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stable-diffusion-webui-stable-horde
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Update process_images_inner

main
natanjunges 2023-01-15 17:12:34 -03:00
parent 476c360954
commit dd78372981
1 changed files with 13 additions and 6 deletions
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19
scripts/main.py
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@ -14,7 +14,7 @@
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
from modules import scripts, processing, shared, images, devices, ui
from modules import scripts, processing, shared, images, devices, ui, lowvram
import gradio
import requests
import time
@ -217,7 +217,7 @@ class Main(scripts.Script):
def process_images_inner(self, p, model, nsfw, shared_laion, seed_variation, post_processing):
# Copyright (C) 2022 AUTOMATIC1111
# https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/d7aec59c4eb02f723b3d55c6f927a42e97acd679/modules/processing.py#L493-L687
# https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/ce13ced5dc5ce06634b3313bbfed6d479f8a4538/modules/processing.py#L493-L687
fake_model = FakeModel(model)
@ -257,6 +257,9 @@ class Main(scripts.Script):
shared.sd_model = old_model
return ret
if p.scripts is not None:
p.scripts.process(p)
with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
old_model = shared.sd_model
shared.sd_model = fake_model
@ -264,9 +267,6 @@ class Main(scripts.Script):
file.write(processed.infotext(p, 0))
shared.sd_model = old_model
if p.scripts is not None:
p.scripts.process(p)
infotexts = []
output_images = []
@ -302,6 +302,14 @@ class Main(scripts.Script):
if x_samples_ddim is None:
break
x_samples_ddim = [s.cpu() for s in x_samples_ddim]
x_samples_ddim = torch.stack(x_samples_ddim).float()
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()
devices.torch_gc()
if p.scripts is not None:
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
@ -453,7 +461,6 @@ class Main(scripts.Script):
images = [PIL.Image.open(io.BytesIO(base64.b64decode(image["img"]))) for image in images]
images = [numpy.moveaxis(numpy.array(image).astype(numpy.float32) / 255.0, 2, 0) for image in images]
images = [torch.from_numpy(image) for image in images]
images = torch.stack(images).to(shared.device)
return (images, models)
elif status["faulted"]:
raise StableHordeError("This request caused an internal server error and could not be completed.")