diff options
| -rw-r--r-- | javascript/hints.js | 3 | ||||
| -rw-r--r-- | modules/generation_parameters_copypaste.py | 1 | ||||
| -rw-r--r-- | modules/processing.py | 3 | ||||
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 43 |
4 files changed, 30 insertions, 20 deletions
diff --git a/javascript/hints.js b/javascript/hints.js index c6bae360..44d418da 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -111,7 +111,8 @@ titles = { "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.", "Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.", "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.", - "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited." + "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.", + "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction." } diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index e7269363..99f1a0d3 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -309,6 +309,7 @@ infotext_to_setting_name_mapping = [ ('UniPC order', 'uni_pc_order'),
('UniPC lower order final', 'uni_pc_lower_order_final'),
('RNG', 'randn_source'),
+ ('NGMS', 's_min_uncond'),
]
diff --git a/modules/processing.py b/modules/processing.py index 04a06290..c50784f4 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -480,7 +480,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Clip skip": None if clip_skip <= 1 else clip_skip,
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
"Init image hash": getattr(p, 'init_img_hash', None),
- "RNG": (opts.randn_source if opts.randn_source != "GPU" else None)
+ "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+ "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
}
generation_params.update(p.extra_generation_params)
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index d42d5fcf..f8aaac59 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -115,20 +115,21 @@ class CFGDenoiser(torch.nn.Module): sigma_in = denoiser_params.sigma
tensor = denoiser_params.text_cond
uncond = denoiser_params.text_uncond
+ skip_uncond = False
- if self.step % 2 and s_min_uncond > 0 and not is_edit_model:
- # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
- sigma_threshold = s_min_uncond
- if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_threshold*sigma_threshold) ):
- uncond = torch.zeros([0,0,uncond.shape[2]])
- x_in=x_in[:x_in.shape[0]//2]
- sigma_in=sigma_in[:sigma_in.shape[0]//2]
+ # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
+ if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+ skip_uncond = True
+ x_in = x_in[:-batch_size]
+ sigma_in = sigma_in[:-batch_size]
- if tensor.shape[1] == uncond.shape[1]:
- if not is_edit_model:
- cond_in = torch.cat([tensor, uncond])
- else:
+ if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+ if is_edit_model:
cond_in = torch.cat([tensor, uncond, uncond])
+ elif skip_uncond:
+ cond_in = tensor
+ else:
+ cond_in = torch.cat([tensor, uncond])
if shared.batch_cond_uncond:
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
@@ -152,9 +153,15 @@ class CFGDenoiser(torch.nn.Module): x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
- if uncond.shape[0]:
+ if not skip_uncond:
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
+ if skip_uncond:
+ #x_out = torch.cat([x_out, x_out[0:batch_size]]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
+ denoised_image_indexes = [x[0][0] for x in conds_list]
+ fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
+ x_out = torch.cat([x_out, fake_uncond])
+
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
cfg_denoised_callback(denoised_params)
@@ -165,13 +172,12 @@ class CFGDenoiser(torch.nn.Module): elif opts.live_preview_content == "Negative prompt":
sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
- if not is_edit_model:
- if uncond.shape[0]:
- denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
- else:
- denoised = x_out
- else:
+ if is_edit_model:
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
+ elif skip_uncond:
+ denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
+ else:
+ denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
if self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
@@ -221,6 +227,7 @@ class KDiffusionSampler: self.eta = None
self.config = None
self.last_latent = None
+ self.s_min_uncond = None
self.conditioning_key = sd_model.model.conditioning_key
|