Merge branch 'comfyanonymous:master' into master

comfy/k_diffusion/sampling.py

@@ -1336,3 +1336,26 @@ def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disab
 @torch.no_grad()
 def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None):
     return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise, noise_sampler=noise_sampler, cfg_pp=True)
+
+@torch.no_grad()
+def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
+    """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    old_d = None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        d = to_d(x, sigmas[i], denoised)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        dt = sigmas[i + 1] - sigmas[i]
+        if i == 0:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Gradient estimation
+            d_bar = ge_gamma * d + (1 - ge_gamma) * old_d
+            x = x + d_bar * dt
+        old_d = d
+    return x

comfy/samplers.py

@@ -686,7 +686,7 @@ class Sampler:
 KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
-                  "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp"]
+                  "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "gradient_estimation"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

comfy_extras/nodes_latent.py

@@ -2,10 +2,14 @@ import comfy.utils
 import comfy_extras.nodes_post_processing
 import torch
 
-def reshape_latent_to(target_shape, latent):
+
+def reshape_latent_to(target_shape, latent, repeat_batch=True):
     if latent.shape[1:] != target_shape[1:]:
-        latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center")
-    return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
+        latent = comfy.utils.common_upscale(latent, target_shape[-1], target_shape[-2], "bilinear", "center")
+    if repeat_batch:
+        return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
+    else:
+        return latent
 
 
 class LatentAdd:
@@ -116,8 +120,7 @@ class LatentBatch:
         s1 = samples1["samples"]
         s2 = samples2["samples"]
 
-        if s1.shape[1:] != s2.shape[1:]:
-            s2 = comfy.utils.common_upscale(s2, s1.shape[-1], s1.shape[-2], "bilinear", "center")
+        s2 = reshape_latent_to(s1.shape, s2, repeat_batch=False)
         s = torch.cat((s1, s2), dim=0)
         samples_out["samples"] = s
         samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])