1580158
/
infinite-zoom-automatic1111-webui
mirror of https://github.com/v8hid/infinite-zoom-automatic1111-webui.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #46 from v8hid/develop 

Post Process (Upsclaing), Vlad a1111 fork Integration, Prompts loading error fixes, choose Txt2img and Img2img model in setting, default output folder changed to /output/infinite-zoom, and other improvements
pull/48/head 1.0
vahid khroasani 2023-04-21 08:30:55 +04:00 committed by GitHub
parent 6daf25745d c5f77a50e3
commit 660aca308e
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 343 additions and 56 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
iz_helpers/video.py
View File

@ -13,8 +13,9 @@ def write_video(file_path, frames, fps, reversed=True, start_frame_dupe_amount=1
if reversed == True:
frames = frames[::-1]
# Get dimensions of the frames
# w, h = frames[0].size
# Get dimensions of the first frames, all subsequent has to be same sized
for k in frames:
assert (k.size == frames[0].size,"Different frame sizes found!")
# Create an imageio video writer, avoid block size of 512.
writer = imageio.get_writer(file_path, fps=fps, macro_block_size=None)

337
scripts/infinite-zoom.py
View File

@ -1,9 +1,9 @@
import sys
import os
import time
import json
from jsonschema import validate
basedir = os.getcwd()
sys.path.extend(basedir + "/extensions/infinite-zoom-automatic1111-webui/")
import numpy as np
import gradio as gr
from PIL import Image
@ -12,7 +12,7 @@ import json
from iz_helpers import shrink_and_paste_on_blank, write_video
from webui import wrap_gradio_gpu_call
from modules import script_callbacks
from modules import script_callbacks, scripts
import modules.shared as shared
from modules.processing import (
process_images,
@ -20,30 +20,47 @@ from modules.processing import (
StableDiffusionProcessingImg2Img,
)
from modules.ui import create_output_panel, plaintext_to_html
from scripts import postprocessing_upscale
available_samplers = [
"DDIM",
"Euler a",
"Euler",
"LMS",
"Heun",
"DPM2",
"DPM2 a",
"DPM++ 2S a",
"DPM++ 2M",
"DPM++ SDE",
"DPM fast",
"DPM adaptive",
"LMS Karras",
"DPM2 Karras",
"DPM2 a Karras",
"DPM++ 2S a Karras",
"DPM++ 2M Karras",
"DPM++ SDE Karras",
]
default_prompt = "A psychedelic jungle with trees that have glowing, fractal-like patterns, Simon stalenhag poster 1920s style, street level view, hyper futuristic, 8k resolution, hyper realistic"
default_negative_prompt = "frames, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur"
from modules.ui import create_output_panel, plaintext_to_html
import modules.sd_models
import modules.sd_samplers
from modules import scripts
usefulDirs = scripts.basedir().split(os.sep)[
-2:
] # contains install and our extension foldername
jsonprompt_schemafile = (
usefulDirs[0] + "/" + usefulDirs[1] + "/scripts/promptschema.json"
)
available_samplers = [s.name for s in modules.sd_samplers.samplers if "UniPc" not in s.name]
default_prompt = """
{
"prompts":{
"headers":["outpaint steps","prompt"],
"data":[
[0,"Huge spectacular Waterfall in a dense tropical forest,epic perspective,(vegetation overgrowth:1.3)(intricate, ornamentation:1.1),(baroque:1.1), fantasy, (realistic:1) digital painting , (magical,mystical:1.2) , (wide angle shot:1.4), (landscape composed:1.2)(medieval:1.1), divine,cinematic,(tropical forest:1.4),(river:1.3)mythology,india, volumetric lighting, Hindu ,epic, Alex Horley Wenjun Lin greg rutkowski Ruan Jia (Wayne Barlowe:1.2) <lora:epiNoiseoffset_v2:0.6> "],
]
},
"negPrompt":"frames, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur bad-artist"
}
"""
empty_prompt = (
'{"prompts":{"data":[],"headers":["outpaint steps","prompt"]},"negPrompt":""}'
)
# must be python dict
invalid_prompt = {
"prompts": {
"data": [[0, "Your prompt-json is invalid, please check Settings"]],
"headers": ["outpaint steps", "prompt"],
},
"negPrompt": "Invalid prompt-json",
}
def closest_upper_divisible_by_eight(num):
@ -53,6 +70,49 @@ def closest_upper_divisible_by_eight(num):
return math.ceil(num / 8) * 8
# example fail: 720 px width * 1.66 upscale => 1195.2 => 1195 crash
# 512 px * 1.66 = 513.66 = ?
# assume ffmpeg will CUT to integer
# 721 /720
def do_upscaleImg(curImg, upscale_do, upscaler_name, upscale_by):
if not upscale_do:
return curImg
# ensure even width and even height for ffmpeg
# if odd, switch to scale to mode
rwidth = round(curImg.width * upscale_by)
rheight = round(curImg.height * upscale_by)
ups_mode = 2 # upscale_by
if ( (rwidth %2) == 1 ):
ups_mode = 1
rwidth += 1
if ( (rheight %2) == 1 ):
ups_mode = 1
rheight += 1
if (1 == ups_mode ):
print ("Infinite Zoom: aligning output size to even width and height: " + str(rwidth) +" x "+str(rheight), end='\r' )
pp = postprocessing_upscale.scripts_postprocessing.PostprocessedImage(
curImg
)
ups = postprocessing_upscale.ScriptPostprocessingUpscale()
ups.process(
pp,
upscale_mode=ups_mode,
upscale_by=upscale_by,
upscale_to_width=rwidth,
upscale_to_height=rheight,
upscale_crop=False,
upscaler_1_name=upscaler_name,
upscaler_2_name=None,
upscaler_2_visibility=0.0,
)
return pp.image
def renderTxt2Img(prompt, negative_prompt, sampler, steps, cfg_scale, width, height):
processed = None
p = StableDiffusionProcessingTxt2Img(
@ -90,6 +150,7 @@ def renderImg2Img(
inpainting_padding,
):
processed = None
p = StableDiffusionProcessingImg2Img(
sd_model=shared.sd_model,
outpath_samples=shared.opts.outdir_img2img_samples,
@ -126,6 +187,28 @@ def fix_env_Path_ffprobe():
os.environ["PATH"] = envpath + path_sep + ffppath
def load_model_from_setting(model_field_name, progress, progress_desc):
# fix typo in Automatic1111 vs Vlad111
if hasattr(modules.sd_models, "checkpoint_alisases"):
checkPList = modules.sd_models.checkpoint_alisases
elif hasattr(modules.sd_models, "checkpoint_aliases"):
checkPList = modules.sd_models.checkpoint_aliases
else:
raise Exception("This is not a compatible StableDiffusion Platform, can not access checkpoints")
model_name = shared.opts.data.get(model_field_name)
if model_name is not None and model_name != "":
checkinfo = checkPList[model_name]
if not checkinfo:
raise NameError(model_field_name + " Does not exist in your models.")
if progress:
progress(0, desc=progress_desc + checkinfo.name)
modules.sd_models.load_model(checkinfo)
def create_zoom(
prompts_array,
negative_prompt,
@ -133,6 +216,7 @@ def create_zoom(
guidance_scale,
num_inference_steps,
custom_init_image,
custom_exit_image,
video_frame_rate,
video_zoom_mode,
video_start_frame_dupe_amount,
@ -147,8 +231,12 @@ def create_zoom(
outputsizeH,
batchcount,
sampler,
upscale_do,
upscaler_name,
upscale_by,
progress=None,
):
for i in range(batchcount):
print(f"Batch {i+1}/{batchcount}")
result = create_zoom_single(
@ -158,6 +246,7 @@ def create_zoom(
guidance_scale,
num_inference_steps,
custom_init_image,
custom_exit_image,
video_frame_rate,
video_zoom_mode,
video_start_frame_dupe_amount,
@ -171,6 +260,9 @@ def create_zoom(
outputsizeW,
outputsizeH,
sampler,
upscale_do,
upscaler_name,
upscale_by,
progress,
)
return result
@ -183,6 +275,7 @@ def create_zoom_single(
guidance_scale,
num_inference_steps,
custom_init_image,
custom_exit_image,
video_frame_rate,
video_zoom_mode,
video_start_frame_dupe_amount,
@ -196,6 +289,9 @@ def create_zoom_single(
outputsizeW,
outputsizeH,
sampler,
upscale_do,
upscaler_name,
upscale_by,
progress=None,
):
# try:
@ -229,6 +325,8 @@ def create_zoom_single(
(width, height), resample=Image.LANCZOS
)
else:
load_model_from_setting("infzoom_txt2img_model", progress, "Loading Model for txt2img: ")
processed = renderTxt2Img(
prompts[min(k for k in prompts.keys() if k >= 0)],
negative_prompt,
@ -246,19 +344,26 @@ def create_zoom_single(
num_interpol_frames = round(video_frame_rate * zoom_speed)
all_frames = []
all_frames.append(current_image)
if upscale_do and progress:
progress(0, desc="upscaling inital image")
all_frames.append(
do_upscaleImg(current_image, upscale_do, upscaler_name, upscale_by)
if upscale_do
else current_image
)
load_model_from_setting("infzoom_inpainting_model", progress, "Loading Model for inpainting/img2img: " )
for i in range(num_outpainting_steps):
print_out = "Outpaint step: " + str(i + 1) + " / " + str(num_outpainting_steps)
print(print_out)
# if progress is not None:
# progress(
# ((i + 1) / num_outpainting_steps),
# desc=print_out,
# )
if progress:
progress(((i + 1) / num_outpainting_steps), desc=print_out)
prev_image_fix = current_image
prev_image = shrink_and_paste_on_blank(current_image, mask_width, mask_height)
current_image = prev_image
# create mask (black image with white mask_width width edges)
@ -267,6 +372,7 @@ def create_zoom_single(
# inpainting step
current_image = current_image.convert("RGB")
processed = renderImg2Img(
prompts[max(k for k in prompts.keys() if k <= i)],
negative_prompt,
@ -341,8 +447,23 @@ def create_zoom_single(
interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
all_frames.append(interpol_image)
all_frames.append(current_image)
if upscale_do and progress:
progress(((i + 1) / num_outpainting_steps), desc="upscaling interpol")
all_frames.append(
do_upscaleImg(interpol_image, upscale_do, upscaler_name, upscale_by)
if upscale_do
else interpol_image
)
if upscale_do and progress:
progress(((i + 1) / num_outpainting_steps), desc="upscaling current")
all_frames.append(
do_upscaleImg(current_image, upscale_do, upscaler_name, upscale_by)
if upscale_do
else current_image
)
video_file_name = "infinite_zoom_" + str(int(time.time())) + ".mp4"
output_path = shared.opts.data.get(
@ -372,17 +493,38 @@ def create_zoom_single(
)
def exportPrompts(p, np):
print("prompts:" + str(p) + "\n" + str(np))
def validatePromptJson_throws(data):
with open(jsonprompt_schemafile, "r") as s:
schema = json.load(s)
validate(instance=data, schema=schema)
def putPrompts(files):
file_paths = [file.name for file in files]
with open(files.name, "r") as f:
file_contents = f.read()
data = json.loads(file_contents)
print(data)
return [gr.DataFrame.update(data["prompts"]), gr.Textbox.update(data["negPrompt"])]
try:
with open(files.name, "r") as f:
file_contents = f.read()
data = json.loads(file_contents)
validatePromptJson_throws(data)
return [
gr.DataFrame.update(data["prompts"]),
gr.Textbox.update(data["negPrompt"]),
]
except Exception:
gr.Error(
"loading your prompt failed. It seems to be invalid. Your prompt table is preserved."
)
print(
"[InfiniteZoom:] Loading your prompt failed. It seems to be invalid. Your prompt table is preserved."
)
return [gr.DataFrame.update(), gr.Textbox.update()]
def clearPrompts():
return [
gr.DataFrame.update(value=[[0, "Infinite Zoom. Start over"]]),
gr.Textbox.update(""),
]
def on_ui_tabs():
@ -396,8 +538,9 @@ def on_ui_tabs():
"""
)
generate_btn = gr.Button(value="Generate video", variant="primary")
interrupt = gr.Button(value="Interrupt", elem_id="interrupt_training")
with gr.Row():
generate_btn = gr.Button(value="Generate video", variant="primary")
interrupt = gr.Button(value="Interrupt", elem_id="interrupt_training")
with gr.Row():
with gr.Column(scale=1, variant="panel"):
with gr.Tab("Main"):
@ -409,18 +552,30 @@ def on_ui_tabs():
label="Total Outpaint Steps",
info="The more it is, the longer your videos will be",
)
# safe reading json prompt
pr = shared.opts.data.get("infzoom_defPrompt", default_prompt)
if not pr:
pr = empty_prompt
try:
jpr = json.loads(pr)
validatePromptJson_throws(jpr)
except Exception:
jpr = invalid_prompt
main_prompts = gr.Dataframe(
type="array",
headers=["outpaint step", "prompt"],
datatype=["number", "str"],
row_count=1,
col_count=(2, "fixed"),
value=[[0, default_prompt]],
value=jpr["prompts"],
wrap=True,
)
main_negative_prompt = gr.Textbox(
value=default_negative_prompt, label="Negative Prompt"
value=jpr["negPrompt"], label="Negative Prompt"
)
# these button will be moved using JS unde the dataframe view as small ones
@ -431,7 +586,7 @@ def on_ui_tabs():
elem_id="infzoom_exP_butt",
)
importPrompts_button = gr.UploadButton(
value="Import prompts",
label="Import prompts",
variant="secondary",
elem_classes="sm infzoom_tab_butt",
elem_id="infzoom_imP_butt",
@ -447,6 +602,19 @@ def on_ui_tabs():
outputs=[main_prompts, main_negative_prompt],
inputs=[importPrompts_button],
)
clearPrompts_button = gr.Button(
value="Clear prompts",
variant="secondary",
elem_classes="sm infzoom_tab_butt",
elem_id="infzoom_clP_butt",
)
clearPrompts_button.click(
fn=clearPrompts,
inputs=[],
outputs=[main_prompts, main_negative_prompt],
)
main_sampler = gr.Dropdown(
label="Sampler",
choices=available_samplers,
@ -483,7 +651,12 @@ def on_ui_tabs():
value=50,
label="Sampling Steps for each outpaint",
)
init_image = gr.Image(type="pil", label="custom initial image")
with gr.Row():
init_image = gr.Image(type="pil", label="custom initial image")
exit_image = gr.Image(
type="pil", label="custom exit image", visible=False
) # TODO: implement exit-image rendering
batchcount_slider = gr.Slider(
minimum=1,
maximum=25,
@ -545,6 +718,26 @@ def on_ui_tabs():
label="masked padding", minimum=0, maximum=256, value=0
)
with gr.Tab("Post proccess"):
upscale_do = gr.Checkbox(False, label="Enable Upscale")
upscaler_name = gr.Dropdown(
label="Upscaler",
elem_id="infZ_upscaler",
choices=[x.name for x in shared.sd_upscalers],
value=shared.sd_upscalers[0].name,
)
upscale_by = gr.Slider(
label="Upscale by factor", minimum=1, maximum=8, value=1
)
with gr.Accordion("Help", open=False):
gr.Markdown(
"""# Performance critical
Depending on amount of frames and which upscaler you choose it might took a long time to render.
Our best experience and trade-off is the R-ERSGAn4x upscaler.
"""
)
with gr.Column(scale=1, variant="compact"):
output_video = gr.Video(label="Output").style(width=512, height=512)
(
@ -553,10 +746,10 @@ def on_ui_tabs():
html_info,
html_log,
) = create_output_panel(
"infinit-zoom", shared.opts.outdir_img2img_samples
"infinite-zoom", shared.opts.outdir_img2img_samples
)
generate_btn.click(
fn=wrap_gradio_gpu_call(create_zoom, extra_outputs=[None, '', '']),
fn=wrap_gradio_gpu_call(create_zoom, extra_outputs=[None, "", ""]),
inputs=[
main_prompts,
main_negative_prompt,
@ -564,6 +757,7 @@ def on_ui_tabs():
main_guidance_scale,
sampling_step,
init_image,
exit_image,
video_frame_rate,
video_zoom_mode,
video_start_frame_dupe_amount,
@ -578,6 +772,9 @@ def on_ui_tabs():
main_height,
batchcount_slider,
main_sampler,
upscale_do,
upscaler_name,
upscale_by,
],
outputs=[output_video, out_image, generation_info, html_info, html_log],
)
@ -590,10 +787,11 @@ def on_ui_settings():
section = ("infinite-zoom", "Infinite Zoom")
shared.opts.add_option(
"outputs"
"infzoom_outpath",
shared.OptionInfo(
"",
"Path where to store your infinite video. Let empty to use img2img-output",
"Path where to store your infinite video. Default is Outputs",
gr.Textbox,
{"interactive": True},
section=section,
@ -644,6 +842,39 @@ def on_ui_settings():
),
)
shared.opts.add_option(
"infzoom_txt2img_model",
shared.OptionInfo(
None,
"Name of your desired model to render keyframes (txt2img)",
gr.Dropdown,
lambda: {"choices": shared.list_checkpoint_tiles()},
section=section,
),
)
shared.opts.add_option(
"infzoom_inpainting_model",
shared.OptionInfo(
None,
"Name of your desired inpaint model (img2img-inpaint). Default is vanilla sd-v1-5-inpainting.ckpt ",
gr.Dropdown,
lambda: {"choices": shared.list_checkpoint_tiles()},
section=section,
),
)
shared.opts.add_option(
"infzoom_defPrompt",
shared.OptionInfo(
default_prompt,
"Default prompt-setup to start with'",
gr.Code,
{"interactive": True, "language": "json"},
section=section,
),
)
script_callbacks.on_ui_tabs(on_ui_tabs)
script_callbacks.on_ui_settings(on_ui_settings)

49
scripts/promptschema.json Normal file
View File

@ -0,0 +1,49 @@
{
"$schema": "http://json-schema.org/draft-07/schema#",
"type": "object",
"properties": {
"prompts": {
"type": "object",
"properties": {
"data": {
"type": "array",
"items": {
"type": "array",
"items": [
{
"oneOf": [
{
"type": "integer",
"minimum": 0
},
{
"type": "string"
}
]
},
{
"type": "string"
}
],
"minItems": 0,
"maxItems": 999,
"uniqueItems": false
},
"minItems": 0
},
"headers": {
"type": "array",
"items": {
"type": "string"
},
"minItems": 2
}
},
"required": ["data", "headers"]
},
"negPrompt": {
"type": "string"
}
},
"required": ["prompts", "negPrompt"]
}

8
style.css
View File

@ -1,4 +1,10 @@
#tab_iz_interface .gradio-dataframe .controls-wrap {
flex-direction: row-reverse;
justify-content: space-between;
}
}
/* first column min width */
#tab_iz_interface th:first-child {
flex: 0 0 0%;
width: 0;
}