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| author | C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> | 2022-09-28 02:09:22 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2022-09-28 02:09:22 +0000 |
| commit | 8644e494be720a2a898eb4ed771d6109fec34858 (patch) | |
| tree | 163162b4647f0109bf9f24174a1206d7f06325a3 /modules/sd_samplers.py | |
| parent | f2a4a2c3a672e22f088a7455d6039557370dd3f2 (diff) | |
| download | stable-diffusion-webui-gfx803-8644e494be720a2a898eb4ed771d6109fec34858.tar.gz stable-diffusion-webui-gfx803-8644e494be720a2a898eb4ed771d6109fec34858.tar.bz2 stable-diffusion-webui-gfx803-8644e494be720a2a898eb4ed771d6109fec34858.zip | |
add eta to k ancestral
Diffstat (limited to 'modules/sd_samplers.py')
| -rw-r--r-- | modules/sd_samplers.py | 6 |
1 files changed, 4 insertions, 2 deletions
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 666ee1ee..17faeab1 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -39,8 +39,10 @@ samplers_for_img2img = [x for x in samplers if x.name != 'PLMS'] sampler_extra_params = {
'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'],
+ 'sample_euler_ancestral':['eta'],
'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'],
'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'],
+ 'sample_dpm_2_ancestral':['eta'],
}
def setup_img2img_steps(p, steps=None):
@@ -154,9 +156,9 @@ class VanillaStableDiffusionSampler: # existing code fails with cetin step counts, like 9
try:
- samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)
+ samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.eta)
except Exception:
- samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)
+ samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.eta)
return samples_ddim
|