cxllm-vendors/stable-diffusion-webui-forge
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

ini mask support

Browse Source
This commit is contained in:
lllyasviel
2024-02-01 21:21:41 -08:00
parent 07977a1fec
commit 9f6ee2a688
9 changed files with 109 additions and 236 deletions
Download Patch File Download Diff File Expand all files Collapse all files
extensions-builtin/forge_preprocessor_inpaint/scripts/preprocessor_inpaint.py
+19 -14
View File
@@ -29,10 +29,11 @@ class PreprocessorInpaintOnly(PreprocessorInpaint):
self.image = None
self.mask = None
self.latent = None
self.fill_mask_with_one_when_resize_and_fill = True
def process_before_every_sampling(self, process, cond, *args, **kwargs):
self.image = kwargs['cond_before_inpaint_fix'][:, 0:3]
self.mask = kwargs['cond_before_inpaint_fix'][:, 3:]
def process_before_every_sampling(self, process, cond, mask, *args, **kwargs):
self.image = cond
self.mask = mask
vae = process.sd_model.forge_objects.vae
# This is a powerful VAE with integrated memory management, bf16, and tiled fallback.
@@ -58,7 +59,10 @@ class PreprocessorInpaintOnly(PreprocessorInpaint):
process.sd_model.forge_objects.unet = unet
self.latent = latent_image
return
mixed_cond = cond * (1.0 - mask) - mask
return mixed_cond, None
def process_after_every_sampling(self, process, params, *args, **kwargs):
a1111_batch_result = args[0]
@@ -95,19 +99,21 @@ class PreprocessorInpaintLama(PreprocessorInpaintOnly):
self.setup_model_patcher(model)
return
def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, **kwargs):
def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, input_mask=None, **kwargs):
H, W, C = input_image.shape
raw_color = input_image[:, :, 0:3].copy()
raw_mask = input_image[:, :, 3:4].copy()
raw_color = input_image.copy()
raw_mask = input_mask.copy()
input_image, remove_pad = resize_image_with_pad(input_image, 256)
input_mask, remove_pad = resize_image_with_pad(input_mask, 256)
input_mask = input_mask[..., :1]
self.load_model()
self.move_all_model_patchers_to_gpu()
color = np.ascontiguousarray(input_image[:, :, 0:3]).astype(np.float32) / 255.0
mask = np.ascontiguousarray(input_image[:, :, 3:4]).astype(np.float32) / 255.0
color = np.ascontiguousarray(input_image).astype(np.float32) / 255.0
mask = np.ascontiguousarray(input_mask).astype(np.float32) / 255.0
with torch.no_grad():
color = self.send_tensor_to_model_device(torch.from_numpy(color))
mask = self.send_tensor_to_model_device(torch.from_numpy(mask))
@@ -128,15 +134,14 @@ class PreprocessorInpaintLama(PreprocessorInpaintOnly):
fin_color = prd_color.astype(np.float32) * alpha + raw_color.astype(np.float32) * (1 - alpha)
fin_color = fin_color.clip(0, 255).astype(np.uint8)
result = np.concatenate([fin_color, raw_mask], axis=2)
return result
return fin_color
def process_before_every_sampling(self, process, cond, *args, **kwargs):
super().process_before_every_sampling(process, cond, *args, **kwargs)
def process_before_every_sampling(self, process, cond, mask, *args, **kwargs):
cond, mask = super().process_before_every_sampling(process, cond, mask, *args, **kwargs)
sigma_max = process.sd_model.forge_objects.unet.model.model_sampling.sigma_max
original_noise = kwargs['noise']
process.modified_noise = original_noise + self.latent.to(original_noise) / sigma_max.to(original_noise)
return
return cond, mask
add_supported_preprocessor(PreprocessorInpaint())