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fix a bug in 2nd order schedulers when using in ensemble of experts config #5511

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Oct 25, 2023
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fix a bug in 2nd order schedulers when using in ensemble of experts config #5511

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4b1e7d2
fix
Oct 24, 2023
8ea162b
fix copies
Oct 24, 2023
5a0ac58
remove heun from tests
Oct 24, 2023
443aa6b
add back heun and fix the tests to include 2nd order
Oct 24, 2023
de85e0b
fix the other test too
Oct 24, 2023
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patrickvonplaten Oct 25, 2023
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8 changes: 6 additions & 2 deletions src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -867,8 +867,12 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
- (denoising_start * self.scheduler.config.num_train_timesteps)
)
)
timesteps = list(filter(lambda ts: ts < discrete_timestep_cutoff, timesteps))
return torch.tensor(timesteps), len(timesteps)

num_inference_steps = (timesteps < discrete_timestep_cutoff).sum()
if self.scheduler.order == 2:
num_inference_steps = num_inference_steps + 1
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I don't think num_inference_steps will always be even. Consider the case where the scheduler has been set up by calling set_timesteps(25) and then supplied denoising_start = 0.6. The following code prints an even number:

sched = SCHEDULERS["DPM2"]
sched.set_timesteps(25)
denoising_start = 0.6
discrete_timestep_cutoff = int(
    round(
        sched.config.num_train_timesteps
        - (denoising_start * sched.config.num_train_timesteps)
    )
)
num_inference_steps = (sched.timesteps < discrete_timestep_cutoff).sum().item()
print(num_inference_steps)

This prints 20. For denoising_start=0.5, it prints 25. So num_inference_steps may be either odd or even depending on num_inference_steps and denoising_start passed into the pipeline call.

I think we should add 1 to it only if it's even, and not if it's odd. No?

@yiyixuxu @patrickvonplaten

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Ah DPM2 can actually be different indeed if it interpolates, (Heun can't)

Nice catch! I'll open a PR to fix it

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This is the result I get with hnf = 0.5 and steps = 25 with DPM2 with the code from this PR:
image

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#5526 (comment)

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Thanks @patrickvonplaten, all testcases I threw at it now produce good images.

I am just curious why we don't need to do any logic in the base step (where denoising_end is supplied to the base SDXL pipeline without img2img) since I would expect that we would want to ensure that the final timestep there is a second order timestep, and as far as I can tell it still has the odd/even issue when splitting. Maybe I am wrong!

In any case, latest main now produces good images for all my testcases like I said. Thank you! 🙏

timesteps = timesteps[-num_inference_steps:]
return timesteps, num_inference_steps

return timesteps, num_inference_steps - t_start

Expand Down
8 changes: 6 additions & 2 deletions src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -515,8 +515,12 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
- (denoising_start * self.scheduler.config.num_train_timesteps)
)
)
timesteps = list(filter(lambda ts: ts < discrete_timestep_cutoff, timesteps))
return torch.tensor(timesteps), len(timesteps)

num_inference_steps = (timesteps < discrete_timestep_cutoff).sum()
if self.scheduler.order == 2:
num_inference_steps = num_inference_steps + 1
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timesteps = timesteps[-num_inference_steps:]
return timesteps, num_inference_steps

return timesteps, num_inference_steps - t_start

Expand Down
8 changes: 6 additions & 2 deletions src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -799,8 +799,12 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
- (denoising_start * self.scheduler.config.num_train_timesteps)
)
)
timesteps = list(filter(lambda ts: ts < discrete_timestep_cutoff, timesteps))
return torch.tensor(timesteps), len(timesteps)

num_inference_steps = (timesteps < discrete_timestep_cutoff).sum()
if self.scheduler.order == 2:
num_inference_steps = num_inference_steps + 1
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timesteps = timesteps[-num_inference_steps:]
return timesteps, num_inference_steps

return timesteps, num_inference_steps - t_start

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26 changes: 18 additions & 8 deletions tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py
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Expand Up @@ -324,8 +324,13 @@ class scheduler_cls(scheduler_cls_orig):
pipe_1.scheduler.set_timesteps(num_steps)
expected_steps = pipe_1.scheduler.timesteps.tolist()

expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss))
if pipe_1.scheduler.order == 2:
expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split, expected_tss))
expected_steps = expected_steps_1 + expected_steps_2
else:
expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss))

# now we monkey patch step `done_steps`
# list into the step function for testing
Expand Down Expand Up @@ -607,13 +612,18 @@ class scheduler_cls(scheduler_cls_orig):

split_1_ts = num_train_timesteps - int(round(num_train_timesteps * split_1))
split_2_ts = num_train_timesteps - int(round(num_train_timesteps * split_2))
expected_steps_1 = expected_steps[:split_1_ts]
expected_steps_2 = expected_steps[split_1_ts:split_2_ts]
expected_steps_3 = expected_steps[split_2_ts:]

expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
expected_steps_2 = list(filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps))
expected_steps_3 = list(filter(lambda ts: ts < split_2_ts, expected_steps))
if pipe_1.scheduler.order == 2:
expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
expected_steps_2 = expected_steps_1[-1:] + list(
filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps)
)
expected_steps_3 = expected_steps_2[-1:] + list(filter(lambda ts: ts < split_2_ts, expected_steps))
expected_steps = expected_steps_1 + expected_steps_2 + expected_steps_3
else:
expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
expected_steps_2 = list(filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps))
expected_steps_3 = list(filter(lambda ts: ts < split_2_ts, expected_steps))

# now we monkey patch step `done_steps`
# list into the step function for testing
Expand Down
28 changes: 18 additions & 10 deletions tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -318,11 +318,14 @@ class scheduler_cls(scheduler_cls_orig):
expected_steps = pipe_1.scheduler.timesteps.tolist()

split_ts = num_train_timesteps - int(round(num_train_timesteps * split))
expected_steps_1 = expected_steps[:split_ts]
expected_steps_2 = expected_steps[split_ts:]

expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps))
expected_steps_2 = list(filter(lambda ts: ts < split_ts, expected_steps))
if pipe_1.scheduler.order == 2:
expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps))
expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split_ts, expected_steps))
expected_steps = expected_steps_1 + expected_steps_2
else:
expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps))
expected_steps_2 = list(filter(lambda ts: ts < split_ts, expected_steps))

# now we monkey patch step `done_steps`
# list into the step function for testing
Expand Down Expand Up @@ -389,13 +392,18 @@ class scheduler_cls(scheduler_cls_orig):

split_1_ts = num_train_timesteps - int(round(num_train_timesteps * split_1))
split_2_ts = num_train_timesteps - int(round(num_train_timesteps * split_2))
expected_steps_1 = expected_steps[:split_1_ts]
expected_steps_2 = expected_steps[split_1_ts:split_2_ts]
expected_steps_3 = expected_steps[split_2_ts:]

expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
expected_steps_2 = list(filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps))
expected_steps_3 = list(filter(lambda ts: ts < split_2_ts, expected_steps))
if pipe_1.scheduler.order == 2:
expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
expected_steps_2 = expected_steps_1[-1:] + list(
filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps)
)
expected_steps_3 = expected_steps_2[-1:] + list(filter(lambda ts: ts < split_2_ts, expected_steps))
expected_steps = expected_steps_1 + expected_steps_2 + expected_steps_3
else:
expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
expected_steps_2 = list(filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps))
expected_steps_3 = list(filter(lambda ts: ts < split_2_ts, expected_steps))

# now we monkey patch step `done_steps`
# list into the step function for testing
Expand Down
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