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improve human eyes again by using crop adm rather than interpolate adm (#544)

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and add an debug slider
This commit is contained in:
lllyasviel
2023-10-04 21:42:43 -07:00
committed by GitHub
parent aebfc56641
commit 6189e482bd
4 changed files with 14 additions and 8 deletions
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modules/async_worker.py
+3 -2
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@@ -48,7 +48,7 @@ def worker():
execution_start_time = time.perf_counter()
prompt, negative_prompt, style_selections, performance_selection, \
aspect_ratios_selection, image_number, image_seed, sharpness, adm_scaler_positive, adm_scaler_negative, guidance_scale, adaptive_cfg, sampler_name, scheduler_name, \
aspect_ratios_selection, image_number, image_seed, sharpness, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, guidance_scale, adaptive_cfg, sampler_name, scheduler_name, \
overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength, overwrite_upscale_strength, \
base_model_name, refiner_model_name, \
l1, w1, l2, w2, l3, w3, l4, w4, l5, w5, \
@@ -80,7 +80,8 @@ def worker():
modules.patch.positive_adm_scale = adm_scaler_positive
modules.patch.negative_adm_scale = adm_scaler_negative
print(f'[Parameters] ADM Scale = {modules.patch.positive_adm_scale} / {modules.patch.negative_adm_scale}')
modules.patch.adm_scaler_end = adm_scaler_end
print(f'[Parameters] ADM Scale = {modules.patch.positive_adm_scale} : {modules.patch.negative_adm_scale} : {modules.patch.adm_scaler_end}')
cfg_scale = float(guidance_scale)
print(f'[Parameters] CFG = {cfg_scale}')
modules/patch.py
+6 -4
View File
@@ -20,6 +20,8 @@ from comfy.ldm.modules.diffusionmodules.openaimodel import timestep_embedding, f
sharpness = 2.0
adm_scaler_end = 0.3
positive_adm_scale = 1.5
negative_adm_scale = 0.8
@@ -340,10 +342,10 @@ def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=
transformer_patches = transformer_options.get("patches", {})
if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632:
t = (timesteps / 999.0)[:, None].clone().to(x) ** 2.0
ya = y[..., :2816].clone()
yb = y[..., 2816:].clone()
y = t * ya + (1 - t) * yb
y_mask = (timesteps > 999.0 * (1.0 - float(adm_scaler_end))).to(y)[..., None]
y_with_adm = y[..., :2816].clone()
y_without_adm = y[..., 2816:].clone()
y = y_with_adm * y_mask + y_without_adm * (1.0 - y_mask)
hs = []
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)