preparing for merge into main

README.md

@@ -73,6 +73,14 @@ The --init_img (-I) option gives the path to the seed picture. --strength (-f) c
 the original will be modified, ranging from 0.0 (keep the original intact), to 1.0 (ignore the original
 completely). The default is 0.75, and ranges from 0.25-0.75 give interesting results.
 
+## Changes
+
+*v1.01 (21 August 2022)
+-added k_lms sampling **Please run "conda update -f environment.yaml" to load the k_lms dependencies**
+-*use half precision arithmetic by default, resulting in faster execution and lower memory requirements
+Pass argument --full_precision to dream.py to get slower but more accurate image generation
+
+
 ## Installation
 
 ### Linux/Mac

environment.yaml

@@ -25,7 +25,7 @@ dependencies:
     - torchmetrics==0.6.0
     - kornia==0.6
     - accelerate==0.12.0
-    - git+https://github.com/crowsonkb/k-diffusion.git@master
-    - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
     - -e git+https://github.com/openai/CLIP.git@main#egg=clip
+    - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
+    - -e git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion
     - -e .

ldm/models/diffusion/ksampler.py

@@ -1,12 +1,29 @@
 '''wrapper around part of Karen Crownson's k-duffsion library, making it call compatible with other Samplers'''
 import k_diffusion as K
+import torch
 import torch.nn as nn
+import accelerate
 
 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       = self.accelerator.device
+        self.schedule = schedule
+
         def forward(self, x, sigma, uncond, cond, cond_scale):
             x_in = torch.cat([x] * 2)
             sigma_in = torch.cat([sigma] * 2)
@@ -14,13 +31,6 @@ class CFGDenoiser(nn.Module):
             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
@@ -54,10 +64,10 @@ class KSampler(object):
         if x_T:
             x = x_T
         else:
-            x = torch.randn([batch_size, *shape], device=device) * sigmas[0] # for GPU draw
+            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.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args, disable=not accelerator.is_main_process),
+        return (K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args, disable=not self.accelerator.is_main_process),
                 None)
 
     def gather(samples_ddim):

ldm/simplet2i.py

@@ -108,6 +108,7 @@ class T2I:
                  ddim_eta=0.0,  # deterministic
                  fixed_code=False,
                  precision='autocast',
+                 full_precision=False,
                  strength=0.75 # default in scripts/img2img.py
     ):
         self.outdir     = outdir
@@ -126,6 +127,7 @@ class T2I:
         self.downsampling_factor = downsampling_factor
         self.ddim_eta            = ddim_eta
         self.precision           = precision
+        self.full_precision      = full_precision
         self.strength            = strength
         self.model      = None     # empty for now
         self.sampler    = None
@@ -407,7 +409,12 @@ class T2I:
         m, u = model.load_state_dict(sd, strict=False)
         model.cuda()
         model.eval()
-        model.half()
+        if self.full_precision:
+            print('Using slower but more accurate full precision math')
+            model.full()
+        else:
+            print('Using half precision math. Call with --full_precision to use full precision')
+            model.half()
         return model
 
     def _load_img(self,path):

scripts/dream.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import argparse
 import shlex
 import atexit
@@ -11,7 +12,7 @@ try:
 except:
     readline_available = False
 
-debugging = True
+debugging = False
 
 def main():
     ''' Initialize command-line parsers and the diffusion model '''
@@ -49,6 +50,7 @@ def main():
               outdir=opt.outdir,
               sampler=opt.sampler,
               weights=weights,
+              full_precision=opt.full_precision,
               config=config)
 
     # make sure the output directory exists
@@ -165,14 +167,18 @@ def create_argv_parser():
                         type=int,
                         default=1,
                         help="number of images to generate")
+    parser.add_argument('-F','--full_precision',
+                        dest='full_precision',
+                        action='store_true',
+                        help="use slower full precision math for calculations")
     parser.add_argument('-b','--batch_size',
                         type=int,
                         default=1,
                         help="number of images to produce per iteration (currently not working properly - producing too many images)")
     parser.add_argument('--sampler',
                         choices=['plms','ddim', 'klms'],
-                        default='plms',
-                        help="which sampler to use")
+                        default='klms',
+                        help="which sampler to use (klms)")
     parser.add_argument('-o',
                         '--outdir',
                         type=str,

scripts/preload_models.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 # Before running stable-diffusion on an internet-isolated machine,
 # run this script from one with internet connectivity. The
 # two machines must share a common .cache directory.