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SD - Add repeatable (batch) seeds option (#1654)

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* Generates the seeds for all batch_count batches being run up front
rather than generating the seed for a batch just before it is run.
* Adds a --repeatable_seeds argument defaulting to False
* When repeatable_seeds=True, the first seed for a set of batches will
also be used as the rng seed for the subsequent batch seeds in the run.
The rng seed is then reset.
* When repeatable_seeds=False, batch seeding works as currently.
* Update scripts under apps/scripts that support the batch_count
argument to also support the repeatable_seeds argument.
* UI/Web: Adds a checkbox element on each SD tab after batch count/size
for toggling repeatable seeds, and update _inf functions to take
this into account.
* UI/Web: Moves the Stop buttons out of the Advanced sections and next
to Generate to make things not fit quite so badly with the extra UI
elements.
* UI/Web: Fixes logging to the upscaler output text box not working
correctly when running multiple batches.
This commit is contained in:
Stefan Kapusniak
2023-07-16 00:22:41 +01:00
committed by GitHub
parent 1adb51b29d
commit a2a436eb0c
11 changed files with 231 additions and 163 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
apps/stable_diffusion/scripts/inpaint.py
View File

@@ -58,11 +58,8 @@ def main():
ondemand=args.ondemand,
)
seeds = utils.batch_seeds(seed, args.batch_count, args.repeatable_seeds)
for current_batch in range(args.batch_count):
if current_batch > 0:
seed = -1
seed = utils.sanitize_seed(seed)
start_time = time.time()
generated_imgs = inpaint_obj.generate_images(
args.prompts,
@@ -76,7 +73,7 @@ def main():
args.inpaint_full_res_padding,
args.steps,
args.guidance_scale,
seed,
seeds[current_batch],
args.max_length,
dtype,
args.use_base_vae,
@@ -90,7 +87,10 @@ def main():
f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
)
text_output += f"\nscheduler={args.scheduler}, device={args.device}"
text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
text_output += (
f"\nsteps={args.steps}, guidance_scale={args.guidance_scale},"
)
text_output += f"seed={seed}, size={args.height}x{args.width}"
text_output += (
f", batch size={args.batch_size}, max_length={args.max_length}"
)

12
apps/stable_diffusion/scripts/outpaint.py
View File

@@ -51,11 +51,8 @@ def main():
ondemand=args.ondemand,
)
seeds = utils.batch_seeds(seed, args.batch_count, args.repeatable_seeds)
for current_batch in range(args.batch_count):
if current_batch > 0:
seed = -1
seed = utils.sanitize_seed(seed)
start_time = time.time()
generated_imgs = outpaint_obj.generate_images(
args.prompts,
@@ -74,7 +71,7 @@ def main():
args.width,
args.steps,
args.guidance_scale,
seed,
seeds[current_batch],
args.max_length,
dtype,
args.use_base_vae,
@@ -88,7 +85,10 @@ def main():
f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
)
text_output += f"\nscheduler={args.scheduler}, device={args.device}"
text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
text_output += (
f"\nsteps={args.steps}, guidance_scale={args.guidance_scale},"
)
text_output += f"seed={seed}, size={args.height}x{args.width}"
text_output += (
f", batch size={args.batch_size}, max_length={args.max_length}"
)

14
apps/stable_diffusion/scripts/txt2img.py
View File

@@ -42,11 +42,8 @@ def main():
ondemand=args.ondemand,
)
seeds = utils.batch_seeds(seed, args.batch_count, args.repeatable_seeds)
for current_batch in range(args.batch_count):
if current_batch > 0:
seed = -1
seed = utils.sanitize_seed(seed)
start_time = time.time()
generated_imgs = txt2img_obj.generate_images(
args.prompts,
@@ -56,7 +53,7 @@ def main():
args.width,
args.steps,
args.guidance_scale,
seed,
seeds[current_batch],
args.max_length,
dtype,
args.use_base_vae,
@@ -70,7 +67,12 @@ def main():
f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
)
text_output += f"\nscheduler={args.scheduler}, device={args.device}"
text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
text_output += (
f"\nsteps={args.steps}, guidance_scale={args.guidance_scale},"
)
text_output += (
f"seed={seeds[current_batch]}, size={args.height}x{args.width}"
)
text_output += (
f", batch size={args.batch_size}, max_length={args.max_length}"
)

1
apps/stable_diffusion/src/utils/__init__.py
View File

@@ -28,6 +28,7 @@ from apps.stable_diffusion.src.utils.utils import (
fetch_and_update_base_model_id,
get_path_to_diffusers_checkpoint,
sanitize_seed,
batch_seeds,
get_path_stem,
get_extended_name,
get_generated_imgs_path,

12
apps/stable_diffusion/src/utils/stable_args.py
View File

@@ -320,10 +320,18 @@ p.add_argument(
"--batch_count",
type=int,
default=1,
help="Number of batch to be generated with random seeds in "
help="Number of batches to be generated with random seeds in "
"single execution.",
)
p.add_argument(
"--repeatable_seeds",
default=False,
action=argparse.BooleanOptionalAction,
help="The seed of the first batch will be used as the rng seed to "
"generate the subsequent seeds for subsequent batches in that run.",
)
p.add_argument(
"--ckpt_loc",
type=str,
@@ -524,6 +532,8 @@ p.add_argument(
help="If import_mlir is True, saves mlir via the debug option "
"in shark importer. Does nothing if import_mlir is false (the default).",
)
##############################################################################
# Web UI flags
##############################################################################

29
apps/stable_diffusion/src/utils/utils.py
View File

@@ -8,7 +8,12 @@ from datetime import datetime as dt
from csv import DictWriter
from pathlib import Path
import numpy as np
from random import randint
from random import (
randint,
seed as seed_random,
getstate as random_getstate,
setstate as random_setstate,
)
import tempfile
import torch
from safetensors.torch import load_file
@@ -730,6 +735,28 @@ def sanitize_seed(seed):
return seed
# Generate a set of seeds, using as the first seed of the set,
# optionally using it as the rng seed for subsequent seeds in the set
def batch_seeds(seed, batch_count, repeatable=False):
# use the passed seed as the initial seed of the batch
seeds = [sanitize_seed(seed)]
if repeatable:
# use the initial seed as the rng generator seed
saved_random_state = random_getstate()
seed_random(seed)
# generate the additional seeds
for i in range(1, batch_count):
seeds.append(sanitize_seed(-1))
if repeatable:
# reset the rng back to normal
random_setstate(saved_random_state)
return seeds
# clear all the cached objects to recompile cleanly.
def clear_all():
print("CLEARING ALL, EXPECT SEVERAL MINUTES TO RECOMPILE")

64
apps/stable_diffusion/web/ui/img2img_ui.py
View File

@@ -66,6 +66,7 @@ def img2img_inf(
lora_weights: str,
lora_hf_id: str,
ondemand: bool,
repeatable_seeds: bool,
):
from apps.stable_diffusion.web.ui.utils import (
get_custom_model_pathfile,
@@ -230,12 +231,11 @@ def img2img_inf(
global_obj.get_sd_obj().log = ""
generated_imgs = []
seeds = []
img_seed = utils.sanitize_seed(seed)
seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
extra_info = {"STRENGTH": strength}
text_output = ""
for current_batch in range(batch_count):
if current_batch > 0:
img_seed = utils.sanitize_seed(-1)
out_imgs = global_obj.get_sd_obj().generate_images(
prompt,
negative_prompt,
@@ -246,7 +246,7 @@ def img2img_inf(
steps,
strength,
guidance_scale,
img_seed,
seeds[current_batch],
args.max_length,
dtype,
args.use_base_vae,
@@ -254,9 +254,10 @@ def img2img_inf(
args.max_embeddings_multiples,
use_stencil=use_stencil,
)
seeds.append(img_seed)
total_time = time.time() - start_time
text_output = get_generation_text_info(seeds, device)
text_output = get_generation_text_info(
seeds[: current_batch + 1], device
)
text_output += "\n" + global_obj.get_sd_obj().log
text_output += f"\nTotal image(s) generation time: {total_time:.4f}sec"
@@ -265,7 +266,7 @@ def img2img_inf(
else:
save_output_img(
out_imgs[0],
img_seed,
seeds[current_batch],
extra_info,
)
generated_imgs.extend(out_imgs)
@@ -344,6 +345,7 @@ def img2img_api(
lora_weights="None",
lora_hf_id="",
ondemand=False,
repeatable_seeds=False,
)
# Converts generator type to subscriptable
@@ -565,16 +567,18 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
visible=False,
)
with gr.Row():
steps = gr.Slider(
1, 100, value=args.steps, step=1, label="Steps"
)
strength = gr.Slider(
0,
1,
value=args.strength,
step=0.01,
label="Denoising Strength",
)
with gr.Column(scale=3):
steps = gr.Slider(
1, 100, value=args.steps, step=1, label="Steps"
)
with gr.Column(scale=3):
strength = gr.Slider(
0,
1,
value=args.strength,
step=0.01,
label="Denoising Strength",
)
ondemand = gr.Checkbox(
value=args.ondemand,
label="Low VRAM",
@@ -598,6 +602,11 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
label="Batch Count",
interactive=True,
)
repeatable_seeds = gr.Checkbox(
args.repeatable_seeds,
label="Repeatable Seeds",
)
with gr.Row():
batch_size = gr.Slider(
1,
4,
@@ -607,7 +616,6 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
interactive=False,
visible=False,
)
stop_batch = gr.Button("Stop Batch")
with gr.Row():
seed = gr.Number(
value=args.seed, precision=0, label="Seed"
@@ -619,16 +627,15 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
choices=available_devices,
)
with gr.Row():
with gr.Column(scale=2):
random_seed = gr.Button("Randomize Seed")
random_seed.click(
lambda: -1,
inputs=[],
outputs=[seed],
queue=False,
)
with gr.Column(scale=6):
stable_diffusion = gr.Button("Generate Image(s)")
random_seed = gr.Button("Randomize Seed")
random_seed.click(
lambda: -1,
inputs=[],
outputs=[seed],
queue=False,
)
stop_batch = gr.Button("Stop Batch")
stable_diffusion = gr.Button("Generate Image(s)")
with gr.Column(scale=1, min_width=600):
with gr.Group():
@@ -683,6 +690,7 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
lora_weights,
lora_hf_id,
ondemand,
repeatable_seeds,
],
outputs=[img2img_gallery, std_output, img2img_status],
show_progress="minimal" if args.progress_bar else "none",

47
apps/stable_diffusion/web/ui/inpaint_ui.py
View File

@@ -64,6 +64,7 @@ def inpaint_inf(
lora_weights: str,
lora_hf_id: str,
ondemand: bool,
repeatable_seeds: int,
):
from apps.stable_diffusion.web.ui.utils import (
get_custom_model_pathfile,
@@ -180,14 +181,12 @@ def inpaint_inf(
start_time = time.time()
global_obj.get_sd_obj().log = ""
generated_imgs = []
seeds = []
img_seed = utils.sanitize_seed(seed)
seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
image = image_dict["image"]
mask_image = image_dict["mask"]
text_output = ""
for i in range(batch_count):
if i > 0:
img_seed = utils.sanitize_seed(-1)
for current_batch in range(batch_count):
out_imgs = global_obj.get_sd_obj().generate_images(
prompt,
negative_prompt,
@@ -200,26 +199,27 @@ def inpaint_inf(
inpaint_full_res_padding,
steps,
guidance_scale,
img_seed,
seeds[current_batch],
args.max_length,
dtype,
args.use_base_vae,
cpu_scheduling,
args.max_embeddings_multiples,
)
seeds.append(img_seed)
total_time = time.time() - start_time
text_output = get_generation_text_info(seeds, device)
text_output = get_generation_text_info(
seeds[: current_batch + 1], device
)
text_output += "\n" + global_obj.get_sd_obj().log
text_output += f"\nTotal image(s) generation time: {total_time:.4f}sec"
if global_obj.get_sd_status() == SD_STATE_CANCEL:
break
else:
save_output_img(out_imgs[0], img_seed)
save_output_img(out_imgs[0], seeds[current_batch])
generated_imgs.extend(out_imgs)
yield generated_imgs, text_output, status_label(
"Inpaint", i + 1, batch_count, batch_size
"Inpaint", current_batch + 1, batch_count, batch_size
)
return generated_imgs, text_output
@@ -292,6 +292,7 @@ def inpaint_api(
lora_weights="None",
lora_hf_id="",
ondemand=False,
repeatable_seeds=False,
)
# Converts generator type to subscriptable
@@ -498,6 +499,11 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
label="Batch Count",
interactive=True,
)
repeatable_seeds = gr.Checkbox(
args.repeatable_seeds,
label="Repeatable Seeds",
)
with gr.Row():
batch_size = gr.Slider(
1,
4,
@@ -507,7 +513,6 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
interactive=False,
visible=False,
)
stop_batch = gr.Button("Stop Batch")
with gr.Row():
seed = gr.Number(
value=args.seed, precision=0, label="Seed"
@@ -519,16 +524,15 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
choices=available_devices,
)
with gr.Row():
with gr.Column(scale=2):
random_seed = gr.Button("Randomize Seed")
random_seed.click(
lambda: -1,
inputs=[],
outputs=[seed],
queue=False,
)
with gr.Column(scale=6):
stable_diffusion = gr.Button("Generate Image(s)")
random_seed = gr.Button("Randomize Seed")
random_seed.click(
lambda: -1,
inputs=[],
outputs=[seed],
queue=False,
)
stop_batch = gr.Button("Stop Batch")
stable_diffusion = gr.Button("Generate Image(s)")
with gr.Column(scale=1, min_width=600):
with gr.Group():
@@ -584,6 +588,7 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
lora_weights,
lora_hf_id,
ondemand,
repeatable_seeds,
],
outputs=[inpaint_gallery, std_output, inpaint_status],
show_progress="minimal" if args.progress_bar else "none",

47
apps/stable_diffusion/web/ui/outpaint_ui.py
View File

@@ -64,6 +64,7 @@ def outpaint_inf(
lora_weights: str,
lora_hf_id: str,
ondemand: bool,
repeatable_seeds: bool,
):
from apps.stable_diffusion.web.ui.utils import (
get_custom_model_pathfile,
@@ -177,8 +178,7 @@ def outpaint_inf(
start_time = time.time()
global_obj.get_sd_obj().log = ""
generated_imgs = []
seeds = []
img_seed = utils.sanitize_seed(seed)
seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
left = True if "left" in directions else False
right = True if "right" in directions else False
@@ -186,9 +186,7 @@ def outpaint_inf(
bottom = True if "down" in directions else False
text_output = ""
for i in range(batch_count):
if i > 0:
img_seed = utils.sanitize_seed(-1)
for current_batch in range(batch_count):
out_imgs = global_obj.get_sd_obj().generate_images(
prompt,
negative_prompt,
@@ -206,26 +204,27 @@ def outpaint_inf(
width,
steps,
guidance_scale,
img_seed,
seeds[current_batch],
args.max_length,
dtype,
args.use_base_vae,
cpu_scheduling,
args.max_embeddings_multiples,
)
seeds.append(img_seed)
total_time = time.time() - start_time
text_output = get_generation_text_info(seeds, device)
text_output = get_generation_text_info(
seeds[: current_batch + 1], device
)
text_output += "\n" + global_obj.get_sd_obj().log
text_output += f"\nTotal image(s) generation time: {total_time:.4f}sec"
if global_obj.get_sd_status() == SD_STATE_CANCEL:
break
else:
save_output_img(out_imgs[0], img_seed)
save_output_img(out_imgs[0], seeds[current_batch])
generated_imgs.extend(out_imgs)
yield generated_imgs, text_output, status_label(
"Outpaint", i + 1, batch_count, batch_size
"Outpaint", current_batch + 1, batch_count, batch_size
)
return generated_imgs, text_output, ""
@@ -300,6 +299,7 @@ def outpaint_api(
lora_weights="None",
lora_hf_id="",
ondemand=False,
repeatable_seeds=False,
)
# Convert Generator to Subscriptable
@@ -526,6 +526,12 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
label="Batch Count",
interactive=True,
)
repeatable_seeds = gr.Checkbox(
args.repeatable_seeds,
label="Repeatable Seeds",
)
with gr.Row():
batch_size = gr.Slider(
1,
4,
@@ -535,7 +541,6 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
interactive=False,
visible=False,
)
stop_batch = gr.Button("Stop Batch")
with gr.Row():
seed = gr.Number(
value=args.seed, precision=0, label="Seed"
@@ -547,16 +552,15 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
choices=available_devices,
)
with gr.Row():
with gr.Column(scale=2):
random_seed = gr.Button("Randomize Seed")
random_seed.click(
lambda: -1,
inputs=[],
outputs=[seed],
queue=False,
)
with gr.Column(scale=6):
stable_diffusion = gr.Button("Generate Image(s)")
random_seed = gr.Button("Randomize Seed")
random_seed.click(
lambda: -1,
inputs=[],
outputs=[seed],
queue=False,
)
stop_batch = gr.Button("Stop Batch")
stable_diffusion = gr.Button("Generate Image(s)")
with gr.Column(scale=1, min_width=600):
with gr.Group():
@@ -612,6 +616,7 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
lora_weights,
lora_hf_id,
ondemand,
repeatable_seeds,
],
outputs=[outpaint_gallery, std_output, outpaint_status],
show_progress="minimal" if args.progress_bar else "none",

69
apps/stable_diffusion/web/ui/txt2img_ui.py
View File

@@ -61,6 +61,7 @@ def txt2img_inf(
lora_weights: str,
lora_hf_id: str,
ondemand: bool,
repeatable_seeds: bool,
):
from apps.stable_diffusion.web.ui.utils import (
get_custom_model_pathfile,
@@ -177,12 +178,10 @@ def txt2img_inf(
start_time = time.time()
global_obj.get_sd_obj().log = ""
generated_imgs = []
seeds = []
img_seed = utils.sanitize_seed(seed)
seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
text_output = ""
for i in range(batch_count):
if i > 0:
img_seed = utils.sanitize_seed(-1)
for current_batch in range(batch_count):
out_imgs = global_obj.get_sd_obj().generate_images(
prompt,
negative_prompt,
@@ -191,26 +190,27 @@ def txt2img_inf(
width,
steps,
guidance_scale,
img_seed,
seeds[current_batch],
args.max_length,
dtype,
args.use_base_vae,
cpu_scheduling,
args.max_embeddings_multiples,
)
seeds.append(img_seed)
total_time = time.time() - start_time
text_output = get_generation_text_info(seeds, device)
text_output = get_generation_text_info(
seeds[: current_batch + 1], device
)
text_output += "\n" + global_obj.get_sd_obj().log
text_output += f"\nTotal image(s) generation time: {total_time:.4f}sec"
if global_obj.get_sd_status() == SD_STATE_CANCEL:
break
else:
save_output_img(out_imgs[0], img_seed)
save_output_img(out_imgs[0], seeds[current_batch])
generated_imgs.extend(out_imgs)
yield generated_imgs, text_output, status_label(
"Text-to-Image", i + 1, batch_count, batch_size
"Text-to-Image", current_batch + 1, batch_count, batch_size
)
return generated_imgs, text_output, ""
@@ -267,6 +267,7 @@ def txt2img_api(
lora_weights="None",
lora_hf_id="",
ondemand=False,
repeatable_seeds=False,
)
# Convert Generator to Subscriptable
@@ -439,16 +440,18 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
visible=False,
)
with gr.Row():
steps = gr.Slider(
1, 100, value=args.steps, step=1, label="Steps"
)
guidance_scale = gr.Slider(
0,
50,
value=args.guidance_scale,
step=0.1,
label="CFG Scale",
)
with gr.Column(scale=3):
steps = gr.Slider(
1, 100, value=args.steps, step=1, label="Steps"
)
with gr.Column(scale=3):
guidance_scale = gr.Slider(
0,
50,
value=args.guidance_scale,
step=0.1,
label="CFG Scale",
)
ondemand = gr.Checkbox(
value=args.ondemand,
label="Low VRAM",
@@ -473,7 +476,10 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
label="Batch Size",
interactive=True,
)
stop_batch = gr.Button("Stop Batch")
repeatable_seeds = gr.Checkbox(
args.repeatable_seeds,
label="Repeatable Seeds",
)
with gr.Row():
seed = gr.Number(
value=args.seed, precision=0, label="Seed"
@@ -485,17 +491,15 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
choices=available_devices,
)
with gr.Row():
with gr.Column(scale=2):
random_seed = gr.Button("Randomize Seed")
random_seed.click(
lambda: -1,
inputs=[],
outputs=[seed],
queue=False,
)
with gr.Column(scale=6):
stable_diffusion = gr.Button("Generate Image(s)")
random_seed = gr.Button("Randomize Seed")
random_seed.click(
lambda: -1,
inputs=[],
outputs=[seed],
queue=False,
)
stop_batch = gr.Button("Stop Batch")
stable_diffusion = gr.Button("Generate Image(s)")
with gr.Accordion(label="Prompt Examples!", open=False):
ex = gr.Examples(
examples=prompt_examples,
@@ -555,6 +559,7 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
lora_weights,
lora_hf_id,
ondemand,
repeatable_seeds,
],
outputs=[txt2img_gallery, std_output, txt2img_status],
show_progress="minimal" if args.progress_bar else "none",

87
apps/stable_diffusion/web/ui/upscaler_ui.py
View File

@@ -57,6 +57,7 @@ def upscaler_inf(
lora_weights: str,
lora_hf_id: str,
ondemand: bool,
repeatable_seeds: bool,
):
from apps.stable_diffusion.web.ui.utils import (
get_custom_model_pathfile,
@@ -176,12 +177,10 @@ def upscaler_inf(
start_time = time.time()
global_obj.get_sd_obj().log = ""
generated_imgs = []
seeds = []
img_seed = utils.sanitize_seed(seed)
seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
extra_info = {"NOISE LEVEL": noise_level}
for current_batch in range(batch_count):
if current_batch > 0:
img_seed = utils.sanitize_seed(-1)
low_res_img = image
high_res_img = Image.new("RGB", (height * 4, width * 4))
@@ -198,7 +197,7 @@ def upscaler_inf(
steps,
noise_level,
guidance_scale,
img_seed,
seeds[current_batch],
args.max_length,
dtype,
args.use_base_vae,
@@ -213,39 +212,40 @@ def upscaler_inf(
if global_obj.get_sd_status() == SD_STATE_CANCEL:
break
total_time = time.time() - start_time
text_output = f"prompt={args.prompts}"
text_output += f"\nnegative prompt={args.negative_prompts}"
text_output += (
f"\nmodel_id={args.hf_model_id}, " f"ckpt_loc={args.ckpt_loc}"
)
text_output += f"\nscheduler={args.scheduler}, " f"device={device}"
text_output += (
f"\nsteps={steps}, "
f"noise_level={noise_level}, "
f"guidance_scale={guidance_scale}, "
f"seed={seeds[:current_batch + 1]}"
)
text_output += (
f"\ninput size={height}x{width}, "
f"output size={height*4}x{width*4}, "
f"batch_count={batch_count}, "
f"batch_size={batch_size}, "
f"max_length={args.max_length}\n"
)
text_output += global_obj.get_sd_obj().log
text_output += f"\nTotal image generation time: {total_time:.4f}sec"
if global_obj.get_sd_status() == SD_STATE_CANCEL:
break
else:
save_output_img(high_res_img, img_seed, extra_info)
save_output_img(high_res_img, seeds[current_batch], extra_info)
generated_imgs.append(high_res_img)
seeds.append(img_seed)
global_obj.get_sd_obj().log += "\n"
yield generated_imgs, global_obj.get_sd_obj().log, status_label(
yield generated_imgs, text_output, status_label(
"Upscaler", current_batch + 1, batch_count, batch_size
)
total_time = time.time() - start_time
text_output = f"prompt={args.prompts}"
text_output += f"\nnegative prompt={args.negative_prompts}"
text_output += (
f"\nmodel_id={args.hf_model_id}, " f"ckpt_loc={args.ckpt_loc}"
)
text_output += f"\nscheduler={args.scheduler}, " f"device={device}"
text_output += (
f"\nsteps={steps}, "
f"noise_level={noise_level}, "
f"guidance_scale={guidance_scale}, "
f"seed={seeds}"
)
text_output += (
f"\nsize={height}x{width}, "
f"batch_count={batch_count}, "
f"batch_size={batch_size}, "
f"max_length={args.max_length}"
)
text_output += global_obj.get_sd_obj().log
text_output += f"\nTotal image generation time: {total_time:.4f}sec"
yield generated_imgs, text_output, ""
@@ -314,6 +314,7 @@ def upscaler_api(
lora_weights="None",
lora_hf_id="",
ondemand=False,
repeatable_seeds=False,
)
# Converts generator type to subscriptable
res = next(res)
@@ -518,6 +519,11 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
label="Batch Count",
interactive=True,
)
repeatable_seeds = gr.Checkbox(
args.repeatable_seeds,
label="Repeatable Seeds",
)
with gr.Row():
batch_size = gr.Slider(
1,
4,
@@ -527,7 +533,6 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
interactive=False,
visible=False,
)
stop_batch = gr.Button("Stop Batch")
with gr.Row():
seed = gr.Number(
value=args.seed, precision=0, label="Seed"
@@ -539,16 +544,15 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
choices=available_devices,
)
with gr.Row():
with gr.Column(scale=2):
random_seed = gr.Button("Randomize Seed")
random_seed.click(
lambda: -1,
inputs=[],
outputs=[seed],
queue=False,
)
with gr.Column(scale=6):
stable_diffusion = gr.Button("Generate Image(s)")
random_seed = gr.Button("Randomize Seed")
random_seed.click(
lambda: -1,
inputs=[],
outputs=[seed],
queue=False,
)
stop_batch = gr.Button("Stop Batch")
stable_diffusion = gr.Button("Generate Image(s)")
with gr.Column(scale=1, min_width=600):
with gr.Group():
@@ -601,6 +605,7 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
lora_weights,
lora_hf_id,
ondemand,
repeatable_seeds,
],
outputs=[upscaler_gallery, std_output, upscaler_status],
show_progress="minimal" if args.progress_bar else "none",