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Fooocus
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Merge branch 'main_upstream' into feature/add-nsfw-filter

This commit is contained in:
Manuel Schmid 2023-12-27 18:41:56 +01:00
parent e97008d4fb 7b5bced6c2
commit 8a2141c9fc
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GPG Key ID: 32C4F7569B40B84B
30 changed files with 1155 additions and 417 deletions
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4
css/style.css
View File

@ -94,6 +94,10 @@
overflow:inherit !important;
}
.gradio-container{
overflow: visible;
}
/* fullpage image viewer */
#lightboxModal{

16
extras/ip_adapter.py
View File

@ -167,21 +167,19 @@ def preprocess(img, ip_adapter_path):
ldm_patched.modules.model_management.load_model_gpu(clip_vision.patcher)
pixel_values = clip_preprocess(numpy_to_pytorch(img).to(clip_vision.load_device))
if clip_vision.dtype != torch.float32:
precision_scope = torch.autocast
else:
precision_scope = lambda a, b: contextlib.nullcontext(a)
with precision_scope(ldm_patched.modules.model_management.get_autocast_device(clip_vision.load_device), torch.float32):
outputs = clip_vision.model(pixel_values=pixel_values, intermediate_output=-2)
outputs = clip_vision.model(pixel_values=pixel_values, output_hidden_states=True)
ip_adapter = entry['ip_adapter']
ip_layers = entry['ip_layers']
image_proj_model = entry['image_proj_model']
ip_unconds = entry['ip_unconds']
cond = outputs[1].to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)
if ip_adapter.plus:
cond = outputs.hidden_states[-2]
else:
cond = outputs.image_embeds
cond = cond.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)
ldm_patched.modules.model_management.load_model_gpu(image_proj_model)
cond = image_proj_model.model(cond).to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)

2
fooocus_version.py
View File

@ -1 +1 @@
version = '2.1.839'
version = '2.1.855'

291
javascript/zoom.js
View File

@ -1,18 +1,5 @@
onUiLoaded(async() => {
// Helper functions
// Get active tab
/**
* Waits for an element to be present in the DOM.
*/
const waitForElement = (id) => new Promise(resolve => {
const checkForElement = () => {
const element = document.querySelector(id);
if (element) return resolve(element);
setTimeout(checkForElement, 100);
};
checkForElement();
});
// Detect whether the element has a horizontal scroll bar
function hasHorizontalScrollbar(element) {
@ -33,140 +20,40 @@ onUiLoaded(async() => {
}
}
// Check if hotkey is valid
function isValidHotkey(value) {
const specialKeys = ["Ctrl", "Alt", "Shift", "Disable"];
return (
(typeof value === "string" &&
value.length === 1 &&
/[a-z]/i.test(value)) ||
specialKeys.includes(value)
);
}
// Normalize hotkey
function normalizeHotkey(hotkey) {
return hotkey.length === 1 ? "Key" + hotkey.toUpperCase() : hotkey;
}
// Format hotkey for display
function formatHotkeyForDisplay(hotkey) {
return hotkey.startsWith("Key") ? hotkey.slice(3) : hotkey;
}
// Create hotkey configuration with the provided options
function createHotkeyConfig(defaultHotkeysConfig) {
const result = {}; // Resulting hotkey configuration
for (const key in defaultHotkeysConfig) {
result[key] = defaultHotkeysConfig[key];
}
return result;
}
// Disables functions in the config object based on the provided list of function names
function disableFunctions(config, disabledFunctions) {
// Bind the hasOwnProperty method to the functionMap object to avoid errors
const hasOwnProperty =
Object.prototype.hasOwnProperty.bind(functionMap);
// Loop through the disabledFunctions array and disable the corresponding functions in the config object
disabledFunctions.forEach(funcName => {
if (hasOwnProperty(funcName)) {
const key = functionMap[funcName];
config[key] = "disable";
}
});
// Return the updated config object
return config;
}
/**
* The restoreImgRedMask function displays a red mask around an image to indicate the aspect ratio.
* If the image display property is set to 'none', the mask breaks. To fix this, the function
* temporarily sets the display property to 'block' and then hides the mask again after 300 milliseconds
* to avoid breaking the canvas. Additionally, the function adjusts the mask to work correctly on
* very long images.
*/
function restoreImgRedMask(elements) {
const mainTabId = getTabId(elements);
if (!mainTabId) return;
const mainTab = gradioApp().querySelector(mainTabId);
const img = mainTab.querySelector("img");
const imageARPreview = gradioApp().querySelector("#imageARPreview");
if (!img || !imageARPreview) return;
imageARPreview.style.transform = "";
if (parseFloat(mainTab.style.width) > 865) {
const transformString = mainTab.style.transform;
const scaleMatch = transformString.match(
/scale\(([-+]?[0-9]*\.?[0-9]+)\)/
);
let zoom = 1; // default zoom
if (scaleMatch && scaleMatch[1]) {
zoom = Number(scaleMatch[1]);
}
imageARPreview.style.transformOrigin = "0 0";
imageARPreview.style.transform = `scale(${zoom})`;
}
if (img.style.display !== "none") return;
img.style.display = "block";
setTimeout(() => {
img.style.display = "none";
}, 400);
}
// Default config
const defaultHotkeysConfig = {
canvas_hotkey_zoom: "Alt",
canvas_hotkey_zoom: "Shift",
canvas_hotkey_adjust: "Ctrl",
canvas_zoom_undo_extra_key: "Ctrl",
canvas_zoom_hotkey_undo: "KeyZ",
canvas_hotkey_reset: "KeyR",
canvas_hotkey_fullscreen: "KeyS",
canvas_hotkey_move: "KeyF",
canvas_hotkey_overlap: "KeyO",
canvas_disabled_functions: [],
canvas_show_tooltip: true,
canvas_auto_expand: true,
canvas_blur_prompt: false,
};
const functionMap = {
"Zoom": "canvas_hotkey_zoom",
"Adjust brush size": "canvas_hotkey_adjust",
"Moving canvas": "canvas_hotkey_move",
"Fullscreen": "canvas_hotkey_fullscreen",
"Reset Zoom": "canvas_hotkey_reset",
"Overlap": "canvas_hotkey_overlap"
canvas_blur_prompt: true,
};
// Loading the configuration from opts
const preHotkeysConfig = createHotkeyConfig(
const hotkeysConfig = createHotkeyConfig(
defaultHotkeysConfig
);
// Disable functions that are not needed by the user
const hotkeysConfig = disableFunctions(
preHotkeysConfig,
preHotkeysConfig.canvas_disabled_functions
);
let isMoving = false;
let mouseX, mouseY;
let activeElement;
const elemData = {};
function applyZoomAndPan(elemId, isExtension = true) {
function applyZoomAndPan(elemId) {
const targetElement = gradioApp().querySelector(elemId);
if (!targetElement) {
@ -181,6 +68,7 @@ onUiLoaded(async() => {
panX: 0,
panY: 0
};
let fullScreenMode = false;
// Create tooltip
@ -211,44 +99,46 @@ onUiLoaded(async() => {
action: "Adjust brush size",
keySuffix: " + wheel"
},
{configKey: "canvas_zoom_hotkey_undo", action: "Undo last action", keyPrefix: `${hotkeysConfig.canvas_zoom_undo_extra_key} + ` },
{configKey: "canvas_hotkey_reset", action: "Reset zoom"},
{
configKey: "canvas_hotkey_fullscreen",
action: "Fullscreen mode"
},
{configKey: "canvas_hotkey_move", action: "Move canvas"},
{configKey: "canvas_hotkey_overlap", action: "Overlap"}
{configKey: "canvas_hotkey_move", action: "Move canvas"}
];
// Create hotkeys array with disabled property based on the config values
const hotkeys = hotkeysInfo.map(info => {
// Create hotkeys array based on the config values
const hotkeys = hotkeysInfo.map((info) => {
const configValue = hotkeysConfig[info.configKey];
const key = info.keySuffix ?
`${configValue}${info.keySuffix}` :
configValue.charAt(configValue.length - 1);
return {
key,
action: info.action,
disabled: configValue === "disable"
};
});
for (const hotkey of hotkeys) {
if (hotkey.disabled) {
continue;
let key = configValue.slice(-1);
if (info.keySuffix) {
key = `${configValue}${info.keySuffix}`;
}
if (info.keyPrefix && info.keyPrefix !== "None + ") {
key = `${info.keyPrefix}${configValue[3]}`;
}
return {
key,
action: info.action,
};
});
hotkeys
.forEach(hotkey => {
const p = document.createElement("p");
p.innerHTML = `<b>${hotkey.key}</b> - ${hotkey.action}`;
tooltipContent.appendChild(p);
});
tooltip.append(info, tooltipContent);
const p = document.createElement("p");
p.innerHTML = `<b>${hotkey.key}</b> - ${hotkey.action}`;
tooltipContent.appendChild(p);
}
// Add information and content elements to the tooltip element
tooltip.appendChild(info);
tooltip.appendChild(tooltipContent);
// Add a hint element to the target element
toolTipElemnt.appendChild(tooltip);
// Add a hint element to the target element
toolTipElemnt.appendChild(tooltip);
}
//Show tool tip if setting enable
@ -264,9 +154,7 @@ onUiLoaded(async() => {
panY: 0
};
if (isExtension) {
targetElement.style.overflow = "hidden";
}
targetElement.style.overflow = "hidden";
targetElement.isZoomed = false;
@ -284,7 +172,7 @@ onUiLoaded(async() => {
closeBtn.addEventListener("click", resetZoom);
}
if (canvas && isExtension) {
if (canvas) {
const parentElement = targetElement.closest('[id^="component-"]');
if (
canvas &&
@ -297,16 +185,6 @@ onUiLoaded(async() => {
}
if (
canvas &&
!isExtension &&
parseFloat(canvas.style.width) > 865 &&
parseFloat(targetElement.style.width) > 865
) {
fitToElement();
return;
}
targetElement.style.width = "";
}
@ -372,12 +250,10 @@ onUiLoaded(async() => {
targetElement.style.transformOrigin = "0 0";
targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`;
targetElement.style.overflow = "visible";
toggleOverlap("on");
if (isExtension) {
targetElement.style.overflow = "visible";
}
return newZoomLevel;
}
@ -388,6 +264,7 @@ onUiLoaded(async() => {
let zoomPosX, zoomPosY;
let delta = 0.2;
if (elemData[elemId].zoomLevel > 7) {
delta = 0.9;
} else if (elemData[elemId].zoomLevel > 2) {
@ -421,12 +298,7 @@ onUiLoaded(async() => {
let parentElement;
if (isExtension) {
parentElement = targetElement.closest('[id^="component-"]');
} else {
parentElement = targetElement.parentElement;
}
parentElement = targetElement.closest('[id^="component-"]');
// Get element and screen dimensions
const elementWidth = targetElement.offsetWidth;
@ -455,6 +327,26 @@ onUiLoaded(async() => {
toggleOverlap("off");
}
// Undo last action
function undoLastAction(e) {
let isCtrlPressed = isModifierKey(e, hotkeysConfig.canvas_zoom_undo_extra_key)
const isAuxButton = e.button >= 3;
if (isAuxButton) {
isCtrlPressed = true
} else {
if (!isModifierKey(e, hotkeysConfig.canvas_zoom_undo_extra_key)) return;
}
// Move undoBtn query outside the if statement to avoid unnecessary queries
const undoBtn = document.querySelector(`${activeElement} button[aria-label="Undo"]`);
if ((isCtrlPressed) && undoBtn ) {
e.preventDefault();
undoBtn.click();
}
}
/**
* This function fits the target element to the screen by calculating
* the required scale and offsets. It also updates the global variables
@ -469,13 +361,8 @@ onUiLoaded(async() => {
if (!canvas) return;
if (canvas.offsetWidth > 862 || isExtension) {
targetElement.style.width = (canvas.offsetWidth + 2) + "px";
}
if (isExtension) {
targetElement.style.overflow = "visible";
}
targetElement.style.width = (canvas.offsetWidth + 2) + "px";
targetElement.style.overflow = "visible";
if (fullScreenMode) {
resetZoom();
@ -549,11 +436,11 @@ onUiLoaded(async() => {
}
}
const hotkeyActions = {
[hotkeysConfig.canvas_hotkey_reset]: resetZoom,
[hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,
[hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen
[hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen,
[hotkeysConfig.canvas_zoom_hotkey_undo]: undoLastAction,
};
const action = hotkeyActions[event.code];
@ -597,26 +484,27 @@ onUiLoaded(async() => {
}
targetElement.addEventListener("mousemove", getMousePosition);
targetElement.addEventListener("auxclick", undoLastAction);
//observers
// Creating an observer with a callback function to handle DOM changes
const observer = new MutationObserver((mutationsList, observer) => {
for (let mutation of mutationsList) {
// If the style attribute of the canvas has changed, by observation it happens only when the picture changes
if (mutation.type === 'attributes' && mutation.attributeName === 'style' &&
mutation.target.tagName.toLowerCase() === 'canvas') {
targetElement.isExpanded = false;
setTimeout(resetZoom, 10);
}
// If the style attribute of the canvas has changed, by observation it happens only when the picture changes
if (mutation.type === 'attributes' && mutation.attributeName === 'style' &&
mutation.target.tagName.toLowerCase() === 'canvas') {
targetElement.isExpanded = false;
setTimeout(resetZoom, 10);
}
}
});
// Apply auto expand if enabled
if (hotkeysConfig.canvas_auto_expand) {
});
// Apply auto expand if enabled
if (hotkeysConfig.canvas_auto_expand) {
targetElement.addEventListener("mousemove", autoExpand);
// Set up an observer to track attribute changes
observer.observe(targetElement, {attributes: true, childList: true, subtree: true});
}
observer.observe(targetElement, { attributes: true, childList: true, subtree: true });
}
// Handle events only inside the targetElement
let isKeyDownHandlerAttached = false;
@ -661,7 +549,7 @@ onUiLoaded(async() => {
function handleMoveKeyDown(e) {
// Disable key locks to make pasting from the buffer work correctly
if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && event.code === 'KeyC') || e.code === "F5") {
if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && e.code === 'KeyC') || e.code === "F5") {
return;
}
@ -713,11 +601,7 @@ onUiLoaded(async() => {
if (isMoving && elemId === activeElement) {
updatePanPosition(e.movementX, e.movementY);
targetElement.style.pointerEvents = "none";
if (isExtension) {
targetElement.style.overflow = "visible";
}
targetElement.style.overflow = "visible";
} else {
targetElement.style.pointerEvents = "auto";
}
@ -745,18 +629,13 @@ onUiLoaded(async() => {
}
}
if (isExtension) {
targetElement.addEventListener("mousemove", checkForOutBox);
}
targetElement.addEventListener("mousemove", checkForOutBox);
window.addEventListener('resize', (e) => {
resetZoom();
if (isExtension) {
targetElement.isExpanded = false;
targetElement.isZoomed = false;
}
targetElement.isExpanded = false;
targetElement.isZoomed = false;
});
gradioApp().addEventListener("mousemove", handleMoveByKey);

1
ldm_patched/contrib/external.py
View File

@ -1870,6 +1870,7 @@ def init_custom_nodes():
"nodes_images.py",
"nodes_video_model.py",
"nodes_sag.py",
"nodes_perpneg.py",
]
for node_file in extras_files:

4
ldm_patched/contrib/external_images.py
View File

@ -76,7 +76,7 @@ class SaveAnimatedWEBP:
OUTPUT_NODE = True
CATEGORY = "_for_testing"
CATEGORY = "image/animation"
def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None):
method = self.methods.get(method)
@ -138,7 +138,7 @@ class SaveAnimatedPNG:
OUTPUT_NODE = True
CATEGORY = "_for_testing"
CATEGORY = "image/animation"
def save_images(self, images, fps, compress_level, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None):
filename_prefix += self.prefix_append

25
ldm_patched/contrib/external_latent.py
View File

@ -5,9 +5,7 @@ import torch
def reshape_latent_to(target_shape, latent):
if latent.shape[1:] != target_shape[1:]:
latent.movedim(1, -1)
latent = ldm_patched.modules.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center")
latent.movedim(-1, 1)
return ldm_patched.modules.utils.repeat_to_batch_size(latent, target_shape[0])
@ -104,9 +102,32 @@ class LatentInterpolate:
samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio))
return (samples_out,)
class LatentBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "batch"
CATEGORY = "latent/batch"
def batch(self, samples1, samples2):
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
if s1.shape[1:] != s2.shape[1:]:
s2 = ldm_patched.modules.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center")
s = torch.cat((s1, s2), dim=0)
samples_out["samples"] = s
samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])
return (samples_out,)
NODE_CLASS_MAPPINGS = {
"LatentAdd": LatentAdd,
"LatentSubtract": LatentSubtract,
"LatentMultiply": LatentMultiply,
"LatentInterpolate": LatentInterpolate,
"LatentBatch": LatentBatch,
}

48
ldm_patched/contrib/external_model_advanced.py
View File

@ -19,41 +19,19 @@ class LCM(ldm_patched.modules.model_sampling.EPS):
return c_out * x0 + c_skip * model_input
class ModelSamplingDiscreteDistilled(torch.nn.Module):
class ModelSamplingDiscreteDistilled(ldm_patched.modules.model_sampling.ModelSamplingDiscrete):
original_timesteps = 50
def __init__(self):
super().__init__()
self.sigma_data = 1.0
timesteps = 1000
beta_start = 0.00085
beta_end = 0.012
def __init__(self, model_config=None):
super().__init__(model_config)
betas = torch.linspace(beta_start**0.5, beta_end**0.5, timesteps, dtype=torch.float32) ** 2
alphas = 1.0 - betas
alphas_cumprod = torch.cumprod(alphas, dim=0)
self.skip_steps = self.num_timesteps // self.original_timesteps
self.skip_steps = timesteps // self.original_timesteps
alphas_cumprod_valid = torch.zeros((self.original_timesteps), dtype=torch.float32)
sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32)
for x in range(self.original_timesteps):
alphas_cumprod_valid[self.original_timesteps - 1 - x] = alphas_cumprod[timesteps - 1 - x * self.skip_steps]
sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps]
sigmas = ((1 - alphas_cumprod_valid) / alphas_cumprod_valid) ** 0.5
self.set_sigmas(sigmas)
def set_sigmas(self, sigmas):
self.register_buffer('sigmas', sigmas)
self.register_buffer('log_sigmas', sigmas.log())
@property
def sigma_min(self):
return self.sigmas[0]
@property
def sigma_max(self):
return self.sigmas[-1]
self.set_sigmas(sigmas_valid)
def timestep(self, sigma):
log_sigma = sigma.log()
@ -68,14 +46,6 @@ class ModelSamplingDiscreteDistilled(torch.nn.Module):
log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
return log_sigma.exp().to(timestep.device)
def percent_to_sigma(self, percent):
if percent <= 0.0:
return 999999999.9
if percent >= 1.0:
return 0.0
percent = 1.0 - percent
return self.sigma(torch.tensor(percent * 999.0)).item()
def rescale_zero_terminal_snr_sigmas(sigmas):
alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
@ -124,7 +94,7 @@ class ModelSamplingDiscrete:
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced()
model_sampling = ModelSamplingAdvanced(model.model.model_config)
if zsnr:
model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))
@ -156,7 +126,7 @@ class ModelSamplingContinuousEDM:
class ModelSamplingAdvanced(ldm_patched.modules.model_sampling.ModelSamplingContinuousEDM, sampling_type):
pass
model_sampling = ModelSamplingAdvanced()
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_sigma_range(sigma_min, sigma_max)
m.add_object_patch("model_sampling", model_sampling)
return (m, )

57
ldm_patched/contrib/external_perpneg.py Normal file
View File

@ -0,0 +1,57 @@
# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
import torch
import ldm_patched.modules.model_management
import ldm_patched.modules.sample
import ldm_patched.modules.samplers
import ldm_patched.modules.utils
class PerpNeg:
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL", ),
"empty_conditioning": ("CONDITIONING", ),
"neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "_for_testing"
def patch(self, model, empty_conditioning, neg_scale):
m = model.clone()
nocond = ldm_patched.modules.sample.convert_cond(empty_conditioning)
def cfg_function(args):
model = args["model"]
noise_pred_pos = args["cond_denoised"]
noise_pred_neg = args["uncond_denoised"]
cond_scale = args["cond_scale"]
x = args["input"]
sigma = args["sigma"]
model_options = args["model_options"]
nocond_processed = ldm_patched.modules.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative")
(noise_pred_nocond, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, nocond_processed, None, x, sigma, model_options)
pos = noise_pred_pos - noise_pred_nocond
neg = noise_pred_neg - noise_pred_nocond
perp = ((torch.mul(pos, neg).sum())/(torch.norm(neg)**2)) * neg
perp_neg = perp * neg_scale
cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg)
cfg_result = x - cfg_result
return cfg_result
m.set_model_sampler_cfg_function(cfg_function)
return (m, )
NODE_CLASS_MAPPINGS = {
"PerpNeg": PerpNeg,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"PerpNeg": "Perp-Neg",
}

8
ldm_patched/contrib/external_sag.py
View File

@ -29,9 +29,7 @@ def attention_basic_with_sim(q, k, v, heads, mask=None):
# force cast to fp32 to avoid overflowing
if _ATTN_PRECISION =="fp32":
with torch.autocast(enabled=False, device_type = 'cuda'):
q, k = q.float(), k.float()
sim = einsum('b i d, b j d -> b i j', q, k) * scale
sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
else:
sim = einsum('b i d, b j d -> b i j', q, k) * scale
@ -62,7 +60,7 @@ def create_blur_map(x0, attn, sigma=3.0, threshold=1.0):
attn = attn.reshape(b, -1, hw1, hw2)
# Global Average Pool
mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold
ratio = round(math.sqrt(lh * lw / hw1))
ratio = math.ceil(math.sqrt(lh * lw / hw1))
mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)]
# Reshape
@ -113,7 +111,6 @@ class SelfAttentionGuidance:
m = model.clone()
attn_scores = None
mid_block_shape = None
# TODO: make this work properly with chunked batches
# currently, we can only save the attn from one UNet call
@ -136,7 +133,6 @@ class SelfAttentionGuidance:
def post_cfg_function(args):
nonlocal attn_scores
nonlocal mid_block_shape
uncond_attn = attn_scores
sag_scale = scale

4
ldm_patched/ldm/modules/attention.py
View File

@ -104,9 +104,7 @@ def attention_basic(q, k, v, heads, mask=None):
# force cast to fp32 to avoid overflowing
if _ATTN_PRECISION =="fp32":
with torch.autocast(enabled=False, device_type = 'cuda'):
q, k = q.float(), k.float()
sim = einsum('b i d, b j d -> b i j', q, k) * scale
sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
else:
sim = einsum('b i d, b j d -> b i j', q, k) * scale

2
ldm_patched/modules/args_parser.py
View File

@ -102,7 +102,7 @@ vram_group.add_argument("--always-cpu", action="store_true")
parser.add_argument("--always-offload-from-vram", action="store_true")
parser.add_argument("--pytorch-deterministic", action="store_true")
parser.add_argument("--disable-server-log", action="store_true")
parser.add_argument("--debug-mode", action="store_true")

31
ldm_patched/modules/clip_vision.py
View File

@ -19,11 +19,13 @@ class Output:
def clip_preprocess(image, size=224):
mean = torch.tensor([ 0.48145466,0.4578275,0.40821073], device=image.device, dtype=image.dtype)
std = torch.tensor([0.26862954,0.26130258,0.27577711], device=image.device, dtype=image.dtype)
scale = (size / min(image.shape[1], image.shape[2]))
image = torch.nn.functional.interpolate(image.movedim(-1, 1), size=(round(scale * image.shape[1]), round(scale * image.shape[2])), mode="bicubic", antialias=True)
h = (image.shape[2] - size)//2
w = (image.shape[3] - size)//2
image = image[:,:,h:h+size,w:w+size]
image = image.movedim(-1, 1)
if not (image.shape[2] == size and image.shape[3] == size):
scale = (size / min(image.shape[2], image.shape[3]))
image = torch.nn.functional.interpolate(image, size=(round(scale * image.shape[2]), round(scale * image.shape[3])), mode="bicubic", antialias=True)
h = (image.shape[2] - size)//2
w = (image.shape[3] - size)//2
image = image[:,:,h:h+size,w:w+size]
image = torch.clip((255. * image), 0, 255).round() / 255.0
return (image - mean.view([3,1,1])) / std.view([3,1,1])
@ -34,11 +36,9 @@ class ClipVisionModel():
self.load_device = ldm_patched.modules.model_management.text_encoder_device()
offload_device = ldm_patched.modules.model_management.text_encoder_offload_device()
self.dtype = torch.float32
if ldm_patched.modules.model_management.should_use_fp16(self.load_device, prioritize_performance=False):
self.dtype = torch.float16
self.model = ldm_patched.modules.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, ldm_patched.modules.ops.disable_weight_init)
self.dtype = ldm_patched.modules.model_management.text_encoder_dtype(self.load_device)
self.model = ldm_patched.modules.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, ldm_patched.modules.ops.manual_cast)
self.model.eval()
self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
def load_sd(self, sd):
@ -46,15 +46,8 @@ class ClipVisionModel():
def encode_image(self, image):
ldm_patched.modules.model_management.load_model_gpu(self.patcher)
pixel_values = clip_preprocess(image.to(self.load_device))
if self.dtype != torch.float32:
precision_scope = torch.autocast
else:
precision_scope = lambda a, b: contextlib.nullcontext(a)
with precision_scope(ldm_patched.modules.model_management.get_autocast_device(self.load_device), torch.float32):
out = self.model(pixel_values=pixel_values, intermediate_output=-2)
pixel_values = clip_preprocess(image.to(self.load_device)).float()
out = self.model(pixel_values=pixel_values, intermediate_output=-2)
outputs = Output()
outputs["last_hidden_state"] = out[0].to(ldm_patched.modules.model_management.intermediate_device())

4
ldm_patched/modules/model_management.py
View File

@ -28,6 +28,10 @@ total_vram = 0
lowvram_available = True
xpu_available = False
if args.pytorch_deterministic:
print("Using deterministic algorithms for pytorch")
torch.use_deterministic_algorithms(True, warn_only=True)
directml_enabled = False
if args.directml is not None:
import torch_directml

49
ldm_patched/modules/samplers.py
View File

@ -2,6 +2,7 @@ from ldm_patched.k_diffusion import sampling as k_diffusion_sampling
from ldm_patched.unipc import uni_pc
import torch
import enum
import collections
from ldm_patched.modules import model_management
import math
from ldm_patched.modules import model_base
@ -61,9 +62,7 @@ def get_area_and_mult(conds, x_in, timestep_in):
for c in model_conds:
conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
control = None
if 'control' in conds:
control = conds['control']
control = conds.get('control', None)
patches = None
if 'gligen' in conds:
@ -78,7 +77,8 @@ def get_area_and_mult(conds, x_in, timestep_in):
patches['middle_patch'] = [gligen_patch]
return (input_x, mult, conditioning, area, control, patches)
cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches'])
return cond_obj(input_x, mult, conditioning, area, control, patches)
def cond_equal_size(c1, c2):
if c1 is c2:
@ -91,24 +91,24 @@ def cond_equal_size(c1, c2):
return True
def can_concat_cond(c1, c2):
if c1[0].shape != c2[0].shape:
if c1.input_x.shape != c2.input_x.shape:
return False
#control
if (c1[4] is None) != (c2[4] is None):
return False
if c1[4] is not None:
if c1[4] is not c2[4]:
def objects_concatable(obj1, obj2):
if (obj1 is None) != (obj2 is None):
return False
if obj1 is not None:
if obj1 is not obj2:
return False
return True
#patches
if (c1[5] is None) != (c2[5] is None):
if not objects_concatable(c1.control, c2.control):
return False
if (c1[5] is not None):
if c1[5] is not c2[5]:
return False
return cond_equal_size(c1[2], c2[2])
if not objects_concatable(c1.patches, c2.patches):
return False
return cond_equal_size(c1.conditioning, c2.conditioning)
def cond_cat(c_list):
c_crossattn = []
@ -184,13 +184,13 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
for x in to_batch:
o = to_run.pop(x)
p = o[0]
input_x += [p[0]]
mult += [p[1]]
c += [p[2]]
area += [p[3]]
cond_or_uncond += [o[1]]
control = p[4]
patches = p[5]
input_x.append(p.input_x)
mult.append(p.mult)
c.append(p.conditioning)
area.append(p.area)
cond_or_uncond.append(o[1])
control = p.control
patches = p.patches
batch_chunks = len(cond_or_uncond)
input_x = torch.cat(input_x)
@ -251,7 +251,8 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option
cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
if "sampler_cfg_function" in model_options:
args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep}
args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
"cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
cfg_result = x - model_options["sampler_cfg_function"](args)
else:
cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale

25
modules/async_worker.py
View File

@ -34,6 +34,7 @@ def worker():
import modules.advanced_parameters as advanced_parameters
import extras.ip_adapter as ip_adapter
import extras.face_crop
import fooocus_version
from modules.censor import censor_batch
from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion
@ -281,9 +282,10 @@ def worker():
inpaint_image = HWC3(inpaint_image)
if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0):
progressbar(async_task, 1, 'Downloading upscale models ...')
modules.config.downloading_upscale_model()
if inpaint_parameterized:
progressbar(async_task, 1, 'Downloading inpainter ...')
modules.config.downloading_upscale_model()
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
advanced_parameters.inpaint_engine)
base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
@ -401,8 +403,8 @@ def worker():
uc=None,
positive_top_k=len(positive_basic_workloads),
negative_top_k=len(negative_basic_workloads),
log_positive_prompt='; '.join([task_prompt] + task_extra_positive_prompts),
log_negative_prompt='; '.join([task_negative_prompt] + task_extra_negative_prompts),
log_positive_prompt='\n'.join([task_prompt] + task_extra_positive_prompts),
log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts),
))
if use_expansion:
@ -497,7 +499,7 @@ def worker():
if direct_return:
d = [('Upscale (Fast)', '2x')]
log(uov_input_image, d, single_line_number=1)
log(uov_input_image, d)
yield_result(async_task, uov_input_image, do_not_show_finished_images=True)
return
@ -779,12 +781,13 @@ def worker():
('Refiner Switch', refiner_switch),
('Sampler', sampler_name),
('Scheduler', scheduler_name),
('Seed', task['task_seed'])
('Seed', task['task_seed']),
]
for n, w in loras:
for li, (n, w) in enumerate(loras):
if n != 'None':
d.append((f'LoRA [{n}] weight', w))
log(x, d, single_line_number=3)
d.append((f'LoRA {li + 1}', f'{n} : {w}'))
d.append(('Version', 'v' + fooocus_version.version))
log(x, d)
yield_result(async_task, imgs, do_not_show_finished_images=len(tasks) == 1, progressbar_index=int(15.0 + 85.0 * float((current_task_id + 1) * steps) / float(all_steps)))
except ldm_patched.modules.model_management.InterruptProcessingException as e:
@ -806,12 +809,12 @@ def worker():
task = async_tasks.pop(0)
try:
handler(task)
except:
traceback.print_exc()
finally:
build_image_wall(task)
task.yields.append(['finish', task.results])
pipeline.prepare_text_encoder(async_call=True)
except:
traceback.print_exc()
task.yields.append(['finish', task.results])
pass

2
modules/core.py
View File

@ -191,7 +191,7 @@ def encode_vae_inpaint(vae, pixels, mask):
latent_mask = mask[:, None, :, :]
latent_mask = torch.nn.functional.interpolate(latent_mask, size=(H * 8, W * 8), mode="bilinear").round()
latent_mask = torch.nn.functional.max_pool2d(latent_mask, (8, 8)).round()
latent_mask = torch.nn.functional.max_pool2d(latent_mask, (8, 8)).round().to(latent)
return latent, latent_mask

144
modules/meta_parser.py Normal file
View File

@ -0,0 +1,144 @@
import json
import gradio as gr
import modules.config
def load_parameter_button_click(raw_prompt_txt):
loaded_parameter_dict = json.loads(raw_prompt_txt)
assert isinstance(loaded_parameter_dict, dict)
results = [True, 1]
try:
h = loaded_parameter_dict.get('Prompt', None)
assert isinstance(h, str)
results.append(h)
except:
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Negative Prompt', None)
assert isinstance(h, str)
results.append(h)
except:
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Styles', None)
h = eval(h)
assert isinstance(h, list)
results.append(h)
except:
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Performance', None)
assert isinstance(h, str)
results.append(h)
except:
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Resolution', None)
width, height = eval(h)
formatted = modules.config.add_ratio(f'{width}*{height}')
if formatted in modules.config.available_aspect_ratios:
results.append(formatted)
results.append(-1)
results.append(-1)
else:
results.append(gr.update())
results.append(width)
results.append(height)
except:
results.append(gr.update())
results.append(gr.update())
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Sharpness', None)
assert h is not None
h = float(h)
results.append(h)
except:
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Guidance Scale', None)
assert h is not None
h = float(h)
results.append(h)
except:
results.append(gr.update())
try:
h = loaded_parameter_dict.get('ADM Guidance', None)
p, n, e = eval(h)
results.append(float(p))
results.append(float(n))
results.append(float(e))
except:
results.append(gr.update())
results.append(gr.update())
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Base Model', None)
assert isinstance(h, str)
results.append(h)
except:
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Refiner Model', None)
assert isinstance(h, str)
results.append(h)
except:
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Refiner Switch', None)
assert h is not None
h = float(h)
results.append(h)
except:
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Sampler', None)
assert isinstance(h, str)
results.append(h)
except:
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Scheduler', None)
assert isinstance(h, str)
results.append(h)
except:
results.append(gr.update())
try:
h = loaded_parameter_dict.get('Seed', None)
assert h is not None
h = int(h)
results.append(False)
results.append(h)
except:
results.append(gr.update())
results.append(gr.update())
results.append(gr.update(visible=True))
results.append(gr.update(visible=False))
for i in range(1, 6):
try:
n, w = loaded_parameter_dict.get(f'LoRA {i}').split(' : ')
w = float(w)
results.append(n)
results.append(w)
except:
results.append(gr.update())
results.append(gr.update())
return results

68
modules/patch.py
View File

@ -25,6 +25,8 @@ import modules.constants as constants
from ldm_patched.modules.samplers import calc_cond_uncond_batch
from ldm_patched.k_diffusion.sampling import BatchedBrownianTree
from ldm_patched.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, apply_control
from modules.patch_precision import patch_all_precision
from modules.patch_clip import patch_all_clip
sharpness = 2.0
@ -214,16 +216,20 @@ def compute_cfg(uncond, cond, cfg_scale, t):
def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None):
if math.isclose(cond_scale, 1.0):
return calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0]
global eps_record
if math.isclose(cond_scale, 1.0):
final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0]
if eps_record is not None:
eps_record = ((x - final_x0) / timestep).cpu()
return final_x0
positive_x0, negative_x0 = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options)
positive_eps = x - positive_x0
negative_eps = x - negative_x0
sigma = timestep
alpha = 0.001 * sharpness * global_diffusion_progress
@ -234,7 +240,7 @@ def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, mode
cfg_scale=cond_scale, t=global_diffusion_progress)
if eps_record is not None:
eps_record = (final_eps / sigma).cpu()
eps_record = (final_eps / timestep).cpu()
return x - final_eps
@ -265,11 +271,11 @@ def sdxl_encode_adm_patched(self, **kwargs):
height = float(height) * positive_adm_scale
def embedder(number_list):
h = [self.embedder(torch.Tensor([number])) for number in number_list]
y = torch.flatten(torch.cat(h)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
return y
h = self.embedder(torch.tensor(number_list, dtype=torch.float32))
h = torch.flatten(h).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
return h
width, height = round_to_64(width), round_to_64(height)
width, height = int(width), int(height)
target_width, target_height = round_to_64(target_width), round_to_64(target_height)
adm_emphasized = embedder([height, width, 0, 0, target_height, target_width])
@ -281,46 +287,6 @@ def sdxl_encode_adm_patched(self, **kwargs):
return final_adm
def encode_token_weights_patched_with_a1111_method(self, token_weight_pairs):
to_encode = list()
max_token_len = 0
has_weights = False
for x in token_weight_pairs:
tokens = list(map(lambda a: a[0], x))
max_token_len = max(len(tokens), max_token_len)
has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))
to_encode.append(tokens)
sections = len(to_encode)
if has_weights or sections == 0:
to_encode.append(ldm_patched.modules.sd1_clip.gen_empty_tokens(self.special_tokens, max_token_len))
out, pooled = self.encode(to_encode)
if pooled is not None:
first_pooled = pooled[0:1].to(ldm_patched.modules.model_management.intermediate_device())
else:
first_pooled = pooled
output = []
for k in range(0, sections):
z = out[k:k + 1]
if has_weights:
original_mean = z.mean()
z_empty = out[-1]
for i in range(len(z)):
for j in range(len(z[i])):
weight = token_weight_pairs[k][j][1]
if weight != 1.0:
z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]
new_mean = z.mean()
z = z * (original_mean / new_mean)
output.append(z)
if len(output) == 0:
return out[-1:].to(ldm_patched.modules.model_management.intermediate_device()), first_pooled
return torch.cat(output, dim=-2).to(ldm_patched.modules.model_management.intermediate_device()), first_pooled
def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None):
if inpaint_worker.current_task is not None:
latent_processor = self.inner_model.inner_model.process_latent_in
@ -514,6 +480,9 @@ def build_loaded(module, loader_name):
def patch_all():
patch_all_precision()
patch_all_clip()
if not hasattr(ldm_patched.modules.model_management, 'load_models_gpu_origin'):
ldm_patched.modules.model_management.load_models_gpu_origin = ldm_patched.modules.model_management.load_models_gpu
@ -522,7 +491,6 @@ def patch_all():
ldm_patched.controlnet.cldm.ControlNet.forward = patched_cldm_forward
ldm_patched.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward
ldm_patched.modules.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = encode_token_weights_patched_with_a1111_method
ldm_patched.modules.samplers.KSamplerX0Inpaint.forward = patched_KSamplerX0Inpaint_forward
ldm_patched.k_diffusion.sampling.BrownianTreeNoiseSampler = BrownianTreeNoiseSamplerPatched
ldm_patched.modules.samplers.sampling_function = patched_sampling_function

213
modules/patch_clip.py Normal file
View File

@ -0,0 +1,213 @@
# Consistent with Kohya/A1111 to reduce differences between model training and inference.
import os
import torch
import ldm_patched.controlnet.cldm
import ldm_patched.k_diffusion.sampling
import ldm_patched.ldm.modules.attention
import ldm_patched.ldm.modules.diffusionmodules.model
import ldm_patched.ldm.modules.diffusionmodules.openaimodel
import ldm_patched.ldm.modules.diffusionmodules.openaimodel
import ldm_patched.modules.args_parser
import ldm_patched.modules.model_base
import ldm_patched.modules.model_management
import ldm_patched.modules.model_patcher
import ldm_patched.modules.samplers
import ldm_patched.modules.sd
import ldm_patched.modules.sd1_clip
import ldm_patched.modules.clip_vision
import ldm_patched.modules.model_management as model_management
import ldm_patched.modules.ops as ops
import contextlib
from transformers import CLIPTextModel, CLIPTextConfig, modeling_utils, CLIPVisionConfig, CLIPVisionModelWithProjection
@contextlib.contextmanager
def use_patched_ops(operations):
op_names = ['Linear', 'Conv2d', 'Conv3d', 'GroupNorm', 'LayerNorm']
backups = {op_name: getattr(torch.nn, op_name) for op_name in op_names}
try:
for op_name in op_names:
setattr(torch.nn, op_name, getattr(operations, op_name))
yield
finally:
for op_name in op_names:
setattr(torch.nn, op_name, backups[op_name])
return
def patched_encode_token_weights(self, token_weight_pairs):
to_encode = list()
max_token_len = 0
has_weights = False
for x in token_weight_pairs:
tokens = list(map(lambda a: a[0], x))
max_token_len = max(len(tokens), max_token_len)
has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))
to_encode.append(tokens)
sections = len(to_encode)
if has_weights or sections == 0:
to_encode.append(ldm_patched.modules.sd1_clip.gen_empty_tokens(self.special_tokens, max_token_len))
out, pooled = self.encode(to_encode)
if pooled is not None:
first_pooled = pooled[0:1].to(ldm_patched.modules.model_management.intermediate_device())
else:
first_pooled = pooled
output = []
for k in range(0, sections):
z = out[k:k + 1]
if has_weights:
original_mean = z.mean()
z_empty = out[-1]
for i in range(len(z)):
for j in range(len(z[i])):
weight = token_weight_pairs[k][j][1]
if weight != 1.0:
z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]
new_mean = z.mean()
z = z * (original_mean / new_mean)
output.append(z)
if len(output) == 0:
return out[-1:].to(ldm_patched.modules.model_management.intermediate_device()), first_pooled
return torch.cat(output, dim=-2).to(ldm_patched.modules.model_management.intermediate_device()), first_pooled
def patched_SDClipModel__init__(self, max_length=77, freeze=True, layer="last", layer_idx=None,
textmodel_json_config=None, dtype=None, special_tokens=None,
layer_norm_hidden_state=True, **kwargs):
torch.nn.Module.__init__(self)
assert layer in self.LAYERS
if special_tokens is None:
special_tokens = {"start": 49406, "end": 49407, "pad": 49407}
if textmodel_json_config is None:
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(ldm_patched.modules.sd1_clip.__file__)),
"sd1_clip_config.json")
config = CLIPTextConfig.from_json_file(textmodel_json_config)
self.num_layers = config.num_hidden_layers
with use_patched_ops(ops.manual_cast):
with modeling_utils.no_init_weights():
self.transformer = CLIPTextModel(config)
if dtype is not None:
self.transformer.to(dtype)
self.transformer.text_model.embeddings.to(torch.float32)
if freeze:
self.freeze()
self.max_length = max_length
self.layer = layer
self.layer_idx = None
self.special_tokens = special_tokens
self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1]))
self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
self.enable_attention_masks = False
self.layer_norm_hidden_state = layer_norm_hidden_state
if layer == "hidden":
assert layer_idx is not None
assert abs(layer_idx) < self.num_layers
self.clip_layer(layer_idx)
self.layer_default = (self.layer, self.layer_idx)
def patched_SDClipModel_forward(self, tokens):
backup_embeds = self.transformer.get_input_embeddings()
device = backup_embeds.weight.device
tokens = self.set_up_textual_embeddings(tokens, backup_embeds)
tokens = torch.LongTensor(tokens).to(device)
attention_mask = None
if self.enable_attention_masks:
attention_mask = torch.zeros_like(tokens)
max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1
for x in range(attention_mask.shape[0]):
for y in range(attention_mask.shape[1]):
attention_mask[x, y] = 1
if tokens[x, y] == max_token:
break
outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask,
output_hidden_states=self.layer == "hidden")
self.transformer.set_input_embeddings(backup_embeds)
if self.layer == "last":
z = outputs.last_hidden_state
elif self.layer == "pooled":
z = outputs.pooler_output[:, None, :]
else:
z = outputs.hidden_states[self.layer_idx]
if self.layer_norm_hidden_state:
z = self.transformer.text_model.final_layer_norm(z)
if hasattr(outputs, "pooler_output"):
pooled_output = outputs.pooler_output.float()
else:
pooled_output = None
if self.text_projection is not None and pooled_output is not None:
pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()
return z.float(), pooled_output
def patched_ClipVisionModel__init__(self, json_config):
config = CLIPVisionConfig.from_json_file(json_config)
self.load_device = ldm_patched.modules.model_management.text_encoder_device()
self.offload_device = ldm_patched.modules.model_management.text_encoder_offload_device()
if ldm_patched.modules.model_management.should_use_fp16(self.load_device, prioritize_performance=False):
self.dtype = torch.float16
else:
self.dtype = torch.float32
with use_patched_ops(ops.manual_cast):
with modeling_utils.no_init_weights():
self.model = CLIPVisionModelWithProjection(config)
self.model.to(self.dtype)
self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(
self.model,
load_device=self.load_device,
offload_device=self.offload_device
)
def patched_ClipVisionModel_encode_image(self, image):
ldm_patched.modules.model_management.load_model_gpu(self.patcher)
pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(self.load_device))
outputs = self.model(pixel_values=pixel_values, output_hidden_states=True)
for k in outputs:
t = outputs[k]
if t is not None:
if k == 'hidden_states':
outputs["penultimate_hidden_states"] = t[-2].to(ldm_patched.modules.model_management.intermediate_device())
outputs["hidden_states"] = None
else:
outputs[k] = t.to(ldm_patched.modules.model_management.intermediate_device())
return outputs
def patch_all_clip():
ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = patched_encode_token_weights
ldm_patched.modules.sd1_clip.SDClipModel.__init__ = patched_SDClipModel__init__
ldm_patched.modules.sd1_clip.SDClipModel.forward = patched_SDClipModel_forward
ldm_patched.modules.clip_vision.ClipVisionModel.__init__ = patched_ClipVisionModel__init__
ldm_patched.modules.clip_vision.ClipVisionModel.encode_image = patched_ClipVisionModel_encode_image
return

60
modules/patch_precision.py Normal file
View File

@ -0,0 +1,60 @@
# Consistent with Kohya to reduce differences between model training and inference.
import torch
import math
import einops
import numpy as np
import ldm_patched.ldm.modules.diffusionmodules.openaimodel
import ldm_patched.modules.model_sampling
import ldm_patched.modules.sd1_clip
from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule
def patched_timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
# Consistent with Kohya to reduce differences between model training and inference.
if not repeat_only:
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=timesteps.device)
args = timesteps[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
else:
embedding = einops.repeat(timesteps, 'b -> b d', d=dim)
return embedding
def patched_register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
# Consistent with Kohya to reduce differences between model training and inference.
if given_betas is not None:
betas = given_betas
else:
betas = make_beta_schedule(
beta_schedule,
timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
sigmas = torch.tensor(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, dtype=torch.float32)
self.set_sigmas(sigmas)
return
def patch_all_precision():
ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding = patched_timestep_embedding
ldm_patched.modules.model_sampling.ModelSamplingDiscrete._register_schedule = patched_register_schedule
return

77
modules/private_logger.py
View File

@ -1,6 +1,8 @@
import os
import args_manager
import modules.config
import json
import urllib.parse
from PIL import Image
from modules.util import generate_temp_filename
@ -16,7 +18,7 @@ def get_current_html_path():
return html_name
def log(img, dic, single_line_number=3):
def log(img, dic):
if args_manager.args.disable_image_log:
return
@ -25,36 +27,67 @@ def log(img, dic, single_line_number=3):
Image.fromarray(img).save(local_temp_filename)
html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')
existing_log = log_cache.get(html_name, None)
css_styles = (
"<style>"
"body { background-color: #121212; color: #E0E0E0; } "
"a { color: #BB86FC; } "
".metadata { border-collapse: collapse; width: 100%; } "
".metadata .key { width: 15%; } "
".metadata .value { width: 85%; font-weight: bold; } "
".metadata th, .metadata td { border: 1px solid #4d4d4d; padding: 4px; } "
".image-container img { height: auto; max-width: 512px; display: block; padding-right:10px; } "
".image-container div { text-align: center; padding: 4px; } "
"hr { border-color: gray; } "
"button { background-color: black; color: white; border: 1px solid grey; border-radius: 5px; padding: 5px 10px; text-align: center; display: inline-block; font-size: 16px; cursor: pointer; }"
"button:hover {background-color: grey; color: black;}"
"</style>"
)
if existing_log is None:
js = (
"<script>"
"function to_clipboard(txt) { "
"txt = decodeURIComponent(txt);"
"navigator.clipboard.writeText(txt);"
"alert('Copied to Clipboard!\\nPaste to prompt area to load parameters.\\nCurrent clipboard content is:\\n\\n' + txt);"
"}"
"</script>"
)
begin_part = f"<html><head><title>Fooocus Log {date_string}</title>{css_styles}</head><body>{js}<p>Fooocus Log {date_string} (private)</p>\n<p>All images are clean, without any hidden data/meta, and safe to share with others.</p><!--fooocus-log-split-->\n\n"
end_part = f'\n<!--fooocus-log-split--></body></html>'
middle_part = log_cache.get(html_name, "")
if middle_part == "":
if os.path.exists(html_name):
existing_log = open(html_name, encoding='utf-8').read()
else:
existing_log = f'<p>Fooocus Log {date_string} (private)</p>\n<p>All images do not contain any hidden data.</p>'
existing_split = open(html_name, 'r', encoding='utf-8').read().split('<!--fooocus-log-split-->')
if len(existing_split) == 3:
middle_part = existing_split[1]
else:
middle_part = existing_split[0]
div_name = only_name.replace('.', '_')
item = f'<div id="{div_name}">\n'
item += "<table><tr>"
item += f"<td><img src=\"{only_name}\" width=auto height=100% loading=lazy style=\"height:auto;max-width:512px\" onerror=\"document.getElementById('{div_name}').style.display = 'none';\"></img></p></td>"
item += f"<td style=\"padding-left:10px;\"><p>{only_name}</p>\n"
for i, (k, v) in enumerate(dic):
if i < single_line_number:
item += f"<p>{k}: <b>{v}</b></p>\n"
else:
if (i - single_line_number) % 2 == 0:
item += f"<p>{k}: <b>{v}</b>, "
else:
item += f"{k}: <b>{v}</b></p>\n"
item = f"<div id=\"{div_name}\" class=\"image-container\"><hr><table><tr>\n"
item += f"<td><a href=\"{only_name}\" target=\"_blank\"><img src='{only_name}' onerror=\"this.closest('.image-container').style.display='none';\" loading='lazy'></img></a><div>{only_name}</div></td>"
item += "<td><table class='metadata'>"
for key, value in dic:
value_txt = str(value).replace('\n', ' </br> ')
item += f"<tr><td class='key'>{key}</td><td class='value'>{value_txt}</td></tr>\n"
item += "</table>"
js_txt = urllib.parse.quote(json.dumps({k: v for k, v in dic}, indent=0), safe='')
item += f"</br><button onclick=\"to_clipboard('{js_txt}')\">Copy to Clipboard</button>"
item += "</td>"
item += "</tr></table><hr></div>\n"
existing_log = item + existing_log
item += "</tr></table></div>\n\n"
middle_part = item + middle_part
with open(html_name, 'w', encoding='utf-8') as f:
f.write(existing_log)
f.write(begin_part + middle_part + end_part)
print(f'Image generated with private log at: {html_name}')
log_cache[html_name] = existing_log
log_cache[html_name] = middle_part
return

3
modules/sdxl_styles.py
View File

@ -31,7 +31,8 @@ for x in ['sdxl_styles_fooocus.json',
'sdxl_styles_sai.json',
'sdxl_styles_mre.json',
'sdxl_styles_twri.json',
'sdxl_styles_diva.json']:
'sdxl_styles_diva.json',
'sdxl_styles_marc_k3nt3l.json']:
if x in styles_files:
styles_files.remove(x)
styles_files.append(x)

13
modules/style_sorter.py
View File

@ -15,11 +15,14 @@ def try_load_sorted_styles(style_names, default_selected):
try:
if os.path.exists('sorted_styles.json'):
with open('sorted_styles.json', 'rt', encoding='utf-8') as fp:
sorted_styles = json.load(fp)
if len(sorted_styles) == len(all_styles):
if all(x in all_styles for x in sorted_styles):
if all(x in sorted_styles for x in all_styles):
all_styles = sorted_styles
sorted_styles = []
for x in json.load(fp):
if x in all_styles:
sorted_styles.append(x)
for x in all_styles:
if x not in sorted_styles:
sorted_styles.append(x)
all_styles = sorted_styles
except Exception as e:
print('Load style sorting failed.')
print(e)

6
readme.md
View File

@ -28,6 +28,8 @@ Fooocus has simplified the installation. Between pressing "download" and generat
`[1]` David Holz, 2019.
**Recently many fake websites exist on Google when you search “fooocus”. Do not trust those – here is the only official source of Fooocus.**
## [Installing Fooocus](#download)
# Moving from Midjourney to Fooocus
@ -121,7 +123,7 @@ See also the common problems and troubleshoots [here](troubleshoot.md).
In Colab, you can modify the last line to `!python entry_with_update.py --share` or `!python entry_with_update.py --preset anime --share` or `!python entry_with_update.py --preset realistic --share` for Fooocus Default/Anime/Realistic Edition.
Note that this Colab will disable refiner by default because Colab free's resource is relatively limited.
Note that this Colab will disable refiner by default because Colab free's resource is relatively limited (and some "big" features like image prompt may cause free-tier Colab to disconnect). We make sure that basic text-to-image is always working on free-tier Colab.
Thanks to [camenduru](https://github.com/camenduru)!
@ -384,7 +386,7 @@ See also [About Forking and Promotion of Forks](https://github.com/lllyasviel/Fo
## Thanks
Special thanks to [twri](https://github.com/twri) and [3Diva](https://github.com/3Diva) for creating additional SDXL styles available in Fooocus.
Special thanks to [twri](https://github.com/twri) and [3Diva](https://github.com/3Diva) and [Marc K3nt3L](https://github.com/K3nt3L) for creating additional SDXL styles available in Fooocus. Thanks [daswer123](https://github.com/daswer123) for contributing the Canvas Zoom!
## Update Log

1
requirements_versions.txt
View File

@ -8,7 +8,6 @@ Pillow==9.2.0
scipy==1.9.3
tqdm==4.64.1
psutil==5.9.5
numpy==1.23.5
pytorch_lightning==1.9.4
omegaconf==2.2.3
gradio==3.41.2

312
sdxl_styles/sdxl_styles_marc_k3nt3l.json Normal file
View File

@ -0,0 +1,312 @@
[
{
"name": "MK Chromolithography",
"prompt": "Chromolithograph {prompt}. Vibrant colors, intricate details, rich color saturation, meticulous registration, multi-layered printing, decorative elements, historical charm, artistic reproductions, commercial posters, nostalgic, ornate compositions.",
"negative_prompt": "monochromatic, simple designs, limited color palette, imprecise registration, minimalistic, modern aesthetic, digital appearance."
},
{
"name": "MK Cross Processing Print",
"prompt": "Cross processing print {prompt}. Experimental color shifts, unconventional tonalities, vibrant and surreal hues, heightened contrasts, unpredictable results, artistic unpredictability, retro and vintage feel, dynamic color interplay, abstract and dreamlike.",
"negative_prompt": "predictable color tones, traditional processing, realistic color representation, subdued contrasts, standard photographic aesthetics."
},
{
"name": "MK Dufaycolor Photograph",
"prompt": "Dufaycolor photograph {prompt}. Vintage color palette, distinctive color rendering, soft and dreamy atmosphere, historical charm, unique color process, grainy texture, evocative mood, nostalgic aesthetic, hand-tinted appearance, artistic patina.",
"negative_prompt": "modern color reproduction, hyperrealistic tones, sharp and clear details, digital precision, contemporary aesthetic."
},
{
"name": "MK Herbarium",
"prompt": "Herbarium drawing{prompt}. Botanical accuracy, old botanical book illustration, detailed illustrations, pressed plants, delicate and precise linework, scientific documentation, meticulous presentation, educational purpose, organic compositions, timeless aesthetic, naturalistic beauty.",
"negative_prompt": "abstract representation, vibrant colors, artistic interpretation, chaotic compositions, fantastical elements, digital appearance."
},
{
"name": "MK Punk Collage",
"prompt": "punk collage style {prompt} . mixed media, papercut,textured paper, overlapping, ripped posters, safety pins, chaotic layers, graffiti-style elements, anarchy symbols, vintage photos, cut-and-paste aesthetic, bold typography, distorted images, political messages, urban decay, distressed textures, newspaper clippings, spray paint, rebellious icons, DIY spirit, vivid colors, punk band logos, edgy and raw compositions, ",
"negative_prompt": "conventional,blurry, noisy, low contrast"
},
{
"name": "MK mosaic",
"prompt": "mosaic style {prompt} . fragmented, assembled, colorful, highly detailed",
"negative_prompt": "whole, unbroken, monochrome"
},
{
"name": "MK Van Gogh",
"prompt": "Oil painting by Van Gogh {prompt} . Expressive, impasto, swirling brushwork, vibrant, brush strokes, Brushstroke-heavy, Textured, Impasto, Colorful, Dynamic, Bold, Distinctive, Vibrant, Whirling, Expressive, Dramatic, Swirling, Layered, Intense, Contrastive, Atmospheric, Luminous, Textural, Evocative, SpiraledVan Gogh style",
"negative_prompt": "realistic, photorealistic, calm, straight lines, signature, frame, text, watermark"
},
{
"name": "MK Coloring Book",
"prompt": "centered black and white high contrast line drawing, coloring book style,{prompt} . monochrome, blank white background",
"negative_prompt": "greyscale, gradients,shadows,shadow, colored, Red, Blue, Yellow, Green, Orange, Purple, Pink, Brown, Gray, Beige, Turquoise, Lavender, Cyan, Magenta, Olive, Indigo, black background"
},
{
"name": "MK Singer Sargent",
"prompt": "Oil painting by John Singer Sargent {prompt}. Elegant, refined, masterful technique,realistic portrayal, subtle play of light, captivating expression, rich details, harmonious colors, skillful composition, brush strokes, chiaroscuro.",
"negative_prompt": "realistic, photorealistic, abstract, overly stylized, excessive contrasts, distorted,bright colors,disorder."
},
{
"name": "MK Pollock",
"prompt": "Oil painting by Jackson Pollock {prompt}. Abstract expressionism, drip painting, chaotic composition, energetic, spontaneous, unconventional technique, dynamic, bold, distinctive, vibrant, intense, expressive, energetic, layered, non-representational, gestural.",
"negative_prompt": "(realistic:1.5), (photorealistic:1.5), representational, calm, ordered composition, precise lines, detailed forms, subdued colors, quiet, static, traditional, figurative."
},
{
"name": "MK Basquiat",
"prompt": "Artwork by Jean-Michel Basquiat {prompt}. Neo-expressionism, street art influence, graffiti-inspired, raw, energetic, bold colors, dynamic composition, chaotic, layered, textural, expressive, spontaneous, distinctive, symbolic,energetic brushstrokes.",
"negative_prompt": "(realistic:1.5), (photorealistic:1.5), calm, precise lines, conventional composition, subdued"
},
{
"name": "MK Andy Warhol",
"prompt": "Artwork in the style of Andy Warhol {prompt}. Pop art, vibrant colors, bold compositions, repetition of iconic imagery, celebrity culture, commercial aesthetics, mass production influence, stylized simplicity, cultural commentary, graphical elements, distinctive portraits.",
"negative_prompt": "subdued colors, realistic, lack of repetition, minimalistic."
},
{
"name": "MK Halftone print",
"prompt": "Halftone print of {prompt}. Dot matrix pattern, grayscale tones, vintage aesthetic, newspaper print vibe, stylized dots, visual texture, black and white contrasts, retro appearance, artistic pointillism,pop culture, (Roy Lichtenstein style:1.5).",
"negative_prompt": "smooth gradients, continuous tones, vibrant colors."
},
{
"name": "MK Gond Painting",
"prompt": "Gond painting {prompt}. Intricate patterns, vibrant colors, detailed motifs, nature-inspired themes, tribal folklore, fine lines, intricate detailing, storytelling compositions, mystical and folkloric, cultural richness.",
"negative_prompt": "monochromatic, abstract shapes, minimalistic."
},
{
"name": "MK Albumen Print",
"prompt": "Albumen print {prompt}. Sepia tones, fine details, subtle tonal gradations, delicate highlights, vintage aesthetic, soft and muted atmosphere, historical charm, rich textures, meticulous craftsmanship, classic photographic technique, vignetting.",
"negative_prompt": "vibrant colors, high contrast, modern, digital appearance, sharp details, contemporary style."
},
{
"name": "MK Aquatint Print",
"prompt": "Aquatint print {prompt}. Soft tonal gradations, atmospheric effects, velvety textures, rich contrasts, fine details, etching process, delicate lines, nuanced shading, expressive and moody atmosphere, artistic depth.",
"negative_prompt": "sharp contrasts, bold lines, minimalistic."
},
{
"name": "MK Anthotype Print",
"prompt": "Anthotype print {prompt}. Monochrome dye, soft and muted colors, organic textures, ephemeral and delicate appearance, low details, watercolor canvas, low contrast, overexposed, silhouette, textured paper.",
"negative_prompt": "vibrant synthetic dyes, bold and saturated colors."
},
{
"name": "MK Inuit Carving",
"prompt": "A sculpture made of ivory, {prompt} made of . Sculptures, Inuit art style, intricate carvings, natural materials, storytelling motifs, arctic wildlife themes, symbolic representations, cultural traditions, earthy tones, harmonious compositions, spiritual and mythological elements.",
"negative_prompt": "abstract, vibrant colors."
},
{
"name": "MK Bromoil Print",
"prompt": "Bromoil print {prompt}. Painterly effects, sepia tones, textured surfaces, rich contrasts, expressive brushwork, tonal variations, vintage aesthetic, atmospheric mood, handmade quality, artistic experimentation, darkroom craftsmanship, vignetting.",
"negative_prompt": "smooth surfaces, minimal brushwork, contemporary digital appearance."
},
{
"name": "MK Calotype Print",
"prompt": "Calotype print {prompt}. Soft focus, subtle tonal range, paper negative process, fine details, vintage aesthetic, artistic experimentation, atmospheric mood, early photographic charm, handmade quality, vignetting.",
"negative_prompt": "sharp focus, bold contrasts, modern aesthetic, digital photography."
},
{
"name": "MK Color Sketchnote",
"prompt": "Color sketchnote {prompt}. Hand-drawn elements, vibrant colors, visual hierarchy, playful illustrations, varied typography, graphic icons, organic and dynamic layout, personalized touches, creative expression, engaging storytelling.",
"negative_prompt": "monochromatic, geometric layout."
},
{
"name": "MK Cibulak Porcelain",
"prompt": "A sculpture made of blue pattern porcelain of {prompt}. Classic design, blue and white color scheme, intricate detailing, floral motifs, onion-shaped elements, historical charm, rococo, white ware, cobalt blue, underglaze pattern, fine craftsmanship, traditional elegance, delicate patterns, vintage aesthetic, Meissen, Blue Onion pattern, Cibulak.",
"negative_prompt": "tea, teapot, cup, teacup,bright colors, bold and modern design, absence of intricate detailing, lack of floral motifs, non-traditional shapes."
},
{
"name": "MK Alcohol Ink Art",
"prompt": "Alcohol ink art {prompt}. Fluid and vibrant colors, unpredictable patterns, organic textures, translucent layers, abstract compositions, ethereal and dreamy effects, free-flowing movement, expressive brushstrokes, contemporary aesthetic, wet textured paper.",
"negative_prompt": "monochromatic, controlled patterns."
},
{
"name": "MK One Line Art",
"prompt": "One line art {prompt}. Continuous and unbroken black line, minimalistic, simplicity, economical use of space, flowing and dynamic, symbolic representations, contemporary aesthetic, evocative and abstract, white background.",
"negative_prompt": "disjointed lines, complexity, complex detailing."
},
{
"name": "MK Blacklight Paint",
"prompt": "Blacklight paint {prompt}. Fluorescent pigments, vibrant and surreal colors, ethereal glow, otherworldly effects, dynamic and psychedelic compositions, neon aesthetics, transformative in ultraviolet light, contemporary and experimental.",
"negative_prompt": "muted colors, traditional and realistic compositions."
},
{
"name": "MK Carnival Glass",
"prompt": "A sculpture made of Carnival glass, {prompt}. Iridescent surfaces, vibrant colors, intricate patterns, opalescent hues, reflective and prismatic effects, Art Nouveau and Art Deco influences, vintage charm, intricate detailing, lustrous and luminous appearance, Carnival Glass style.",
"negative_prompt": "non-iridescent surfaces, muted colors, absence of intricate patterns, lack of opalescent hues, modern and minimalist aesthetic."
},
{
"name": "MK Cyanotype Print",
"prompt": "Cyanotype print {prompt}. Prussian blue tones, distinctive coloration, high contrast, blueprint aesthetics, atmospheric mood, sun-exposed paper, silhouette effects, delicate details, historical charm, handmade and experimental quality.",
"negative_prompt": "vibrant colors, low contrast, modern and polished appearance."
},
{
"name": "MK Cross-Stitching",
"prompt": "Cross-stitching {prompt}. Intricate patterns, embroidery thread, sewing, fine details, precise stitches, textile artistry, symmetrical designs, varied color palette, traditional and contemporary motifs, handmade and crafted,canvas, nostalgic charm.",
"negative_prompt": "paper, paint, ink, photography."
},
{
"name": "MK Encaustic Paint",
"prompt": "Encaustic paint {prompt}. Textured surfaces, translucent layers, luminous quality, wax medium, rich color saturation, fluid and organic shapes, contemporary and historical influences, mixed media elements, atmospheric depth.",
"negative_prompt": "flat surfaces, opaque layers, lack of wax medium, muted color palette, absence of textured surfaces, non-mixed media."
},
{
"name": "MK Embroidery",
"prompt": "Embroidery {prompt}. Intricate stitching, embroidery thread, fine details, varied thread textures, textile artistry, embellished surfaces, diverse color palette, traditional and contemporary motifs, handmade and crafted, tactile and ornate.",
"negative_prompt": "minimalist, monochromatic."
},
{
"name": "MK Gyotaku",
"prompt": "Gyotaku {prompt}. Fish impressions, realistic details, ink rubbings, textured surfaces, traditional Japanese art form, nature-inspired compositions, artistic representation of marine life, black and white contrasts, cultural significance.",
"negative_prompt": "photography."
},
{
"name": "MK Luminogram",
"prompt": "Luminogram {prompt}. Photogram technique, ethereal and abstract effects, light and shadow interplay, luminous quality, experimental process, direct light exposure, unique and unpredictable results, artistic experimentation.",
"negative_prompt": ""
},
{
"name": "MK Lite Brite Art",
"prompt": "Lite Brite art {prompt}. Luminous and colorful designs, pixelated compositions, retro aesthetic, glowing effects, creative patterns, interactive and playful, nostalgic charm, vibrant and dynamic arrangements.",
"negative_prompt": "monochromatic."
},
{
"name": "MK Mokume-gane",
"prompt": "Mokume-gane {prompt}. Wood-grain patterns, mixed metal layers, intricate and organic designs, traditional Japanese metalwork, harmonious color combinations, artisanal craftsmanship, unique and layered textures, cultural and historical significance.",
"negative_prompt": "uniform metal surfaces."
},
{
"name": "Pebble Art",
"prompt": "a sculpture made of peebles, {prompt}. Pebble art style,natural materials, textured surfaces, balanced compositions, organic forms, harmonious arrangements, tactile and 3D effects, beach-inspired aesthetic, creative storytelling, artisanal craftsmanship.",
"negative_prompt": "non-natural materials, lack of textured surfaces, imbalanced compositions, absence of organic forms, non-tactile appearance."
},
{
"name": "MK Palekh",
"prompt": "Palekh art {prompt}. Miniature paintings, intricate details, vivid colors, folkloric themes, lacquer finish, storytelling compositions, symbolic elements, Russian folklore influence, cultural and historical significance.",
"negative_prompt": "large-scale paintings."
},
{
"name": "MK Suminagashi",
"prompt": "Suminagashi {prompt}. Floating ink patterns, marbled effects, delicate and ethereal designs, water-based ink, fluid and unpredictable compositions, meditative process, monochromatic or subtle color palette, Japanese artistic tradition.",
"negative_prompt": "vibrant and bold color palette."
},
{
"name": "MK Scrimshaw",
"prompt": "A Scrimshaw engraving of {prompt}. Intricate engravings on a spermwhale's teeth, marine motifs, detailed scenes, nautical themes, black and white contrasts, historical craftsmanship, artisanal carving, storytelling compositions, maritime heritage.",
"negative_prompt": "colorful, modern."
},
{
"name": "MK Shibori",
"prompt": "Shibori {prompt}. Textured fabric, intricate patterns, resist-dyeing technique, indigo or vibrant colors, organic and flowing designs, Japanese textile art, cultural tradition, tactile and visual interest.",
"negative_prompt": "monochromatic."
},
{
"name": "MK Vitreous Enamel",
"prompt": "A sculpture made of Vitreous enamel {prompt}. Smooth and glossy surfaces, vibrant colors, glass-like finish, durable and resilient, intricate detailing, traditional and contemporary applications, artistic craftsmanship, jewelry and decorative objects, , Vitreous enamel, colored glass.",
"negative_prompt": "rough surfaces, muted colors."
},
{
"name": "MK Ukiyo-e",
"prompt": "Ukiyo-e {prompt}. Woodblock prints, vibrant colors, intricate details, depictions of landscapes, kabuki actors, beautiful women, cultural scenes, traditional Japanese art, artistic craftsmanship, historical significance.",
"negative_prompt": "absence of woodblock prints, muted colors, lack of intricate details, non-traditional Japanese themes, absence of cultural scenes."
},
{
"name": "MK vintage-airline-poster",
"prompt": "vintage airline poster {prompt} . classic aviation fonts, pastel colors, elegant aircraft illustrations, scenic destinations, distressed textures, retro travel allure",
"negative_prompt": "modern fonts, bold colors, hyper-realistic, sleek design"
},
{
"name": "MK vintage-travel-poster",
"prompt": "vintage travel poster {prompt} . retro fonts, muted colors, scenic illustrations, iconic landmarks, distressed textures, nostalgic vibes",
"negative_prompt": "modern fonts, vibrant colors, hyper-realistic, sleek design"
},
{
"name": "MK bauhaus-style",
"prompt": "Bauhaus-inspired {prompt} . minimalism, geometric precision, primary colors, sans-serif typography, asymmetry, functional design",
"negative_prompt": "ornate, intricate, excessive detail, complex patterns, serif typography"
},
{
"name": "MK afrofuturism",
"prompt": "Afrofuturism illustration {prompt} . vibrant colors, futuristic elements, cultural symbolism, cosmic imagery, dynamic patterns, empowering narratives",
"negative_prompt": "monochromatic"
},
{
"name": "MK atompunk",
"prompt": "Atompunk illustation, {prompt} . retro-futuristic, atomic age aesthetics, sleek lines, metallic textures, futuristic technology, optimism, energy",
"negative_prompt": "organic, natural textures, rustic, dystopian"
},
{
"name": "MK constructivism",
"prompt": "Constructivism {prompt} . geometric abstraction, bold colors, industrial aesthetics, dynamic compositions, utilitarian design, revolutionary spirit",
"negative_prompt": "organic shapes, muted colors, ornate elements, traditional"
},
{
"name": "MK chicano-art",
"prompt": "Chicano art {prompt} . bold colors, cultural symbolism, muralism, lowrider aesthetics, barrio life, political messages, social activism, Mexico",
"negative_prompt": "monochromatic, minimalist, mainstream aesthetics"
},
{
"name": "MK de-stijl",
"prompt": "De Stijl Art {prompt} . neoplasticism, primary colors, geometric abstraction, horizontal and vertical lines, simplicity, harmony, utopian ideals",
"negative_prompt": "complex patterns, muted colors, ornate elements, asymmetry"
},
{
"name": "MK dayak-art",
"prompt": "Dayak art sculpture of {prompt} . intricate patterns, nature-inspired motifs, vibrant colors, traditional craftsmanship, cultural symbolism, storytelling",
"negative_prompt": "minimalist, monochromatic, modern"
},
{
"name": "MK fayum-portrait",
"prompt": "Fayum portrait {prompt} . encaustic painting, realistic facial features, warm earth tones, serene expressions, ancient Egyptian influences",
"negative_prompt": "abstract, vibrant colors, exaggerated features, modern"
},
{
"name": "MK illuminated-manuscript",
"prompt": "Illuminated manuscript {prompt} . intricate calligraphy, rich colors, detailed illustrations, gold leaf accents, ornate borders, religious, historical, medieval",
"negative_prompt": "modern typography, minimalist design, monochromatic, abstract themes"
},
{
"name": "MK kalighat-painting",
"prompt": "Kalighat painting {prompt} . bold lines, vibrant colors, narrative storytelling, cultural motifs, flat compositions, expressive characters",
"negative_prompt": "subdued colors, intricate details, realistic portrayal, modern aesthetics"
},
{
"name": "MK madhubani-painting",
"prompt": "Madhubani painting {prompt} . intricate patterns, vibrant colors, nature-inspired motifs, cultural storytelling, symmetry, folk art aesthetics",
"negative_prompt": "abstract, muted colors, minimalistic design, modern aesthetics"
},
{
"name": "MK pictorialism",
"prompt": "Pictorialism illustration{prompt} . soft focus, atmospheric effects, artistic interpretation, tonality, muted colors, evocative storytelling",
"negative_prompt": "sharp focus, high contrast, realistic depiction, vivid colors"
},
{
"name": "MK pichwai-painting",
"prompt": "Pichwai painting {prompt} . intricate detailing, vibrant colors, religious themes, nature motifs, devotional storytelling, gold leaf accents",
"negative_prompt": "minimalist, subdued colors, abstract design"
},
{
"name": "MK patachitra-painting",
"prompt": "Patachitra painting {prompt} . bold outlines, vibrant colors, intricate detailing, mythological themes, storytelling, traditional craftsmanship",
"negative_prompt": "subdued colors, minimalistic, abstract, modern aesthetics"
},
{
"name": "MK samoan-art-inspired",
"prompt": "Samoan art-inspired wooden sculpture {prompt} . traditional motifs, natural elements, bold colors, cultural symbolism, storytelling, craftsmanship",
"negative_prompt": "modern aesthetics, minimalist, abstract"
},
{
"name": "MK tlingit-art",
"prompt": "Tlingit art {prompt} . formline design, natural elements, animal motifs, bold colors, cultural storytelling, traditional craftsmanship, Alaska traditional art, (totem:1.5)",
"negative_prompt": ""
},
{
"name": "MK adnate-style",
"prompt": "Painting by Adnate {prompt} . realistic portraits, street art, large-scale murals, subdued color palette, social narratives",
"negative_prompt": "abstract, vibrant colors, small-scale art"
},
{
"name": "MK ron-english-style",
"prompt": "Painting by Ron English {prompt} . pop-surrealism, cultural subversion, iconic mash-ups, vibrant and bold colors, satirical commentary",
"negative_prompt": "traditional, monochromatic"
},
{
"name": "MK shepard-fairey-style",
"prompt": "Painting by Shepard Fairey {prompt} . street art, political activism, iconic stencils, bold typography, high contrast, red, black, and white color palette",
"negative_prompt": "traditional, muted colors"
}
]

22
troubleshoot.md
View File

@ -118,12 +118,12 @@ If you get this error elsewhere in the world, then you may need to look at [this
### CUDA kernel errors might be asynchronously reported at some other API call
This problem is fixed two months ago. Please make sure that you are using the latest version of Fooocus (try fresh install).
If it still does not work, try to upgrade your Nvidia driver.
If it still does not work, open an issue with full log, and we will take a look.
A very small amount of devices does have this problem. The cause can be complicated but usually can be resolved after following these steps:
1. Make sure that you are using official version and latest version installed from [here](https://github.com/lllyasviel/Fooocus#download). (Some forks and other versions are more likely to cause this problem.)
2. Upgrade your Nvidia driver to the latest version. (Usually the version of your Nvidia driver should be 53X, not 3XX or 4XX.)
3. If things still do not work, then perhaps it is a problem with CUDA 12. You can use CUDA 11 and Xformers to try to solve this problem. We have prepared all files for you, and please do NOT install any CUDA or other environment on you own. The only one official way to do this is: (1) Backup and delete your `python_embeded` folder (near the `run.bat`); (2) Download the "previous_old_xformers_env.7z" from the [release page](https://github.com/lllyasviel/Fooocus/releases/tag/release), decompress it, and put the newly extracted `python_embeded` folder near your `run.bat`; (3) run Fooocus.
4. If it still does not work, please open an issue for us to take a look.
### Found no NVIDIA driver on your system
@ -143,19 +143,19 @@ Besides, the current support for MAC is very experimental, and we encourage user
### I am using Nvidia with 8GB VRAM, I get CUDA Out Of Memory
It is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](readme.md#minimal-requirement).
It is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).
### I am using Nvidia with 6GB VRAM, I get CUDA Out Of Memory
It is very likely a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](readme.md#minimal-requirement).
It is very likely a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).
### I am using Nvidia with 4GB VRAM with Float16 support, like RTX 3050, I get CUDA Out Of Memory
It is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](readme.md#minimal-requirement).
It is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).
### I am using Nvidia with 4GB VRAM without Float16 support, like GTX 960, I get CUDA Out Of Memory
Supporting GPU with 4GB VRAM without fp16 is extremely difficult, and you may not be able to use SDXL. However, you may still make an issue and let us know. You may try SD1.5 in Automatic1111 or other software for your device. See also [minimal requirements](readme.md#minimal-requirement).
Supporting GPU with 4GB VRAM without fp16 is extremely difficult, and you may not be able to use SDXL. However, you may still make an issue and let us know. You may try SD1.5 in Automatic1111 or other software for your device. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).
### I am using AMD GPU on Windows, I get CUDA Out Of Memory
@ -163,11 +163,11 @@ Current AMD support is very experimental for Windows. If you see this, then perh
However, if you re able to run SDXL on this same device on any other software, please let us know immediately, and we will support it as soon as possible. If no other software can enable your device to run SDXL on Windows, then we also do not have much to help.
Besides, the AMD support on Linux is slightly better because it will use ROCM. You may also try it if you are willing to change OS to linux. See also [minimal requirements](readme.md#minimal-requirement).
Besides, the AMD support on Linux is slightly better because it will use ROCM. You may also try it if you are willing to change OS to linux. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).
### I am using AMD GPU on Linux, I get CUDA Out Of Memory
Current AMD support for Linux is better than that for Windows, but still, very experimental. However, if you re able to run SDXL on this same device on any other software, please let us know immediately, and we will support it as soon as possible. If no other software can enable your device to run SDXL on Windows, then we also do not have much to help. See also [minimal requirements](readme.md#minimal-requirement).
Current AMD support for Linux is better than that for Windows, but still, very experimental. However, if you re able to run SDXL on this same device on any other software, please let us know immediately, and we will support it as soon as possible. If no other software can enable your device to run SDXL on Windows, then we also do not have much to help. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).
### I tried flags like --lowvram or --gpu-only or --bf16 or so on, and things are not getting any better?

31
update_log.md
View File

@ -1,3 +1,34 @@
**(2023 Dec 21) Hi all, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads. See you soon and we will come back in mid or late Jan. However, you may still see updates if other collaborators are fixing bugs or solving problems.**
# 2.1.854
* Add a button to copy parameters to clipboard in log.
* Allow users to load parameters directly by pasting parameters to prompt.
# 2.1.853
* Add Marc K3nt3L's styles. Thanks [Marc K3nt3L](https://github.com/K3nt3L)!
# 2.1.852
* New Log System: Log system now uses tables. If this is breaking some other browser extension or javascript developments, see also [use previous version](https://github.com/lllyasviel/Fooocus/discussions/1405).
# 2.1.846
* Many users reported that image quality is different from 2.1.824. We reviewed all codes and fixed several precision problems in 2.1.846.
# 2.1.843
* Many improvements to Canvas. Thanks CanvasZoom author!
# 2.1.841
* Backend maintain.
* Fix some potential frozen after model mismatch.
* Fix crash when cfg=1 when using anime preset.
* Added some guidelines for troubleshoot the "CUDA kernel errors asynchronously" problem.
* Fix inpaint device problem in `--always-gpu` mode.
# 2.1.839
* Maintained some computation codes in backend for efficiency.

49
webui.py
View File

@ -1,6 +1,7 @@
import gradio as gr
import random
import os
import json
import time
import shared
import modules.config
@ -12,6 +13,7 @@ import modules.flags as flags
import modules.gradio_hijack as grh
import modules.advanced_parameters as advanced_parameters
import modules.style_sorter as style_sorter
import modules.meta_parser
import args_manager
import copy
@ -100,7 +102,7 @@ with shared.gradio_root:
elem_id='final_gallery')
with gr.Row(elem_classes='type_row'):
with gr.Column(scale=17):
prompt = gr.Textbox(show_label=False, placeholder="Type prompt here.", elem_id='positive_prompt',
prompt = gr.Textbox(show_label=False, placeholder="Type prompt here or paste parameters.", elem_id='positive_prompt',
container=False, autofocus=True, elem_classes='type_row', lines=1024)
default_prompt = modules.config.default_prompt
@ -109,6 +111,7 @@ with shared.gradio_root:
with gr.Column(scale=3, min_width=0):
generate_button = gr.Button(label="Generate", value="Generate", elem_classes='type_row', elem_id='generate_button', visible=True)
load_parameter_button = gr.Button(label="Load Parameters", value="Load Parameters", elem_classes='type_row', elem_id='load_parameter_button', visible=False)
skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', visible=False)
stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False)
@ -518,6 +521,50 @@ with shared.gradio_root:
ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt]
ctrls += ip_ctrls
def parse_meta(raw_prompt_txt):
loaded_json = None
try:
if '{' in raw_prompt_txt:
if '}' in raw_prompt_txt:
if ':' in raw_prompt_txt:
loaded_json = json.loads(raw_prompt_txt)
assert isinstance(loaded_json, dict)
except:
loaded_json = None
if loaded_json is None:
return gr.update(), gr.update(visible=True), gr.update(visible=False)
return json.dumps(loaded_json), gr.update(visible=False), gr.update(visible=True)
prompt.input(parse_meta, inputs=prompt, outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False)
load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=prompt, outputs=[
advanced_checkbox,
image_number,
prompt,
negative_prompt,
style_selections,
performance_selection,
aspect_ratios_selection,
overwrite_width,
overwrite_height,
sharpness,
guidance_scale,
adm_scaler_positive,
adm_scaler_negative,
adm_scaler_end,
base_model,
refiner_model,
refiner_switch,
sampler_name,
scheduler_name,
seed_random,
image_seed,
generate_button,
load_parameter_button
] + lora_ctrls, queue=False, show_progress=False)
generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False), []), outputs=[stop_button, skip_button, generate_button, gallery]) \
.then(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed) \
.then(advanced_parameters.set_all_advanced_parameters, inputs=adps) \