Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fixes

Browse Source
This commit is contained in:
Sergey Borisov 2023-07-18 16:51:16 +03:00
parent bc11296a5e
commit fbbc4b3f69
3 changed files with 16 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

13
invokeai/app/invocations/generate.py
View File

@ -146,9 +146,13 @@ class InpaintInvocation(BaseInvocation):
source_node_id=source_node_id,
)
def get_conditioning(self, context):
c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name)
uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
def get_conditioning(self, context, unet):
positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
c = positive_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype)
extra_conditioning_info = positive_cond_data.conditionings[0].extra_conditioning
negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
uc = negative_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype)
return (uc, c, extra_conditioning_info)
@ -209,7 +213,6 @@ class InpaintInvocation(BaseInvocation):
)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
conditioning = self.get_conditioning(context)
scheduler = get_scheduler(
context=context,
scheduler_info=self.unet.scheduler,
@ -217,6 +220,8 @@ class InpaintInvocation(BaseInvocation):
)
with self.load_model_old_way(context, scheduler) as model:
conditioning = self.get_conditioning(context, model.context.model.unet)
outputs = Inpaint(model).generate(
conditioning=conditioning,
scheduler=scheduler,

5
invokeai/app/invocations/latent.py
View File

@ -334,6 +334,8 @@ class TextToLatentsInvocation(BaseInvocation):
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
unet_info as unet:
noise = noise.to(device=unet.device, dtype=unet.dtype)
scheduler = get_scheduler(
context=context,
scheduler_info=self.unet.scheduler,
@ -425,6 +427,9 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
unet_info as unet:
noise = noise.to(device=unet.device, dtype=unet.dtype)
latent = latent.to(device=unet.device, dtype=unet.dtype)
scheduler = get_scheduler(
context=context,
scheduler_info=self.unet.scheduler,

4
invokeai/app/invocations/sdxl.py
View File

@ -305,7 +305,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
latents = latents.to(device=unet.device, dtype=unet.dtype)
with tqdm(total=self.steps) as progress_bar:
with tqdm(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
@ -351,7 +351,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
latents = latents.to(device=unet.device, dtype=unet.dtype)
with tqdm(total=self.steps) as progress_bar:
with tqdm(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
#latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents