1085 lines
39 KiB
Python
1085 lines
39 KiB
Python
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import sys
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import os
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import time
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import json
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from jsonschema import validate
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import numpy as np
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import gradio as gr
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from PIL import Image, ImageDraw, ImageFilter, ImageOps
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import math
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import json
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from iz_helpers import shrink_and_paste_on_blank, write_video
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from webui import wrap_gradio_gpu_call
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from modules import script_callbacks, scripts
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import modules.shared as shared
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from modules.processing import (
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process_images,
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StableDiffusionProcessingTxt2Img,
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StableDiffusionProcessingImg2Img,
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)
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from scripts import postprocessing_upscale
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from modules.ui import create_output_panel, plaintext_to_html
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import modules.sd_models
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import modules.sd_samplers
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from modules import scripts
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usefulDirs = scripts.basedir().split(os.sep)[
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-2:
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] # contains install and our extension foldername
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jsonprompt_schemafile = (
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usefulDirs[0] + "/" + usefulDirs[1] + "/scripts/promptschema.json"
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)
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available_samplers = [s.name for s in modules.sd_samplers.samplers if "UniPc" not in s.name]
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default_prompt = """
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{
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"prompts":{
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"headers":["outpaint steps","prompt"],
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"data":[
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[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> "]
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]
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},
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"negPrompt":"frames, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur bad-artist"
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}
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"""
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empty_prompt = (
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'{"prompts":{"data":[],"headers":["outpaint steps","prompt"]},"negPrompt":""}'
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)
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# must be python dict
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invalid_prompt = {
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"prompts": {
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"data": [[0, "Your prompt-json is invalid, please check Settings"]],
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"headers": ["outpaint steps", "prompt"],
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},
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"negPrompt": "Invalid prompt-json",
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}
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def closest_upper_divisible_by_eight(num):
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if num % 8 == 0:
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return num
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else:
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return math.ceil(num / 8) * 8
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# example fail: 720 px width * 1.66 upscale => 1195.2 => 1195 crash
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# 512 px * 1.66 = 513.66 = ?
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# assume ffmpeg will CUT to integer
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# 721 /720
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def do_upscaleImg(curImg, upscale_do, upscaler_name, upscale_by, desc=""):
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if not upscale_do:
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return curImg
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# ensure even width and even height for ffmpeg
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# if odd, switch to scale to mode
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rwidth = round(curImg.width * upscale_by)
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rheight = round(curImg.height * upscale_by)
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ups_mode = 2 # upscale_by
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if ( (rwidth %2) == 1 ):
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ups_mode = 1
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rwidth += 1
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if ( (rheight %2) == 1 ):
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ups_mode = 1
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rheight += 1
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if (1 == ups_mode ):
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print ("Infinite Zoom: aligning output size to even width and height: " + str(rwidth) +" x "+str(rheight), end='\r' )
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print (f"Infinite Zoom: Upscaling by {str(upscale_by)} with {str(upscaler_name)}. {desc}", end='\r' )
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pp = postprocessing_upscale.scripts_postprocessing.PostprocessedImage(
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curImg
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)
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ups = postprocessing_upscale.ScriptPostprocessingUpscale()
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ups.process(
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pp,
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upscale_mode=ups_mode,
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upscale_by=upscale_by,
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upscale_to_width=rwidth,
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upscale_to_height=rheight,
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upscale_crop=False,
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upscaler_1_name=upscaler_name,
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upscaler_2_name=None,
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upscaler_2_visibility=0.0,
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)
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return pp.image
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def renderTxt2Img(prompt, negative_prompt, sampler, steps, cfg_scale, seed, width, height):
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processed = None
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p = StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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outpath_samples=shared.opts.outdir_txt2img_samples,
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outpath_grids=shared.opts.outdir_txt2img_grids,
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prompt=prompt,
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negative_prompt=negative_prompt,
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seed=seed,
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sampler_name=sampler,
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n_iter=1,
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steps=steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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)
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processed = process_images(p)
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newseed = p.seed
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return processed, newseed
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def renderImg2Img(
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prompt,
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negative_prompt,
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sampler,
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steps,
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cfg_scale,
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seed,
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width,
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height,
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init_image,
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mask_image,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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):
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processed = None
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p = StableDiffusionProcessingImg2Img(
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sd_model=shared.sd_model,
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outpath_samples=shared.opts.outdir_img2img_samples,
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outpath_grids=shared.opts.outdir_img2img_grids,
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prompt=prompt,
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negative_prompt=negative_prompt,
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seed=seed,
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sampler_name=sampler,
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n_iter=1,
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steps=steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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init_images=[init_image],
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denoising_strength=inpainting_denoising_strength,
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mask_blur=inpainting_mask_blur,
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inpainting_fill=inpainting_fill_mode,
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inpaint_full_res=inpainting_full_res,
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inpaint_full_res_padding=inpainting_padding,
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mask=mask_image,
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)
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# p.latent_mask = Image.new("RGB", (p.width, p.height), "white")
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processed = process_images(p)
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newseed = p.seed
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return processed, newseed
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def fix_env_Path_ffprobe():
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envpath = os.environ["PATH"]
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ffppath = shared.opts.data.get("infzoom_ffprobepath", "")
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if ffppath and not ffppath in envpath:
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path_sep = ";" if os.name == "nt" else ":"
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os.environ["PATH"] = envpath + path_sep + ffppath
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def load_model_from_setting(model_field_name, progress, progress_desc):
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# fix typo in Automatic1111 vs Vlad111
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if hasattr(modules.sd_models, "checkpoint_alisases"):
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checkPList = modules.sd_models.checkpoint_alisases
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elif hasattr(modules.sd_models, "checkpoint_aliases"):
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checkPList = modules.sd_models.checkpoint_aliases
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else:
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raise Exception("This is not a compatible StableDiffusion Platform, can not access checkpoints")
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model_name = shared.opts.data.get(model_field_name)
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if model_name is not None and model_name != "":
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checkinfo = checkPList[model_name]
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if not checkinfo:
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raise NameError(model_field_name + " Does not exist in your models.")
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if progress:
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progress(0, desc=progress_desc + checkinfo.name)
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modules.sd_models.load_model(checkinfo)
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def create_zoom(
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prompts_array,
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negative_prompt,
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num_outpainting_steps,
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guidance_scale,
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num_inference_steps,
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custom_init_image,
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custom_exit_image,
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video_frame_rate,
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video_zoom_mode,
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video_start_frame_dupe_amount,
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video_last_frame_dupe_amount,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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zoom_speed,
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seed,
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outputsizeW,
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outputsizeH,
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batchcount,
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sampler,
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upscale_do,
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upscaler_name,
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upscalerinterpol_name,
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upscale_by,
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exitgamma,
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maskwidth_slider,
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maskheight_slider,
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progress=None,
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):
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for i in range(batchcount):
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print(f"Batch {i+1}/{batchcount}")
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result = create_zoom_single(
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prompts_array,
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negative_prompt,
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num_outpainting_steps,
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guidance_scale,
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num_inference_steps,
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custom_init_image,
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custom_exit_image,
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video_frame_rate,
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video_zoom_mode,
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video_start_frame_dupe_amount,
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video_last_frame_dupe_amount,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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zoom_speed,
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seed,
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outputsizeW,
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outputsizeH,
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sampler,
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upscale_do,
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upscaler_name,
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upscalerinterpol_name,
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upscale_by,
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exitgamma,
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maskwidth_slider,
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maskheight_slider,
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progress
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)
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return result
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def calculate_interpolframes_curve(width,height,mask_width,mask_height,num_interpol_frames):
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from scipy.signal import savgol_filter
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#import matplotlib.pyplot as plt
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# Create a list to store the values of interpol_width for each frame
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interpol_width_values = []
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interpol_height_values = []
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# Calculate interpol_width for each frame and add it to the list
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for j in range(num_interpol_frames):
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interpol_width = round((1 - (1 - 2 * mask_width / width)**(1 - (j + 1) / num_interpol_frames)) * width / 2)
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interpol_width_values.append(interpol_width)
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# Apply a SAVGOL filter to the interpol_width values
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window_size = 7 # adjust the window size as needed
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interpol_width_smoothed = savgol_filter(interpol_width_values, window_size, 2)
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interpol_width_smoothed = np.round(interpol_width_smoothed).astype(int) # Round the output to the nearest integer
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# Calculate interpol_width for each frame and add it to the list
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for j in range(num_interpol_frames):
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interpol_height = round((1 - (1 - 2 * mask_height / height)**(1 - (j + 1) / num_interpol_frames)) * height / 2)
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interpol_height_values.append(interpol_height)
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# Apply a SAVGOL filter to the interpol_width values
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window_size = 7 # adjust the window size as needed
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interpol_height_smoothed = savgol_filter(interpol_width_values, window_size, 2)
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interpol_height_smoothed = np.round(interpol_width_smoothed).astype(int) # Round the output to the nearest integer
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return interpol_width_smoothed,interpol_height_smoothed
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def calculate_interpolframes2_curve(width,height,mask_width,mask_height,num_interpol_frames, interpol_LUT_W, interpol_LUT_H):
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from scipy.signal import savgol_filter
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#import matplotlib.pyplot as plt
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# Create a list to store the values of interpol_width for each frame
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interpol_width_values = []
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interpol_height_values = []
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# Calculate interpol_width for each frame and add it to the list
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for j in range(num_interpol_frames):
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interpol_width = round( (1 - (width - 2 * mask_width) / (width - 2 * interpol_LUT_W[j])) / 2 * width)
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interpol_width_values.append(interpol_width)
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# Apply a SAVGOL filter to the interpol_width values
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window_size = 7 # adjust the window size as needed
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interpol_width_smoothed = savgol_filter(interpol_width_values, window_size, 2)
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# Calculate interpol_width for each frame and add it to the list
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for j in range(num_interpol_frames):
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interpol_height = round( (1 - (height - 2 * mask_height) / (height - 2 * interpol_LUT_H[j])) / 2 * height)
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interpol_height_values.append(interpol_height)
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# Apply a SAVGOL filter to the interpol_width values
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window_size = 7 # adjust the window size as needed
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interpol_height_smoothed = savgol_filter(interpol_width_values, window_size, 2)
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return interpol_width_smoothed,interpol_height_smoothed
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def create_zoom_single(
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prompts_array,
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negative_prompt,
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num_outpainting_steps,
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guidance_scale,
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num_inference_steps,
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custom_init_image,
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custom_exit_image,
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video_frame_rate,
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video_zoom_mode,
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video_start_frame_dupe_amount,
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video_last_frame_dupe_amount,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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||
inpainting_fill_mode,
|
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inpainting_full_res,
|
||
inpainting_padding,
|
||
zoom_speed,
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||
seed,
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||
outputsizeW,
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outputsizeH,
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||
sampler,
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||
upscale_do,
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||
upscaler_name,
|
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upscalerinterpol_name,
|
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upscale_by,
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exitgamma,
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maskwidth_slider,
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maskheight_slider,
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progress=None,
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||
):
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# try:
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# if gr.Progress() is not None:
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# progress = gr.Progress()
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# progress(0, desc="Preparing Initial Image")
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# except Exception:
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# pass
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fix_env_Path_ffprobe()
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prompts = {}
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for x in prompts_array:
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try:
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key = int(x[0])
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value = str(x[1])
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prompts[key] = value
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except ValueError:
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pass
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assert len(prompts_array) > 0, "prompts is empty"
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width = closest_upper_divisible_by_eight(outputsizeW)
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height = closest_upper_divisible_by_eight(outputsizeH)
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current_image = Image.new(mode="RGBA", size=(width, height))
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mask_image = np.array(current_image)[:, :, 3]
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mask_image = Image.fromarray(255 - mask_image).convert("RGB")
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current_image = current_image.convert("RGB")
|
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current_seed = seed
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||
|
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if custom_init_image:
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current_image = custom_init_image.resize(
|
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(width, height), resample=Image.LANCZOS
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)
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print("using Custom Initial Image")
|
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else:
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load_model_from_setting("infzoom_txt2img_model", progress, "Loading Model for txt2img: ")
|
||
|
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processed, newseed = renderTxt2Img(
|
||
prompts[min(k for k in prompts.keys() if k >= 0)],
|
||
negative_prompt,
|
||
sampler,
|
||
num_inference_steps,
|
||
guidance_scale,
|
||
current_seed,
|
||
width,
|
||
height,
|
||
)
|
||
current_image = processed.images[0]
|
||
current_seed = newseed
|
||
|
||
# if custom_exit_image and ((i + 1) == num_outpainting_steps):
|
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# mask_width = 4 # fade out whole interpol
|
||
# mask_height =4 #
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# mask_width = width*(20//30) # fade out whole interpol
|
||
# mask_height = height*(20//30) #
|
||
# else:
|
||
mask_width = math.trunc(width / 4) # was initially 512px => 128px
|
||
mask_height = math.trunc(height / 4) # was initially 512px => 128px
|
||
|
||
num_interpol_frames = round(video_frame_rate * zoom_speed)
|
||
|
||
all_frames = []
|
||
|
||
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: " )
|
||
|
||
# setup filesystem paths
|
||
video_file_name = "infinite_zoom_" + str(int(time.time())) + ".mp4"
|
||
output_path = shared.opts.data.get(
|
||
"infzoom_outpath", shared.opts.data.get("outdir_img2img_samples")
|
||
)
|
||
save_path = os.path.join(
|
||
output_path, shared.opts.data.get("infzoom_outSUBpath", "infinite-zooms")
|
||
)
|
||
if not os.path.exists(save_path):
|
||
os.makedirs(save_path)
|
||
out = os.path.join(save_path, video_file_name)
|
||
|
||
|
||
for i in range(num_outpainting_steps):
|
||
print_out = "Outpaint step: " + str(i + 1) + " / " + str(num_outpainting_steps) + " Seed: " + str(current_seed)
|
||
print(print_out)
|
||
if progress:
|
||
progress(((i + 1) / num_outpainting_steps), desc=print_out)
|
||
|
||
if custom_exit_image and ((i + 1) == num_outpainting_steps):
|
||
mask_width=round(width*maskwidth_slider)
|
||
mask_height=round(height*maskheight_slider)
|
||
|
||
# 30 fps@ maskw 0.25 => 30
|
||
# normalize to default speed of 30 fps for 0.25 mask factor
|
||
num_interpol_frames = round(num_interpol_frames * (1 + (max(maskheight_slider,maskwidth_slider)/0.5) * exitgamma))
|
||
|
||
#interpol_LUT_W, interpol_LUT_H = calculate_interpolframes_curve(width,height,mask_width,mask_height,num_interpol_frames)
|
||
#interpol2_LUT_W, interpol2_LUT_H = calculate_interpolframes2_curve(width,height,mask_width,mask_height,num_interpol_frames, interpol_LUT_W, interpol_LUT_H)
|
||
|
||
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)
|
||
mask_image = np.array(current_image)[:, :, 3]
|
||
mask_image = Image.fromarray(255 - mask_image).convert("RGB")
|
||
|
||
# inpainting step
|
||
current_image = current_image.convert("RGB")
|
||
|
||
if custom_exit_image and ((i + 1) == num_outpainting_steps):
|
||
current_image = custom_exit_image.resize(
|
||
(width, height), resample=Image.LANCZOS
|
||
)
|
||
print("using Custom Exit Image")
|
||
else:
|
||
processed, newseed = renderImg2Img(
|
||
prompts[max(k for k in prompts.keys() if k <= i)],
|
||
negative_prompt,
|
||
sampler,
|
||
num_inference_steps,
|
||
guidance_scale,
|
||
current_seed,
|
||
width,
|
||
height,
|
||
current_image,
|
||
mask_image,
|
||
inpainting_denoising_strength,
|
||
inpainting_mask_blur,
|
||
inpainting_fill_mode,
|
||
inpainting_full_res,
|
||
inpainting_padding,
|
||
)
|
||
current_image = processed.images[0]
|
||
current_seed = newseed
|
||
|
||
current_image.paste(prev_image, mask=prev_image)
|
||
|
||
# interpolation steps between 2 inpainted images (=sequential zoom and crop)
|
||
for j in range(num_interpol_frames - 1):
|
||
interpol_image = current_image
|
||
|
||
if (True):
|
||
interpol_width = round(
|
||
(1 - (1 - 2 * mask_width / width)** (1 - (j + 1) / num_interpol_frames))
|
||
* width/2
|
||
)
|
||
|
||
interpol_height = round(
|
||
( 1 - (1 - 2 * mask_height / height) ** (1 - (j + 1) / num_interpol_frames) )
|
||
* height/2
|
||
)
|
||
else:
|
||
interpol_width = interpol_LUT_W[j]
|
||
interpol_height = interpol_LUT_H[j]
|
||
|
||
interpol_image = interpol_image.crop(
|
||
(
|
||
interpol_width,
|
||
interpol_height,
|
||
width - interpol_width,
|
||
height - interpol_height,
|
||
)
|
||
)
|
||
|
||
print (f"interpol: width x height: {interpol_width} {interpol_height}")
|
||
|
||
interpol_image = interpol_image.resize((width, height))
|
||
|
||
# paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
|
||
if (True):
|
||
interpol_width2 = round(
|
||
(1 - (width - 2 * mask_width) / (width - 2 * interpol_width))
|
||
/ 2 * width
|
||
)
|
||
|
||
interpol_height2 = round(
|
||
(1 - (height - 2 * mask_height) / (height - 2 * interpol_height))
|
||
/ 2 * height
|
||
)
|
||
|
||
|
||
"""
|
||
interpol_width2 = mask_width - interpol_width
|
||
interpol_height2 = mask_height - interpol_height
|
||
"""
|
||
print (f"interpol2: width2 x height2: {interpol_width2} {interpol_height2}")
|
||
|
||
else:
|
||
interpol_width2 = round(interpol2_LUT_W[j])
|
||
interpol_height2 = round(interpol2_LUT_H[j])
|
||
|
||
if custom_exit_image and ((i + 1) == num_outpainting_steps):
|
||
opacity = 1 - ((j+1)/num_interpol_frames )
|
||
else:
|
||
opacity = 1
|
||
|
||
prev_image_fix_crop = shrink_and_paste_on_blank(
|
||
prev_image_fix, interpol_width2, interpol_height2,
|
||
opacity
|
||
)
|
||
|
||
interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
|
||
|
||
# exit image: from now we see the last prompt on the exit image
|
||
if custom_exit_image and ((i + 1) == num_outpainting_steps):
|
||
"""
|
||
mask_img = Image.new("L", (width,height), 0)
|
||
in_center_x = interpol_image.width/2
|
||
in_center_y = interpol_image.height/2
|
||
|
||
# Draw a circular brush on the mask image with 64px diameter and 8px softness
|
||
draw = ImageDraw.Draw(mask_img)
|
||
brush_size = 64
|
||
brush_softness = 8
|
||
brush = Image.new("L", (brush_size, brush_size), 255)
|
||
draw_brush = ImageDraw.Draw(brush)
|
||
draw_brush.ellipse((brush_softness, brush_softness, brush_size-brush_softness, brush_size-brush_softness), fill=255, outline=None)
|
||
brush = brush.filter(ImageFilter.GaussianBlur(radius=brush_softness))
|
||
brush_width, brush_height = brush.size
|
||
|
||
# Draw the rectangular frame on the mask image using the circular brush
|
||
frame_width = width-2*interpol_width2
|
||
frame_height = height-2*interpol_height2
|
||
frame_left = in_center_x - (frame_width // 2)
|
||
frame_top = in_center_y - (frame_height // 2)
|
||
frame_right = frame_left + frame_width
|
||
frame_bottom = frame_top + frame_height
|
||
draw.ellipse((frame_left, frame_top, frame_left+brush_width, frame_top+brush_height), fill=255, outline=None)
|
||
draw.ellipse((frame_right-brush_width, frame_top, frame_right, frame_top+brush_height), fill=255, outline=None)
|
||
draw.ellipse((frame_left, frame_bottom-brush_height, frame_left+brush_width, frame_bottom), fill=255, outline=None)
|
||
draw.ellipse((frame_right-brush_width, frame_bottom-brush_height, frame_right, frame_bottom), fill=255, outline=None)
|
||
|
||
draw.rectangle((max(0,frame_left-brush_size/2), max(0,frame_top+brush_size/2), max(0,frame_right-brush_size/2), max(0,frame_bottom-brush_size/2)), fill=255)
|
||
|
||
# inner rect, now we have a bordermask
|
||
draw.rectangle((max(0,frame_left+brush_size/2), max(0,frame_top-brush_size/2), max(0,frame_right+brush_size/2), max(0,frame_bottom+brush_size/2)), fill=0)
|
||
|
||
# Blur the mask image to soften the edges
|
||
#mask_img = mask_img.filter(ImageFilter.GaussianBlur(radius=8))
|
||
#mask_img = ImageOps.invert(mask_img)
|
||
#mask_img.save(output_path+os.pathsep+"Mask"+str(int(time.time()))+".png")
|
||
"""
|
||
"""processed, newseed = renderImg2Img(
|
||
prompts[max(k for k in prompts.keys() if k <= i)],
|
||
negative_prompt,
|
||
sampler,
|
||
num_inference_steps,
|
||
guidance_scale,
|
||
current_seed,
|
||
width,
|
||
height,
|
||
interpol_image,
|
||
mask_img,
|
||
inpainting_denoising_strength,
|
||
inpainting_mask_blur,
|
||
|
||
inpainting_fill_mode,
|
||
inpainting_full_res,
|
||
inpainting_padding,
|
||
)
|
||
#interpol_image = processed.images[0]
|
||
"""
|
||
|
||
if upscale_do and progress:
|
||
progress(((i + 1) / num_outpainting_steps), desc="upscaling interpol")
|
||
|
||
all_frames.append(
|
||
do_upscaleImg(interpol_image, upscale_do, upscalerinterpol_name, upscale_by, desc=f"(interpol: {i}/{num_interpol_frames})")
|
||
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, desc=f"(outpaint: {i}/{num_outpainting_steps}")
|
||
if upscale_do
|
||
else current_image
|
||
)
|
||
|
||
write_video(
|
||
out,
|
||
all_frames,
|
||
video_frame_rate,
|
||
video_zoom_mode,
|
||
int(video_start_frame_dupe_amount),
|
||
int(video_last_frame_dupe_amount),
|
||
)
|
||
|
||
return (
|
||
out,
|
||
processed.images,
|
||
processed.js(),
|
||
plaintext_to_html(processed.info),
|
||
plaintext_to_html(""),
|
||
)
|
||
|
||
|
||
def validatePromptJson_throws(data):
|
||
with open(jsonprompt_schemafile, "r") as s:
|
||
schema = json.load(s)
|
||
validate(instance=data, schema=schema)
|
||
|
||
|
||
def putPrompts(files):
|
||
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():
|
||
with gr.Blocks(analytics_enabled=False) as infinite_zoom_interface:
|
||
gr.HTML(
|
||
"""
|
||
<p style="text-align: center;">
|
||
<a target="_blank" href="https://github.com/v8hid/infinite-zoom-automatic1111-webui"><img src="https://img.shields.io/static/v1?label=github&message=repository&color=blue&style=flat&logo=github&logoColor=white" style="display: inline;" alt="GitHub Repo"/></a>
|
||
<a href="https://discord.gg/v2nHqSrWdW"><img src="https://img.shields.io/discord/1095469311830806630?color=blue&label=discord&logo=discord&logoColor=white" style="display: inline;" alt="Discord server"></a>
|
||
</p>
|
||
|
||
"""
|
||
)
|
||
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"):
|
||
main_outpaint_steps = gr.Slider(
|
||
minimum=1,
|
||
maximum=100,
|
||
step=1,
|
||
value=8,
|
||
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=jpr["prompts"],
|
||
wrap=True,
|
||
)
|
||
|
||
main_negative_prompt = gr.Textbox(
|
||
value=jpr["negPrompt"], label="Negative Prompt"
|
||
)
|
||
|
||
# these button will be moved using JS unde the dataframe view as small ones
|
||
exportPrompts_button = gr.Button(
|
||
value="Export prompts",
|
||
variant="secondary",
|
||
elem_classes="sm infzoom_tab_butt",
|
||
elem_id="infzoom_exP_butt",
|
||
)
|
||
importPrompts_button = gr.UploadButton(
|
||
label="Import prompts",
|
||
variant="secondary",
|
||
elem_classes="sm infzoom_tab_butt",
|
||
elem_id="infzoom_imP_butt",
|
||
)
|
||
exportPrompts_button.click(
|
||
None,
|
||
_js="exportPrompts",
|
||
inputs=[main_prompts, main_negative_prompt],
|
||
outputs=None,
|
||
)
|
||
importPrompts_button.upload(
|
||
fn=putPrompts,
|
||
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],
|
||
)
|
||
with gr.Row():
|
||
seed = gr.Number(label="Seed", value=-1, precision=0, interactive=True)
|
||
main_sampler = gr.Dropdown(
|
||
label="Sampler",
|
||
choices=available_samplers,
|
||
value="Euler a",
|
||
type="value",
|
||
)
|
||
with gr.Row():
|
||
main_width = gr.Slider(
|
||
minimum=16,
|
||
maximum=2048,
|
||
value=shared.opts.data.get("infzoom_outsizeW", 512),
|
||
step=16,
|
||
label="Output Width",
|
||
)
|
||
main_height = gr.Slider(
|
||
minimum=16,
|
||
maximum=2048,
|
||
value=shared.opts.data.get("infzoom_outsizeH", 512),
|
||
step=16,
|
||
label="Output Height",
|
||
)
|
||
with gr.Row():
|
||
main_guidance_scale = gr.Slider(
|
||
minimum=0.1,
|
||
maximum=15,
|
||
step=0.1,
|
||
value=7,
|
||
label="Guidance Scale",
|
||
)
|
||
sampling_step = gr.Slider(
|
||
minimum=1,
|
||
maximum=100,
|
||
step=1,
|
||
value=50,
|
||
label="Sampling Steps for each outpaint",
|
||
)
|
||
with gr.Row():
|
||
init_image = gr.Image(type="pil", label="custom initial image")
|
||
exit_image = gr.Image(type="pil", label="custom exit image")
|
||
|
||
with gr.Row():
|
||
batchcount_slider = gr.Slider(
|
||
minimum=1,
|
||
maximum=25,
|
||
value=shared.opts.data.get("infzoom_batchcount", 1),
|
||
step=1,
|
||
label="Batch Count",
|
||
)
|
||
with gr.Accordion("I know what I´m doing"):
|
||
gamma_slider = gr.Slider(value=0.0,minimum=0.0,maximum=10,label="Exit Image speed - Gamma")
|
||
maskwidth_slider = gr.Slider(value=0.25,minimum=0.1,maximum=0.49,label="MaskWidth ratio ")
|
||
maskheight_slider = gr.Slider(value=0.25,minimum=0.1,maximum=0.49,label="MaskHeight ratio")
|
||
|
||
with gr.Tab("Video"):
|
||
video_frame_rate = gr.Slider(
|
||
label="Frames per second",
|
||
value=30,
|
||
minimum=1,
|
||
maximum=60,
|
||
)
|
||
video_zoom_mode = gr.Radio(
|
||
label="Zoom mode",
|
||
choices=["Zoom-out", "Zoom-in"],
|
||
value="Zoom-out",
|
||
type="index",
|
||
)
|
||
video_start_frame_dupe_amount = gr.Slider(
|
||
label="number of start frame dupe",
|
||
info="Frames to freeze at the start of the video",
|
||
value=0,
|
||
minimum=1,
|
||
maximum=60,
|
||
)
|
||
video_last_frame_dupe_amount = gr.Slider(
|
||
label="number of last frame dupe",
|
||
info="Frames to freeze at the end of the video",
|
||
value=0,
|
||
minimum=1,
|
||
maximum=60,
|
||
)
|
||
video_zoom_speed = gr.Slider(
|
||
label="Zoom Speed",
|
||
value=1.0,
|
||
minimum=0.1,
|
||
maximum=20.0,
|
||
step=0.1,
|
||
info="Zoom speed in seconds (higher values create slower zoom)",
|
||
)
|
||
|
||
with gr.Tab("Outpaint"):
|
||
inpainting_denoising_strength = gr.Slider(
|
||
label="Denoising Strength", minimum=0.75, maximum=1, value=1
|
||
)
|
||
inpainting_mask_blur = gr.Slider(
|
||
label="Mask Blur", minimum=0, maximum=64, value=0
|
||
)
|
||
inpainting_fill_mode = gr.Radio(
|
||
label="Masked content",
|
||
choices=["fill", "original", "latent noise", "latent nothing"],
|
||
value="latent noise",
|
||
type="index",
|
||
)
|
||
inpainting_full_res = gr.Checkbox(label="Inpaint Full Resolution")
|
||
inpainting_padding = gr.Slider(
|
||
label="masked padding", minimum=0, maximum=256, value=0
|
||
)
|
||
|
||
with gr.Tab("Post proccess"):
|
||
upscale_do = gr.Checkbox(False, label="Enable Upscale")
|
||
with gr.Row():
|
||
upscaler_name = gr.Dropdown(
|
||
label="Upscaler for keyframes (txt2img;inpaint)",
|
||
elem_id="infZ_upscaler",
|
||
choices=[x.name for x in shared.sd_upscalers],
|
||
value=shared.sd_upscalers[0].name,
|
||
)
|
||
|
||
upscalerinterpol_name = gr.Dropdown(
|
||
label="Upscaler for interpolation",
|
||
elem_id="infZ_upscaler_interpol",
|
||
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-ESRGAN 4x upscaler.
|
||
"""
|
||
)
|
||
|
||
with gr.Column(scale=1, variant="compact"):
|
||
output_video = gr.Video(label="Output").style(width=512, height=512)
|
||
(
|
||
out_image,
|
||
generation_info,
|
||
html_info,
|
||
html_log,
|
||
) = create_output_panel(
|
||
"infinite-zoom", shared.opts.outdir_img2img_samples
|
||
)
|
||
generate_btn.click(
|
||
fn=wrap_gradio_gpu_call(create_zoom, extra_outputs=[None, "", ""]),
|
||
inputs=[
|
||
main_prompts,
|
||
main_negative_prompt,
|
||
main_outpaint_steps,
|
||
main_guidance_scale,
|
||
sampling_step,
|
||
init_image,
|
||
exit_image,
|
||
video_frame_rate,
|
||
video_zoom_mode,
|
||
video_start_frame_dupe_amount,
|
||
video_last_frame_dupe_amount,
|
||
inpainting_denoising_strength,
|
||
inpainting_mask_blur,
|
||
inpainting_fill_mode,
|
||
inpainting_full_res,
|
||
inpainting_padding,
|
||
video_zoom_speed,
|
||
seed,
|
||
main_width,
|
||
main_height,
|
||
batchcount_slider,
|
||
main_sampler,
|
||
upscale_do,
|
||
upscaler_name,
|
||
upscalerinterpol_name,
|
||
upscale_by,
|
||
gamma_slider,
|
||
maskwidth_slider,
|
||
maskheight_slider,
|
||
|
||
],
|
||
outputs=[output_video, out_image, generation_info, html_info, html_log],
|
||
)
|
||
interrupt.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[])
|
||
infinite_zoom_interface.queue()
|
||
return [(infinite_zoom_interface, "Infinite Zoom", "iz_interface")]
|
||
|
||
|
||
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. Default is Outputs",
|
||
gr.Textbox,
|
||
{"interactive": True},
|
||
section=section,
|
||
),
|
||
)
|
||
|
||
shared.opts.add_option(
|
||
"infzoom_outSUBpath",
|
||
shared.OptionInfo(
|
||
"infinite-zooms",
|
||
"Which subfolder name to be created in the outpath. Default is 'infinite-zooms'",
|
||
gr.Textbox,
|
||
{"interactive": True},
|
||
section=section,
|
||
),
|
||
)
|
||
|
||
shared.opts.add_option(
|
||
"infzoom_outsizeW",
|
||
shared.OptionInfo(
|
||
512,
|
||
"Default width of your video",
|
||
gr.Slider,
|
||
{"minimum": 16, "maximum": 2048, "step": 16},
|
||
section=section,
|
||
),
|
||
)
|
||
|
||
shared.opts.add_option(
|
||
"infzoom_outsizeH",
|
||
shared.OptionInfo(
|
||
512,
|
||
"Default height your video",
|
||
gr.Slider,
|
||
{"minimum": 16, "maximum": 2048, "step": 16},
|
||
section=section,
|
||
),
|
||
)
|
||
|
||
shared.opts.add_option(
|
||
"infzoom_ffprobepath",
|
||
shared.OptionInfo(
|
||
"",
|
||
"Writing videos has dependency to an existing FFPROBE executable on your machine. D/L here (https://github.com/BtbN/FFmpeg-Builds/releases) your OS variant and point to your installation path",
|
||
gr.Textbox,
|
||
{"interactive": True},
|
||
section=section,
|
||
),
|
||
)
|
||
|
||
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)
|