Coyote-A/ultimate-upscale-for-automatic1111
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fix initial info

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This commit is contained in:
Andrey Pavlov
2023-01-02 18:03:59 +03:00
parent dc7f92fab8
commit 4b6007ace3
1 changed files with 35 additions and 25 deletions
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60
scripts/ultimate-upscale.py
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@@ -8,6 +8,7 @@ from modules import processing, shared, sd_samplers, images, devices
from modules.processing import Processed
from modules.shared import opts, cmd_opts, state
def upscale(p, init_img, upscaler_index, tileSize, padding):
scale_factor = max(p.width, p.height) // max(init_img.width, init_img.height)
print(f"Canva size: {p.width}x{p.height}")
@@ -41,13 +42,15 @@ def upscale(p, init_img, upscaler_index, tileSize, padding):
if current_scale_factor == 1:
current_scale_factor = 2
print(f"Upscaling iteration {i} with scale factor {current_scale_factor}")
upscaled_img = upscaler.scaler.upscale(upscaled_img, current_scale_factor, upscaler.data_path)
return upscaled_img.resize((p.width, p.height), resample=Image.LANCZOS)
def redraw_image(p, upscaled_img, rows, cols, tileSize, padding):
initial_info = None
for yi in range(rows):
for xi in range(cols):
p.width = tileSize
@@ -61,29 +64,30 @@ def redraw_image(p, upscaled_img, rows, cols, tileSize, padding):
yi * tileSize,
xi * tileSize + tileSize,
yi * tileSize + tileSize
), fill="white")
), fill="white")
p.init_images = [upscaled_img]
p.image_mask = mask
processed = processing.process_images(p)
initial_info = processed.info
if (len(processed.images)>0):
if (len(processed.images) > 0):
upscaled_img = processed.images[0]
return upscaled_img
return upscaled_img, initial_info
def seam_draw(p, upscaled_img, seam_pass_width, seam_pass_padding, seam_pass_denoise, padding, tileSize, cols, rows):
for xi in range(1, cols):
p.width = seam_pass_width + seam_pass_padding*2
p.width = seam_pass_width + seam_pass_padding * 2
p.height = upscaled_img.height
p.inpaint_full_res = True
p.inpaint_full_res_padding = padding
mask = Image.new("L", (upscaled_img.width, upscaled_img.height), "black")
draw = ImageDraw.Draw(mask)
draw.rectangle((
xi * tileSize - seam_pass_width//2,
xi * tileSize - seam_pass_width // 2,
0,
xi * tileSize + seam_pass_width//2,
xi * tileSize + seam_pass_width // 2,
mask.height
), fill="white")
@@ -94,16 +98,16 @@ def seam_draw(p, upscaled_img, seam_pass_width, seam_pass_padding, seam_pass_den
upscaled_img = processed.images[0]
for yi in range(1, rows):
p.width = upscaled_img.width
p.height = seam_pass_width + seam_pass_padding*2
p.height = seam_pass_width + seam_pass_padding * 2
p.inpaint_full_res = True
p.inpaint_full_res_padding = padding
mask = Image.new("L", (upscaled_img.width, upscaled_img.height), "black")
draw = ImageDraw.Draw(mask)
draw.rectangle((
0,
yi * tileSize - seam_pass_width//2,
yi * tileSize - seam_pass_width // 2,
mask.width,
yi * tileSize + seam_pass_width//2
yi * tileSize + seam_pass_width // 2
), fill="white")
p.init_images = [upscaled_img]
@@ -113,6 +117,7 @@ def seam_draw(p, upscaled_img, seam_pass_width, seam_pass_padding, seam_pass_den
upscaled_img = processed.images[0]
return upscaled_img
class Script(scripts.Script):
def title(self):
return "Ultimate SD upscale"
@@ -121,32 +126,35 @@ class Script(scripts.Script):
return is_img2img
def ui(self, is_img2img):
info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
info = gr.HTML(
"<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers],
value=shared.sd_upscalers[0].name, type="index")
tileSize = gr.Slider(minimum=256, maximum=2048, step=64, label='Tile size', value=512)
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8)
padding = gr.Slider(label='Padding', minimum=0, maximum=128, step=1, value=32)
seam_pass_enabled = gr.Checkbox(label= "Seam pass enabled")
seam_pass_enabled = gr.Checkbox(label="Seam pass enabled")
seam_pass_width = gr.Slider(label='Seam pass width', minimum=0, maximum=128, step=1, value=16)
seam_pass_denoise = gr.Slider(label='Seam pass denoise', minimum=0, maximum=1, step=0.01, value=0.25)
seam_pass_padding = gr.Slider(label='Seam pass padding', minimum=0, maximum=128, step=1, value=32)
return [info, tileSize, mask_blur, padding, seam_pass_enabled, seam_pass_width, seam_pass_denoise, seam_pass_padding, upscaler_index]
return [info, tileSize, mask_blur, padding, seam_pass_enabled, seam_pass_width, seam_pass_denoise,
seam_pass_padding, upscaler_index]
def run(self, p, _, tileSize, mask_blur, padding, seam_pass_enabled, seam_pass_width, seam_pass_denoise, seam_pass_padding, upscaler_index):
def run(self, p, _, tileSize, mask_blur, padding, seam_pass_enabled, seam_pass_width, seam_pass_denoise,
seam_pass_padding, upscaler_index):
processing.fix_seed(p)
p.extra_generation_params["SD upscale tileSize"] = tileSize
p.mask_blur = mask_blur
initial_info = None
seed = p.seed
init_img = p.init_images[0]
init_img = images.flatten(init_img, opts.img2img_background_color)
#Upscaling
# Upscaling
upscaled_img = upscale(p, init_img, upscaler_index, tileSize, padding)
#Drawing
# Drawing
devices.torch_gc()
p.inpaint_full_res = False
@@ -154,22 +162,24 @@ class Script(scripts.Script):
rows = math.ceil(p.height / tileSize)
cols = math.ceil(p.width / tileSize)
print(f"Tiles amount: {rows*cols}")
print(f"Tiles amount: {rows * cols}")
print(f"Grid: {rows}x{cols}")
print(f"Seam path: {seam_pass_enabled}")
seams = 0
if seam_pass_enabled:
seams = rows-1 + cols - 1
seams = rows - 1 + cols - 1
result_images = []
state.job_count = rows*cols + seams
result_images.append(redraw_image(p, upscaled_img, rows, cols, tileSize, padding))
state.job_count = rows * cols + seams
result_image, initial_info = redraw_image(p, upscaled_img, rows, cols, tileSize, padding)
result_images.append()
if seam_pass_enabled:
print(f"Starting seam path drawing")
result_images.append(seam_draw(p, upscaled_img, seam_pass_width, seam_pass_padding, seam_pass_denoise, padding, tileSize, cols, rows))
result_images.append(
seam_draw(p, upscaled_img, seam_pass_width, seam_pass_padding, seam_pass_denoise, padding, tileSize, cols, rows))
processed = Processed(p, result_images, seed, initial_info)