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multidiffusion-upscaler-for-automatic1111
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notify #317, #318; AttributeError: 'KDiffusionSampler' object has no attribute 'step'

pull/356/head
Kahsolt 2023-10-20 11:20:05 +08:00
parent aa47321b10
commit 60081979d0
3 changed files with 21 additions and 15 deletions
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28
tile_methods/abstractdiffusion.py
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@ -28,8 +28,8 @@ class AbstractDiffusion:
# fix. Kdiff 'AND' support and image editing model support
if self.is_kdiff and not hasattr(self, 'is_edit_model'):
self.is_edit_model = (shared.sd_model.cond_stage_key == "edit" # "txt"
and self.sampler.image_cfg_scale is not None
and self.sampler.image_cfg_scale != 1.0)
and self.sampler.model_wrap_cfg.image_cfg_scale is not None
and self.sampler.model_wrap_cfg.image_cfg_scale != 1.0)
# cache. final result of current sampling step, [B, C=4, H//8, W//8]
# avoiding overhead of creating new tensors and weight summing
@ -249,8 +249,9 @@ class AbstractDiffusion:
icond = icond[bbox.slicer]
image_conditioning = icond
tensor = Condition.reconstruct_cond(custom_cond, self.sampler.step)
custom_uncond = Condition.reconstruct_uncond(custom_uncond, self.sampler.step)
sampler_step = self.sampler.model_wrap_cfg.step
tensor = Condition.reconstruct_cond(custom_cond, sampler_step)
custom_uncond = Condition.reconstruct_uncond(custom_uncond, sampler_step)
return tensor, custom_uncond, image_conditioning
@custom_bbox
@ -261,21 +262,22 @@ class AbstractDiffusion:
This can be extremely tricky.
'''
if self.kdiff_step != self.sampler.step:
self.kdiff_step = self.sampler.step
sampler_step = self.sampler.model_wrap_cfg.step
if self.kdiff_step != sampler_step:
self.kdiff_step = sampler_step
self.kdiff_step_bbox = [-1 for _ in range(len(self.custom_bboxes))]
self.tensor = {} # {int: Tensor[cond]}
self.uncond = {} # {int: Tensor[cond]}
self.image_cond_in = {}
# Initialize global prompts just for estimate the behavior of kdiff
self.real_tensor = Condition.reconstruct_cond(self.cond_basis, self.sampler.step)
self.real_uncond = Condition.reconstruct_uncond(self.uncond_basis, self.sampler.step)
self.real_tensor = Condition.reconstruct_cond(self.cond_basis, sampler_step)
self.real_uncond = Condition.reconstruct_uncond(self.uncond_basis, sampler_step)
# reset the progress for all bboxes
self.a = [0 for _ in range(len(self.custom_bboxes))]
if self.kdiff_step_bbox[bbox_id] != self.sampler.step:
if self.kdiff_step_bbox[bbox_id] != sampler_step:
# When a new step starts for a bbox, we need to judge whether the tensor is batched.
self.kdiff_step_bbox[bbox_id] = self.sampler.step
self.kdiff_step_bbox[bbox_id] = sampler_step
tensor, uncond, image_cond_in = self.reconstruct_custom_cond(original_cond, bbox.cond, bbox.uncond, bbox)
@ -710,13 +712,13 @@ class AbstractDiffusion:
}
cond_in = self.make_cond_dict(cond_dict_dummy, cond, self.p.image_conditioning)
sigmas = dnw.get_sigmas(steps).flip(0)
shared.state.sampling_steps = steps
state.sampling_steps = steps
pbar = tqdm(total=steps, desc='Noise Inversion')
for i in range(1, len(sigmas)):
if shared.state.interrupted:
if state.interrupted:
return x
shared.state.sampling_step += 1
state.sampling_step += 1
x_in = x
sigma_in = torch.cat([sigmas[i] * s_in])

4
tile_methods/multidiffusion.py
View File

@ -21,10 +21,14 @@ class MultiDiffusion(AbstractDiffusion):
# For K-Diffusion sampler with uniform prompt, we hijack into the inner model for simplicity
# Otherwise, the masked-redraw will break due to the init_latent
self.sampler: KDiffusionSampler
self.sampler.model_wrap_cfg: CFGDenoiserKDiffusion
self.sampler.model_wrap_cfg.inner_model: Union[CompVisDenoiser, CompVisVDenoiser]
self.sampler_forward = self.sampler.model_wrap_cfg.inner_model.forward
self.sampler.model_wrap_cfg.inner_model.forward = self.kdiff_forward
else:
self.sampler: CompVisSampler
self.sampler.model_wrap_cfg: CFGDenoiserTimesteps
self.sampler.model_wrap_cfg.inner_model: Union[CompVisTimestepsDenoiser, CompVisTimestepsVDenoiser]
self.sampler_forward = self.sampler.model_wrap_cfg.inner_model.forward
self.sampler.model_wrap_cfg.inner_model.forward = self.ddim_forward

4
tile_utils/typing.py
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@ -10,8 +10,8 @@ from ldm.models.diffusion.ddpm import LatentDiffusion
from modules.processing import StableDiffusionProcessing as Processing, StableDiffusionProcessingImg2Img as ProcessingImg2Img, Processed
from modules.prompt_parser import MulticondLearnedConditioning, ScheduledPromptConditioning
from modules.extra_networks import ExtraNetworkParams
from modules.sd_samplers_kdiffusion import KDiffusionSampler, CFGDenoiser
from modules.sd_samplers_timesteps import CompVisSampler, CompVisTimestepsDenoiser, CompVisTimestepsVDenoiser
from modules.sd_samplers_kdiffusion import KDiffusionSampler, CFGDenoiserKDiffusion, CFGDenoiser
from modules.sd_samplers_timesteps import CompVisSampler, CFGDenoiserTimesteps, CompVisTimestepsDenoiser, CompVisTimestepsVDenoiser
ModuleType = type(sys)