From 6c6ae28bf5fd1e8bc3e8f64a3430b6f29f338f77 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 4 Oct 2022 12:32:22 +0300 Subject: [PATCH] send all three of GFPGAN's and codeformer's models to CPU memory instead of just one for #1283 --- modules/codeformer_model.py | 12 ++++++++++-- modules/devices.py | 10 ++++++++++ modules/gfpgan_model.py | 14 ++++++++++++-- modules/processing.py | 16 +++++++++------- 4 files changed, 41 insertions(+), 11 deletions(-) diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index a29f38550..e6d9fa4f4 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -69,10 +69,14 @@ def setup_model(dirname): self.net = net self.face_helper = face_helper - self.net.to(devices.device_codeformer) return net, face_helper + def send_model_to(self, device): + self.net.to(device) + self.face_helper.face_det.to(device) + self.face_helper.face_parse.to(device) + def restore(self, np_image, w=None): np_image = np_image[:, :, ::-1] @@ -82,6 +86,8 @@ def setup_model(dirname): if self.net is None or self.face_helper is None: return np_image + self.send_model_to(devices.device_codeformer) + self.face_helper.clean_all() self.face_helper.read_image(np_image) self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) @@ -113,8 +119,10 @@ def setup_model(dirname): if original_resolution != restored_img.shape[0:2]: 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) + self.face_helper.clean_all() + if shared.opts.face_restoration_unload: - self.net.to(devices.cpu) + self.send_model_to(devices.cpu) return restored_img diff --git a/modules/devices.py b/modules/devices.py index ff82f2f64..12aab6652 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -1,3 +1,5 @@ +import contextlib + import torch # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility @@ -57,3 +59,11 @@ def randn_without_seed(shape): return torch.randn(shape, device=device) + +def autocast(): + from modules import shared + + if dtype == torch.float32 or shared.cmd_opts.precision == "full": + return contextlib.nullcontext() + + return torch.autocast("cuda") diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py index dd3fbcab1..5586b554b 100644 --- a/modules/gfpgan_model.py +++ b/modules/gfpgan_model.py @@ -37,22 +37,32 @@ def gfpgann(): print("Unable to load gfpgan model!") return None model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None) - model.gfpgan.to(shared.device) loaded_gfpgan_model = model return model +def send_model_to(model, device): + model.gfpgan.to(device) + model.face_helper.face_det.to(device) + model.face_helper.face_parse.to(device) + + def gfpgan_fix_faces(np_image): model = gfpgann() if model is None: return np_image + + send_model_to(model, devices.device) + np_image_bgr = np_image[:, :, ::-1] cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True) np_image = gfpgan_output_bgr[:, :, ::-1] + model.face_helper.clean_all() + if shared.opts.face_restoration_unload: - model.gfpgan.to(devices.cpu) + send_model_to(model, devices.cpu) return np_image diff --git a/modules/processing.py b/modules/processing.py index 0a4b6198f..9cbecdd83 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -1,4 +1,3 @@ -import contextlib import json import math import os @@ -330,9 +329,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: infotexts = [] output_images = [] - precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext - ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope) - with torch.no_grad(), precision_scope("cuda"), ema_scope(): + + with torch.no_grad(): p.init(all_prompts, all_seeds, all_subseeds) if state.job_count == -1: @@ -351,8 +349,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed: #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) #c = p.sd_model.get_learned_conditioning(prompts) - uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps) - c = prompt_parser.get_learned_conditioning(prompts, p.steps) + with devices.autocast(): + uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps) + c = prompt_parser.get_learned_conditioning(prompts, p.steps) if len(model_hijack.comments) > 0: for comment in model_hijack.comments: @@ -361,7 +360,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if p.n_iter > 1: shared.state.job = f"Batch {n+1} out of {p.n_iter}" - samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) + with devices.autocast(): + samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength).to(devices.dtype) + if state.interrupted: # if we are interruped, sample returns just noise @@ -386,6 +387,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: devices.torch_gc() x_sample = modules.face_restoration.restore_faces(x_sample) + devices.torch_gc() image = Image.fromarray(x_sample)