KeyError: 0

[adamfils2]

I get this error when inferring.

Iteration 0 | Loss: 0.2740
Iteration 1 | Loss: 0.0576
Iteration 2 | Loss: 0.1228
Iteration 3 | Loss: 0.0293
Iteration 4 | Loss: 0.0415
Iteration 5 | Loss: 0.0324
Iteration 6 | Loss: 0.0325
Iteration 7 | Loss: 0.0340
Iteration 8 | Loss: 0.0391
Iteration 9 | Loss: 0.0454
Iteration 10 | Loss: 0.0406
Iteration 11 | Loss: 0.0393
Iteration 12 | Loss: 0.0386
Iteration 13 | Loss: 0.0374
Iteration 14 | Loss: 0.0359
Iteration 15 | Loss: 0.0335
Iteration 16 | Loss: 0.0307
Iteration 17 | Loss: 0.0289
Iteration 18 | Loss: 0.0283
Iteration 19 | Loss: 0.0278
Iteration 20 | Loss: 0.0281
Iteration 21 | Loss: 0.0292
Iteration 22 | Loss: 0.0311
Iteration 23 | Loss: 0.0328
Iteration 24 | Loss: 0.0339

KeyError Traceback (most recent call last)
in
----> 1 generate_images_for_method(
2 prompt="a cat and a frog",
3 seeds=[6141],
4 indices_to_alter=[2,5],
5 is_attend_and_excite=True

8 frames
in generate_images_for_method(prompt, seeds, indices_to_alter, is_attend_and_excite)
10 controller = AttentionStore()
11 run_standard_sd = False if is_attend_and_excite else True
---> 12 image = run_and_display(prompts=prompts,
13 controller=controller,
14 indices_to_alter=token_indices,

in run_and_display(prompts, controller, indices_to_alter, generator, run_standard_sd, scale_factor, thresholds, max_iter_to_alter, display_output)
17 thresholds=thresholds,
18 max_iter_to_alter=max_iter_to_alter)
---> 19 image = run_on_prompt(model=stable,
20 prompt=prompts,
21 controller=controller,

/content/excite/run.py in run_on_prompt(prompt, model, controller, token_indices, seed, config)
41 if controller is not None:
42 ptp_utils.register_attention_control(model, controller)
---> 43 outputs = model(prompt=prompt,
44 attention_store=controller,
45 indices_to_alter=token_indices,

/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
25 def decorate_context(*args, **kwargs):
26 with self.clone():
---> 27 return func(*args, **kwargs)
28 return cast(F, decorate_context)
29

/content/excite/pipeline_attend_and_excite.py in call(self, prompt, attention_store, indices_to_alter, attention_res, height, width, num_inference_steps, guidance_scale, eta, generator, latents, output_type, return_dict, max_iter_to_alter, run_standard_sd, thresholds, scale_factor, scale_range, smooth_attentions, sigma, kernel_size, **kwargs)
259 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
260
--> 261 outputs = self._prepare_output(latents, output_type, return_dict)
262 return outputs

/content/excite/pipeline_stable_diffusion.py in _prepare_output(self, latents, output_type, return_dict)
160
161 # run safety checker
--> 162 safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
163 image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
164

/usr/local/lib/python3.8/dist-packages/transformers/models/clip/feature_extraction_clip.py in call(self, images, return_tensors, **kwargs)
150 images = [self.convert_rgb(image) for image in images]
151 if self.do_resize and self.size is not None and self.resample is not None:
--> 152 images = [
153 self.resize(image=image, size=self.size, resample=self.resample, default_to_square=False)
154 for image in images

/usr/local/lib/python3.8/dist-packages/transformers/models/clip/feature_extraction_clip.py in (.0)
151 if self.do_resize and self.size is not None and self.resample is not None:
152 images = [
--> 153 self.resize(image=image, size=self.size, resample=self.resample, default_to_square=False)
154 for image in images
155 ]

/usr/local/lib/python3.8/dist-packages/transformers/image_utils.py in resize(self, image, size, resample, default_to_square, max_size)
282 # specified size only for the smallest edge
283 short, long = (width, height) if width <= height else (height, width)
--> 284 requested_new_short = size if isinstance(size, int) else size[0]
285
286 if short == requested_new_short:

KeyError: 0

[AttendAndExcite]

It seems like this happened at the very end of inference for the NSFW of SD (after generating the image with our method).
What version of diffusers are you using?

[AttendAndExcite]

@adamfils2 closing due to inactivity, please reopen if necessary