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* Updates for thresholding and perlin noise options, added warmup for thresholding.

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This commit is contained in:
Peter Baylies 2022-09-05 21:40:05 -04:00
parent 1a4bed2e55
commit 0891910cac
6 changed files with 55 additions and 37 deletions
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10
ldm/dream/server.py
View File

@ -79,6 +79,8 @@ class DreamServer(BaseHTTPRequestHandler):
upscale = [int(upscale_level),float(upscale_strength)] if upscale_level != '' else None
progress_images = 'progress_images' in post_data
seed = self.model.seed if int(post_data['seed']) == -1 else int(post_data['seed'])
threshold = float(post_data['threshold'])
perlin = float(post_data['perlin'])
self.canceled.clear()
print(f">> Request to generate with prompt: {prompt}")
@ -165,7 +167,9 @@ class DreamServer(BaseHTTPRequestHandler):
upscale = upscale,
sampler_name = sampler_name,
step_callback=image_progress,
image_callback=image_done)
image_callback=image_done,
threshold=threshold,
perlin=perlin)
else:
# Decode initimg as base64 to temp file
with open("./img2img-tmp.png", "wb") as f:
@ -188,7 +192,9 @@ class DreamServer(BaseHTTPRequestHandler):
gfpgan_strength=gfpgan_strength,
upscale = upscale,
step_callback=image_progress,
image_callback=image_done)
image_callback=image_done,
threshold=threshold,
perlin=perlin)
finally:
# Remove the temp file
os.remove("./img2img-tmp.png")

26
ldm/models/diffusion/ksampler.py
View File

@ -3,9 +3,9 @@ import k_diffusion as K
import torch
import torch.nn as nn
from ldm.dream.devices import choose_torch_device
from ldm.modules.diffusionmodules.util import rand_perlin_2d
from ldm.util import rand_perlin_2d
def cfg_apply_threshold(result, threshold = 0.0, scale = 0.707):
def cfg_apply_threshold(result, threshold = 0.0, scale = 0.7):
if threshold <= 0.0:
return result
maxval = 0.0 + torch.max(result).cpu().numpy()
@ -20,17 +20,26 @@ def cfg_apply_threshold(result, threshold = 0.0, scale = 0.707):
class CFGDenoiser(nn.Module):
def __init__(self, model, threshold = 0):
def __init__(self, model, threshold = 0, warmup = 0):
super().__init__()
self.inner_model = model
self.threshold = threshold
self.warmup_max = warmup
self.warmup = 0
def forward(self, x, sigma, uncond, cond, cond_scale):
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigma] * 2)
cond_in = torch.cat([uncond, cond])
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
return cfg_apply_threshold(uncond + (cond - uncond) * cond_scale, self.threshold)
if self.warmup < self.warmup_max:
thresh = max(1, 1 + (self.threshold - 1) * (self.warmup / self.warmup_max))
self.warmup += 1
else:
thresh = self.threshold
if thresh > self.threshold:
thresh = self.threshold
return cfg_apply_threshold(uncond + (cond - uncond) * cond_scale, thresh)
class KSampler(object):
@ -39,7 +48,6 @@ class KSampler(object):
self.model = K.external.CompVisDenoiser(model)
self.schedule = schedule
self.device = device or choose_torch_device()
#self.threshold = threshold or 0
def forward(self, x, sigma, uncond, cond, cond_scale):
x_in = torch.cat([x] * 2)
@ -49,7 +57,6 @@ class KSampler(object):
x_in, sigma_in, cond=cond_in
).chunk(2)
return uncond + (cond - uncond) * cond_scale
#return cfg_apply_threshold(uncond + (cond - uncond) * cond_scale, self.threshold)
@ -95,12 +102,7 @@ class KSampler(object):
torch.randn([batch_size, *shape], device=self.device)
* sigmas[0]
) # for GPU draw
if perlin > 0.0:
print(shape)
x = (1 - perlin / 2) * x + perlin * rand_perlin_2d((shape[1], shape[2]), (8, 8)).to(self.device)
model_wrap_cfg = CFGDenoiser(self.model, threshold=threshold)
model_wrap_cfg = CFGDenoiser(self.model, threshold=threshold, warmup=max(0.8*S,S-10))
extra_args = {
'cond': conditioning,
'uncond': unconditional_conditioning,

19
ldm/modules/diffusionmodules/util.py
View File

@ -18,25 +18,6 @@ from einops import repeat
from ldm.util import instantiate_from_config
def rand_perlin_2d(shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
delta = (res[0] / shape[0], res[1] / shape[1])
d = (shape[0] // res[0], shape[1] // res[1])
grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1])), dim = -1) % 1
angles = 2*math.pi*torch.rand(res[0]+1, res[1]+1)
gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1)
tile_grads = lambda slice1, slice2: gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)
dot = lambda grad, shift: (torch.stack((grid[:shape[0],:shape[1],0] + shift[0], grid[:shape[0],:shape[1], 1] + shift[1] ), dim = -1) * grad[:shape[0], :shape[1]]).sum(dim = -1)
n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0])
n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0])
n01 = dot(tile_grads([0, -1],[1, None]), [0, -1])
n11 = dot(tile_grads([1, None], [1, None]), [-1,-1])
t = fade(grid[:shape[0], :shape[1]])
return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1])
def make_beta_schedule(
schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3
):

14
ldm/simplet2i.py
View File

@ -9,6 +9,7 @@ import numpy as np
import random
import os
import traceback
from ldm.modules.diffusionmodules.util import noise_like
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
@ -23,7 +24,7 @@ import time
import re
import sys
from ldm.util import instantiate_from_config
from ldm.util import instantiate_from_config, rand_perlin_2d
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.models.diffusion.ksampler import KSampler
@ -352,13 +353,18 @@ class T2I:
def get_noise():
if init_img:
return torch.randn_like(init_latent, device=self.device)
x = torch.randn_like(init_latent, device=self.device)
else:
return torch.randn([1,
x = torch.randn([1,
self.latent_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
device=self.device)
if perlin > 0.0:
shape = x.shape
perlin_noise = torch.stack([rand_perlin_2d((shape[2], shape[3]), (8, 8)).to(self.device) for _ in range(shape[1])], dim=0)
x = (1 - perlin) * x + perlin * perlin_noise
return x
initial_noise = None
if variation_amount > 0 or len(with_variations) > 0:
@ -387,7 +393,7 @@ class T2I:
x_T = initial_noise
else:
seed_everything(seed)
# make_image will do the equivalent of get_noise itself
x_T = get_noise()
image = make_image(x_T)
results.append([image, seed])
if image_callback is not None:

19
ldm/util.py
View File

@ -2,6 +2,7 @@ import importlib
import torch
import numpy as np
import math
from collections import abc
from einops import rearrange
from functools import partial
@ -212,3 +213,21 @@ def parallel_data_prefetch(
return out
else:
return gather_res
def rand_perlin_2d(shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
delta = (res[0] / shape[0], res[1] / shape[1])
d = (shape[0] // res[0], shape[1] // res[1])
grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1])), dim = -1) % 1
angles = 2*math.pi*torch.rand(res[0]+1, res[1]+1)
gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1)
tile_grads = lambda slice1, slice2: gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)
dot = lambda grad, shift: (torch.stack((grid[:shape[0],:shape[1],0] + shift[0], grid[:shape[0],:shape[1], 1] + shift[1] ), dim = -1) * grad[:shape[0], :shape[1]]).sum(dim = -1)
n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0])
n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0])
n01 = dot(tile_grads([0, -1],[1, None]), [0, -1])
n11 = dot(tile_grads([1, None], [1, None]), [-1,-1])
t = fade(grid[:shape[0], :shape[1]])
return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1])

4
static/dream_web/index.html
View File

@ -63,6 +63,10 @@
<label title="Set to -1 for random seed" for="seed">Seed:</label>
<input value="-1" type="number" id="seed" name="seed">
<button type="button" id="reset-seed">&olarr;</button>
<label title="Threshold" for="threshold">Threshold:</label>
<input value="0" type="number" id="threshold" name="threshold" step="any">
<label title="Perlin" for="perlin">Perlin:</label>
<input value="0" type="number" id="perlin" name="perlin" step="any">
<input type="checkbox" name="progress_images" id="progress_images">
<label for="progress_images">Display in-progress images (slows down generation):</label>
<button type="button" id="reset-all">Reset to Defaults</button>