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Initial port of controlnet node support from generator-based TextToImageInvocation node to latent-based TextToLatentsInvocation node

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user1 2023-04-30 07:44:50 -07:00 committed by Kent Keirsey
parent 113129daf9
commit 3a645c4e80
1 changed files with 58 additions and 1 deletions
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57
invokeai/app/invocations/latent.py
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@ -342,6 +342,63 @@ class TextToLatentsInvocation(BaseInvocation):
multi_control = MultiControlNetModel(control_models) multi_control = MultiControlNetModel(control_models)
model.control_model = multi_control model.control_model = multi_control
# loading controlnet model
if (self.control_model is None or self.control_model==''):
control_model = None
else:
# FIXME: change this to dropdown menu?
# FIXME: generalize so don't have to hardcode torch_dtype and device
control_model = ControlNetModel.from_pretrained(self.control_model,
torch_dtype=torch.float16).to("cuda")
model.control_model = control_model
# loading controlnet image (currently requires pre-processed image)
control_image = (
None if self.control_image is None
else context.services.images.get(
self.control_image.image_type, self.control_image.image_name
)
)
# copied from old backend/txt2img.py
# FIXME: still need to test with different widths, heights, devices, dtypes
# and add in batch_size, num_images_per_prompt?
if control_image is not None:
if isinstance(control_model, ControlNetModel):
control_image = model.prepare_control_image(
image=control_image,
# do_classifier_free_guidance=do_classifier_free_guidance,
do_classifier_free_guidance=True,
# width=width,
# height=height,
width=512,
height=512,
# batch_size=batch_size * num_images_per_prompt,
# num_images_per_prompt=num_images_per_prompt,
device=control_model.device,
dtype=control_model.dtype,
)
elif isinstance(control_model, MultiControlNetModel):
images = []
for image_ in control_image:
image_ = model.prepare_control_image(
image=image_,
# do_classifier_free_guidance=do_classifier_free_guidance,
do_classifier_free_guidance=True,
# width=width,
# height=height,
width=512,
height=512,
# batch_size=batch_size * num_images_per_prompt,
# num_images_per_prompt=num_images_per_prompt,
device=control_model.device,
dtype=control_model.dtype,
)
images.append(image_)
control_image = images
# TODO: Verify the noise is the right size # TODO: Verify the noise is the right size
result_latents, result_attention_map_saver = model.latents_from_embeddings( result_latents, result_attention_map_saver = model.latents_from_embeddings(
latents=torch.zeros_like(noise, dtype=torch_dtype(model.device)), latents=torch.zeros_like(noise, dtype=torch_dtype(model.device)),