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10 changes: 8 additions & 2 deletions src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
Original file line number Diff line number Diff line change
Expand Up @@ -1104,15 +1104,22 @@ def __call__(
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}

# controlnet(s) inference
if guess_mode and do_classifier_free_guidance:
# Infer ControlNet only for the conditional batch.
control_model_input = latents
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
controlnet_added_cond_kwargs = {
"text_embeds": add_text_embeds.chunk(2)[1],
"time_ids": add_time_ids.chunk(2)[1],
}
else:
control_model_input = latent_model_input
controlnet_prompt_embeds = prompt_embeds
controlnet_added_cond_kwargs = added_cond_kwargs

if isinstance(controlnet_keep[i], list):
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
Expand All @@ -1122,15 +1129,14 @@ def __call__(
controlnet_cond_scale = controlnet_cond_scale[0]
cond_scale = controlnet_cond_scale * controlnet_keep[i]

added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
down_block_res_samples, mid_block_res_sample = self.controlnet(
control_model_input,
t,
encoder_hidden_states=controlnet_prompt_embeds,
controlnet_cond=image,
conditioning_scale=cond_scale,
guess_mode=guess_mode,
added_cond_kwargs=added_cond_kwargs,
added_cond_kwargs=controlnet_added_cond_kwargs,
return_dict=False,
)

Expand Down
22 changes: 22 additions & 0 deletions tests/pipelines/controlnet/test_controlnet_sdxl.py
Original file line number Diff line number Diff line change
Expand Up @@ -300,6 +300,28 @@ def test_stable_diffusion_xl_prompt_embeds(self):
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4

def test_controlnet_sdxl_guess(self):
device = "cpu"

components = self.get_dummy_components()

sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)

sd_pipe.set_progress_bar_config(disable=None)

inputs = self.get_dummy_inputs(device)
inputs["guess_mode"] = True

output = sd_pipe(**inputs)
image_slice = output.images[0, -3:, -3:, -1]
expected_slice = np.array(
[0.7330834, 0.590667, 0.5667336, 0.6029023, 0.5679491, 0.5968194, 0.4032986, 0.47612396, 0.5089609]
)

# make sure that it's equal
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4


class StableDiffusionXLMultiControlNetPipelineFastTests(
PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
Expand Down