Merge remote-tracking branch 'upstream/master' into ModelLoader
This commit is contained in:
d8ahazard
commit
5756d517a6
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@ -21,3 +21,5 @@ __pycache__
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/interrogate
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/user.css
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/.idea
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notification.mp3
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/SwinIR
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@ -68,13 +68,19 @@ window.addEventListener('paste', e => {
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if ( ! isValidImageList( files ) ) {
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return;
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}
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[...gradioApp().querySelectorAll('input[type=file][accept="image/x-png,image/gif,image/jpeg"]')]
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.filter(input => !input.matches('.\\!hidden input[type=file]'))
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.forEach(input => {
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input.files = files;
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input.dispatchEvent(new Event('change'))
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});
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[...gradioApp().querySelectorAll('[data-testid="image"]')]
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.filter(imgWrap => !imgWrap.closest('.\\!hidden'))
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.forEach(imgWrap => dropReplaceImage( imgWrap, files ));
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const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
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.filter(el => uiElementIsVisible(el));
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if ( ! visibleImageFields.length ) {
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return;
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}
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const firstFreeImageField = visibleImageFields
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.filter(el => el.querySelector('input[type=file]'))?.[0];
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dropReplaceImage(
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firstFreeImageField ?
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firstFreeImageField :
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visibleImageFields[visibleImageFields.length - 1]
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, files );
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});
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@ -25,6 +25,9 @@ onUiUpdate(function(){
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lastHeadImg = headImg;
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// play notification sound if available
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gradioApp().querySelector('#audio_notification audio')?.play();
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if (document.hasFocus()) return;
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// Multiple copies of the images are in the DOM when one is selected. Dedup with a Set to get the real number generated.
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@ -1,9 +1,8 @@
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// various functions for interation with ui.py not large enough to warrant putting them in separate files
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function selected_gallery_index(){
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var gr = gradioApp()
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var buttons = gradioApp().querySelectorAll(".gallery-item")
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var button = gr.querySelector(".gallery-item.\\!ring-2")
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var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
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var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')
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var result = -1
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buttons.forEach(function(v, i){ if(v==button) { result = i } })
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@ -3,6 +3,9 @@ import os
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import numpy as np
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from PIL import Image
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import torch
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import tqdm
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from modules import processing, shared, images, devices
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from modules.shared import opts
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import modules.gfpgan_model
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@ -137,3 +140,57 @@ def run_pnginfo(image):
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info = f"<div><p>{message}<p></div>"
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return '', geninfo, info
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def run_modelmerger(modelname_0, modelname_1, interp_method, interp_amount):
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# Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
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def weighted_sum(theta0, theta1, alpha):
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return ((1 - alpha) * theta0) + (alpha * theta1)
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# Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
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def sigmoid(theta0, theta1, alpha):
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alpha = alpha * alpha * (3 - (2 * alpha))
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return theta0 + ((theta1 - theta0) * alpha)
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if os.path.exists(modelname_0):
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model0_filename = modelname_0
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modelname_0 = os.path.splitext(os.path.basename(modelname_0))[0]
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else:
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model0_filename = 'models/' + modelname_0 + '.ckpt'
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if os.path.exists(modelname_1):
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model1_filename = modelname_1
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modelname_1 = os.path.splitext(os.path.basename(modelname_1))[0]
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else:
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model1_filename = 'models/' + modelname_1 + '.ckpt'
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print(f"Loading {model0_filename}...")
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model_0 = torch.load(model0_filename, map_location='cpu')
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print(f"Loading {model1_filename}...")
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model_1 = torch.load(model1_filename, map_location='cpu')
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theta_0 = model_0['state_dict']
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theta_1 = model_1['state_dict']
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theta_funcs = {
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"Weighted Sum": weighted_sum,
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"Sigmoid": sigmoid,
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}
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theta_func = theta_funcs[interp_method]
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print(f"Merging...")
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for key in tqdm.tqdm(theta_0.keys()):
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if 'model' in key and key in theta_1:
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theta_0[key] = theta_func(theta_0[key], theta_1[key], interp_amount)
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for key in theta_1.keys():
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if 'model' in key and key not in theta_0:
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theta_0[key] = theta_1[key]
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output_modelname = 'models/' + modelname_0 + '-' + modelname_1 + '-merged.ckpt'
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print(f"Saving to {output_modelname}...")
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torch.save(model_0, output_modelname)
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print(f"Checkpoint saved.")
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return "Checkpoint saved to " + output_modelname
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@ -78,7 +78,14 @@ class StableDiffusionProcessing:
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self.paste_to = None
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self.color_corrections = None
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self.denoising_strength: float = 0
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self.ddim_eta = opts.ddim_eta
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self.ddim_discretize = opts.ddim_discretize
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self.s_churn = opts.s_churn
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self.s_tmin = opts.s_tmin
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self.s_tmax = float('inf') # not representable as a standard ui option
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self.s_noise = opts.s_noise
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if not seed_enable_extras:
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self.subseed = -1
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self.subseed_strength = 0
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@ -117,6 +124,13 @@ class Processed:
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self.extra_generation_params = p.extra_generation_params
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self.index_of_first_image = index_of_first_image
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self.ddim_eta = p.ddim_eta
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self.ddim_discretize = p.ddim_discretize
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self.s_churn = p.s_churn
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self.s_tmin = p.s_tmin
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self.s_tmax = p.s_tmax
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self.s_noise = p.s_noise
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self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
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self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
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self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
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@ -406,7 +420,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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index_of_first_image = 1
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if opts.grid_save:
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images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p)
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images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
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devices.torch_gc()
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return Processed(p, output_images, all_seeds[0], infotext(), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image)
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@ -37,6 +37,11 @@ samplers = [
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]
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samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
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sampler_extra_params = {
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'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'],
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'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'],
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'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'],
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}
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def setup_img2img_steps(p, steps=None):
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if opts.img2img_fix_steps or steps is not None:
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@ -120,9 +125,9 @@ class VanillaStableDiffusionSampler:
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# existing code fails with cetain step counts, like 9
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try:
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self.sampler.make_schedule(ddim_num_steps=steps, verbose=False)
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self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
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except Exception:
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self.sampler.make_schedule(ddim_num_steps=steps+1, verbose=False)
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self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
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x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
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@ -149,9 +154,9 @@ class VanillaStableDiffusionSampler:
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# existing code fails with cetin step counts, like 9
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try:
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samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
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samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)
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except Exception:
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samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
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samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)
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return samples_ddim
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@ -224,6 +229,7 @@ class KDiffusionSampler:
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self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
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self.funcname = funcname
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self.func = getattr(k_diffusion.sampling, self.funcname)
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self.extra_params = sampler_extra_params.get(funcname,[])
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self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
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self.sampler_noises = None
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self.sampler_noise_index = 0
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@ -269,7 +275,12 @@ class KDiffusionSampler:
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if self.sampler_noises is not None:
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k_diffusion.sampling.torch = TorchHijack(self)
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return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
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extra_params_kwargs = {}
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for val in self.extra_params:
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if hasattr(p,val):
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extra_params_kwargs[val] = getattr(p,val)
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return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
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def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
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steps = steps or p.steps
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@ -286,7 +297,12 @@ class KDiffusionSampler:
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if self.sampler_noises is not None:
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k_diffusion.sampling.torch = TorchHijack(self)
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samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
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extra_params_kwargs = {}
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for val in self.extra_params:
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if hasattr(p,val):
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extra_params_kwargs[val] = getattr(p,val)
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samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
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return samples
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|
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@ -76,7 +76,7 @@ class State:
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job = ""
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job_no = 0
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job_count = 0
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job_timestamp = 0
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job_timestamp = '0'
|
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sampling_step = 0
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sampling_steps = 0
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current_latent = None
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@ -90,6 +90,7 @@ class State:
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self.job_no += 1
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self.sampling_step = 0
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self.current_image_sampling_step = 0
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|
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def get_job_timestamp(self):
|
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return datetime.datetime.now().strftime("%Y%m%d%H%M%S")
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@ -229,6 +230,13 @@ options_templates.update(options_section(('ui', "User interface"), {
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"js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
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}))
|
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|
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options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
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"ddim_eta": OptionInfo(0.0, "DDIM eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
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"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}),
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's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
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's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
}))
|
||||
|
||||
class Options:
|
||||
data = None
|
||||
|
|
|
|||
|
|
@ -49,6 +49,7 @@ sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
|
|||
|
||||
css_hide_progressbar = """
|
||||
.wrap .m-12 svg { display:none!important; }
|
||||
.wrap .m-12::before { content:"Loading..." }
|
||||
.progress-bar { display:none!important; }
|
||||
.meta-text { display:none!important; }
|
||||
"""
|
||||
|
|
@ -393,7 +394,7 @@ def setup_progressbar(progressbar, preview, id_part):
|
|||
)
|
||||
|
||||
|
||||
def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
||||
def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
||||
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
|
||||
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False)
|
||||
dummy_component = gr.Label(visible=False)
|
||||
|
|
@ -564,13 +565,13 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
with gr.TabItem('Inpaint', id='inpaint'):
|
||||
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")
|
||||
|
||||
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False)
|
||||
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False)
|
||||
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
|
||||
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
|
||||
|
||||
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
|
||||
|
||||
with gr.Row():
|
||||
mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask")
|
||||
mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
|
||||
inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
|
||||
|
||||
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index")
|
||||
|
|
@ -853,6 +854,33 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
outputs=[html, generation_info, html2],
|
||||
)
|
||||
|
||||
with gr.Blocks() as modelmerger_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Column(variant='panel'):
|
||||
gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>/models</b> directory.</p>")
|
||||
|
||||
modelname_0 = gr.Textbox(elem_id="modelmerger_modelname_0", label="Model Name (to)")
|
||||
modelname_1 = gr.Textbox(elem_id="modelmerger_modelname_1", label="Model Name (from)")
|
||||
interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid"], value="Weighted Sum", label="Interpolation Method")
|
||||
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
|
||||
submit = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
|
||||
|
||||
with gr.Column(variant='panel'):
|
||||
submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
|
||||
|
||||
submit.click(
|
||||
fn=run_modelmerger,
|
||||
inputs=[
|
||||
modelname_0,
|
||||
modelname_1,
|
||||
interp_method,
|
||||
interp_amount
|
||||
],
|
||||
outputs=[
|
||||
submit_result,
|
||||
]
|
||||
)
|
||||
|
||||
def create_setting_component(key):
|
||||
def fun():
|
||||
return opts.data[key] if key in opts.data else opts.data_labels[key].default
|
||||
|
|
@ -950,6 +978,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
(img2img_interface, "img2img", "img2img"),
|
||||
(extras_interface, "Extras", "extras"),
|
||||
(pnginfo_interface, "PNG Info", "pnginfo"),
|
||||
(modelmerger_interface, "Checkpoint Merger", "modelmerger"),
|
||||
(settings_interface, "Settings", "settings"),
|
||||
]
|
||||
|
||||
|
|
@ -970,6 +999,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
for interface, label, ifid in interfaces:
|
||||
with gr.TabItem(label, id=ifid):
|
||||
interface.render()
|
||||
|
||||
if os.path.exists(os.path.join(script_path, "notification.mp3")):
|
||||
audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
|
||||
|
||||
text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
|
||||
settings_submit.click(
|
||||
|
|
|
|||
21
script.js
21
script.js
|
|
@ -39,3 +39,24 @@ document.addEventListener("DOMContentLoaded", function() {
|
|||
});
|
||||
mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
|
||||
});
|
||||
|
||||
/**
|
||||
* checks that a UI element is not in another hidden element or tab content
|
||||
*/
|
||||
function uiElementIsVisible(el) {
|
||||
let isVisible = !el.closest('.\\!hidden');
|
||||
if ( ! isVisible ) {
|
||||
return false;
|
||||
}
|
||||
|
||||
while( isVisible = el.closest('.tabitem')?.style.display !== 'none' ) {
|
||||
if ( ! isVisible ) {
|
||||
return false;
|
||||
} else if ( el.parentElement ) {
|
||||
el = el.parentElement
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
return isVisible;
|
||||
}
|
||||
|
|
@ -59,7 +59,55 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
|
|||
return x / x.std()
|
||||
|
||||
|
||||
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt"])
|
||||
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"])
|
||||
|
||||
|
||||
# Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
|
||||
def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
|
||||
x = p.init_latent
|
||||
|
||||
s_in = x.new_ones([x.shape[0]])
|
||||
dnw = K.external.CompVisDenoiser(shared.sd_model)
|
||||
sigmas = dnw.get_sigmas(steps).flip(0)
|
||||
|
||||
shared.state.sampling_steps = steps
|
||||
|
||||
for i in trange(1, len(sigmas)):
|
||||
shared.state.sampling_step += 1
|
||||
|
||||
x_in = torch.cat([x] * 2)
|
||||
sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
|
||||
cond_in = torch.cat([uncond, cond])
|
||||
|
||||
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
|
||||
|
||||
if i == 1:
|
||||
t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
|
||||
else:
|
||||
t = dnw.sigma_to_t(sigma_in)
|
||||
|
||||
eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
|
||||
denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
|
||||
|
||||
denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
|
||||
|
||||
if i == 1:
|
||||
d = (x - denoised) / (2 * sigmas[i])
|
||||
else:
|
||||
d = (x - denoised) / sigmas[i - 1]
|
||||
|
||||
dt = sigmas[i] - sigmas[i - 1]
|
||||
x = x + d * dt
|
||||
|
||||
sd_samplers.store_latent(x)
|
||||
|
||||
# This shouldn't be necessary, but solved some VRAM issues
|
||||
del x_in, sigma_in, cond_in, c_out, c_in, t,
|
||||
del eps, denoised_uncond, denoised_cond, denoised, d, dt
|
||||
|
||||
shared.state.nextjob()
|
||||
|
||||
return x / sigmas[-1]
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
|
|
@ -78,9 +126,10 @@ class Script(scripts.Script):
|
|||
cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
|
||||
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
|
||||
randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
|
||||
return [original_prompt, original_negative_prompt, cfg, st, randomness]
|
||||
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False)
|
||||
return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
|
||||
|
||||
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness):
|
||||
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
|
||||
p.batch_size = 1
|
||||
p.batch_count = 1
|
||||
|
||||
|
|
@ -88,7 +137,10 @@ class Script(scripts.Script):
|
|||
def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
|
||||
lat = (p.init_latent.cpu().numpy() * 10).astype(int)
|
||||
|
||||
same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st and self.cache.original_prompt == original_prompt and self.cache.original_negative_prompt == original_negative_prompt
|
||||
same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \
|
||||
and self.cache.original_prompt == original_prompt \
|
||||
and self.cache.original_negative_prompt == original_negative_prompt \
|
||||
and self.cache.sigma_adjustment == sigma_adjustment
|
||||
same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
|
||||
|
||||
if same_everything:
|
||||
|
|
@ -97,8 +149,11 @@ class Script(scripts.Script):
|
|||
shared.state.job_count += 1
|
||||
cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
|
||||
uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])
|
||||
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
|
||||
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt)
|
||||
if sigma_adjustment:
|
||||
rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st)
|
||||
else:
|
||||
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
|
||||
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
|
||||
|
||||
rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
|
||||
|
||||
|
|
@ -121,6 +176,7 @@ class Script(scripts.Script):
|
|||
p.extra_generation_params["Decode CFG scale"] = cfg
|
||||
p.extra_generation_params["Decode steps"] = st
|
||||
p.extra_generation_params["Randomness"] = randomness
|
||||
p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment
|
||||
|
||||
processed = processing.process_images(p)
|
||||
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ from collections import namedtuple
|
|||
from copy import copy
|
||||
import random
|
||||
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
import modules.scripts as scripts
|
||||
|
|
@ -86,7 +87,12 @@ axis_options = [
|
|||
AxisOption("Prompt S/R", str, apply_prompt, format_value),
|
||||
AxisOption("Sampler", str, apply_sampler, format_value),
|
||||
AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
|
||||
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
|
||||
AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
|
||||
AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
|
||||
AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
|
||||
AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
|
||||
AxisOption("DDIM Eta", float, apply_field("ddim_eta"), format_value_add_label),
|
||||
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),# as it is now all AxisOptionImg2Img items must go after AxisOption ones
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -108,7 +114,10 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
|
|||
if first_pocessed is None:
|
||||
first_pocessed = processed
|
||||
|
||||
res.append(processed.images[0])
|
||||
try:
|
||||
res.append(processed.images[0])
|
||||
except:
|
||||
res.append(Image.new(res[0].mode, res[0].size))
|
||||
|
||||
grid = images.image_grid(res, rows=len(ys))
|
||||
if draw_legend:
|
||||
|
|
|
|||
3
webui.py
3
webui.py
|
|
@ -85,7 +85,8 @@ def webui():
|
|||
txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
|
||||
img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
|
||||
run_extras=wrap_gradio_gpu_call(modules.extras.run_extras),
|
||||
run_pnginfo=modules.extras.run_pnginfo
|
||||
run_pnginfo=modules.extras.run_pnginfo,
|
||||
run_modelmerger=modules.extras.run_modelmerger
|
||||
)
|
||||
|
||||
demo.launch(
|
||||
|
|
|
|||
Loading…
Reference in New Issue