diff options
| -rw-r--r-- | modules/esrgan_model.py | 7 | ||||
| -rw-r--r-- | modules/img2img.py | 35 | ||||
| -rw-r--r-- | modules/lowvram.py | 9 | ||||
| -rw-r--r-- | modules/sd_hijack.py | 2 | ||||
| -rw-r--r-- | modules/shared.py | 9 | ||||
| -rw-r--r-- | modules/ui.py | 2 | ||||
| -rw-r--r-- | requirements.txt | 1 | ||||
| -rw-r--r-- | requirements_versions.txt | 1 | ||||
| -rw-r--r-- | script.js | 16 | ||||
| -rw-r--r-- | webui.bat | 2 |
10 files changed, 69 insertions, 15 deletions
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index 3dcef5a6..2ed1d273 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -14,8 +14,11 @@ import modules.images def load_model(filename):
# this code is adapted from https://github.com/xinntao/ESRGAN
-
- pretrained_net = torch.load(filename)
+ if torch.has_mps:
+ map_l = 'cpu'
+ else:
+ map_l = None
+ pretrained_net = torch.load(filename, map_location=map_l)
crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
if 'conv_first.weight' in pretrained_net:
diff --git a/modules/img2img.py b/modules/img2img.py index 3129798d..c2392305 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -1,5 +1,7 @@ import math
-from PIL import Image
+import cv2
+import numpy as np
+from PIL import Image, ImageOps, ImageChops
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
@@ -16,7 +18,9 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index if is_inpaint:
image = init_img_with_mask['image']
- mask = init_img_with_mask['mask']
+ alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
+ mask = ImageChops.lighter(alpha_mask, init_img_with_mask['mask'].convert('L')).convert('RGBA')
+ image = image.convert('RGB')
else:
image = init_img
mask = None
@@ -57,8 +61,19 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index state.job_count = n_iter
+ do_color_correction = False
+ try:
+ from skimage import exposure
+ do_color_correction = True
+ except:
+ print("Install scikit-image to perform color correction on loopback")
+
+
for i in range(n_iter):
+ if do_color_correction and i == 0:
+ correction_target = cv2.cvtColor(np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB)
+
p.n_iter = 1
p.batch_size = 1
p.do_not_save_grid = True
@@ -69,8 +84,20 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
-
- p.init_images = [processed.images[0]]
+
+ init_img = processed.images[0]
+
+ if do_color_correction and correction_target is not None:
+ init_img = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
+ cv2.cvtColor(
+ np.asarray(init_img),
+ cv2.COLOR_RGB2LAB
+ ),
+ correction_target,
+ channel_axis=2
+ ), cv2.COLOR_LAB2RGB).astype("uint8"))
+
+ p.init_images = [init_img]
p.seed = processed.seed + 1
p.denoising_strength = max(p.denoising_strength * 0.95, 0.1)
history.append(processed.images[0])
diff --git a/modules/lowvram.py b/modules/lowvram.py index 4b78deab..bd117491 100644 --- a/modules/lowvram.py +++ b/modules/lowvram.py @@ -2,9 +2,12 @@ import torch module_in_gpu = None
cpu = torch.device("cpu")
-gpu = torch.device("cuda")
-device = gpu if torch.cuda.is_available() else cpu
-
+if torch.has_cuda:
+ device = gpu = torch.device("cuda")
+elif torch.has_mps:
+ device = gpu = torch.device("mps")
+else:
+ device = gpu = torch.device("cpu")
def setup_for_low_vram(sd_model, use_medvram):
parents = {}
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 2d26b5f7..1084e248 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -232,7 +232,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): z = outputs.last_hidden_state
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
- batch_multipliers = torch.asarray(np.array(batch_multipliers)).to(device)
+ batch_multipliers = torch.asarray(batch_multipliers).to(device)
original_mean = z.mean()
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
new_mean = z.mean()
diff --git a/modules/shared.py b/modules/shared.py index beb6f9bb..e529ec27 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -36,9 +36,12 @@ parser.add_argument("--opt-split-attention", action='store_true', help="enable o parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
cmd_opts = parser.parse_args()
-cpu = torch.device("cpu")
-gpu = torch.device("cuda")
-device = gpu if torch.cuda.is_available() else cpu
+if torch.has_cuda:
+ device = torch.device("cuda")
+elif torch.has_mps:
+ device = torch.device("mps")
+else:
+ device = torch.device("cpu")
batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
diff --git a/modules/ui.py b/modules/ui.py index f5564d0e..b1a8c776 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -323,7 +323,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo): with gr.Group():
switch_mode = gr.Radio(label='Mode', elem_id="img2img_mode", choices=['Redraw whole image', 'Inpaint a part of image', 'Loopback', 'SD upscale'], value='Redraw whole image', type="index", show_label=False)
init_img = gr.Image(label="Image for img2img", source="upload", interactive=True, type="pil")
- init_img_with_mask = gr.Image(label="Image for inpainting with mask", elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", visible=False)
+ init_img_with_mask = gr.Image(label="Image for inpainting with mask", elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", visible=False, image_mode="RGBA")
resize_mode = gr.Radio(label="Resize mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
diff --git a/requirements.txt b/requirements.txt index c9e3f2fc..ba1bc281 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,5 +10,6 @@ omegaconf pytorch_lightning
diffusers
invisible-watermark
+scikit-image
git+https://github.com/crowsonkb/k-diffusion.git
git+https://github.com/TencentARC/GFPGAN.git
diff --git a/requirements_versions.txt b/requirements_versions.txt index 177c6b58..e8a7470c 100644 --- a/requirements_versions.txt +++ b/requirements_versions.txt @@ -8,3 +8,4 @@ torch transformers==4.19.2
omegaconf==2.1.1
pytorch_lightning==1.7.2
+scikit-image==0.19.2
@@ -172,3 +172,19 @@ function submit(){ } return res } + +window.addEventListener('paste', e => { + const files = e.clipboardData.files; + if (!files || files.length !== 1) { + return; + } + if (!['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type)) { + return; + } + [...gradioApp().querySelectorAll('input[type=file][accept="image/x-png,image/gif,image/jpeg"]')] + .filter(input => !input.matches('.\\!hidden input[type=file]')) + .forEach(input => { + input.files = files; + input.dispatchEvent(new Event('change')) + }); +}); @@ -35,7 +35,7 @@ echo Unable to create venv in directory %VENV_DIR% goto :show_stdout_stderr
:activate_venv
-set PYTHON=%~dp0%VENV_DIR%\Scripts\Python.exe
+set PYTHON="%~dp0%VENV_DIR%\Scripts\Python.exe"
%PYTHON% --version
echo venv %PYTHON%
goto :install_torch
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