feat(txt2img): allow from_file to work with len(lines) < batch_size (#349)

Author: mgcrea

scripts/orig_scripts/txt2img.py

@@ -232,7 +232,12 @@ def forward(self, x, sigma, uncond, cond, cond_scale):
         print(f"reading prompts from {opt.from_file}")
         with open(opt.from_file, "r") as f:
             data = f.read().splitlines()
-            data = list(chunk(data, batch_size))
+            if (len(data) >= batch_size):
+                data = list(chunk(data, batch_size))
+            else:
+                while (len(data) < batch_size):
+                    data.append(data[-1])
+                data = [data]
 
     sample_path = os.path.join(outpath, "samples")
     os.makedirs(sample_path, exist_ok=True)
@@ -264,7 +269,7 @@ def forward(self, x, sigma, uncond, cond, cond_scale):
                             prompts = list(prompts)
                         c = model.get_learned_conditioning(prompts)
                         shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
-                        
+
                         if not opt.klms:
                             samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
                                                             conditioning=c,
@@ -284,7 +289,7 @@ def forward(self, x, sigma, uncond, cond, cond_scale):
                             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)
-                        
+
                         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)