finish

scripts/txt2img.py

@@ -19,8 +19,10 @@ from ldm.models.diffusion.plms import PLMSSampler
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from transformers import AutoFeatureExtractor
 
-feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-v-1-3", use_auth_token=True)
-safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-v-1-3", use_auth_token=True)
+# load safety model
+safety_model_id = "CompVis/stable-diffusion-v-1-3"
+safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id, use_auth_token=True)
+safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id, use_auth_token=True)
 
 def chunk(it, size):
     it = iter(it)
@@ -266,16 +268,23 @@ def main():
 
                         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)
+                        x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
+
+                        x_image = x_samples_ddim
+                        safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
+                        x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
+
+                        x_checked_image_torch = torch.from_numpy(x_checked_image).permute(0, 3, 2, 1)
 
                         if not opt.skip_save:
-                            for x_sample in x_samples_ddim:
+                            for x_sample in x_checked_image_torch:
                                 x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
                                 Image.fromarray(x_sample.astype(np.uint8)).save(
                                     os.path.join(sample_path, f"{base_count:05}.png"))
                                 base_count += 1
 
                         if not opt.skip_grid:
-                            all_samples.append(x_samples_ddim)
+                            all_samples.append(x_checked_image_torch)
 
                 if not opt.skip_grid:
                     # additionally, save as grid
@@ -288,12 +297,6 @@ def main():
                     Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
                     grid_count += 1
 
-                image = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
-
-                # run safety checker
-                safety_checker_input = pipe.feature_extractor(numpy_to_pil(image), return_tensors="pt")
-                image, has_nsfw_concept = pipe.safety_checker(images=image, clip_input=safety_checker_input.pixel_values)
-
     print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
           f" \nEnjoy.")