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Update SD Upscaler to include user selectable Scale Factor

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This commit is contained in:
kaneda2004 2022-11-28 12:24:53 -08:00
parent 0b5dcb3d7c
commit 0202547696
1 changed files with 18 additions and 12 deletions
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26
scripts/sd_upscale.py
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@ -17,13 +17,16 @@ class Script(scripts.Script):
return is_img2img return is_img2img
def ui(self, is_img2img): def ui(self, is_img2img):
info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image to twice the dimensions; use width and height sliders to set tile size</p>") 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>")
overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64) overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") scale_factor = gr.Slider(minimum=0, maximum=4, step=1, label='Scale Factor', value=2)
upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers],
value=shared.sd_upscalers[0].name, type="index")
return [info, overlap, upscaler_index] return [info, overlap, upscaler_index, scale_factor]
def run(self, p, _, overlap, upscaler_index): def run(self, p, _, overlap, upscaler_index, scale_factor):
processing.fix_seed(p) processing.fix_seed(p)
upscaler = shared.sd_upscalers[upscaler_index] upscaler = shared.sd_upscalers[upscaler_index]
@ -35,8 +38,8 @@ class Script(scripts.Script):
init_img = p.init_images[0] init_img = p.init_images[0]
if(upscaler.name != "None"): if (upscaler.name != "None"):
img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path) img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)
else: else:
img = init_img img = init_img
@ -59,7 +62,8 @@ class Script(scripts.Script):
batch_count = math.ceil(len(work) / batch_size) batch_count = math.ceil(len(work) / batch_size)
state.job_count = batch_count * upscale_count state.job_count = batch_count * upscale_count
print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.") print(
f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")
result_images = [] result_images = []
for n in range(upscale_count): for n in range(upscale_count):
@ -69,7 +73,7 @@ class Script(scripts.Script):
work_results = [] work_results = []
for i in range(batch_count): for i in range(batch_count):
p.batch_size = batch_size p.batch_size = batch_size
p.init_images = work[i*batch_size:(i+1)*batch_size] p.init_images = work[i * batch_size:(i + 1) * batch_size]
state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}" state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"
processed = processing.process_images(p) processed = processing.process_images(p)
@ -83,14 +87,16 @@ class Script(scripts.Script):
image_index = 0 image_index = 0
for y, h, row in grid.tiles: for y, h, row in grid.tiles:
for tiledata in row: for tiledata in row:
tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height)) tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (
p.width, p.height))
image_index += 1 image_index += 1
combined_image = images.combine_grid(grid) combined_image = images.combine_grid(grid)
result_images.append(combined_image) result_images.append(combined_image)
if opts.samples_save: if opts.samples_save:
images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p) images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format,
info=initial_info, p=p)
processed = Processed(p, result_images, seed, initial_info) processed = Processed(p, result_images, seed, initial_info)