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infinite-zoom-automatic1111-webui
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Merge pull request #53 from Oncorporation/dev 

Extending Exit Image and Adding Key frames
exit_image
vahid khroasani 2023-04-25 08:33:11 +04:00 committed by GitHub
parent 365bb7e82a 8e14adcf80
commit fe43a5ecbf
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 95 additions and 53 deletions
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21
iz_helpers/image.py
View File

@ -1,4 +1,8 @@
from PIL import Image
import requests
import base64
from io import BytesIO
def shrink_and_paste_on_blank(current_image, mask_width, mask_height):
"""
@ -21,3 +25,20 @@ def shrink_and_paste_on_blank(current_image, mask_width, mask_height):
blank_image.paste(prev_image, (mask_width, mask_height))
return blank_image
def open_image(image_path):
if image_path.startswith('http'):
# If the image path is a URL, download the image using requests
response = requests.get(image_path)
img = Image.open(BytesIO(response.content))
elif image_path.startswith('data'):
# If the image path is a DataURL, decode the base64 string
encoded_data = image_path.split(',')[1]
decoded_data = base64.b64decode(encoded_data)
img = Image.open(BytesIO(decoded_data))
else:
# Assume that the image path is a file path
img = Image.open(image_path)
return img

5
iz_helpers/promptschema.json
View File

@ -21,6 +21,9 @@
}
]
},
{
"type": "string"
},
{
"type": "string"
}
@ -36,7 +39,7 @@
"items": {
"type": "string"
},
"minItems": 2
"minItems": 3
}
},
"required": ["data", "headers"]

106
iz_helpers/run.py
View File

@ -11,7 +11,7 @@ from .helpers import (
do_upscaleImg,
)
from .sd_helpers import renderImg2Img, renderTxt2Img
from .image import shrink_and_paste_on_blank
from .image import shrink_and_paste_on_blank, open_image
from .video import write_video
@ -111,14 +111,20 @@ def create_zoom_single(
fix_env_Path_ffprobe()
prompts = {}
prompt_images = {}
for x in prompts_array:
try:
key = int(x[0])
value = str(x[1])
file_loc = str(x[2])
prompts[key] = value
prompt_images[key] = file_loc
except ValueError:
pass
assert len(prompts_array) > 0, "prompts is empty"
print(str(len(prompts)) + " prompts found")
print(str(len(prompt_images)) + " prompts Images found")
width = closest_upper_divisible_by_eight(outputsizeW)
height = closest_upper_divisible_by_eight(outputsizeH)
@ -128,6 +134,7 @@ def create_zoom_single(
mask_image = Image.fromarray(255 - mask_image).convert("RGB")
current_image = current_image.convert("RGB")
current_seed = seed
extra_frames = 0
if custom_init_image:
current_image = custom_init_image.resize(
@ -135,23 +142,26 @@ def create_zoom_single(
)
print("using Custom Initial Image")
else:
load_model_from_setting(
"infzoom_txt2img_model", progress, "Loading Model for txt2img: "
)
if prompt_images[min(k for k in prompt_images.keys() if k >= 0)] == "":
load_model_from_setting(
"infzoom_txt2img_model", progress, "Loading Model for txt2img: "
)
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,
)
if(len(processed.images) > 0):
processed, current_seed = 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
else:
current_image = open_image(prompt_images[min(k for k in prompt_images.keys() if k >= 0)]).resize(
(width, height), resample=Image.LANCZOS
)
mask_width = math.trunc(width / 4) # was initially 512px => 128px
mask_height = math.trunc(height / 4) # was initially 512px => 128px
@ -169,16 +179,17 @@ def create_zoom_single(
else current_image
)
load_model_from_setting(
"infzoom_inpainting_model", progress, "Loading Model for inpainting/img2img: "
)
load_model_from_setting("infzoom_inpainting_model", progress, "Loading Model for inpainting/img2img: " )
for i in range(num_outpainting_steps):
if custom_exit_image:
extra_frames += 2
for i in range(num_outpainting_steps + extra_frames):
print_out = (
"Outpaint step: "
+ str(i + 1)
+ " / "
+ str(num_outpainting_steps)
+ str(num_outpainting_steps + extra_frames)
+ " Seed: "
+ str(current_seed)
)
@ -197,34 +208,40 @@ def create_zoom_single(
# inpainting step
current_image = current_image.convert("RGB")
if custom_exit_image and ((i + 1) == num_outpainting_steps):
# Custom and specified images work like keyframes
if custom_exit_image and (i + 1) >= (num_outpainting_steps + extra_frames):
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,
)
if(len(processed.images) > 0):
else:
if prompt_images[max(k for k in prompt_images.keys() if k <= (i + 1))] == "":
processed, current_seed = renderImg2Img(
prompts[max(k for k in prompts.keys() if k <= (i + 1))],
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
if(len(processed.images) > 0):
current_image.paste(prev_image, mask=prev_image)
# only paste previous image when generating a new image
current_image.paste(prev_image, mask=prev_image)
else:
current_image = open_image(prompt_images[max(k for k in prompt_images.keys() if k <= (i + 1))]).resize(
(width, height), resample=Image.LANCZOS
)
# interpolation steps between 2 inpainted images (=sequential zoom and crop)
for j in range(num_interpol_frames - 1):
@ -305,6 +322,7 @@ def create_zoom_single(
save_path = os.path.join(
output_path, shared.opts.data.get("infzoom_outSUBpath", "infinite-zooms")
)
print("save to: " + save_path)
if not os.path.exists(save_path):
os.makedirs(save_path)
out = os.path.join(save_path, video_file_name)

10
iz_helpers/static_variables.py
View File

@ -5,9 +5,9 @@ import modules.sd_samplers
default_prompt = """
{
"prompts":{
"headers":["outpaint steps","prompt"],
"headers":["outpaint steps","prompt","image location"],
"data":[
[0,"Huge spectacular Waterfall in a dense tropical forest,epic perspective,(vegetation overgrowth:1.3)(intricate, ornamentation:1.1),(baroque:1.1), fantasy, (realistic:1) digital painting , (magical,mystical:1.2) , (wide angle shot:1.4), (landscape composed:1.2)(medieval:1.1), divine,cinematic,(tropical forest:1.4),(river:1.3)mythology,india, volumetric lighting, Hindu ,epic, Alex Horley Wenjun Lin greg rutkowski Ruan Jia (Wayne Barlowe:1.2) <lora:epiNoiseoffset_v2:0.6> "]
[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> ","C:\\path\\to\\image.png"]
]
},
"negPrompt":"frames, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur bad-artist"
@ -18,13 +18,13 @@ available_samplers = [
]
empty_prompt = (
'{"prompts":{"data":[],"headers":["outpaint steps","prompt"]},"negPrompt":""}'
'{"prompts":{"data":[],"headers":["outpaint steps","prompt","image location"]},"negPrompt":""}'
)
invalid_prompt = {
"prompts": {
"data": [[0, "Your prompt-json is invalid, please check Settings"]],
"headers": ["outpaint steps", "prompt"],
"data": [[0, "Your prompt-json is invalid, please check Settings",""]],
"headers": ["outpaint steps", "prompt","image location"],
},
"negPrompt": "Invalid prompt-json",
}

6
iz_helpers/ui.py
View File

@ -51,10 +51,10 @@ def on_ui_tabs():
main_prompts = gr.Dataframe(
type="array",
headers=["outpaint step", "prompt"],
datatype=["number", "str"],
headers=["outpaint step", "prompt", "image location"],
datatype=["number", "str", "str"],
row_count=1,
col_count=(2, "fixed"),
col_count=(3, "fixed"),
value=jpr["prompts"],
wrap=True,
)