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flip and fix h/v coords for regions

This commit is contained in:
Sean Sube 2023-11-05 19:25:13 -06:00
parent 911f87f7ec
commit 05f63a32b7
Signed by: ssube
GPG Key ID: 3EED7B957D362AF1
1 changed files with 16 additions and 15 deletions
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31
api/onnx_web/diffusers/pipelines/panorama_xl.py
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@ -450,29 +450,30 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
logger.debug("running region prompt: %s, %s, %s, %s, %s, %s", top, left, bottom, right, mult, prompt)
# convert coordinates to latent space
h_start = top // 8
h_end = bottom // 8
w_start = left // 8
w_end = right // 8
h_start = left // 8
h_end = right // 8
w_start = top // 8
w_end = bottom // 8
# get the latents corresponding to the current view coordinates
latents_for_view = latents[:, :, h_start:h_end, w_start:w_end]
latents_for_region = latents[:, :, h_start:h_end, w_start:w_end]
logger.trace("region latent shape: %s", latents_for_region.shape)
# expand the latents if we are doing classifier free guidance
latent_model_input = (
np.concatenate([latents_for_view] * 2)
latent_region_input = (
np.concatenate([latents_for_region] * 2)
if do_classifier_free_guidance
else latents_for_view
else latents_for_region
)
latent_model_input = self.scheduler.scale_model_input(
torch.from_numpy(latent_model_input), t
latent_region_input = self.scheduler.scale_model_input(
torch.from_numpy(latent_region_input), t
)
latent_model_input = latent_model_input.cpu().numpy()
latent_region_input = latent_region_input.cpu().numpy()
# predict the noise residual
timestep = np.array([t], dtype=timestep_dtype)
noise_pred = self.unet(
sample=latent_model_input,
sample=latent_region_input,
timestep=timestep,
encoder_hidden_states=region_embeds[i],
text_embeds=add_region_embeds[i],
@ -498,12 +499,12 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
scheduler_output = self.scheduler.step(
torch.from_numpy(noise_pred),
t,
torch.from_numpy(latents_for_view),
torch.from_numpy(latents_for_region),
**extra_step_kwargs,
)
latents_view_denoised = scheduler_output.prev_sample.numpy()
latents_region_denoised = scheduler_output.prev_sample.numpy()
value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised * mult
value[:, :, h_start:h_end, w_start:w_end] += latents_region_denoised * mult
count[:, :, h_start:h_end, w_start:w_end] += mult
# take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113