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| author | captin411 <captindave@gmail.com> | 2022-10-25 20:22:27 +0000 |
|---|---|---|
| committer | captin411 <captindave@gmail.com> | 2022-10-25 20:22:27 +0000 |
| commit | 6629446a2f9bb3ade1c271854aae1530ba1a8cc3 (patch) | |
| tree | ad7cfd2b3f0208c24da64c7f08e0550e783228ec /scripts/img2imgalt.py | |
| parent | 3e6c2420c1177e9e79f2b566a5a7795b7416e34a (diff) | |
| parent | 3e15f8e0f5cc87507f77546d92435670644dbd18 (diff) | |
| download | stable-diffusion-webui-gfx803-6629446a2f9bb3ade1c271854aae1530ba1a8cc3.tar.gz stable-diffusion-webui-gfx803-6629446a2f9bb3ade1c271854aae1530ba1a8cc3.tar.bz2 stable-diffusion-webui-gfx803-6629446a2f9bb3ade1c271854aae1530ba1a8cc3.zip | |
Merge branch 'master' into focal-point-cropping
Diffstat (limited to 'scripts/img2imgalt.py')
| -rw-r--r-- | scripts/img2imgalt.py | 8 |
1 files changed, 7 insertions, 1 deletions
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index d438175c..88abc093 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -34,6 +34,9 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps): sigma_in = torch.cat([sigmas[i] * s_in] * 2)
cond_in = torch.cat([uncond, cond])
+ 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)]
t = dnw.sigma_to_t(sigma_in)
@@ -78,6 +81,9 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps): sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
cond_in = torch.cat([uncond, cond])
+ 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)]
if i == 1:
@@ -194,7 +200,7 @@ class Script(scripts.Script): p.seed = p.seed + 1
- return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning)
+ return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
p.sample = sample_extra
|