wangbo/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2026-02-10 13:32:36 +08:00
Code Issues Actions 13 Packages Projects Releases Wiki Activity

Merge branch 'comfyanonymous:master' into refactor/execution

Browse Source
This commit is contained in:
Dr.Lt.Data 2023-06-30 10:47:20 +09:00 committed by GitHub
commit 9aed6cd94c
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 10 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
comfy/model_management.py
View File

@ -216,6 +216,11 @@ current_gpu_controlnets = []
model_accelerated = False
def unet_offload_device():
if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED:
return get_torch_device()
else:
return torch.device("cpu")
def unload_model():
global current_loaded_model
@ -228,10 +233,9 @@ def unload_model():
accelerate.hooks.remove_hook_from_submodules(current_loaded_model.model)
model_accelerated = False
#never unload models from GPU on high vram
if vram_state != VRAMState.HIGH_VRAM:
current_loaded_model.model.cpu()
current_loaded_model.model_patches_to("cpu")
current_loaded_model.model.to(unet_offload_device())
current_loaded_model.model_patches_to(unet_offload_device())
current_loaded_model.unpatch_model()
current_loaded_model = None

1
comfy/sd.py
View File

@ -1142,6 +1142,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
model = model_config.get_model(sd)
model = model.to(model_management.unet_offload_device())
model.load_model_weights(sd, "model.diffusion_model.")
if output_vae:

2
comfy/sd1_clip.py
View File

@ -95,7 +95,7 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
out_tokens += [tokens_temp]
if len(embedding_weights) > 0:
new_embedding = torch.nn.Embedding(next_new_token, current_embeds.weight.shape[1])
new_embedding = torch.nn.Embedding(next_new_token, current_embeds.weight.shape[1], device=self.device)
new_embedding.weight[:token_dict_size] = current_embeds.weight[:]
n = token_dict_size
for x in embedding_weights: