Fix memory leak and reduce memory usage
This commit is contained in:
Jairo Correa
parent
041d2aefc0
commit
c938679de7
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@ -89,7 +89,7 @@ def setup_codeformer():
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output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
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restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
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del output
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torch.cuda.empty_cache()
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devices.torch_gc()
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except Exception as error:
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print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
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restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
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@ -106,7 +106,9 @@ def setup_codeformer():
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restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
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if shared.opts.face_restoration_unload:
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self.net.to(devices.cpu)
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self.net = None
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self.face_helper = None
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devices.torch_gc()
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return restored_img
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@ -1,4 +1,5 @@
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import torch
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import gc
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# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
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from modules import errors
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@ -17,8 +18,8 @@ def get_optimal_device():
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return cpu
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def torch_gc():
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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@ -98,6 +98,8 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
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outputs.append(image)
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devices.torch_gc()
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return outputs, plaintext_to_html(info), ''
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@ -49,6 +49,7 @@ def gfpgan():
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def gfpgan_fix_faces(np_image):
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global loaded_gfpgan_model
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model = gfpgan()
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np_image_bgr = np_image[:, :, ::-1]
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@ -56,7 +57,9 @@ def gfpgan_fix_faces(np_image):
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np_image = gfpgan_output_bgr[:, :, ::-1]
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if shared.opts.face_restoration_unload:
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model.gfpgan.to(devices.cpu)
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del model
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loaded_gfpgan_model = None
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devices.torch_gc()
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return np_image
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@ -83,11 +86,7 @@ def setup_gfpgan():
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return "GFPGAN"
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def restore(self, np_image):
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np_image_bgr = np_image[:, :, ::-1]
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cropped_faces, restored_faces, gfpgan_output_bgr = gfpgan().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
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np_image = gfpgan_output_bgr[:, :, ::-1]
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return np_image
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return gfpgan_fix_faces(np_image)
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shared.face_restorers.append(FaceRestorerGFPGAN())
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except Exception:
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@ -12,7 +12,7 @@ import cv2
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from skimage import exposure
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import modules.sd_hijack
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from modules import devices, prompt_parser, masking
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from modules import devices, prompt_parser, masking, lowvram
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from modules.sd_hijack import model_hijack
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from modules.sd_samplers import samplers, samplers_for_img2img
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from modules.shared import opts, cmd_opts, state
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@ -335,7 +335,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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if state.job_count == -1:
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state.job_count = p.n_iter
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for n in range(p.n_iter):
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for n in range(p.n_iter):
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with torch.no_grad(), precision_scope("cuda"), ema_scope():
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if state.interrupted:
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break
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@ -368,22 +369,32 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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del samples_ddim
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.send_everything_to_cpu()
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devices.torch_gc()
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if opts.filter_nsfw:
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import modules.safety as safety
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x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
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for i, x_sample in enumerate(x_samples_ddim):
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for i, x_sample in enumerate(x_samples_ddim):
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with torch.no_grad(), precision_scope("cuda"), ema_scope():
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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if p.restore_faces:
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if p.restore_faces:
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with torch.no_grad(), precision_scope("cuda"), ema_scope():
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if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
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images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
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devices.torch_gc()
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x_sample = modules.face_restoration.restore_faces(x_sample)
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devices.torch_gc()
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with torch.no_grad(), precision_scope("cuda"), ema_scope():
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image = Image.fromarray(x_sample)
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if p.color_corrections is not None and i < len(p.color_corrections):
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@ -411,8 +422,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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infotexts.append(infotext(n, i))
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output_images.append(image)
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state.nextjob()
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del x_samples_ddim
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devices.torch_gc()
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state.nextjob()
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with torch.no_grad(), precision_scope("cuda"), ema_scope():
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p.color_corrections = None
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index_of_first_image = 0
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@ -648,4 +664,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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if self.mask is not None:
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samples = samples * self.nmask + self.init_latent * self.mask
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del x
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devices.torch_gc()
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return samples
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3
webui.py
3
webui.py
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@ -22,7 +22,10 @@ import modules.txt2img
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import modules.img2img
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import modules.swinir as swinir
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import modules.sd_models
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from torch.nn.functional import silu
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import ldm
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ldm.modules.diffusionmodules.model.nonlinearity = silu
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modules.codeformer_model.setup_codeformer()
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modules.gfpgan_model.setup_gfpgan()
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