added common prompt; todo convert old json prompts to 1.1 schema
GeorgLegato
parent
365bb7e82a
commit
b0cb75399e
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@ -122,4 +122,5 @@ def clearPrompts():
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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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gr.Textbox.update("")
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]
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@ -1,49 +1,60 @@
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{
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"$schema": "http://json-schema.org/draft-07/schema#",
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"type": "object",
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"properties": {
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"prompts": {
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"type": "object",
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"properties": {
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"data": {
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"$schema": "http://json-schema.org/draft-07/schema#",
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"$id": "1.1",
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"type": "object",
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"properties": {
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"prompts": {
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"type": "object",
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"properties": {
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"data": {
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"type": "array",
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"items": {
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"type": "array",
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"items": {
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"type": "array",
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"items": [
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{
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"oneOf": [
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{
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"type": "integer",
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"minimum": 0
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},
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{
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"type": "string"
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}
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]
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},
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{
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"type": "string"
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}
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],
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"minItems": 0,
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"maxItems": 999,
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"uniqueItems": false
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},
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"minItems": 0
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"items": [
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{
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"oneOf": [
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{
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"type": "integer",
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"minimum": 0
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},
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{
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"type": "string"
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}
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]
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},
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{
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"type": "string"
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}
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],
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"minItems": 0,
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"maxItems": 999,
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"uniqueItems": false
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},
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"headers": {
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"type": "array",
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"items": {
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"type": "string"
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},
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"minItems": 2
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}
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"minItems": 0
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},
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"required": ["data", "headers"]
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"headers": {
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"type": "array",
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"items": {
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"type": "string"
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},
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"minItems": 3
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}
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},
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"negPrompt": {
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"type": "string"
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}
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"required": [
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"data",
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"headers"
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]
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},
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"required": ["prompts", "negPrompt"]
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}
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"negPrompt": {
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"type": "string"
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},
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"commonPrompt": {
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"type": "string"
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}
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},
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"required": [
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"prompts",
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"negPrompt",
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"commonPrompt"
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]
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}
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@ -16,6 +16,7 @@ from .video import write_video
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def create_zoom(
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common_prompt,
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prompts_array,
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negative_prompt,
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num_outpainting_steps,
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@ -46,6 +47,7 @@ def create_zoom(
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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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common_prompt,
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prompts_array,
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negative_prompt,
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num_outpainting_steps,
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@ -76,6 +78,7 @@ def create_zoom(
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def create_zoom_single(
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common_prompt,
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prompts_array,
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negative_prompt,
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num_outpainting_steps,
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@ -139,8 +142,9 @@ def create_zoom_single(
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"infzoom_txt2img_model", progress, "Loading Model for txt2img: "
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)
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pr = prompts[min(k for k in prompts.keys() if k >= 0)]
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processed, newseed = renderTxt2Img(
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prompts[min(k for k in prompts.keys() if k >= 0)],
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f"{common_prompt}\n{pr}" if common_prompt else pr,
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negative_prompt,
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sampler,
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num_inference_steps,
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@ -203,8 +207,9 @@ def create_zoom_single(
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)
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print("using Custom Exit Image")
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else:
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pr = prompts[max(k for k in prompts.keys() if k <= i)]
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processed, newseed = renderImg2Img(
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prompts[max(k for k in prompts.keys() if k <= i)],
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f"{common_prompt}\n{pr}" if common_prompt else pr,
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negative_prompt,
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sampler,
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num_inference_steps,
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@ -5,20 +5,18 @@ import modules.sd_samplers
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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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"headers":["outpaint steps","prompt","img"],
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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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[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"]
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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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"negPrompt":"frames, border, edges, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur bad-artist",
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"commonPrompt":"style by 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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available_samplers = [
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s.name for s in modules.sd_samplers.samplers if "UniPc" not in s.name
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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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'{"prompts":{"data":[],"headers":["outpaint steps","prompt"]},"negPrompt":"", commonPrompt:""}'
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)
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invalid_prompt = {
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@ -27,7 +25,13 @@ invalid_prompt = {
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"headers": ["outpaint steps", "prompt"],
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},
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"negPrompt": "Invalid prompt-json",
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"commonPrompt": "Invalid prompt"
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}
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available_samplers = [
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s.name for s in modules.sd_samplers.samplers if "UniPc" not in s.name
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]
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current_script_dir = scripts.basedir().split(os.sep)[
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-2:
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] # contains install and our extension foldername
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@ -49,6 +49,10 @@ def on_ui_tabs():
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except Exception:
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jpr = invalid_prompt
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main_common_prompt = gr.Textbox(
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value=jpr["commonPrompt"], label="Common Prompt"
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)
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main_prompts = gr.Dataframe(
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type="array",
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headers=["outpaint step", "prompt"],
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@ -79,7 +83,7 @@ def on_ui_tabs():
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exportPrompts_button.click(
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None,
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_js="exportPrompts",
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inputs=[main_prompts, main_negative_prompt],
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inputs=[main_common_prompt, main_prompts, main_negative_prompt],
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outputs=None,
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)
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importPrompts_button.upload(
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@ -97,7 +101,7 @@ def on_ui_tabs():
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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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outputs=[main_prompts, main_negative_prompt, main_common_prompt],
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)
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with gr.Row():
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seed = gr.Number(
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@ -237,6 +241,7 @@ Our best experience and trade-off is the R-ERSGAn4x upscaler.
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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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inputs=[
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main_common_prompt,
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main_prompts,
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main_negative_prompt,
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main_outpaint_steps,
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@ -1,7 +1,7 @@
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// Function to download data to a file
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function exportPrompts(p, np, filename = "infinite-zoom-prompts.json") {
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function exportPrompts(cp,p, np, filename = "infinite-zoom-prompts.json") {
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let J = { prompts: p, negPrompt: np }
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let J = { prompts: p, negPrompt: np, commonPrompt: cp }
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var file = new Blob([JSON.stringify(J)], { type: "text/csv" });
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if (window.navigator.msSaveOrOpenBlob) // IE10+
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