Remove accelerate library

This library is not required to use k-diffusion
Make k-diffusion wrapper closer to the other samplers

environment.yaml

@@ -10,7 +10,6 @@ dependencies:
   - torchvision=0.12.0
   - numpy=1.19.2
   - pip:
-    - accelerate==0.12.0
     - albumentations==0.4.3
     - opencv-python==4.1.2.30
     - pudb==2019.2

ldm/models/diffusion/ksampler.py

@@ -2,7 +2,6 @@
 import k_diffusion as K
 import torch
 import torch.nn as nn
-import accelerate
 
 class CFGDenoiser(nn.Module):
     def __init__(self, model):
@@ -17,12 +16,11 @@ class CFGDenoiser(nn.Module):
         return uncond + (cond - uncond) * cond_scale
 
 class KSampler(object):
-    def __init__(self,model,schedule="lms", **kwargs):
+    def __init__(self, model, schedule="lms", device="cuda", **kwargs):
         super().__init__()
         self.model = K.external.CompVisDenoiser(model)
-        self.accelerator  = accelerate.Accelerator()
-        self.device       = self.accelerator.device
         self.schedule = schedule
+        self.device = device
 
         def forward(self, x, sigma, uncond, cond, cond_scale):
             x_in = torch.cat([x] * 2)
@@ -67,8 +65,5 @@ class KSampler(object):
             x = torch.randn([batch_size, *shape], device=self.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.__dict__[f'sample_{self.schedule}'](model_wrap_cfg, x, sigmas, extra_args=extra_args, disable=not self.accelerator.is_main_process),
+        return (K.sampling.__dict__[f'sample_{self.schedule}'](model_wrap_cfg, x, sigmas, extra_args=extra_args),
                 None)
-
-    def gather(samples_ddim):
-        return self.accelerator.gather(samples_ddim)

ldm/simplet2i.py

@@ -467,17 +467,17 @@ The vast majority of these arguments default to reasonable values.
             elif self.sampler_name == 'ddim':
                 self.sampler = DDIMSampler(self.model, device=self.device)
             elif self.sampler_name == 'k_dpm_2_a':
-                self.sampler = KSampler(self.model,'dpm_2_ancestral')
+                self.sampler = KSampler(self.model, 'dpm_2_ancestral', device=self.device)
             elif self.sampler_name == 'k_dpm_2':
-                self.sampler = KSampler(self.model,'dpm_2')
+                self.sampler = KSampler(self.model, 'dpm_2', device=self.device)
             elif self.sampler_name == 'k_euler_a':
-                self.sampler = KSampler(self.model,'euler_ancestral')
+                self.sampler = KSampler(self.model, 'euler_ancestral', device=self.device)
             elif self.sampler_name == 'k_euler':
-                self.sampler = KSampler(self.model,'euler')
+                self.sampler = KSampler(self.model, 'euler', device=self.device)
             elif self.sampler_name == 'k_heun':
-                self.sampler = KSampler(self.model,'heun')
+                self.sampler = KSampler(self.model, 'heun', device=self.device)
             elif self.sampler_name == 'k_lms':
-                self.sampler = KSampler(self.model,'lms')
+                self.sampler = KSampler(self.model, 'lms', device=self.device)
             else:
                 msg = f'unsupported sampler {self.sampler_name}, defaulting to plms'
                 self.sampler = PLMSSampler(self.model, device=self.device)

requirements.txt

@@ -1,4 +1,3 @@
-accelerate==0.12.0
 albumentations==0.4.3
 einops==0.3.0
 huggingface-hub==0.8.1

scripts/orig_scripts/txt2img.py

@@ -12,7 +12,6 @@ from pytorch_lightning import seed_everything
 from torch import autocast
 from contextlib import contextmanager, nullcontext
 
-import accelerate
 import k_diffusion as K
 import torch.nn as nn
 
@@ -201,8 +200,6 @@ def main():
 
     #for klms
     model_wrap = K.external.CompVisDenoiser(model)
-    accelerator = accelerate.Accelerator()
-    device = accelerator.device
     class CFGDenoiser(nn.Module):
         def __init__(self, model):
             super().__init__()
@@ -251,8 +248,8 @@ def main():
             with model.ema_scope():
                 tic = time.time()
                 all_samples = list()
-                for n in trange(opt.n_iter, desc="Sampling", disable =not accelerator.is_main_process):
-                    for prompts in tqdm(data, desc="data", disable =not accelerator.is_main_process):
+                for n in trange(opt.n_iter, desc="Sampling"):
+                    for prompts in tqdm(data, desc="data"):
                         uc = None
                         if opt.scale != 1.0:
                             uc = model.get_learned_conditioning(batch_size * [""])
@@ -279,14 +276,11 @@ def main():
                                 x = torch.randn([opt.n_samples, *shape], device=device) * sigmas[0] # for GPU draw
                             model_wrap_cfg = CFGDenoiser(model_wrap)
                             extra_args = {'cond': c, 'uncond': uc, 'cond_scale': opt.scale}
-                            samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args, disable=not accelerator.is_main_process)
+                            samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args)
                         
                         x_samples_ddim = model.decode_first_stage(samples_ddim)
                         x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
-                        if opt.klms:
-                            x_sample = accelerator.gather(x_samples_ddim)
-
                         if not opt.skip_save:
                             for x_sample in x_samples_ddim:
                                 x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')