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update algorithm with better outpaint

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scraed
2025-06-21 12:56:36 +08:00
parent dbfc1585fc
commit 61c19ac31d
6 changed files with 59 additions and 16 deletions
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README.md
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@@ -14,6 +14,8 @@ This is the official implementation of ["Lanpaint: Training-Free Diffusion Inpai
- **No Workarounds** – Generates 100% new content (no blending or smoothing) without relying on partial denoising.
- **Beyond Inpainting** – You can even use it as a simple way to generate consistent characters.
**Warning**: LanPaint has degraded performance on distillation models, such as Flux.dev, due to a similar [issue with LORA training](https://medium.com/@zhiwangshi28/why-flux-lora-so-hard-to-train-and-how-to-overcome-it-a0c70bc59eaf). Please use low flux guidance (1.0-2.0) to mitigate this [issue](https://github.com/scraed/LanPaint/issues/30).
## How It Works
LanPaint uses Langevin Dynamics as "thinking" steps, which digs deeper into the diffusion process and allows the model to generate more consistent results.
@@ -43,6 +45,9 @@ Once installed, you'll find the LanPaint nodes under the "sampling" category in
## Updates
- 2025/06/21
- Update the algorithm with enhanced stability and outpaint performance.
- Add outpaint example
- 2025/06/04
- Add more sampler support.
- Add early stopping to advanced sampler.
@@ -67,6 +72,12 @@ All examples use a random seed 0 to generate batch of 4 images for fair comparis
You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.org/tutorials/image/hidream/hidream-i1) to download and install the model.
### Example HiDream: OutPaint(LanPaint K Sampler, 5 steps of thinking)
![Inpainting Result 8](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_13(1).jpg)
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_10)
You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.org/tutorials/image/hidream/hidream-i1) to download and install the model.
### Example SD 3.5: InPaint(LanPaint K Sampler, 5 steps of thinking)
![Inpainting Result 8](https://github.com/scraed/LanPaint/blob/master/examples/InpaintChara_12.jpg)
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_9)
examples/Example_10/InPainted_Drag_Me_to_ComfyUI.png
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src/LanPaint/lanpaint.py
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@@ -90,18 +90,37 @@ class LanPaint():
Gamma = Gamma_x * (1-mask) + Gamma_y * mask
def Coef_C(x_t):
x0 = self.x0_evalutation(x_t, score, sigma, args)
C = (abt**0.5 * x0 - x_t )/ (1-abt) + A * x_t
return C
def advance_time(x_t, v, dt, Gamma, A, C, D):
dtype = x_t.dtype
with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
osc = StochasticHarmonicOscillator(Gamma, A, C, D )
x_t, v = osc.dynamics(x_t, v, dt )
x_t = x_t.to(dtype)
v = v.to(dtype)
return x_t, v
if args is None:
#v = torch.zeros_like(x_t)
v = None
C = Coef_C(x_t)
#print(torch.squeeze(dtx), torch.squeeze(dty))
x_t, v = advance_time(x_t, v, dt, Gamma, A, C, D)
else:
v, = args
v, C = args
with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
osc = StochasticHarmonicOscillator(Gamma, A, C, D )
x_t, v = osc.dynamics(x_t, v, dt )
x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
C_new = Coef_C(x_t)
v = v + Gamma**0.5 * ( C_new - C) *dt
x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
C = C_new
return x_t, (v,)
return x_t, (v, C)
def prepare_step_size(self, current_times, step_size, sigma_x, sigma_y):
# -------------------------------------------------------------------------
@@ -125,7 +144,7 @@ class LanPaint():
Gamma_hat_y /= 2.
A_t_x = (1) / ( 1 - abt ) * dtx / 2
A_t_y = (1) / ( 1 - abt ) * dty / 2
A_t_y = (1+self.chara_lamb) / ( 1 - abt ) * dty / 2
A_x = A_t_x / (dtx/2)
A_y = A_t_y / (dty/2)
src/LanPaint/nodes.py
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@@ -260,7 +260,7 @@ class LanPaint_KSampler():
model.LanPaint_StepSize = 0.15
model.LanPaint_Lambda = 16.0
model.LanPaint_Beta = 0.5
model.LanPaint_Beta = 1.
model.LanPaint_NumSteps = LanPaint_NumSteps
model.LanPaint_Friction = 15.
model.LanPaint_EarlyStop = 1
@@ -288,7 +288,7 @@ class LanPaint_KSamplerAdvanced:
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
"return_with_leftover_noise": (["disable", "enable"], ),
"LanPaint_NumSteps": ("INT", {"default": 5, "min": 0, "max": 100, "tooltip": "The number of steps for the Langevin dynamics, representing the turns of thinking per step."}),
"LanPaint_Lambda": ("FLOAT", {"default": 8., "min": 0.1, "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The bidirectional guidance scale. Higher values align with known regions more closely, but may result in instability."}),
"LanPaint_Lambda": ("FLOAT", {"default": 16., "min": 0.1, "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The bidirectional guidance scale. Higher values align with known regions more closely, but may result in instability."}),
"LanPaint_StepSize": ("FLOAT", {"default": 0.15, "min": 0.0001, "max": 1., "step": 0.01, "round": 0.001, "tooltip": "The step size for the Langevin dynamics. Higher values result in faster convergence but may be unstable."}),
"LanPaint_Beta": ("FLOAT", {"default": 1., "min": 0.0001, "max": 5, "step": 0.1, "round": 0.1, "tooltip": "The step size ratio between masked / unmasked regions. Lower value can compensate high values of LanPaint_Lambda."}),
"LanPaint_Friction": ("FLOAT", {"default": 15, "min": 0., "max": 50.0, "step": 0.1, "round": 0.1, "tooltip": "The friction parameter for fast langevin, lower values result in faster convergence but may be unstable."}),
src/LanPaint/utils.py
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@@ -240,7 +240,6 @@ class StochasticHarmonicOscillator:
tuple: (y(t), v(t))
"""
dummyzero = y0.new_zeros(1) # convert scalar to tensor with same device and dtype as y0
Delta = self.Delta + dummyzero
Gamma_hat = self.Gamma * t + dummyzero
@@ -253,8 +252,8 @@ class StochasticHarmonicOscillator:
EE = 1 - Gamma_hat * zeta_2
if v0 is None:
#v0 = torch.randn_like(y0) * D / 2 ** 0.5
v0 = (C - A * y0)/Gamma**0.5
v0 = torch.randn_like(y0) * D / 2 ** 0.5
#v0 = (C - A * y0)/Gamma**0.5
# Calculate mean position and velocity
term1 = (1 - zeta_1) * (C * t - A * t * y0) + zeta_2 * (Gamma ** 0.5) * v0 * t
@@ -273,16 +272,30 @@ class StochasticHarmonicOscillator:
cov_matrix[..., 0, 1] = cov_yv
cov_matrix[..., 1, 0] = cov_yv # symmetric
cov_matrix[..., 1, 1] = cov_vv
# Compute the Cholesky decomposition to get scale_tril
#scale_tril = torch.linalg.cholesky(cov_matrix)
scale_tril = torch.zeros(*batch_shape, 2, 2, device=y0.device, dtype=y0.dtype)
tol = 1e-8
cov_yy = torch.clamp( cov_yy, min = tol )
sd_yy = torch.sqrt( cov_yy )
inv_sd_yy = 1/(sd_yy)
scale_tril[..., 0, 0] = sd_yy
scale_tril[..., 0, 1] = 0.
scale_tril[..., 1, 0] = cov_yv * inv_sd_yy
scale_tril[..., 1, 1] = torch.clamp( cov_vv - cov_yv**2 / cov_yy, min = tol ) ** 0.5
# check if it matches torch.linalg.
#assert torch.allclose(torch.linalg.cholesky(cov_matrix), scale_tril, atol = 1e-4, rtol = 1e-4 )
# Sample correlated noise from multivariate normal
mean = torch.zeros(*batch_shape, 2, device=y0.device, dtype=y0.dtype)
mean[..., 0] = y_mean
mean[..., 1] = v_mean
new_yv = torch.distributions.MultivariateNormal(
loc=mean,
covariance_matrix=cov_matrix
scale_tril=scale_tril
).sample()
return new_yv[...,0], new_yv[...,1]
return new_yv[...,0], new_yv[...,1]