diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/devices.py | 6 | ||||
| -rw-r--r-- | modules/extras.py | 4 | ||||
| -rw-r--r-- | modules/img2img.py | 3 | ||||
| -rw-r--r-- | modules/interrogate.py | 41 | ||||
| -rw-r--r-- | modules/processing.py | 12 |
5 files changed, 38 insertions, 28 deletions
diff --git a/modules/devices.py b/modules/devices.py index 30d30b99..f88e807e 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -14,3 +14,9 @@ def get_optimal_device(): return torch.device("mps") return cpu + + +def torch_gc(): + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.ipc_collect() diff --git a/modules/extras.py b/modules/extras.py index 6aeae6cb..40935f98 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -1,7 +1,7 @@ import numpy as np
from PIL import Image
-from modules import processing, shared, images
+from modules import processing, shared, images, devices
from modules.shared import opts
import modules.gfpgan_model
from modules.ui import plaintext_to_html
@@ -11,7 +11,7 @@ cached_images = {} def run_extras(image, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
- processing.torch_gc()
+ devices.torch_gc()
image = image.convert("RGB")
info = ""
diff --git a/modules/img2img.py b/modules/img2img.py index 779f620d..e046b9a9 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -3,6 +3,7 @@ import cv2 import numpy as np
from PIL import Image, ImageOps, ImageChops
+from modules import devices
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
import modules.shared as shared
@@ -131,7 +132,7 @@ def img2img(prompt: str, negative_prompt: str, prompt_style: str, init_img, init upscaler = shared.sd_upscalers[upscaler_index]
img = upscaler.upscale(init_img, init_img.width * 2, init_img.height * 2)
- processing.torch_gc()
+ devices.torch_gc()
grid = images.split_grid(img, tile_w=width, tile_h=height, overlap=upscale_overlap)
diff --git a/modules/interrogate.py b/modules/interrogate.py index ed97a58b..7ebb79fc 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -1,3 +1,4 @@ +import contextlib
import os
import sys
import traceback
@@ -6,7 +7,6 @@ import re import torch
-from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
@@ -26,6 +26,7 @@ class InterrogateModels: clip_model = None
clip_preprocess = None
categories = None
+ dtype = None
def __init__(self, content_dir):
self.categories = []
@@ -60,14 +61,20 @@ class InterrogateModels: def load(self):
if self.blip_model is None:
self.blip_model = self.load_blip_model()
+ if not shared.cmd_opts.no_half:
+ self.blip_model = self.blip_model.half()
self.blip_model = self.blip_model.to(shared.device)
if self.clip_model is None:
self.clip_model, self.clip_preprocess = self.load_clip_model()
+ if not shared.cmd_opts.no_half:
+ self.clip_model = self.clip_model.half()
self.clip_model = self.clip_model.to(shared.device)
+ self.dtype = next(self.clip_model.parameters()).dtype
+
def unload(self):
if not shared.opts.interrogate_keep_models_in_memory:
if self.clip_model is not None:
@@ -76,14 +83,14 @@ class InterrogateModels: if self.blip_model is not None:
self.blip_model = self.blip_model.to(devices.cpu)
+ devices.torch_gc()
def rank(self, image_features, text_array, top_count=1):
import clip
top_count = min(top_count, len(text_array))
- text_tokens = clip.tokenize([text for text in text_array]).cuda()
- with torch.no_grad():
- text_features = self.clip_model.encode_text(text_tokens).float()
+ text_tokens = clip.tokenize([text for text in text_array]).to(shared.device)
+ text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = torch.zeros((1, len(text_array))).to(shared.device)
@@ -94,13 +101,12 @@ class InterrogateModels: top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]
-
def generate_caption(self, pil_image):
gpu_image = transforms.Compose([
transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
- ])(pil_image).unsqueeze(0).to(shared.device)
+ ])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
with torch.no_grad():
caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)
@@ -116,22 +122,23 @@ class InterrogateModels: caption = self.generate_caption(pil_image)
res = caption
- images = self.clip_preprocess(pil_image).unsqueeze(0).to(shared.device)
+ images = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
- with torch.no_grad():
- image_features = self.clip_model.encode_image(images).float()
+ precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
+ with torch.no_grad(), precision_scope("cuda"):
+ image_features = self.clip_model.encode_image(images).type(self.dtype)
- image_features /= image_features.norm(dim=-1, keepdim=True)
+ image_features /= image_features.norm(dim=-1, keepdim=True)
- if shared.opts.interrogate_use_builtin_artists:
- artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
+ if shared.opts.interrogate_use_builtin_artists:
+ artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
- res += ", " + artist[0]
+ res += ", " + artist[0]
- for name, topn, items in self.categories:
- matches = self.rank(image_features, items, top_count=topn)
- for match, score in matches:
- res += ", " + match
+ for name, topn, items in self.categories:
+ matches = self.rank(image_features, items, top_count=topn)
+ for match, score in matches:
+ res += ", " + match
except Exception:
print(f"Error interrogating", file=sys.stderr)
diff --git a/modules/processing.py b/modules/processing.py index cf2e13d3..b1e8dad2 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -10,6 +10,7 @@ from PIL import Image, ImageFilter, ImageOps import random
import modules.sd_hijack
+from modules import devices
from modules.sd_hijack import model_hijack
from modules.sd_samplers import samplers, samplers_for_img2img
from modules.shared import opts, cmd_opts, state
@@ -23,11 +24,6 @@ opt_C = 4 opt_f = 8
-def torch_gc():
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- torch.cuda.ipc_collect()
-
class StableDiffusionProcessing:
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", prompt_style="None", seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
@@ -157,7 +153,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
assert p.prompt is not None
- torch_gc()
+ devices.torch_gc()
fix_seed(p)
@@ -258,7 +254,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: x_sample = x_sample.astype(np.uint8)
if p.restore_faces:
- torch_gc()
+ devices.torch_gc()
x_sample = modules.face_restoration.restore_faces(x_sample)
@@ -297,7 +293,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
- torch_gc()
+ devices.torch_gc()
return Processed(p, output_images, all_seeds[0], infotext())
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