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fix img2img by working around pytorch bug (#2458)

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horribly, temporarily send the vae to `.cpu()` so that good latents can
be produced

closes #2418
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Lincoln Stein 2023-01-31 21:46:05 -05:00 committed by GitHub
commit bd57793a65
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1 changed files with 10 additions and 1 deletions
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11
ldm/invoke/generator/diffusers_pipeline.py
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@ -597,11 +597,20 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_maps) output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_maps)
return self.check_for_safety(output, dtype=conditioning_data.dtype) return self.check_for_safety(output, dtype=conditioning_data.dtype)
def non_noised_latents_from_image(self, init_image, *, device, dtype): def non_noised_latents_from_image(self, init_image, *, device: torch.device, dtype):
init_image = init_image.to(device=device, dtype=dtype) init_image = init_image.to(device=device, dtype=dtype)
with torch.inference_mode(): with torch.inference_mode():
if device.type == 'mps':
# workaround for torch MPS bug that has been fixed in https://github.com/kulinseth/pytorch/pull/222
# TODO remove this workaround once kulinseth#222 is merged to pytorch mainline
self.vae.to('cpu')
init_image = init_image.to('cpu')
init_latent_dist = self.vae.encode(init_image).latent_dist init_latent_dist = self.vae.encode(init_image).latent_dist
init_latents = init_latent_dist.sample().to(dtype=dtype) # FIXME: uses torch.randn. make reproducible! init_latents = init_latent_dist.sample().to(dtype=dtype) # FIXME: uses torch.randn. make reproducible!
if device.type == 'mps':
self.vae.to(device)
init_latents = init_latents.to(device)
init_latents = 0.18215 * init_latents init_latents = 0.18215 * init_latents
return init_latents return init_latents