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

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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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21
modules/sd_samplers_timesteps.py
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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_timesteps = [
('DDIM', sd_samplers_timesteps_impl.ddim, ['ddim'], {}),
@@ -54,6 +56,7 @@ class CFGDenoiserTimesteps(CFGDenoiser):
def get_pred_x0(self, x_in, x_out, sigma):
ts = sigma.to(dtype=int)
self.alphas = self.alphas.to(ts.device)
a_t = self.alphas[ts][:, None, None, None]
sqrt_one_minus_at = (1 - a_t).sqrt()
@@ -95,6 +98,14 @@ class CompVisSampler(sd_samplers_common.Sampler):
return timesteps
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.inner_model.alphas_cumprod = self.model_wrap.inner_model.alphas_cumprod.to(unet_patcher.current_device)
steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
timesteps = self.get_timesteps(p, steps)
@@ -104,7 +115,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
sqrt_alpha_cumprod = torch.sqrt(alphas_cumprod[timesteps[t_enc]])
sqrt_one_minus_alpha_cumprod = torch.sqrt(1 - alphas_cumprod[timesteps[t_enc]])
xi = x * sqrt_alpha_cumprod + noise * sqrt_one_minus_alpha_cumprod
xi = x.to(noise) * sqrt_alpha_cumprod + noise * sqrt_one_minus_alpha_cumprod
if opts.img2img_extra_noise > 0:
p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
@@ -138,6 +149,14 @@ class CompVisSampler(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.inner_model.alphas_cumprod = self.model_wrap.inner_model.alphas_cumprod.to(unet_patcher.current_device)
steps = steps or p.steps
timesteps = self.get_timesteps(p, steps)