cxllm-vendors/stable-diffusion-webui-forge
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use better context manager to fix potential problems

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lllyasviel
2024-02-08 01:51:18 -08:00
parent 4c9db26541
commit 760f727eb9
2 changed files with 4 additions and 1 deletions
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modules/processing.py
+1 -1
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@@ -814,7 +814,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
infotexts = [] infotexts = []
output_images = [] output_images = []
with torch.no_grad(): with torch.inference_mode():
with devices.autocast(): with devices.autocast():
p.init(p.all_prompts, p.all_seeds, p.all_subseeds) p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
modules_forge/forge_loader.py
+3
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@@ -130,6 +130,7 @@ def load_checkpoint_guess_config(sd, output_vae=True, output_clip=True, output_c
return ForgeSD(model_patcher, clip, vae, clipvision) return ForgeSD(model_patcher, clip, vae, clipvision)
@torch.inference_mode()
def load_model_for_a1111(timer, checkpoint_info=None, state_dict=None): def load_model_for_a1111(timer, checkpoint_info=None, state_dict=None):
a1111_config_filename = find_checkpoint_config(state_dict, checkpoint_info) a1111_config_filename = find_checkpoint_config(state_dict, checkpoint_info)
a1111_config = OmegaConf.load(a1111_config_filename) a1111_config = OmegaConf.load(a1111_config_filename)
@@ -232,11 +233,13 @@ def load_model_for_a1111(timer, checkpoint_info=None, state_dict=None):
sd_model.sd_model_checkpoint = checkpoint_info.filename sd_model.sd_model_checkpoint = checkpoint_info.filename
sd_model.sd_checkpoint_info = checkpoint_info sd_model.sd_checkpoint_info = checkpoint_info
@torch.inference_mode()
def patched_decode_first_stage(x): def patched_decode_first_stage(x):
sample = forge_objects.unet.model.model_config.latent_format.process_out(x) sample = forge_objects.unet.model.model_config.latent_format.process_out(x)
sample = forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0 sample = forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
return sample.to(x) return sample.to(x)
@torch.inference_mode()
def patched_encode_first_stage(x): def patched_encode_first_stage(x):
sample = forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5) sample = forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
sample = forge_objects.unet.model.model_config.latent_format.process_in(sample) sample = forge_objects.unet.model.model_config.latent_format.process_in(sample)