diff options
Diffstat (limited to 'scripts')
| -rw-r--r-- | scripts/img2imgalt.py | 68 | ||||
| -rw-r--r-- | scripts/outpainting_mk_2.py | 40 | ||||
| -rw-r--r-- | scripts/sd_upscale.py | 2 | ||||
| -rw-r--r-- | scripts/xy_grid.py | 23 |
4 files changed, 80 insertions, 53 deletions
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index 7b4ba244..0ef137f7 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -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)
diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py index 9719bb8f..11613ca3 100644 --- a/scripts/outpainting_mk_2.py +++ b/scripts/outpainting_mk_2.py @@ -11,46 +11,8 @@ from modules import images, processing, devices from modules.processing import Processed, process_images
from modules.shared import opts, cmd_opts, state
-# https://github.com/parlance-zz/g-diffuser-bot
-def expand(x, dir, amount, power=0.75):
- is_left = dir == 3
- is_right = dir == 1
- is_up = dir == 0
- is_down = dir == 2
-
- if is_left or is_right:
- noise = np.zeros((x.shape[0], amount, 3), dtype=float)
- indexes = np.random.random((x.shape[0], amount)) ** power * (1 - np.arange(amount) / amount)
- if is_right:
- indexes = 1 - indexes
- indexes = (indexes * (x.shape[1] - 1)).astype(int)
-
- for row in range(x.shape[0]):
- if is_left:
- noise[row] = x[row][indexes[row]]
- else:
- noise[row] = np.flip(x[row][indexes[row]], axis=0)
-
- x = np.concatenate([noise, x] if is_left else [x, noise], axis=1)
- return x
-
- if is_up or is_down:
- noise = np.zeros((amount, x.shape[1], 3), dtype=float)
- indexes = np.random.random((x.shape[1], amount)) ** power * (1 - np.arange(amount) / amount)
- if is_down:
- indexes = 1 - indexes
- indexes = (indexes * x.shape[0] - 1).astype(int)
-
- for row in range(x.shape[1]):
- if is_up:
- noise[:, row] = x[:, row][indexes[row]]
- else:
- noise[:, row] = np.flip(x[:, row][indexes[row]], axis=0)
-
- x = np.concatenate([noise, x] if is_up else [x, noise], axis=0)
- return x
-
+# this function is taken from https://github.com/parlance-zz/g-diffuser-bot
def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05):
# helper fft routines that keep ortho normalization and auto-shift before and after fft
def _fft2(data):
diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py index b87a145b..2653e2d4 100644 --- a/scripts/sd_upscale.py +++ b/scripts/sd_upscale.py @@ -34,7 +34,7 @@ class Script(scripts.Script): seed = p.seed
init_img = p.init_images[0]
- img = upscaler.upscale(init_img, init_img.width * 2, init_img.height * 2)
+ img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)
devices.torch_gc()
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py index 3a2e103f..146663b0 100644 --- a/scripts/xy_grid.py +++ b/scripts/xy_grid.py @@ -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
@@ -44,11 +45,8 @@ def apply_sampler(p, x, xs): def apply_checkpoint(p, x, xs):
- applicable = [info for info in modules.sd_models.checkpoints_list.values() if x in info.title]
- assert len(applicable) > 0, f'Checkpoint {x} for found'
-
- info = applicable[0]
-
+ info = modules.sd_models.get_closet_checkpoint_match(x)
+ assert info is not None, f'Checkpoint for {x} not found'
modules.sd_models.reload_model_weights(shared.sd_model, info)
@@ -86,7 +84,12 @@ 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("Eta", float, apply_field("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 +111,10 @@ 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:
@@ -150,6 +156,9 @@ class Script(scripts.Script): p.batch_size = 1
def process_axis(opt, vals):
+ if opt.label == 'Nothing':
+ return [0]
+
valslist = [x.strip() for x in vals.split(",")]
if opt.type == int:
|