Commit 2162be51 authored by d8ahazard's avatar d8ahazard Committed by GitHub

Merge branch 'master' into master

parents 9fc1e49b 3a876b16
...@@ -67,7 +67,7 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler): ...@@ -67,7 +67,7 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
else: else:
filename = path filename = path
if not os.path.exists(filename) or filename is None: if not os.path.exists(filename) or filename is None:
print("Unable to load %s from %s" % (self.model_path, filename)) print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
return None return None
model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=2) # define network model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=2) # define network
model.load_state_dict(torch.load(filename), strict=True) model.load_state_dict(torch.load(filename), strict=True)
......
...@@ -213,17 +213,19 @@ def resize_image(resize_mode, im, width, height): ...@@ -213,17 +213,19 @@ def resize_image(resize_mode, im, width, height):
if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None" or im.mode == 'L': if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None" or im.mode == 'L':
return im.resize((w, h), resample=LANCZOS) return im.resize((w, h), resample=LANCZOS)
upscalers = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img]
assert len(upscalers) > 0, f"could not find upscaler named {opts.upscaler_for_img2img}"
upscaler = upscalers[0]
scale = max(w / im.width, h / im.height) scale = max(w / im.width, h / im.height)
upscaled = upscaler.scaler.upscale(im, scale, upscaler.data_path)
if upscaled.width != w or upscaled.height != h: if scale > 1.0:
upscaled = im.resize((w, h), resample=LANCZOS) upscalers = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img]
assert len(upscalers) > 0, f"could not find upscaler named {opts.upscaler_for_img2img}"
upscaler = upscalers[0]
im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
if im.width != w or im.height != h:
im = im.resize((w, h), resample=LANCZOS)
return upscaled return im
if resize_mode == 0: if resize_mode == 0:
res = resize(im, width, height) res = resize(im, width, height)
......
...@@ -232,7 +232,12 @@ class StableDiffusionModelHijack: ...@@ -232,7 +232,12 @@ class StableDiffusionModelHijack:
for fn in os.listdir(dirname): for fn in os.listdir(dirname):
try: try:
process_file(os.path.join(dirname, fn), fn) fullfn = os.path.join(dirname, fn)
if os.stat(fullfn).st_size == 0:
continue
process_file(fullfn, fn)
except Exception: except Exception:
print(f"Error loading emedding {fn}:", file=sys.stderr) print(f"Error loading emedding {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr) print(traceback.format_exc(), file=sys.stderr)
......
...@@ -599,7 +599,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): ...@@ -599,7 +599,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode") mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index") inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index") inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
with gr.Row(): with gr.Row():
inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False) inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
......
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