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Merge pull request #5179 from kaneda2004/master

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Update SD Upscaler to include user selectable Scale Factor
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AUTOMATIC1111 2022-12-10 13:28:45 +03:00 committed by GitHub
commit 854bb0b56c
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1 changed files with 8 additions and 7 deletions
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scripts/sd_upscale.py
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@ -17,13 +17,14 @@ 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)
scale_factor = gr.Slider(minimum=1, 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") 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 +36,8 @@ 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
@ -69,7 +70,7 @@ 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)