diff options
| -rw-r--r-- | modules/processing.py | 14 | ||||
| -rw-r--r-- | modules/sd_samplers_common.py | 2 | ||||
| -rw-r--r-- | run.ps1 | 1 | ||||
| -rw-r--r-- | run_local.ps1 | 3 | ||||
| -rw-r--r-- | update.ps1 | 1 |
5 files changed, 9 insertions, 12 deletions
diff --git a/modules/processing.py b/modules/processing.py index 544667a4..aae39866 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -16,7 +16,6 @@ from typing import Any, Dict, List import modules.sd_hijack
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
from modules.sd_hijack import model_hijack
-from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.paths as paths
@@ -31,6 +30,7 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion from einops import repeat, rearrange
from blendmodes.blend import blendLayers, BlendType
+decode_first_stage = sd_samplers_common.decode_first_stage
# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
@@ -84,7 +84,7 @@ def txt2img_image_conditioning(sd_model, x, width, height): # The "masked-image" in this case will just be all zeros since the entire image is masked.
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
- image_conditioning = images_tensor_to_samples(image_conditioning, approximation_indexes.get(opts.sd_vae_encode_method))
+ image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
# Add the fake full 1s mask to the first dimension.
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
@@ -203,7 +203,7 @@ class StableDiffusionProcessing: midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
- conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
+ conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
conditioning = torch.nn.functional.interpolate(
self.sd_model.depth_model(midas_in),
size=conditioning_image.shape[2:],
@@ -216,7 +216,7 @@ class StableDiffusionProcessing: return conditioning
def edit_image_conditioning(self, source_image):
- conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
+ conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
return conditioning_image
@@ -255,7 +255,7 @@ class StableDiffusionProcessing: )
# Encode the new masked image using first stage of network.
- conditioning_image = images_tensor_to_samples(conditioning_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
+ conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
# Create the concatenated conditioning tensor to be fed to `c_concat`
conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
@@ -1099,8 +1099,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): decoded_samples = torch.from_numpy(np.array(batch_images))
decoded_samples = decoded_samples.to(shared.device)
+ decoded_samples = 2. * decoded_samples - 1.
- samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
+ samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
@@ -1338,6 +1339,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
image = torch.from_numpy(batch_images)
+ from modules.sd_samplers_common import images_tensor_to_samples, approximation_indexes
self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
devices.torch_gc()
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index 42a29fc9..7269514f 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -75,7 +75,7 @@ def images_tensor_to_samples(image, approximation=None, model=None): if approximation == 3:
image = image.to(devices.device, devices.dtype)
- x_latent = sd_vae_taesd.encoder_model()(image)
+ x_latent = sd_vae_taesd.encoder_model()(image) / 1.5
else:
if model is None:
model = shared.sd_model
diff --git a/run.ps1 b/run.ps1 deleted file mode 100644 index 82c1660b..00000000 --- a/run.ps1 +++ /dev/null @@ -1 +0,0 @@ -.\venv\Scripts\accelerate-launch.exe --num_cpu_threads_per_process=6 --api .\launch.py --listen --port 17415 --xformers --opt-channelslast
\ No newline at end of file diff --git a/run_local.ps1 b/run_local.ps1 deleted file mode 100644 index e2ac43db..00000000 --- a/run_local.ps1 +++ /dev/null @@ -1,3 +0,0 @@ -.\venv\Scripts\Activate.ps1 -python .\launch.py --xformers --opt-channelslast --api -. $PSCommandPath
\ No newline at end of file diff --git a/update.ps1 b/update.ps1 deleted file mode 100644 index 9960bead..00000000 --- a/update.ps1 +++ /dev/null @@ -1 +0,0 @@ -git stash push && git pull --rebase && git stash pop
\ No newline at end of file |