Add hidiffusion support
This commit is contained in:
Kijai
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from .hidiffusion import apply_hidiffusion, remove_hidiffusion
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__all__ = ["apply_hidiffusion", "remove_hidiffusion"]
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Load Diff
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conv_in
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time_proj
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time_embedding
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time_embedding.linear_1
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time_embedding.act
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time_embedding.linear_2
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down_blocks
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|
||||
up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_out.0
|
||||
up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_out.1
|
||||
up_blocks.3.attentions.1.transformer_blocks.0.norm3
|
||||
up_blocks.3.attentions.1.transformer_blocks.0.ff
|
||||
up_blocks.3.attentions.1.transformer_blocks.0.ff.net
|
||||
up_blocks.3.attentions.1.transformer_blocks.0.ff.net.0
|
||||
up_blocks.3.attentions.1.transformer_blocks.0.ff.net.0.proj
|
||||
up_blocks.3.attentions.1.transformer_blocks.0.ff.net.1
|
||||
up_blocks.3.attentions.1.transformer_blocks.0.ff.net.2
|
||||
up_blocks.3.attentions.1.proj_out
|
||||
up_blocks.3.attentions.2
|
||||
up_blocks.3.attentions.2.norm
|
||||
up_blocks.3.attentions.2.proj_in
|
||||
up_blocks.3.attentions.2.transformer_blocks
|
||||
up_blocks.3.attentions.2.transformer_blocks.0
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.norm1
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn1
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_q
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_k
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_v
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_out
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_out.0
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_out.1
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.norm2
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn2
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_q
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_k
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_v
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.1
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.norm3
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.ff
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.ff.net
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.ff.net.0
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.ff.net.0.proj
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.ff.net.1
|
||||
up_blocks.3.attentions.2.transformer_blocks.0.ff.net.2
|
||||
up_blocks.3.attentions.2.proj_out
|
||||
up_blocks.3.resnets
|
||||
up_blocks.3.resnets.0
|
||||
up_blocks.3.resnets.0.norm1
|
||||
up_blocks.3.resnets.0.conv1
|
||||
up_blocks.3.resnets.0.time_emb_proj
|
||||
up_blocks.3.resnets.0.norm2
|
||||
up_blocks.3.resnets.0.dropout
|
||||
up_blocks.3.resnets.0.conv2
|
||||
up_blocks.3.resnets.0.conv_shortcut
|
||||
up_blocks.3.resnets.1
|
||||
up_blocks.3.resnets.1.norm1
|
||||
up_blocks.3.resnets.1.conv1
|
||||
up_blocks.3.resnets.1.time_emb_proj
|
||||
up_blocks.3.resnets.1.norm2
|
||||
up_blocks.3.resnets.1.dropout
|
||||
up_blocks.3.resnets.1.conv2
|
||||
up_blocks.3.resnets.1.conv_shortcut
|
||||
up_blocks.3.resnets.2
|
||||
up_blocks.3.resnets.2.norm1
|
||||
up_blocks.3.resnets.2.conv1
|
||||
up_blocks.3.resnets.2.time_emb_proj
|
||||
up_blocks.3.resnets.2.norm2
|
||||
up_blocks.3.resnets.2.dropout
|
||||
up_blocks.3.resnets.2.conv2
|
||||
up_blocks.3.resnets.2.conv_shortcut
|
||||
mid_block
|
||||
mid_block.attentions
|
||||
mid_block.attentions.0
|
||||
mid_block.attentions.0.norm
|
||||
mid_block.attentions.0.proj_in
|
||||
mid_block.attentions.0.transformer_blocks
|
||||
mid_block.attentions.0.transformer_blocks.0
|
||||
mid_block.attentions.0.transformer_blocks.0.norm1
|
||||
mid_block.attentions.0.transformer_blocks.0.attn1
|
||||
mid_block.attentions.0.transformer_blocks.0.attn1.to_q
|
||||
mid_block.attentions.0.transformer_blocks.0.attn1.to_k
|
||||
mid_block.attentions.0.transformer_blocks.0.attn1.to_v
|
||||
mid_block.attentions.0.transformer_blocks.0.attn1.to_out
|
||||
mid_block.attentions.0.transformer_blocks.0.attn1.to_out.0
|
||||
mid_block.attentions.0.transformer_blocks.0.attn1.to_out.1
|
||||
mid_block.attentions.0.transformer_blocks.0.norm2
|
||||
mid_block.attentions.0.transformer_blocks.0.attn2
|
||||
mid_block.attentions.0.transformer_blocks.0.attn2.to_q
|
||||
mid_block.attentions.0.transformer_blocks.0.attn2.to_k
|
||||
mid_block.attentions.0.transformer_blocks.0.attn2.to_v
|
||||
mid_block.attentions.0.transformer_blocks.0.attn2.to_out
|
||||
mid_block.attentions.0.transformer_blocks.0.attn2.to_out.0
|
||||
mid_block.attentions.0.transformer_blocks.0.attn2.to_out.1
|
||||
mid_block.attentions.0.transformer_blocks.0.norm3
|
||||
mid_block.attentions.0.transformer_blocks.0.ff
|
||||
mid_block.attentions.0.transformer_blocks.0.ff.net
|
||||
mid_block.attentions.0.transformer_blocks.0.ff.net.0
|
||||
mid_block.attentions.0.transformer_blocks.0.ff.net.0.proj
|
||||
mid_block.attentions.0.transformer_blocks.0.ff.net.1
|
||||
mid_block.attentions.0.transformer_blocks.0.ff.net.2
|
||||
mid_block.attentions.0.proj_out
|
||||
mid_block.resnets
|
||||
mid_block.resnets.0
|
||||
mid_block.resnets.0.norm1
|
||||
mid_block.resnets.0.conv1
|
||||
mid_block.resnets.0.time_emb_proj
|
||||
mid_block.resnets.0.norm2
|
||||
mid_block.resnets.0.dropout
|
||||
mid_block.resnets.0.conv2
|
||||
mid_block.resnets.1
|
||||
mid_block.resnets.1.norm1
|
||||
mid_block.resnets.1.conv1
|
||||
mid_block.resnets.1.time_emb_proj
|
||||
mid_block.resnets.1.norm2
|
||||
mid_block.resnets.1.dropout
|
||||
mid_block.resnets.1.conv2
|
||||
conv_norm_out
|
||||
conv_out
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,32 @@
|
||||
import torch
|
||||
|
||||
|
||||
def isinstance_str(x: object, cls_name: str):
|
||||
"""
|
||||
Checks whether x has any class *named* cls_name in its ancestry.
|
||||
Doesn't require access to the class's implementation.
|
||||
|
||||
Useful for patching!
|
||||
"""
|
||||
|
||||
for _cls in x.__class__.__mro__:
|
||||
if _cls.__name__ == cls_name:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def init_generator(device: torch.device, fallback: torch.Generator=None):
|
||||
"""
|
||||
Forks the current default random generator given device.
|
||||
"""
|
||||
if device.type == "cpu":
|
||||
return torch.Generator(device="cpu").set_state(torch.get_rng_state())
|
||||
elif device.type == "cuda":
|
||||
return torch.Generator(device=device).set_state(torch.cuda.get_rng_state())
|
||||
else:
|
||||
if fallback is None:
|
||||
return init_generator(torch.device("cpu"))
|
||||
else:
|
||||
return fallback
|
||||
|
||||
@@ -33,6 +33,8 @@ if is_accelerate_available():
|
||||
from accelerate import init_empty_weights
|
||||
from accelerate.utils import set_module_tensor_to_device
|
||||
|
||||
from .hidiffusion import apply_hidiffusion, remove_hidiffusion
|
||||
|
||||
from omegaconf import OmegaConf
|
||||
from .model import ELLA, T5TextEmbedder
|
||||
from transformers import CLIPTokenizer
|
||||
@@ -218,8 +220,224 @@ class ella_model_loader:
|
||||
}
|
||||
|
||||
return (ella_model,)
|
||||
|
||||
class diffusers_model_loader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"model": ("MODEL",),
|
||||
"clip": ("CLIP",),
|
||||
"vae": ("VAE",),
|
||||
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("DIFFUSERSMODEL",)
|
||||
RETURN_NAMES = ("diffusers_model",)
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "ELLA-Wrapper"
|
||||
|
||||
def loadmodel(self, model, clip, vae):
|
||||
mm.soft_empty_cache()
|
||||
dtype = mm.unet_dtype()
|
||||
vae_dtype = mm.vae_dtype()
|
||||
device = mm.get_torch_device()
|
||||
|
||||
custom_config = {
|
||||
'model': model,
|
||||
'vae': vae,
|
||||
}
|
||||
if not hasattr(self, 'model') or self.model == None or custom_config != self.current_config:
|
||||
pbar = comfy.utils.ProgressBar(5)
|
||||
self.current_config = custom_config
|
||||
# setup pretrained models
|
||||
original_config = OmegaConf.load(os.path.join(script_directory, f"configs/v1-inference.yaml"))
|
||||
|
||||
print("loading ELLA")
|
||||
checkpoint_path = os.path.join(folder_paths.models_dir,'ella')
|
||||
ella_path = os.path.join(checkpoint_path, 'ella-sd1.5-tsc-t5xl.safetensors')
|
||||
if not os.path.exists(ella_path):
|
||||
from huggingface_hub import snapshot_download
|
||||
snapshot_download(repo_id="QQGYLab/ELLA", local_dir=checkpoint_path, local_dir_use_symlinks=False)
|
||||
|
||||
with (init_empty_weights() if is_accelerate_available() else nullcontext()):
|
||||
converted_vae_config = create_vae_diffusers_config(original_config, image_size=512)
|
||||
new_vae = AutoencoderKL(**converted_vae_config)
|
||||
|
||||
converted_unet_config = create_unet_diffusers_config(original_config, image_size=512)
|
||||
unet = UNet2DConditionModel(**converted_unet_config)
|
||||
|
||||
clip_sd = None
|
||||
load_models = [model]
|
||||
load_models.append(clip.load_model())
|
||||
clip_sd = clip.get_sd()
|
||||
comfy.model_management.load_models_gpu(load_models)
|
||||
sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), None)
|
||||
|
||||
converted_vae = convert_ldm_vae_checkpoint(sd, converted_vae_config)
|
||||
if is_accelerate_available():
|
||||
for key in converted_vae:
|
||||
set_module_tensor_to_device(new_vae, key, device=device, dtype=dtype, value=converted_vae[key])
|
||||
else:
|
||||
new_vae.load_state_dict(converted_vae, strict=False)
|
||||
del converted_vae
|
||||
pbar.update(1)
|
||||
|
||||
converted_unet = convert_ldm_unet_checkpoint(sd, converted_unet_config)
|
||||
if is_accelerate_available():
|
||||
for key in converted_unet:
|
||||
set_module_tensor_to_device(unet, key, device=device, dtype=dtype, value=converted_unet[key])
|
||||
else:
|
||||
unet.load_state_dict(converted_unet, strict=False)
|
||||
del converted_unet
|
||||
|
||||
pbar.update(1)
|
||||
# 3. text_model
|
||||
print("loading text model")
|
||||
text_encoder = create_text_encoder_from_ldm_clip_checkpoint("openai/clip-vit-large-patch14",sd)
|
||||
scheduler_config = {
|
||||
'num_train_timesteps': 1000,
|
||||
'beta_start': 0.00085,
|
||||
'beta_end': 0.012,
|
||||
'beta_schedule': "scaled_linear",
|
||||
'steps_offset': 1
|
||||
}
|
||||
# 4. tokenizer
|
||||
tokenizer_path = os.path.join(script_directory, "configs/tokenizer")
|
||||
tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
|
||||
|
||||
scheduler=DPMSolverMultistepScheduler(**scheduler_config)
|
||||
pbar.update(1)
|
||||
del sd
|
||||
|
||||
pbar.update(1)
|
||||
|
||||
print("creating pipeline")
|
||||
self.pipe = StableDiffusionPipeline(
|
||||
unet=unet,
|
||||
vae=new_vae,
|
||||
text_encoder=text_encoder,
|
||||
tokenizer=tokenizer,
|
||||
scheduler=scheduler,
|
||||
safety_checker=None,
|
||||
feature_extractor=None,
|
||||
requires_safety_checker=False,
|
||||
image_encoder=None
|
||||
)
|
||||
print("pipeline created")
|
||||
pbar.update(1)
|
||||
self.pipe.enable_model_cpu_offload()
|
||||
diffusers_model = {
|
||||
'pipe': self.pipe,
|
||||
}
|
||||
|
||||
return (diffusers_model,)
|
||||
|
||||
class diffusers_sampler:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"diffusers_model": ("DIFFUSERSMODEL",),
|
||||
"width": ("INT", {"default": 512, "min": 64, "max": 2048, "step": 64}),
|
||||
"height": ("INT", {"default": 512, "min": 64, "max": 2048, "step": 64}),
|
||||
"steps": ("INT", {"default": 25, "min": 1, "max": 200, "step": 1}),
|
||||
"guidance_scale": ("FLOAT", {"default": 10.0, "min": 1.01, "max": 20.0, "step": 0.01}),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"scheduler": (
|
||||
[
|
||||
'DPMSolverMultistepScheduler',
|
||||
'DPMSolverMultistepScheduler_SDE_karras',
|
||||
'DDPMScheduler',
|
||||
'LCMScheduler',
|
||||
'PNDMScheduler',
|
||||
'DEISMultistepScheduler',
|
||||
'EulerDiscreteScheduler',
|
||||
'EulerAncestralDiscreteScheduler',
|
||||
'UniPCMultistepScheduler',
|
||||
'TCDScheduler'
|
||||
], {
|
||||
"default": 'DPMSolverMultistepScheduler'
|
||||
}),
|
||||
"prompt": ("STRING", {"default": "positive", "multiline": True}),
|
||||
"n_prompt": ("STRING", {"default": "negative", "multiline": True}),
|
||||
"hidiffusion": ("BOOLEAN", {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("images",)
|
||||
FUNCTION = "process"
|
||||
CATEGORY = "ELLA-Wrapper"
|
||||
|
||||
def process(self, diffusers_model, width, height, steps, guidance_scale, seed, scheduler, prompt, n_prompt, hidiffusion):
|
||||
device = mm.get_torch_device()
|
||||
mm.unload_all_models()
|
||||
mm.soft_empty_cache()
|
||||
dtype = mm.unet_dtype()
|
||||
pipe=diffusers_model['pipe']
|
||||
pipe.to(device, dtype=dtype)
|
||||
|
||||
scheduler_config = {
|
||||
'num_train_timesteps': 1000,
|
||||
'beta_start': 0.00085,
|
||||
'beta_end': 0.012,
|
||||
'beta_schedule': "scaled_linear",
|
||||
'steps_offset': 1,
|
||||
}
|
||||
if scheduler == 'DPMSolverMultistepScheduler':
|
||||
noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
|
||||
elif scheduler == 'DPMSolverMultistepScheduler_SDE_karras':
|
||||
scheduler_config.update({"algorithm_type": "sde-dpmsolver++"})
|
||||
scheduler_config.update({"use_karras_sigmas": True})
|
||||
noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
|
||||
elif scheduler == 'DDPMScheduler':
|
||||
noise_scheduler = DDPMScheduler(**scheduler_config)
|
||||
elif scheduler == 'LCMScheduler':
|
||||
noise_scheduler = LCMScheduler(**scheduler_config)
|
||||
elif scheduler == 'PNDMScheduler':
|
||||
scheduler_config.update({"set_alpha_to_one": False})
|
||||
scheduler_config.update({"trained_betas": None})
|
||||
noise_scheduler = PNDMScheduler(**scheduler_config)
|
||||
elif scheduler == 'DEISMultistepScheduler':
|
||||
noise_scheduler = DEISMultistepScheduler(**scheduler_config)
|
||||
elif scheduler == 'EulerDiscreteScheduler':
|
||||
noise_scheduler = EulerDiscreteScheduler(**scheduler_config)
|
||||
elif scheduler == 'EulerAncestralDiscreteScheduler':
|
||||
noise_scheduler = EulerAncestralDiscreteScheduler(**scheduler_config)
|
||||
elif scheduler == 'UniPCMultistepScheduler':
|
||||
noise_scheduler = UniPCMultistepScheduler(**scheduler_config)
|
||||
elif scheduler == 'TCDScheduler':
|
||||
noise_scheduler = TCDScheduler(**scheduler_config)
|
||||
|
||||
pipe.scheduler = noise_scheduler
|
||||
if hidiffusion:
|
||||
apply_hidiffusion(pipe)
|
||||
else:
|
||||
remove_hidiffusion(pipe)
|
||||
|
||||
autocast_condition = (dtype != torch.float32) and not mm.is_device_mps(device)
|
||||
with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
|
||||
|
||||
|
||||
images = pipe(
|
||||
prompt = prompt,
|
||||
negative_prompt = n_prompt,
|
||||
prompt_embeds=None,
|
||||
negative_prompt_embeds=None,
|
||||
guidance_scale=guidance_scale,
|
||||
num_inference_steps=steps,
|
||||
height=height,
|
||||
width=width,
|
||||
generator=[
|
||||
torch.Generator(device=device).manual_seed(seed + i)
|
||||
for i in range(1)
|
||||
],
|
||||
output_type="pt",
|
||||
).images
|
||||
|
||||
image_out = images.permute(0, 2, 3, 1).cpu().float()
|
||||
return (image_out,)
|
||||
|
||||
class ella_sampler:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@@ -246,7 +464,10 @@ class ella_sampler:
|
||||
], {
|
||||
"default": 'DPMSolverMultistepScheduler'
|
||||
}),
|
||||
},
|
||||
},
|
||||
"optional": {
|
||||
"hidiffusion": ("BOOLEAN", {"default": False}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
@@ -254,7 +475,7 @@ class ella_sampler:
|
||||
FUNCTION = "process"
|
||||
CATEGORY = "ELLA-Wrapper"
|
||||
|
||||
def process(self, ella_embeds, width, height, steps, guidance_scale, seed, ella_model, scheduler):
|
||||
def process(self, ella_embeds, width, height, steps, guidance_scale, seed, ella_model, scheduler, hidiffusion=False):
|
||||
device = mm.get_torch_device()
|
||||
mm.unload_all_models()
|
||||
mm.soft_empty_cache()
|
||||
@@ -295,6 +516,10 @@ class ella_sampler:
|
||||
noise_scheduler = TCDScheduler(**scheduler_config)
|
||||
|
||||
pipe.scheduler = noise_scheduler
|
||||
if hidiffusion:
|
||||
apply_hidiffusion(pipe)
|
||||
else:
|
||||
remove_hidiffusion(pipe)
|
||||
|
||||
autocast_condition = (dtype != torch.float32) and not mm.is_device_mps(device)
|
||||
with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
|
||||
@@ -320,6 +545,7 @@ class ella_sampler:
|
||||
image_out = images.permute(0, 2, 3, 1).cpu().float()
|
||||
return (image_out,)
|
||||
|
||||
|
||||
class ella_t5_embeds:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@@ -404,10 +630,15 @@ class ella_t5_embeds:
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"ella_model_loader": ella_model_loader,
|
||||
"ella_sampler": ella_sampler,
|
||||
"ella_t5_embeds": ella_t5_embeds
|
||||
"ella_t5_embeds": ella_t5_embeds,
|
||||
"diffusers_model_loader": diffusers_model_loader,
|
||||
"diffusers_sampler": diffusers_sampler
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"ella_model_loader": "ELLA Model Loader",
|
||||
"ella_sampler": "ELLA Sampler",
|
||||
"ella_t5_embeds": "ELLA T5 Embeds"
|
||||
"ella_t5_embeds": "ELLA T5 Embeds",
|
||||
"diffusers_model_loader": "Diffusers Model Loader",
|
||||
"diffusers_sampler": "Diffusers Sampler"
|
||||
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user