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Fix many precision problems

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Many users reported that image quality is different from 2.1.824. We reviewed all codes and fixed several precision problems in 2.1.846.
This commit is contained in:
lllyasviel
2023-12-16 15:55:53 -08:00
committed by GitHub
parent 3a727fd240
commit ec5dd950a2
6 changed files with 363 additions and 44 deletions
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modules/patch.py
+5 -41
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@@ -25,6 +25,8 @@ import modules.constants as constants
from ldm_patched.modules.samplers import calc_cond_uncond_batch
from ldm_patched.k_diffusion.sampling import BatchedBrownianTree
from ldm_patched.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, apply_control
from modules.patch_precision import patch_all_precision
from modules.patch_clip import patch_all_clip
sharpness = 2.0
@@ -286,46 +288,6 @@ def sdxl_encode_adm_patched(self, **kwargs):
return final_adm
def encode_token_weights_patched_with_a1111_method(self, token_weight_pairs):
to_encode = list()
max_token_len = 0
has_weights = False
for x in token_weight_pairs:
tokens = list(map(lambda a: a[0], x))
max_token_len = max(len(tokens), max_token_len)
has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))
to_encode.append(tokens)
sections = len(to_encode)
if has_weights or sections == 0:
to_encode.append(ldm_patched.modules.sd1_clip.gen_empty_tokens(self.special_tokens, max_token_len))
out, pooled = self.encode(to_encode)
if pooled is not None:
first_pooled = pooled[0:1].to(ldm_patched.modules.model_management.intermediate_device())
else:
first_pooled = pooled
output = []
for k in range(0, sections):
z = out[k:k + 1]
if has_weights:
original_mean = z.mean()
z_empty = out[-1]
for i in range(len(z)):
for j in range(len(z[i])):
weight = token_weight_pairs[k][j][1]
if weight != 1.0:
z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]
new_mean = z.mean()
z = z * (original_mean / new_mean)
output.append(z)
if len(output) == 0:
return out[-1:].to(ldm_patched.modules.model_management.intermediate_device()), first_pooled
return torch.cat(output, dim=-2).to(ldm_patched.modules.model_management.intermediate_device()), first_pooled
def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None):
if inpaint_worker.current_task is not None:
latent_processor = self.inner_model.inner_model.process_latent_in
@@ -519,6 +481,9 @@ def build_loaded(module, loader_name):
def patch_all():
patch_all_precision()
patch_all_clip()
if not hasattr(ldm_patched.modules.model_management, 'load_models_gpu_origin'):
ldm_patched.modules.model_management.load_models_gpu_origin = ldm_patched.modules.model_management.load_models_gpu
@@ -527,7 +492,6 @@ def patch_all():
ldm_patched.controlnet.cldm.ControlNet.forward = patched_cldm_forward
ldm_patched.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward
ldm_patched.modules.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = encode_token_weights_patched_with_a1111_method
ldm_patched.modules.samplers.KSamplerX0Inpaint.forward = patched_KSamplerX0Inpaint_forward
ldm_patched.k_diffusion.sampling.BrownianTreeNoiseSampler = BrownianTreeNoiseSamplerPatched
ldm_patched.modules.samplers.sampling_function = patched_sampling_function