cxllm-vendors/stable-diffusion-webui-forge
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Merge branch 'main' into dev

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This commit is contained in:
lllyasviel
2024-01-27 15:48:30 -08:00
committed by GitHub
parent 67100a3dfe f78e5f783f
commit 40518feb6f
66 changed files with 2746 additions and 2892 deletions
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modules/sd_samplers_kdiffusion.py
+21
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@@ -7,6 +7,8 @@ from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
from modules.shared import opts
import modules.shared as shared
import ldm_patched.modules.model_management
samplers_k_diffusion = [
('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
@@ -139,11 +141,21 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
return sigmas
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
inference_memory = self.model_wrap.inner_model.current_controlnet_required_memory
unet_patcher = self.model_wrap.inner_model.forge_objects.unet
ldm_patched.modules.model_management.load_models_gpu(
[unet_patcher],
unet_patcher.memory_required([x.shape[0] * 2] + list(x.shape[1:])) + inference_memory)
self.model_wrap.log_sigmas = self.model_wrap.log_sigmas.to(unet_patcher.current_device)
self.model_wrap.sigmas = self.model_wrap.sigmas.to(unet_patcher.current_device)
steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
sigmas = self.get_sigmas(p, steps)
sigma_sched = sigmas[steps - t_enc - 1:]
x = x.to(noise)
xi = x + noise * sigma_sched[0]
if opts.img2img_extra_noise > 0:
@@ -192,6 +204,15 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
return samples
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
inference_memory = self.model_wrap.inner_model.current_controlnet_required_memory
unet_patcher = self.model_wrap.inner_model.forge_objects.unet
ldm_patched.modules.model_management.load_models_gpu(
[unet_patcher],
unet_patcher.memory_required([x.shape[0] * 2] + list(x.shape[1:])) + inference_memory)
self.model_wrap.log_sigmas = self.model_wrap.log_sigmas.to(unet_patcher.current_device)
self.model_wrap.sigmas = self.model_wrap.sigmas.to(unet_patcher.current_device)
steps = steps or p.steps
sigmas = self.get_sigmas(p, steps)