSILON)
mask = ~torch.eye(tn.shape[0], dtype=torch.bool, device=tn.device)
A, B, C, D, E = dots.shape
dots = dots[mask].reshape(A, B - 1, C, D, E)
A, B, *rest = dots.shape
rest_1s = (1,) * len(rest)
dots = dots[mask].reshape(A, B - 1, *rest)
omegas = dots.acos()
sin_omega = omegas.sin()
res = t.unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * omegas) / sin_omega
res = t.unsqueeze(1).repeat(1, B - 1, *rest_1s) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, *rest_1s) * omegas) / sin_omega
res = res.sum(dim=[0, 1]).unsqueeze(0)
return res

@torch.no_grad()
def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
orig_shape = t.shape[1:]
if t.shape[1] == 1 and t.ndim == 4:
t = t.squeeze(1)
elif t.ndim < 3:
raise ValueError("Can't handle input with dimensions < 3")
else:
t = t.reshape(t.shape[0], -1, *t.shape[-2 if t.ndim > 3 else -1:])
if t.ndim == 3:
t = t.unsqueeze(-1)
if uncond is not None:
uncond = uncond.reshape(1, *t.shape[1:])
norm = torch.linalg.matrix_norm(t, keepdim=True)
n = t.shape[0]
tn = t.div(norm)
Expand All@@ -43,7 +55,7 @@ def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
else:
res = (t * distances).sum(dim=0).unsqueeze(0)
res = res.div(torch.linalg.matrix_norm(res, keepdim=True)).mul(norm.mul(distances).sum(dim=0).unsqueeze(0))
return res
return res if res.shape == orig_shape else res.reshape(orig_shape)

@torch.no_grad()
def normalize_adjust(a,b,strength=1):
Expand DownExpand Up@@ -127,7 +139,7 @@ def post_cfg_function(args):
d = (new_d + d) / 2
else:
u = uncond if (use_negative and uncond is not None and torch.any(uncond)) else None
d = fast_distance_weights(torch.stack(x_n).squeeze(1), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
d = fast_distance_weights(torch.stack(x_n), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
if sharpen or perp_step:
if sharpen and d_prev is not None:
d = normalize_adjust(d, d_prev, 1)
Expand Down
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
Fix batch handling, attempt to deal with non-4 dimensional latents by blepping · Pull Request #6 · Extraltodeus/DistanceSampler · GitHub
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Fix batch handling, attempt to deal with non-4 dimensional latents#6

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Jun 16, 2025
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Fix batch handling, attempt to deal with non-4 dimensional latents
#6
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22 changes: 17 additions & 5 deletions
custom_samplers.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -11,16 +11,28 @@
def matrix_batch_slerp(t, tn, w):
 dots = torch.mul(tn.unsqueeze(0), tn.unsqueeze(1)).sum(dim=[-1,-2], keepdim=True).clamp(min=-1.0 + EPSILON, max=1.0 - EPSILON)
 mask = ~torch.eye(tn.shape[0], dtype=torch.bool, device=tn.device)
 A, B, C, D, E = dots.shape
 dots = dots[mask].reshape(A, B - 1, C, D, E)
 A, B, *rest = dots.shape
 rest_1s = (1,) * len(rest)
 dots = dots[mask].reshape(A, B - 1, *rest)
 omegas = dots.acos()
 sin_omega = omegas.sin()
 res = t.unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * omegas) / sin_omega
 res = t.unsqueeze(1).repeat(1, B - 1, *rest_1s) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, *rest_1s) * omegas) / sin_omega
 res = res.sum(dim=[0, 1]).unsqueeze(0)
 return res

@torch.no_grad()
def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 orig_shape = t.shape[1:]
 if t.shape[1] == 1 and t.ndim == 4:
 t = t.squeeze(1)
 elif t.ndim < 3:
 raise ValueError("Can't handle input with dimensions < 3")
 else:
 t = t.reshape(t.shape[0], -1, *t.shape[-2 if t.ndim > 3 else -1:])
 if t.ndim == 3:
 t = t.unsqueeze(-1)
 if uncond is not None:
 uncond = uncond.reshape(1, *t.shape[1:])
 norm = torch.linalg.matrix_norm(t, keepdim=True)
 n = t.shape[0]
 tn = t.div(norm)
Expand All@@ -43,7 +55,7 @@ def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 else:
 res = (t * distances).sum(dim=0).unsqueeze(0)
 res = res.div(torch.linalg.matrix_norm(res, keepdim=True)).mul(norm.mul(distances).sum(dim=0).unsqueeze(0))
 return res
 return res if res.shape == orig_shape else res.reshape(orig_shape)

@torch.no_grad()
def normalize_adjust(a,b,strength=1):
Expand DownExpand Up@@ -127,7 +139,7 @@ def post_cfg_function(args):
 d = (new_d + d) / 2
 else:
 u = uncond if (use_negative and uncond is not None and torch.any(uncond)) else None
 d = fast_distance_weights(torch.stack(x_n).squeeze(1), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 d = fast_distance_weights(torch.stack(x_n), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 if sharpen or perp_step:
 if sharpen and d_prev is not None:
 d = normalize_adjust(d, d_prev, 1)
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Fix batch handling, attempt to deal with non-4 dimensional latents by blepping · Pull Request #6 · Extraltodeus/DistanceSampler · GitHub
Skip to content
Fix batch handling, attempt to deal with non-4 dimensional latents#6

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merged 1 commit into

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Jun 16, 2025
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Fix batch handling, attempt to deal with non-4 dimensional latents
#6
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22 changes: 17 additions & 5 deletions
custom_samplers.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -11,16 +11,28 @@
def matrix_batch_slerp(t, tn, w):
 dots = torch.mul(tn.unsqueeze(0), tn.unsqueeze(1)).sum(dim=[-1,-2], keepdim=True).clamp(min=-1.0 + EPSILON, max=1.0 - EPSILON)
 mask = ~torch.eye(tn.shape[0], dtype=torch.bool, device=tn.device)
 A, B, C, D, E = dots.shape
 dots = dots[mask].reshape(A, B - 1, C, D, E)
 A, B, *rest = dots.shape
 rest_1s = (1,) * len(rest)
 dots = dots[mask].reshape(A, B - 1, *rest)
 omegas = dots.acos()
 sin_omega = omegas.sin()
 res = t.unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * omegas) / sin_omega
 res = t.unsqueeze(1).repeat(1, B - 1, *rest_1s) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, *rest_1s) * omegas) / sin_omega
 res = res.sum(dim=[0, 1]).unsqueeze(0)
 return res

@torch.no_grad()
def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 orig_shape = t.shape[1:]
 if t.shape[1] == 1 and t.ndim == 4:
 t = t.squeeze(1)
 elif t.ndim < 3:
 raise ValueError("Can't handle input with dimensions < 3")
 else:
 t = t.reshape(t.shape[0], -1, *t.shape[-2 if t.ndim > 3 else -1:])
 if t.ndim == 3:
 t = t.unsqueeze(-1)
 if uncond is not None:
 uncond = uncond.reshape(1, *t.shape[1:])
 norm = torch.linalg.matrix_norm(t, keepdim=True)
 n = t.shape[0]
 tn = t.div(norm)
Expand All@@ -43,7 +55,7 @@ def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 else:
 res = (t * distances).sum(dim=0).unsqueeze(0)
 res = res.div(torch.linalg.matrix_norm(res, keepdim=True)).mul(norm.mul(distances).sum(dim=0).unsqueeze(0))
 return res
 return res if res.shape == orig_shape else res.reshape(orig_shape)

@torch.no_grad()
def normalize_adjust(a,b,strength=1):
Expand DownExpand Up@@ -127,7 +139,7 @@ def post_cfg_function(args):
 d = (new_d + d) / 2
 else:
 u = uncond if (use_negative and uncond is not None and torch.any(uncond)) else None
 d = fast_distance_weights(torch.stack(x_n).squeeze(1), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 d = fast_distance_weights(torch.stack(x_n), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 if sharpen or perp_step:
 if sharpen and d_prev is not None:
 d = normalize_adjust(d, d_prev, 1)
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
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found = true;
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parts.forEach(function(part, i) {
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frag.appendChild(document.createTextNode(part));
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span.className = 'userscript-highlight';
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frag.appendChild(span);
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node.parentNode.replaceChild(frag, node);
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Array.from(node.childNodes).forEach(highlight);
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highlight(document.body);
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mutations.forEach(function(m) {
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Fix batch handling, attempt to deal with non-4 dimensional latents by blepping · Pull Request #6 · Extraltodeus/DistanceSampler · GitHub
Skip to content
Fix batch handling, attempt to deal with non-4 dimensional latents#6

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 Merged

Extraltodeus
merged 1 commit into

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Jun 16, 2025
 Merged


Fix batch handling, attempt to deal with non-4 dimensional latents
#6
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22 changes: 17 additions & 5 deletions
custom_samplers.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -11,16 +11,28 @@
def matrix_batch_slerp(t, tn, w):
 dots = torch.mul(tn.unsqueeze(0), tn.unsqueeze(1)).sum(dim=[-1,-2], keepdim=True).clamp(min=-1.0 + EPSILON, max=1.0 - EPSILON)
 mask = ~torch.eye(tn.shape[0], dtype=torch.bool, device=tn.device)
 A, B, C, D, E = dots.shape
 dots = dots[mask].reshape(A, B - 1, C, D, E)
 A, B, *rest = dots.shape
 rest_1s = (1,) * len(rest)
 dots = dots[mask].reshape(A, B - 1, *rest)
 omegas = dots.acos()
 sin_omega = omegas.sin()
 res = t.unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * omegas) / sin_omega
 res = t.unsqueeze(1).repeat(1, B - 1, *rest_1s) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, *rest_1s) * omegas) / sin_omega
 res = res.sum(dim=[0, 1]).unsqueeze(0)
 return res

@torch.no_grad()
def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 orig_shape = t.shape[1:]
 if t.shape[1] == 1 and t.ndim == 4:
 t = t.squeeze(1)
 elif t.ndim < 3:
 raise ValueError("Can't handle input with dimensions < 3")
 else:
 t = t.reshape(t.shape[0], -1, *t.shape[-2 if t.ndim > 3 else -1:])
 if t.ndim == 3:
 t = t.unsqueeze(-1)
 if uncond is not None:
 uncond = uncond.reshape(1, *t.shape[1:])
 norm = torch.linalg.matrix_norm(t, keepdim=True)
 n = t.shape[0]
 tn = t.div(norm)
Expand All@@ -43,7 +55,7 @@ def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 else:
 res = (t * distances).sum(dim=0).unsqueeze(0)
 res = res.div(torch.linalg.matrix_norm(res, keepdim=True)).mul(norm.mul(distances).sum(dim=0).unsqueeze(0))
 return res
 return res if res.shape == orig_shape else res.reshape(orig_shape)

@torch.no_grad()
def normalize_adjust(a,b,strength=1):
Expand DownExpand Up@@ -127,7 +139,7 @@ def post_cfg_function(args):
 d = (new_d + d) / 2
 else:
 u = uncond if (use_negative and uncond is not None and torch.any(uncond)) else None
 d = fast_distance_weights(torch.stack(x_n).squeeze(1), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 d = fast_distance_weights(torch.stack(x_n), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 if sharpen or perp_step:
 if sharpen and d_prev is not None:
 d = normalize_adjust(d, d_prev, 1)
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
Fix batch handling, attempt to deal with non-4 dimensional latents by blepping · Pull Request #6 · Extraltodeus/DistanceSampler · GitHub
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Fix batch handling, attempt to deal with non-4 dimensional latents#6

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 Merged

Extraltodeus
merged 1 commit into

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Jun 16, 2025
 Merged


Fix batch handling, attempt to deal with non-4 dimensional latents
#6
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22 changes: 17 additions & 5 deletions
custom_samplers.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -11,16 +11,28 @@
def matrix_batch_slerp(t, tn, w):
 dots = torch.mul(tn.unsqueeze(0), tn.unsqueeze(1)).sum(dim=[-1,-2], keepdim=True).clamp(min=-1.0 + EPSILON, max=1.0 - EPSILON)
 mask = ~torch.eye(tn.shape[0], dtype=torch.bool, device=tn.device)
 A, B, C, D, E = dots.shape
 dots = dots[mask].reshape(A, B - 1, C, D, E)
 A, B, *rest = dots.shape
 rest_1s = (1,) * len(rest)
 dots = dots[mask].reshape(A, B - 1, *rest)
 omegas = dots.acos()
 sin_omega = omegas.sin()
 res = t.unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * omegas) / sin_omega
 res = t.unsqueeze(1).repeat(1, B - 1, *rest_1s) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, *rest_1s) * omegas) / sin_omega
 res = res.sum(dim=[0, 1]).unsqueeze(0)
 return res

@torch.no_grad()
def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 orig_shape = t.shape[1:]
 if t.shape[1] == 1 and t.ndim == 4:
 t = t.squeeze(1)
 elif t.ndim < 3:
 raise ValueError("Can't handle input with dimensions < 3")
 else:
 t = t.reshape(t.shape[0], -1, *t.shape[-2 if t.ndim > 3 else -1:])
 if t.ndim == 3:
 t = t.unsqueeze(-1)
 if uncond is not None:
 uncond = uncond.reshape(1, *t.shape[1:])
 norm = torch.linalg.matrix_norm(t, keepdim=True)
 n = t.shape[0]
 tn = t.div(norm)
Expand All@@ -43,7 +55,7 @@ def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 else:
 res = (t * distances).sum(dim=0).unsqueeze(0)
 res = res.div(torch.linalg.matrix_norm(res, keepdim=True)).mul(norm.mul(distances).sum(dim=0).unsqueeze(0))
 return res
 return res if res.shape == orig_shape else res.reshape(orig_shape)

@torch.no_grad()
def normalize_adjust(a,b,strength=1):
Expand DownExpand Up@@ -127,7 +139,7 @@ def post_cfg_function(args):
 d = (new_d + d) / 2
 else:
 u = uncond if (use_negative and uncond is not None and torch.any(uncond)) else None
 d = fast_distance_weights(torch.stack(x_n).squeeze(1), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 d = fast_distance_weights(torch.stack(x_n), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 if sharpen or perp_step:
 if sharpen and d_prev is not None:
 d = normalize_adjust(d, d_prev, 1)
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
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tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Fix batch handling, attempt to deal with non-4 dimensional latents by blepping · Pull Request #6 · Extraltodeus/DistanceSampler · GitHub
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Fix batch handling, attempt to deal with non-4 dimensional latents#6

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Fix batch handling, attempt to deal with non-4 dimensional latents
#6
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22 changes: 17 additions & 5 deletions
custom_samplers.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -11,16 +11,28 @@
def matrix_batch_slerp(t, tn, w):
 dots = torch.mul(tn.unsqueeze(0), tn.unsqueeze(1)).sum(dim=[-1,-2], keepdim=True).clamp(min=-1.0 + EPSILON, max=1.0 - EPSILON)
 mask = ~torch.eye(tn.shape[0], dtype=torch.bool, device=tn.device)
 A, B, C, D, E = dots.shape
 dots = dots[mask].reshape(A, B - 1, C, D, E)
 A, B, *rest = dots.shape
 rest_1s = (1,) * len(rest)
 dots = dots[mask].reshape(A, B - 1, *rest)
 omegas = dots.acos()
 sin_omega = omegas.sin()
 res = t.unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * omegas) / sin_omega
 res = t.unsqueeze(1).repeat(1, B - 1, *rest_1s) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, *rest_1s) * omegas) / sin_omega
 res = res.sum(dim=[0, 1]).unsqueeze(0)
 return res

@torch.no_grad()
def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 orig_shape = t.shape[1:]
 if t.shape[1] == 1 and t.ndim == 4:
 t = t.squeeze(1)
 elif t.ndim < 3:
 raise ValueError("Can't handle input with dimensions < 3")
 else:
 t = t.reshape(t.shape[0], -1, *t.shape[-2 if t.ndim > 3 else -1:])
 if t.ndim == 3:
 t = t.unsqueeze(-1)
 if uncond is not None:
 uncond = uncond.reshape(1, *t.shape[1:])
 norm = torch.linalg.matrix_norm(t, keepdim=True)
 n = t.shape[0]
 tn = t.div(norm)
Expand All@@ -43,7 +55,7 @@ def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 else:
 res = (t * distances).sum(dim=0).unsqueeze(0)
 res = res.div(torch.linalg.matrix_norm(res, keepdim=True)).mul(norm.mul(distances).sum(dim=0).unsqueeze(0))
 return res
 return res if res.shape == orig_shape else res.reshape(orig_shape)

@torch.no_grad()
def normalize_adjust(a,b,strength=1):
Expand DownExpand Up@@ -127,7 +139,7 @@ def post_cfg_function(args):
 d = (new_d + d) / 2
 else:
 u = uncond if (use_negative and uncond is not None and torch.any(uncond)) else None
 d = fast_distance_weights(torch.stack(x_n).squeeze(1), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 d = fast_distance_weights(torch.stack(x_n), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 if sharpen or perp_step:
 if sharpen and d_prev is not None:
 d = normalize_adjust(d, d_prev, 1)
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Fix batch handling, attempt to deal with non-4 dimensional latents by blepping · Pull Request #6 · Extraltodeus/DistanceSampler · GitHub
Skip to content
Fix batch handling, attempt to deal with non-4 dimensional latents#6

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to your account

 Merged

Extraltodeus
merged 1 commit into

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Jun 16, 2025
 Merged


Fix batch handling, attempt to deal with non-4 dimensional latents
#6
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22 changes: 17 additions & 5 deletions
custom_samplers.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -11,16 +11,28 @@
def matrix_batch_slerp(t, tn, w):
 dots = torch.mul(tn.unsqueeze(0), tn.unsqueeze(1)).sum(dim=[-1,-2], keepdim=True).clamp(min=-1.0 + EPSILON, max=1.0 - EPSILON)
 mask = ~torch.eye(tn.shape[0], dtype=torch.bool, device=tn.device)
 A, B, C, D, E = dots.shape
 dots = dots[mask].reshape(A, B - 1, C, D, E)
 A, B, *rest = dots.shape
 rest_1s = (1,) * len(rest)
 dots = dots[mask].reshape(A, B - 1, *rest)
 omegas = dots.acos()
 sin_omega = omegas.sin()
 res = t.unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * omegas) / sin_omega
 res = t.unsqueeze(1).repeat(1, B - 1, *rest_1s) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, *rest_1s) * omegas) / sin_omega
 res = res.sum(dim=[0, 1]).unsqueeze(0)
 return res

@torch.no_grad()
def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 orig_shape = t.shape[1:]
 if t.shape[1] == 1 and t.ndim == 4:
 t = t.squeeze(1)
 elif t.ndim < 3:
 raise ValueError("Can't handle input with dimensions < 3")
 else:
 t = t.reshape(t.shape[0], -1, *t.shape[-2 if t.ndim > 3 else -1:])
 if t.ndim == 3:
 t = t.unsqueeze(-1)
 if uncond is not None:
 uncond = uncond.reshape(1, *t.shape[1:])
 norm = torch.linalg.matrix_norm(t, keepdim=True)
 n = t.shape[0]
 tn = t.div(norm)
Expand All@@ -43,7 +55,7 @@ def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 else:
 res = (t * distances).sum(dim=0).unsqueeze(0)
 res = res.div(torch.linalg.matrix_norm(res, keepdim=True)).mul(norm.mul(distances).sum(dim=0).unsqueeze(0))
 return res
 return res if res.shape == orig_shape else res.reshape(orig_shape)

@torch.no_grad()
def normalize_adjust(a,b,strength=1):
Expand DownExpand Up@@ -127,7 +139,7 @@ def post_cfg_function(args):
 d = (new_d + d) / 2
 else:
 u = uncond if (use_negative and uncond is not None and torch.any(uncond)) else None
 d = fast_distance_weights(torch.stack(x_n).squeeze(1), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 d = fast_distance_weights(torch.stack(x_n), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 if sharpen or perp_step:
 if sharpen and d_prev is not None:
 d = normalize_adjust(d, d_prev, 1)
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
Fix batch handling, attempt to deal with non-4 dimensional latents by blepping · Pull Request #6 · Extraltodeus/DistanceSampler · GitHub
Skip to content
Fix batch handling, attempt to deal with non-4 dimensional latents#6

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 Merged

Extraltodeus
merged 1 commit into

from

Jun 16, 2025
 Merged


Fix batch handling, attempt to deal with non-4 dimensional latents
#6
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22 changes: 17 additions & 5 deletions
custom_samplers.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -11,16 +11,28 @@
def matrix_batch_slerp(t, tn, w):
 dots = torch.mul(tn.unsqueeze(0), tn.unsqueeze(1)).sum(dim=[-1,-2], keepdim=True).clamp(min=-1.0 + EPSILON, max=1.0 - EPSILON)
 mask = ~torch.eye(tn.shape[0], dtype=torch.bool, device=tn.device)
 A, B, C, D, E = dots.shape
 dots = dots[mask].reshape(A, B - 1, C, D, E)
 A, B, *rest = dots.shape
 rest_1s = (1,) * len(rest)
 dots = dots[mask].reshape(A, B - 1, *rest)
 omegas = dots.acos()
 sin_omega = omegas.sin()
 res = t.unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * omegas) / sin_omega
 res = t.unsqueeze(1).repeat(1, B - 1, *rest_1s) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, *rest_1s) * omegas) / sin_omega
 res = res.sum(dim=[0, 1]).unsqueeze(0)
 return res

@torch.no_grad()
def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 orig_shape = t.shape[1:]
 if t.shape[1] == 1 and t.ndim == 4:
 t = t.squeeze(1)
 elif t.ndim < 3:
 raise ValueError("Can't handle input with dimensions < 3")
 else:
 t = t.reshape(t.shape[0], -1, *t.shape[-2 if t.ndim > 3 else -1:])
 if t.ndim == 3:
 t = t.unsqueeze(-1)
 if uncond is not None:
 uncond = uncond.reshape(1, *t.shape[1:])
 norm = torch.linalg.matrix_norm(t, keepdim=True)
 n = t.shape[0]
 tn = t.div(norm)
Expand All@@ -43,7 +55,7 @@ def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
 else:
 res = (t * distances).sum(dim=0).unsqueeze(0)
 res = res.div(torch.linalg.matrix_norm(res, keepdim=True)).mul(norm.mul(distances).sum(dim=0).unsqueeze(0))
 return res
 return res if res.shape == orig_shape else res.reshape(orig_shape)

@torch.no_grad()
def normalize_adjust(a,b,strength=1):
Expand DownExpand Up@@ -127,7 +139,7 @@ def post_cfg_function(args):
 d = (new_d + d) / 2
 else:
 u = uncond if (use_negative and uncond is not None and torch.any(uncond)) else None
 d = fast_distance_weights(torch.stack(x_n).squeeze(1), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 d = fast_distance_weights(torch.stack(x_n), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
 if sharpen or perp_step:
 if sharpen and d_prev is not None:
 d = normalize_adjust(d, d_prev, 1)
Expand Down