first attempt to fold k_lms changes proposed by hwharrison and bmaltais

ldm/models/diffusion/ksampler.py

@@ -0,0 +1,64 @@
+'''wrapper around part of Karen Crownson's k-duffsion library, making it call compatible with other Samplers'''
+import k_diffusion as K
+import torch.nn as nn
+
+class CFGDenoiser(nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+
+        def forward(self, x, sigma, uncond, cond, cond_scale):
+            x_in = torch.cat([x] * 2)
+            sigma_in = torch.cat([sigma] * 2)
+            cond_in = torch.cat([uncond, cond])
+            uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
+            return uncond + (cond - uncond) * cond_scale
+
+class KSampler(object):
+    def __init__(self,model,schedule="lms", **kwargs):
+        super().__init__()
+        self.model        = K.external.CompVisDenoiser(model)
+        self.accelerator  = accelerate.Accelerator()
+        self.device       = accelerator.device
+        self.schedule = schedule
+
+    # most of these arguments are ignored and are only present for compatibility with
+    # other samples
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               **kwargs
+               ):
+
+        sigmas = self.model.get_sigmas(S)
+        if x_T:
+            x = x_T
+        else:
+            x = torch.randn([batch_size, *shape], device=device) * sigmas[0] # for GPU draw
+        model_wrap_cfg = CFGDenoiser(self.model)
+        extra_args = {'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}
+        return (K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args, disable=not accelerator.is_main_process),
+                None)
+
+    def gather(samples_ddim):
+        return self.accelerator.gather(samples_ddim)

ldm/simplet2i.py

@@ -11,7 +11,7 @@ t2i = T2I(outdir      = <path>        // outputs/txt2img-samples
           batch_size       = <integer>     // how many images to generate per sampling (1)
           steps       = <integer>     // 50
           seed        = <integer>     // current system time
-          sampler     = ['ddim','plms']  // ddim
+          sampler     = ['ddim','plms','klms']  // klms
           grid        = <boolean>     // false
           width       = <integer>     // image width, multiple of 64 (512)
           height      = <integer>     // image height, multiple of 64 (512)
@@ -62,8 +62,9 @@ import time
 import math
 
 from ldm.util import instantiate_from_config
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.plms import PLMSSampler
+from ldm.models.diffusion.ddim     import DDIMSampler
+from ldm.models.diffusion.plms     import PLMSSampler
+from ldm.models.diffusion.ksampler import KSampler
 
 class T2I:
     """T2I class
@@ -101,7 +102,7 @@ class T2I:
                  cfg_scale=7.5,
                  weights="models/ldm/stable-diffusion-v1/model.ckpt",
                  config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml",
-                 sampler="plms",
+                 sampler="klms",
                  latent_channels=4,
                  downsampling_factor=8,
                  ddim_eta=0.0,  # deterministic
@@ -387,6 +388,9 @@ class T2I:
             elif self.sampler_name == 'ddim':
                 print("setting sampler to ddim")
                 self.sampler = DDIMSampler(self.model)
+            elif self.sampler_name == 'klms':
+                print("setting sampler to klms")
+                self.sampler = KSampler(self.model,'lms')
             else:
                 print(f"unsupported sampler {self.sampler_name}, defaulting to plms")
                 self.sampler = PLMSSampler(self.model)