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added resize seeds and variation seeds features

This commit is contained in:
AUTOMATIC 2022-09-09 17:54:04 +03:00
parent 003b60b94e
commit b1707553cf
10 changed files with 171 additions and 28 deletions
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2
modules/images.py
View File

@ -136,7 +136,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
color_active = (0, 0, 0)
color_inactive = (153, 153, 153)
pad_left = width * 3 // 4 if len(ver_texts) > 0 else 0
pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4
cols = im.width // width
rows = im.height // height

6
modules/img2img.py
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@ -11,7 +11,7 @@ from modules.ui import plaintext_to_html
import modules.images as images
import modules.scripts
def img2img(prompt: str, negative_prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, denoising_strength_change_factor: float, seed: int, height: int, width: int, resize_mode: int, upscaler_index: str, upscale_overlap: int, inpaint_full_res: bool, inpainting_mask_invert: int, *args):
def img2img(prompt: str, negative_prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, denoising_strength_change_factor: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, height: int, width: int, resize_mode: int, upscaler_index: str, upscale_overlap: int, inpaint_full_res: bool, inpainting_mask_invert: int, *args):
is_inpaint = mode == 1
is_loopback = mode == 2
is_upscale = mode == 3
@ -34,6 +34,10 @@ def img2img(prompt: str, negative_prompt: str, init_img, init_img_with_mask, ste
prompt=prompt,
negative_prompt=negative_prompt,
seed=seed,
subseed=subseed,
subseed_strength=subseed_strength,
seed_resize_from_h=seed_resize_from_h,
seed_resize_from_w=seed_resize_from_w,
sampler_index=sampler_index,
batch_size=batch_size,
n_iter=n_iter,

93
modules/processing.py
View File

@ -29,7 +29,7 @@ def torch_gc():
class StableDiffusionProcessing:
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
self.sd_model = sd_model
self.outpath_samples: str = outpath_samples
self.outpath_grids: str = outpath_grids
@ -37,6 +37,10 @@ class StableDiffusionProcessing:
self.prompt_for_display: str = None
self.negative_prompt: str = (negative_prompt or "")
self.seed: int = seed
self.subseed: int = subseed
self.subseed_strength: float = subseed_strength
self.seed_resize_from_h: int = seed_resize_from_h
self.seed_resize_from_w: int = seed_resize_from_w
self.sampler_index: int = sampler_index
self.batch_size: int = batch_size
self.n_iter: int = n_iter
@ -84,23 +88,67 @@ class Processed:
return json.dumps(obj)
# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
def slerp(val, low, high):
low_norm = low/torch.norm(low, dim=1, keepdim=True)
high_norm = high/torch.norm(high, dim=1, keepdim=True)
omega = torch.acos((low_norm*high_norm).sum(1))
so = torch.sin(omega)
res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
return res
def create_random_tensors(shape, seeds):
def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):
xs = []
for seed in seeds:
torch.manual_seed(seed)
for i, seed in enumerate(seeds):
noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
subnoise = None
if subseeds is not None:
subseed = 0 if i >= len(subseeds) else subseeds[i]
torch.manual_seed(subseed)
subnoise = torch.randn(noise_shape, device=shared.device)
# randn results depend on device; gpu and cpu get different results for same seed;
# the way I see it, it's better to do this on CPU, so that everyone gets same result;
# but the original script had it like this so I do not dare change it for now because
# but the original script had it like this, so I do not dare change it for now because
# it will break everyone's seeds.
xs.append(torch.randn(shape, device=shared.device))
x = torch.stack(xs)
torch.manual_seed(seed)
noise = torch.randn(noise_shape, device=shared.device)
if subnoise is not None:
#noise = subnoise * subseed_strength + noise * (1 - subseed_strength)
noise = slerp(subseed_strength, noise, subnoise)
if noise_shape != shape:
#noise = torch.nn.functional.interpolate(noise.unsqueeze(1), size=shape[1:], mode="bilinear").squeeze()
# noise_shape = (64, 80)
# shape = (64, 72)
torch.manual_seed(seed)
x = torch.randn(shape, device=shared.device)
dx = (shape[2] - noise_shape[2]) // 2 # -4
dy = (shape[1] - noise_shape[1]) // 2
w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
tx = 0 if dx < 0 else dx
ty = 0 if dy < 0 else dy
dx = max(-dx, 0)
dy = max(-dy, 0)
x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
noise = x
xs.append(noise)
x = torch.stack(xs).to(shared.device)
return x
def set_seed(seed):
return int(random.randrange(4294967294)) if seed is None or seed == -1 else seed
def fix_seed(p):
p.seed = int(random.randrange(4294967294)) if p.seed is None or p.seed == -1 else p.seed
p.subseed = int(random.randrange(4294967294)) if p.subseed is None or p.subseed == -1 else p.subseed
def process_images(p: StableDiffusionProcessing) -> Processed:
@ -111,7 +159,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
assert p.prompt is not None
torch_gc()
seed = set_seed(p.seed)
fix_seed(p)
os.makedirs(p.outpath_samples, exist_ok=True)
os.makedirs(p.outpath_grids, exist_ok=True)
@ -125,20 +173,31 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
else:
all_prompts = p.batch_size * p.n_iter * [prompt]
if type(seed) == list:
all_seeds = seed
if type(p.seed) == list:
all_seeds = int(p.seed)
else:
all_seeds = [int(seed + x) for x in range(len(all_prompts))]
all_seeds = [int(p.seed + x) for x in range(len(all_prompts))]
if type(p.subseed) == list:
all_subseeds = p.subseed
else:
all_subseeds = [int(p.subseed + x) for x in range(len(all_prompts))]
def infotext(iteration=0, position_in_batch=0):
index = position_in_batch + iteration * p.batch_size
generation_params = {
"Steps": p.steps,
"Sampler": samplers[p.sampler_index].name,
"CFG scale": p.cfg_scale,
"Seed": all_seeds[position_in_batch + iteration * p.batch_size],
"Seed": all_seeds[index],
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
"Size": f"{p.width}x{p.height}",
"Batch size": (None if p.batch_size < 2 else p.batch_size),
"Batch pos": (None if p.batch_size < 2 else position_in_batch),
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
}
if p.extra_generation_params is not None:
@ -174,7 +233,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
comments += model_hijack.comments
# we manually generate all input noises because each one should have a specific seed
x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds)
x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=all_subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
@ -231,10 +290,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
output_images.insert(0, grid)
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", seed, all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
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)
torch_gc()
return Processed(p, output_images, seed, infotext())
return Processed(p, output_images, all_seeds[0], infotext())
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

1
modules/shared.py
View File

@ -62,7 +62,6 @@ class State:
current_image = None
current_image_sampling_step = 0
def interrupt(self):
self.interrupted = True

6
modules/txt2img.py
View File

@ -6,7 +6,7 @@ import modules.processing as processing
from modules.ui import plaintext_to_html
def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, *args):
def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, height: int, width: int, *args):
p = StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@ -14,6 +14,10 @@ def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, r
prompt=prompt,
negative_prompt=negative_prompt,
seed=seed,
subseed=subseed,
subseed_strength=subseed_strength,
seed_resize_from_h=seed_resize_from_h,
seed_resize_from_w=seed_resize_from_w,
sampler_index=sampler_index,
batch_size=batch_size,
n_iter=n_iter,

40
modules/ui.py
View File

@ -192,6 +192,40 @@ def visit(x, func, path=""):
func(path + "/" + str(x.label), x)
def create_seed_inputs():
with gr.Row():
seed = gr.Number(label='Seed', value=-1)
subseed = gr.Number(label='Variation seed', value=-1, visible=False)
seed_checkbox = gr.Checkbox(label="Extra", elem_id="subseed_show", value=False)
with gr.Row():
subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, visible=False)
seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0, visible=False)
seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0, visible=False)
def change_visiblity(show):
return {
subseed: gr_show(show),
subseed_strength: gr_show(show),
seed_resize_from_h: gr_show(show),
seed_resize_from_w: gr_show(show),
}
seed_checkbox.change(
change_visiblity,
inputs=[seed_checkbox],
outputs=[
subseed,
subseed_strength,
seed_resize_from_h,
seed_resize_from_w
]
)
return seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w
def create_ui(txt2img, img2img, run_extras, run_pnginfo):
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
with gr.Row():
@ -220,7 +254,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
seed = gr.Number(label='Seed', value=-1)
seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w = create_seed_inputs()
with gr.Group():
custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
@ -260,6 +294,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
batch_size,
cfg_scale,
seed,
subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
height,
width,
] + custom_inputs,
@ -357,7 +392,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
seed = gr.Number(label='Seed', value=-1)
seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w = create_seed_inputs()
with gr.Group():
custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
@ -440,6 +475,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
denoising_strength,
denoising_strength_change_factor,
seed,
subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
height,
width,
resize_mode,

5
script.js
View File

@ -46,6 +46,11 @@ titles = {
"Tile overlap": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.",
"Roll": "Add a random artist to the prompt.",
"Variation seed": "Seed of a different picture to be mixed into the generation.",
"Variation strength": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).",
"Resize seed from height": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution",
"Resize seed from width": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution",
}
function gradioApp(){

2
scripts/prompt_matrix.py
View File

@ -50,7 +50,7 @@ class Script(scripts.Script):
return [put_at_start]
def run(self, p, put_at_start):
seed = modules.processing.set_seed(p.seed)
modules.processing.fix_seed(p)
original_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt

35
scripts/xy_grid.py
View File

@ -2,6 +2,8 @@ from collections import namedtuple
from copy import copy
import random
import numpy as np
import modules.scripts as scripts
import gradio as gr
@ -46,18 +48,27 @@ def format_value_add_label(p, opt, x):
def format_value(p, opt, x):
return x
def do_nothing(p, x, xs):
pass
def format_nothing(p, opt, x):
return ""
AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])
AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])
axis_options = [
AxisOption("Nothing", str, do_nothing, format_nothing),
AxisOption("Seed", int, apply_field("seed"), format_value_add_label),
AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label),
AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label),
AxisOption("Steps", int, apply_field("steps"), format_value_add_label),
AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
AxisOption("Prompt S/R", str, apply_prompt, format_value),
AxisOption("Sampler", str, apply_sampler, 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
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
]
@ -90,6 +101,7 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
class Script(scripts.Script):
def title(self):
@ -99,17 +111,17 @@ class Script(scripts.Script):
current_axis_options = [x for x in axis_options if type(x) == AxisOption or type(x) == AxisOptionImg2Img and is_img2img]
with gr.Row():
x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, visible=False, type="index", elem_id="x_type")
x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, visible=False, type="index", elem_id="x_type")
x_values = gr.Textbox(label="X values", visible=False, lines=1)
with gr.Row():
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, visible=False, type="index", elem_id="y_type")
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[4].label, visible=False, type="index", elem_id="y_type")
y_values = gr.Textbox(label="Y values", visible=False, lines=1)
return [x_type, x_values, y_type, y_values]
def run(self, p, x_type, x_values, y_type, y_values):
p.seed = modules.processing.set_seed(p.seed)
modules.processing.fix_seed(p)
p.batch_size = 1
p.batch_count = 1
@ -132,6 +144,21 @@ class Script(scripts.Script):
valslist_ext.append(val)
valslist = valslist_ext
elif opt.type == float:
valslist_ext = []
for val in valslist:
m = re_range_float.fullmatch(val)
if m is not None:
start = float(m.group(1))
end = float(m.group(2))
step = float(m.group(3)) if m.group(3) is not None else 1
valslist_ext += np.arange(start, end + step, step).tolist()
else:
valslist_ext.append(val)
valslist = valslist_ext
valslist = [opt.type(x) for x in valslist]

9
style.css
View File

@ -5,6 +5,15 @@
max-width: 13em;
}
#subseed_show{
min-width: 6em;
max-width: 6em;
}
#subseed_show label{
height: 100%;
}
#txt2img_roll{
min-width: 1em;
max-width: 4em;