Merge branch 'master' into remove-watermark-option

1 files changed, 16 insertions, 14 deletions

diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index 2572443f..bb00fb3f 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -6,23 +6,21 @@ from tqdm import trange
 import modules.scripts as scripts

 import gradio as gr

 

-from modules import processing, shared, sd_samplers, prompt_parser, sd_samplers_common

-from modules.processing import Processed

-from modules.shared import opts, cmd_opts, state

+from modules import processing, shared, sd_samplers, sd_samplers_common

 

 import torch

 import k_diffusion as K

 

-from PIL import Image

-from torch import autocast

-from einops import rearrange, repeat

-

-

 def find_noise_for_image(p, cond, uncond, cfg_scale, steps):

     x = p.init_latent

 

     s_in = x.new_ones([x.shape[0]])

-    dnw = K.external.CompVisDenoiser(shared.sd_model)

+    if shared.sd_model.parameterization == "v":

+        dnw = K.external.CompVisVDenoiser(shared.sd_model)

+        skip = 1

+    else:

+        dnw = K.external.CompVisDenoiser(shared.sd_model)

+        skip = 0

     sigmas = dnw.get_sigmas(steps).flip(0)

 

     shared.state.sampling_steps = steps

@@ -37,7 +35,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
         image_conditioning = torch.cat([p.image_conditioning] * 2)

         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}

 

-        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]

+        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]

         t = dnw.sigma_to_t(sigma_in)

 

         eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)

@@ -69,7 +67,12 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
     x = p.init_latent

 

     s_in = x.new_ones([x.shape[0]])

-    dnw = K.external.CompVisDenoiser(shared.sd_model)

+    if shared.sd_model.parameterization == "v":

+        dnw = K.external.CompVisVDenoiser(shared.sd_model)

+        skip = 1

+    else:

+        dnw = K.external.CompVisDenoiser(shared.sd_model)

+        skip = 0

     sigmas = dnw.get_sigmas(steps).flip(0)

 

     shared.state.sampling_steps = steps

@@ -84,7 +87,7 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
         image_conditioning = torch.cat([p.image_conditioning] * 2)

         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}

 

-        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]

+        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]

 

         if i == 1:

             t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))

@@ -125,7 +128,7 @@ class Script(scripts.Script):
     def show(self, is_img2img):

         return is_img2img

 

-    def ui(self, is_img2img):     

+    def ui(self, is_img2img):

         info = gr.Markdown('''

         * `CFG Scale` should be 2 or lower.

         ''')

@@ -213,4 +216,3 @@ class Script(scripts.Script):
         processed = processing.process_images(p)

 

         return processed

-