load_model_from_setting function
vahid K. nejad
parent
9ba6f17ce3
commit
2d3bbe91cd
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@ -27,16 +27,30 @@ 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)[-2:] # contains install and our extension foldername
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jsonprompt_schemafile = usefulDirs[0]+"/"+usefulDirs[1]+"/scripts/promptschema.json"
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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]
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default_prompt = '{"prompts":{"data":[[0,"Cat"],["1","Dog"],["2","Happy Pets"]],"headers":["outpaint steps","prompt"]},"negPrompt":"ugly"}'
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empty_prompt = '{"prompts":{"data":[],"headers":["outpaint steps","prompt"]},"negPrompt":""}'
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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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#must be python dict
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invalid_prompt ={"prompts":{"data":[[0,"Your prompt-json is invalid, please check Settings"]],"headers":["outpaint steps","prompt"]},"negPrompt":"Invalid prompt-json"}
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def closest_upper_divisible_by_eight(num):
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if num % 8 == 0:
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@ -44,11 +58,25 @@ def closest_upper_divisible_by_eight(num):
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else:
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return math.ceil(num / 8) * 8
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def do_upscaleImg(curImg,upscale_do, upscaler_name,upscale_by):
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if (not upscale_do): return curImg
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pp= scripts.postprocessing_upscale.scripts_postprocessing.PostprocessedImage(curImg)
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def do_upscaleImg(curImg, upscale_do, upscaler_name, upscale_by):
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if not upscale_do:
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return curImg
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pp = scripts.postprocessing_upscale.scripts_postprocessing.PostprocessedImage(
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curImg
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)
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ups = scripts.postprocessing_upscale.ScriptPostprocessingUpscale()
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ups.process(pp, upscale_mode=2, upscale_by=upscale_by, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=upscaler_name, upscaler_2_name=None, upscaler_2_visibility=0.0)
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ups.process(
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pp,
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upscale_mode=2,
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upscale_by=upscale_by,
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upscale_to_width=None,
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upscale_to_height=None,
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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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@ -71,6 +99,7 @@ def renderTxt2Img(prompt, negative_prompt, sampler, steps, cfg_scale, width, hei
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processed = process_images(p)
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return processed
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def renderImg2Img(
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prompt,
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negative_prompt,
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@ -125,6 +154,15 @@ def fix_env_Path_ffprobe():
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os.environ["PATH"] = envpath + path_sep + ffppath
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def load_model_from_setting(model_field_name):
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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 = modules.sd_models.checkpoint_alisases[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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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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@ -179,7 +217,6 @@ def create_zoom(
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upscaler_name,
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upscale_by,
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progress,
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)
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return result
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@ -241,14 +278,9 @@ def create_zoom_single(
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(width, height), resample=Image.LANCZOS
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)
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else:
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modelname = shared.opts.data.get("infzoom_txt2img_model")
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if (modelname):
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# switch to txt2img model
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checkinfo = modules.sd_models.checkpoint_alisases[modelname]
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if (not checkinfo):
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raise NameError("Checklist not found in registry")
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if progress: progress(0, desc="Loading Model for txt2img: " + checkinfo.name)
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modules.sd_models.load_model(checkinfo)
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load_model_from_setting("infzoom_txt2img_model")
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if progress:
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progress(0, desc="Loading Model for txt2img: " + checkinfo.name)
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processed = renderTxt2Img(
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prompts[min(k for k in prompts.keys() if k >= 0)],
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@ -261,7 +293,6 @@ def create_zoom_single(
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)
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current_image = processed.images[0]
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mask_width = math.trunc(width / 4) # was initially 512px => 128px
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mask_height = math.trunc(height / 4) # was initially 512px => 128px
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@ -269,25 +300,24 @@ def create_zoom_single(
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all_frames = []
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if upscale_do and progress:
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progress(0, desc="upscaling inital image")
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all_frames.append(do_upscaleImg(current_image,upscale_do, upscaler_name,upscale_by) if upscale_do else current_image)
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all_frames.append(
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do_upscaleImg(current_image, upscale_do, upscaler_name, upscale_by)
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if upscale_do
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else current_image
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)
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inmodelname = shared.opts.data.get("infzoom_inpainting_model")
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if (inmodelname):
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# switch to inpaint model now
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checkinfo = modules.sd_models.checkpoint_alisases[inmodelname]
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if (not checkinfo):
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raise NameError("Checklist not found in registry")
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if progress: progress(0, desc="Loading Model for inpainting/img2img: " + checkinfo.name)
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modules.sd_models.load_model(checkinfo)
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load_model_from_setting("infzoom_inpainting_model")
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if progress:
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progress(0, desc="Loading Model for inpainting/img2img: " + checkinfo.name)
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for i in range(num_outpainting_steps):
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print_out = "Outpaint step: " + str(i + 1) + " / " + str(num_outpainting_steps)
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print(print_out)
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if progress: progress( ((i + 1) / num_outpainting_steps), desc=print_out)
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if progress:
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progress(((i + 1) / num_outpainting_steps), desc=print_out)
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prev_image_fix = current_image
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prev_image = shrink_and_paste_on_blank(current_image, mask_width, mask_height)
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@ -374,21 +404,23 @@ def create_zoom_single(
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interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
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if upscale_do and progress:
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progress(
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((i + 1) / num_outpainting_steps),
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desc="upscaling interpol"
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)
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if upscale_do and progress:
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progress(((i + 1) / num_outpainting_steps), desc="upscaling interpol")
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all_frames.append(do_upscaleImg(interpol_image, upscale_do, upscaler_name,upscale_by) if upscale_do else interpol_image)
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if upscale_do and progress:
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progress(
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((i + 1) / num_outpainting_steps),
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desc="upscaling current"
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all_frames.append(
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do_upscaleImg(interpol_image, upscale_do, upscaler_name, upscale_by)
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if upscale_do
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else interpol_image
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)
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all_frames.append(do_upscaleImg(current_image,upscale_do, upscaler_name,upscale_by) if upscale_do else current_image)
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if upscale_do and progress:
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progress(((i + 1) / num_outpainting_steps), desc="upscaling current")
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all_frames.append(
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do_upscaleImg(current_image, upscale_do, upscaler_name, upscale_by)
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if upscale_do
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else current_image
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)
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video_file_name = "infinite_zoom_" + str(int(time.time())) + ".mp4"
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output_path = shared.opts.data.get(
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@ -419,25 +451,37 @@ def create_zoom_single(
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def validatePromptJson_throws(data):
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with open(jsonprompt_schemafile, "r") as s: schema = json.load(s)
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with open(jsonprompt_schemafile, "r") as s:
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schema = json.load(s)
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validate(instance=data, schema=schema)
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def putPrompts(files):
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def putPrompts(files):
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try:
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with open(files.name, 'r') as f:
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with open(files.name, "r") as f:
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file_contents = f.read()
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data = json.loads(file_contents)
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validatePromptJson_throws(data)
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return [gr.DataFrame.update(data["prompts"]), gr.Textbox.update(data["negPrompt"])]
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return [
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gr.DataFrame.update(data["prompts"]),
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gr.Textbox.update(data["negPrompt"]),
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]
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except Exception:
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gr.Error("loading your prompt failed. It seems to be invalid. Your prompt table is preserved.")
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print("[InfiniteZoom:] Loading your prompt failed. It seems to be invalid. Your prompt table is preserved.")
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gr.Error(
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"loading your prompt failed. It seems to be invalid. Your prompt table is preserved."
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)
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print(
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"[InfiniteZoom:] Loading your prompt failed. It seems to be invalid. Your prompt table is preserved."
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)
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return [gr.DataFrame.update(), gr.Textbox.update()]
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def clearPrompts():
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return [gr.DataFrame.update(value=[[0,"Infinite Zoom. Start over"]]), gr.Textbox.update("")]
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return [
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gr.DataFrame.update(value=[[0, "Infinite Zoom. Start over"]]),
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gr.Textbox.update(""),
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]
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def on_ui_tabs():
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@ -456,7 +500,6 @@ def on_ui_tabs():
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interrupt = gr.Button(value="Interrupt", elem_id="interrupt_training")
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with gr.Row():
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with gr.Column(scale=1, variant="panel"):
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with gr.Tab("Main"):
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main_outpaint_steps = gr.Slider(
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minimum=2,
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@ -468,8 +511,9 @@ def on_ui_tabs():
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)
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# safe reading json prompt
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pr = shared.opts.data.get("infzoom_defPrompt",default_prompt)
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if (not pr): pr = empty_prompt
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pr = shared.opts.data.get("infzoom_defPrompt", default_prompt)
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if not pr:
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pr = empty_prompt
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try:
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jpr = json.loads(pr)
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@ -488,7 +532,7 @@ def on_ui_tabs():
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)
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main_negative_prompt = gr.Textbox(
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value=jpr["negPrompt"], label="Negative Prompt"
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value=jpr["negPrompt"], label="Negative Prompt"
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)
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# these button will be moved using JS unde the dataframe view as small ones
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@ -516,8 +560,17 @@ def on_ui_tabs():
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inputs=[importPrompts_button],
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)
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clearPrompts_button= gr.Button(value="Clear prompts",variant="secondary",elem_classes="sm infzoom_tab_butt", elem_id="infzoom_clP_butt")
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clearPrompts_button.click(fn=clearPrompts,inputs=[],outputs=[main_prompts,main_negative_prompt])
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clearPrompts_button = gr.Button(
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value="Clear prompts",
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variant="secondary",
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elem_classes="sm infzoom_tab_butt",
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elem_id="infzoom_clP_butt",
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)
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clearPrompts_button.click(
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fn=clearPrompts,
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inputs=[],
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outputs=[main_prompts, main_negative_prompt],
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)
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main_sampler = gr.Dropdown(
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label="Sampler",
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@ -557,7 +610,9 @@ def on_ui_tabs():
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)
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with gr.Row():
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init_image = gr.Image(type="pil", label="custom initial image")
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exit_image = gr.Image(type="pil", label="custom exit image", visible=False) #TODO: implement exit-image rendering
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exit_image = gr.Image(
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type="pil", label="custom exit image", visible=False
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) # TODO: implement exit-image rendering
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batchcount_slider = gr.Slider(
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minimum=1,
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@ -622,16 +677,23 @@ def on_ui_tabs():
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with gr.Tab("Post proccess"):
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upscale_do = gr.Checkbox(False, label="Enable Upscale")
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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)
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upscaler_name = gr.Dropdown(
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label="Upscaler",
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elem_id="infZ_upscaler",
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choices=[x.name for x in shared.sd_upscalers],
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value=shared.sd_upscalers[0].name,
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)
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upscale_by = gr.Slider(
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label="Upscale by factor", minimum=1, maximum=8, value=1
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)
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with gr.Accordion("Help",open=False):
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gr.Markdown("""# Performance critical
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with gr.Accordion("Help", open=False):
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gr.Markdown(
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"""# Performance critical
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Depending on amount of frames and which upscaler you choose it might took a long time to render.
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Our best experience and trade-off is the R-ERSGAn4x upscaler.
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""")
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"""
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)
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with gr.Column(scale=1, variant="compact"):
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output_video = gr.Video(label="Output").style(width=512, height=512)
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@ -644,7 +706,7 @@ Our best experience and trade-off is the R-ERSGAn4x upscaler.
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"infinite-zoom", shared.opts.outdir_img2img_samples
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)
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generate_btn.click(
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fn=wrap_gradio_gpu_call(create_zoom, extra_outputs=[None, '', '']),
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fn=wrap_gradio_gpu_call(create_zoom, extra_outputs=[None, "", ""]),
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inputs=[
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main_prompts,
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main_negative_prompt,
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@ -669,8 +731,7 @@ Our best experience and trade-off is the R-ERSGAn4x upscaler.
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main_sampler,
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upscale_do,
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upscaler_name,
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upscale_by
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upscale_by,
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],
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outputs=[output_video, out_image, generation_info, html_info, html_log],
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)
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@ -733,42 +794,43 @@ def on_ui_settings():
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"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",
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gr.Textbox,
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{"interactive": True},
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section=section
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)
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section=section,
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),
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)
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shared.opts.add_option(
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"infzoom_txt2img_model",
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"infzoom_txt2img_model",
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shared.OptionInfo(
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None,
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"Name of your desired model to render keyframes (txt2img)",
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gr.Dropdown,
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None,
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"Name of your desired model to render keyframes (txt2img)",
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gr.Dropdown,
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lambda: {"choices": shared.list_checkpoint_tiles()},
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section=section
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)
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)
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shared.opts.add_option(
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"infzoom_inpainting_model",
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shared.OptionInfo(
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None,
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"Name of your desired inpaint model (img2img-inpaint). Default is vanilla sd-v1-5-inpainting.ckpt ",
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gr.Dropdown,
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lambda: {"choices": shared.list_checkpoint_tiles()},
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section=section
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)
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section=section,
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),
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)
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shared.opts.add_option(
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"infzoom_defPrompt",
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"infzoom_inpainting_model",
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shared.OptionInfo(
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default_prompt,
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"Default prompt-setup to start with'",
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gr.Code,
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{"interactive": True, "language":"json"},
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section=section
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)
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None,
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"Name of your desired inpaint model (img2img-inpaint). Default is vanilla sd-v1-5-inpainting.ckpt ",
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gr.Dropdown,
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lambda: {"choices": shared.list_checkpoint_tiles()},
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section=section,
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),
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)
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shared.opts.add_option(
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"infzoom_defPrompt",
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shared.OptionInfo(
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default_prompt,
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"Default prompt-setup to start with'",
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gr.Code,
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{"interactive": True, "language": "json"},
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section=section,
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),
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)
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script_callbacks.on_ui_tabs(on_ui_tabs)
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script_callbacks.on_ui_settings(on_ui_settings)
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Reference in New Issue