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Optimized VRAM usage

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songkey-win
2024-11-11 19:38:01 +08:00
parent 94ebe62410
commit caeb3972fd
11 changed files with 1472 additions and 1407 deletions
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README.md
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@@ -22,7 +22,7 @@
## 🔆 New Features/Updates
- ✅ `11/11/2024` Optimized VRAM usage and added `HMVideoSimplePipeline` (`workflows/hellomeme_video_simple_workflow.json`), which doesn’t use Animatediff and can run on machines with less than 12G VRAM.
- ✅ `11/6/2024` The face proportion in the reference image significantly affects the generation quality. We have encapsulated the **recommended image cropping method** used during training into a `CropReferenceImage` Node. Refer to the workflows in the `ComfyUI_HelloMeme/workflows directory`: `hellomeme_video_cropref_workflow.json` and `hellomeme_image_cropref_workflow.json`.
hellomeme/__init__.py
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@@ -8,5 +8,5 @@
@Desc :
"""
from .pipelines import HMVideoPipeline, HMImagePipeline
from .pipelines import HMVideoPipeline, HMImagePipeline, HMVideoSimplePipeline
from .models import HMDenoising3D, HMDenoisingMotion, HMControlNet, HMReferenceAdapter
hellomeme/pipelines/__init__.py
+2 -1
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@@ -8,5 +8,6 @@
@Desc :
"""
from .pipline_hm_video import HMVideoPipeline
from .pipline_hm_video_simple import HMVideoSimplePipeline
from .pipline_hm_image import HMImagePipeline
from .pipline_hm_video import HMVideoPipeline
hellomeme/pipelines/pipline_hm_video.py
+42 -57
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@@ -8,7 +8,7 @@
@Desc :
adapted from: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
"""
import copy
from typing import Any, Callable, Dict, List, Optional, Union
import torch
@@ -19,42 +19,46 @@ from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import retrieve_timesteps, retrieve_latents
from diffusers import StableDiffusionImg2ImgPipeline
from diffusers import EulerDiscreteScheduler
from diffusers import MotionAdapter
from ..models import HMDenoising3D, HMDenoisingMotion, HMControlNet
from ..models import HMReferenceAdapter
from ..utils import dicts_to_device, cat_dicts
class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
def caryomitosis(self,):
if not hasattr(self, "unet_ref"):
self.unet_ref = HMDenoising3D.from_unet2d(self.unet)
if not isinstance(self.unet, HMDenoisingMotion):
adapter = MotionAdapter.from_pretrained("songkey/hm_animatediff", torch_dtype=torch.float16)
unet = HMDenoisingMotion.from_unet2d(unet=self.unet, motion_adapter=adapter, load_weights=True)
# todo: 不够优雅
del self.unet
self.unet = unet
if hasattr(self, "unet_ref"):
del self.unet_ref
self.unet_ref = HMDenoising3D.from_unet2d(self.unet)
self.num_frames = 16
adapter = MotionAdapter.from_pretrained("songkey/hm_animatediff", torch_dtype=torch.float16)
unet = HMDenoisingMotion.from_unet2d(unet=self.unet, motion_adapter=adapter, load_weights=True)
# todo: 不够优雅
del self.unet
self.unet = unet
def insert_hm_modules(self, dtype, device):
hm_adapter = HMReferenceAdapter.from_pretrained('songkey/hm_reference')
if isinstance(self.unet, HMDenoisingMotion):
hm_adapter = HMReferenceAdapter.from_pretrained('songkey/hm_reference')
self.unet.insert_reference_adapter(hm_adapter)
self.unet_ref.insert_reference_adapter(hm_adapter)
self.unet_ref = self.unet_ref.to(device=device, dtype=dtype).eval()
self.unet = self.unet.to(device=device, dtype=dtype).eval()
if not hasattr(self, "mp_control"):
self.mp_control = HMControlNet.from_pretrained('songkey/hm_control')
self.mp_control = self.mp_control.to(device=device, dtype=dtype).eval()
if hasattr(self, "unet_ref"):
self.unet_ref.insert_reference_adapter(hm_adapter)
self.unet_ref = self.unet_ref.to(device=device, dtype=dtype).eval()
if hasattr(self, "mp_control"):
del self.mp_control
self.mp_control = HMControlNet.from_pretrained('songkey/hm_control')
self.mp_control = self.mp_control.to(device=device, dtype=dtype).eval()
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
num_frames: int = 16,
chunk_overlap: int = 4,
drive_params: Dict[str, Any] = None,
strength: float = 0.8,
@@ -168,21 +172,6 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
raw_video_len = drive_params['condition'].size(2)
chunk_size = num_frames
chunk_overlap = min(max(0, chunk_overlap), chunk_size // 2)
chunk_stride = chunk_size - chunk_overlap
drive_idx_list = list(range(raw_video_len))
steped_video_len = len(drive_idx_list)
post_pad_num = chunk_stride - (steped_video_len - chunk_overlap) % chunk_stride
if post_pad_num > 0:
drive_idx_list = drive_idx_list + [drive_idx_list[-1]] * post_pad_num
paded_video_len = len(drive_idx_list)
drive_idx_chunks = [drive_idx_list[i:i + chunk_size] for i in
range(0, paded_video_len - chunk_stride + 1, chunk_stride)]
# 6. Prepare latent variables
ref_latents = [
@@ -203,7 +192,6 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
else None
)
# if hasattr(self, "unet_ref"):
ref_latents = ref_latents.unsqueeze(2)
cached_res = self.unet_ref(
torch.cat([torch.zeros_like(ref_latents), ref_latents], dim=0) if
@@ -212,8 +200,6 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
encoder_hidden_states=prompt_embeds,
return_dict=False,
)[1]
# else:
# cached_res = None
# 7.2 Optionally get Guidance Scale Embedding
timestep_cond = None
@@ -244,12 +230,7 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
if self.do_classifier_free_guidance:
control_latent = cat_dicts([control_latent_neg, control_latent], dim=0)
scheduler = EulerDiscreteScheduler(
num_train_timesteps=1000,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
)
scheduler = copy.deepcopy(self.scheduler)
tmp_timesteps, _ = retrieve_timesteps(
scheduler, 8, device, None, sigmas
@@ -283,9 +264,27 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
pre_latents.append(pred_latent.cpu())
control_latents.append(dicts_to_device([control_latent], device='cpu')[0])
progress_bar.set_description(f"GEN stage1")
progress_bar.update()
pre_latents = torch.cat(pre_latents, dim=2)
chunk_size = self.num_frames
chunk_overlap = min(max(0, chunk_overlap), chunk_size // 2)
chunk_stride = chunk_size - chunk_overlap
drive_idx_list = list(range(raw_video_len))
steped_video_len = len(drive_idx_list)
post_pad_num = chunk_stride - (steped_video_len - chunk_overlap) % chunk_stride
if post_pad_num > 0:
drive_idx_list = drive_idx_list + [drive_idx_list[-1]] * post_pad_num
paded_video_len = len(drive_idx_list)
drive_idx_chunks = [drive_idx_list[i:i + chunk_size] for i in
range(0, paded_video_len - chunk_stride + 1, chunk_stride)]
if post_pad_num > 0:
pre_latents = torch.cat([pre_latents,
(pre_latents[:, :, -2:-1]).clone().repeat_interleave(post_pad_num, 2)], dim=2)
@@ -362,6 +361,7 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.set_description(f"GEN stage2")
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
@@ -372,19 +372,4 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
res_frames = [self.vae.decode(latents[:,:,i,:,:].to(device=device), return_dict=False, generator=generator)[0]
for i in range(raw_video_len)]
res_frames = [self.image_processor.postprocess(frame, output_type=output_type) for frame in res_frames]
return res_frames
def cat_dicts(dicts, dim=0):
res = {}
for k in dicts[0].keys():
res[k] = torch.cat([d[k].clone() for d in dicts], dim=dim)
return res
def dicts_to_device(dicts, device):
ret = []
for d in dicts:
tmpd = {}
for k in d.keys():
tmpd[k] = d[k].clone().to(device)
ret.append(tmpd)
return ret
return res_frames
hellomeme/pipelines/pipline_hm_video_simple.py
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@@ -0,0 +1,297 @@
# coding: utf-8
"""
@File : hm_pipline_image.py
@Author : Songkey
@Email : songkey@pku.edu.cn
@Date : 8/29/2024
@Desc :
adapted from: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
"""
import copy
import time
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
from diffusers.image_processor import PipelineImageInput
from diffusers.utils import deprecate
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import retrieve_timesteps, retrieve_latents
from diffusers import StableDiffusionImg2ImgPipeline
from ..models import HMDenoising3D, HMControlNet
from ..models import HMReferenceAdapter
from ..utils import dicts_to_device, cat_dicts
class HMVideoSimplePipeline(StableDiffusionImg2ImgPipeline):
def caryomitosis(self,):
if hasattr(self, "unet_ref"):
del self.unet_ref
self.unet_ref = HMDenoising3D.from_unet2d(self.unet)
unet = HMDenoising3D.from_unet2d(self.unet)
# todo: 不够优雅
del self.unet
self.unet = unet
def insert_hm_modules(self, dtype, device):
hm_adapter = HMReferenceAdapter.from_pretrained('songkey/hm_reference')
if isinstance(self.unet, HMDenoising3D):
self.unet.insert_reference_adapter(hm_adapter)
self.unet = self.unet.to(device=device, dtype=dtype).eval()
if hasattr(self, "unet_ref"):
self.unet_ref = self.unet_ref.to(device=device, dtype=dtype).eval()
if hasattr(self, "mp_control"):
del self.mp_control
self.mp_control = HMControlNet.from_pretrained('songkey/hm_control')
self.mp_control = self.mp_control.to(device=device, dtype=dtype).eval()
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
drive_params: Dict[str, Any] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
timesteps: List[int] = None,
sigmas: List[float] = None,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
eta: Optional[float] = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
clip_skip: int = None,
callback_on_step_end: Optional[
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
num_images_per_prompt = 1
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"callback_steps",
"1.0.0",
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
strength,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
ip_adapter_image,
ip_adapter_image_embeds,
callback_on_step_end_tensor_inputs,
)
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
self._interrupt = False
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# 3. Encode input prompt
text_encoder_lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
self.do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
clip_skip=self.clip_skip,
)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
image_embeds = self.prepare_ip_adapter_image_embeds(
ip_adapter_image,
ip_adapter_image_embeds,
device,
batch_size * num_images_per_prompt,
self.do_classifier_free_guidance,
)
# 4. Preprocess image
image = self.image_processor.preprocess(image).to(device=device, dtype=prompt_embeds.dtype)
# 6. Prepare latent variables
ref_latents = [
retrieve_latents(self.vae.encode(image[i: i + 1].to(device=device)), generator=generator)
for i in range(batch_size)
]
ref_latents = torch.cat(ref_latents, dim=0)
ref_latents = self.vae.config.scaling_factor * ref_latents
c, h, w = ref_latents.shape[1:]
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7.1 Add image embeds for IP-Adapter
added_cond_kwargs = (
{"image_embeds": image_embeds}
if ip_adapter_image is not None or ip_adapter_image_embeds is not None
else None
)
ref_latents = ref_latents.unsqueeze(2)
cached_res = self.unet_ref(
torch.cat([torch.zeros_like(ref_latents), ref_latents], dim=0) if
self.do_classifier_free_guidance else ref_latents,
0,
encoder_hidden_states=prompt_embeds,
return_dict=False,
)[1]
# 7.2 Optionally get Guidance Scale Embedding
timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
timestep_cond = self.get_guidance_scale_embedding(
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
).to(device=device, dtype=prompt_embeds.dtype)
raw_video_len = drive_params['condition'].size(2)
pre_latents = []
control_latents = []
control_latent_neg = None
base_noise = randn_tensor([batch_size, c, 1, h, w], dtype=prompt_embeds.dtype, generator=generator).to(device=device)
with self.progress_bar(total=raw_video_len) as progress_bar:
for idx in range(raw_video_len):
condition = drive_params['condition'][:, :, idx:idx+1].clone().to(device=device)
drive_coeff = drive_params['drive_coeff'][:, idx:idx+1].clone().to(device=device)
face_parts = drive_params['face_parts'][:, idx:idx+1].clone().to(device=device)
if control_latent_neg is None and self.do_classifier_free_guidance:
control_latent_neg = self.mp_control(condition=torch.ones_like(condition)*-1,
drive_coeff=torch.zeros_like(drive_coeff),
face_parts=torch.zeros_like(face_parts))
control_latent = self.mp_control(condition=condition,
drive_coeff=drive_coeff,
face_parts=face_parts)
if self.do_classifier_free_guidance:
control_latent = cat_dicts([control_latent_neg, control_latent], dim=0)
scheduler = copy.deepcopy(self.scheduler)
tmp_timesteps, _ = retrieve_timesteps(scheduler, 8, device, None, sigmas)
if idx == 0:
pred_latent = scheduler.add_noise(ref_latents, base_noise, tmp_timesteps[:1])
else:
pred_latent = scheduler.add_noise(pred_latent, base_noise, tmp_timesteps[:1])
for i, t in enumerate(tmp_timesteps):
latent_model_input = torch.cat([pred_latent, pred_latent], dim=0) if \
self.do_classifier_free_guidance else pred_latent
latent_model_input = scheduler.scale_model_input(latent_model_input, t)
noise_pred_chunk = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
reference_hidden_states=cached_res,
control_hidden_states=control_latent,
timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred_chunk.chunk(2)
noise_pred_chunk = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
pred_latent = scheduler.step(noise_pred_chunk, t, pred_latent,
**extra_step_kwargs, return_dict=False)[0]
pre_latents.append(pred_latent.cpu())
control_latents.append(dicts_to_device([control_latent], device='cpu')[0])
progress_bar.set_description(f"GEN stage1")
progress_bar.update()
res_frames = []
with self.progress_bar(total=raw_video_len) as progress_bar:
for idx in range(raw_video_len):
scheduler = copy.deepcopy(self.scheduler)
tmp_timesteps, _ = retrieve_timesteps(scheduler, num_inference_steps, device, None, sigmas)
if idx == 0:
res_latent = scheduler.add_noise(pre_latents[idx].to(device=device),
base_noise, tmp_timesteps[:1])
else:
res_latent = scheduler.add_noise(pre_latents[idx].to(device=device) * 0.8 + res_latent * 0.2,
base_noise, tmp_timesteps[:1])
for i, t in enumerate(tmp_timesteps):
latent_model_input = torch.cat([res_latent, res_latent], dim=0) if \
self.do_classifier_free_guidance else res_latent
latent_model_input = scheduler.scale_model_input(latent_model_input, t)
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
reference_hidden_states=cached_res,
control_hidden_states=dicts_to_device([control_latents[idx]], device=device)[0],
timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidance_scale * (
noise_pred_text - noise_pred_uncond)
res_latent = scheduler.step(noise_pred, t, res_latent,
**extra_step_kwargs, return_dict=False)[0]
res_frame_tensor = self.vae.decode(res_latent[:,:,0,:,:].to(device=device) / self.vae.config.scaling_factor,
return_dict=False, generator=generator)[0]
res_frame = self.image_processor.postprocess(res_frame_tensor, output_type=output_type)
res_frames.append(res_frame)
progress_bar.set_description(f"GEN stage2")
progress_bar.update()
return res_frames
hellomeme/utils.py
+64 -23
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@@ -11,6 +11,7 @@
import os
import cv2
import numpy as np
from tqdm import tqdm
import torch
from PIL import Image
import subprocess
@@ -20,6 +21,27 @@ from diffusers.pipelines.stable_diffusion.convert_from_ckpt import convert_ldm_u
from safetensors import safe_open
def merge_dicts(dictl, dictr, wl=0.5):
res = {}
for k in dictl.keys():
res[k] = dictl[k] * wl + dictr[k] * (1-wl)
return res
def cat_dicts(dicts, dim=0):
res = {}
for k in dicts[0].keys():
res[k] = torch.cat([d[k].clone() for d in dicts], dim=dim)
return res
def dicts_to_device(dicts, device):
ret = []
for d in dicts:
tmpd = {}
for k in d.keys():
tmpd[k] = d[k].clone().to(device)
ret.append(tmpd)
return ret
def load_safetensors(model_path):
tensors = {}
with safe_open(model_path, framework="pt", device=0) as f:
@@ -161,11 +183,16 @@ def get_face_params(h3dmm, harkit_bs, frames, landmarks, save_size=(512, 512), a
rot_list = []
trans_list = []
arkit_bs_list = []
for landmark, frame in zip(new_landmarks, new_frames):
drive_rot, drive_trans = h3dmm.forward_params(frame, landmark)
rot_list.append(drive_rot)
trans_list.append(drive_trans)
arkit_bs_list.append(harkit_bs.forward(frame, landmark))
with tqdm(total=len(new_landmarks)) as pbar:
for landmark, frame in zip(new_landmarks, new_frames):
drive_rot, drive_trans = h3dmm.forward_params(frame, landmark)
rot_list.append(drive_rot)
trans_list.append(drive_trans)
arkit_bs_list.append(harkit_bs.forward(frame, landmark))
pbar.set_description('RT & ARKIT')
pbar.update()
drive_coeff = torch.from_numpy(np.stack(arkit_bs_list, axis=0))
return face_parts, drive_coeff, rot_list, trans_list
@@ -177,10 +204,18 @@ def face_params_to_tensor(clip_encoder, h3dmm, face_parts, drive_rot, drive_tran
face_parts = torch.stack(face_parts_list, dim=2)
face_parts_tensor = rearrange(face_parts, "c f p h w -> (f p) c h w")
face_parts_embedding = clip_encoder(face_parts_tensor.to(device=clip_encoder.device, dtype=clip_encoder.dtype)).image_embeds
face_parts_embedding_list = []
with tqdm(total=face_parts_tensor.size(0)) as pbar:
for i in range(0, face_parts_tensor.size(0)):
face_parts_embedding = clip_encoder(face_parts_tensor[i:i+1].to(device=clip_encoder.device, dtype=clip_encoder.dtype)).image_embeds
face_parts_embedding_list.append(face_parts_embedding.cpu())
pbar.set_description('CLIP IMAGE ENCODER')
pbar.update()
face_parts_embedding = torch.cat(face_parts_embedding_list, dim=0)
face_parts_embedding = rearrange(face_parts_embedding,
"(f p) c -> f p c",
f=face_parts.size(2)).cpu()
f=face_parts.size(2))
control_list = []
control_show_list = []
@@ -198,26 +233,32 @@ def face_params_to_tensor(clip_encoder, h3dmm, face_parts, drive_rot, drive_tran
def det_landmarks(face_aligner, frame_list, save_size=(512, 512), reset=False):
rect_list = []
new_frame_list = []
for frame in frame_list:
frame = cv2.resize(frame, save_size)
faces = face_aligner.forward(frame, reset=reset)
if len(faces) > 0:
face = sorted(faces, key=lambda x: (x['face_rect'][2] - x['face_rect'][0]) * (
x['face_rect'][3] - x['face_rect'][1]))[-1]
rect_list.append(face['face_rect'])
new_frame_list.append(frame)
with tqdm(total=len(frame_list)) as pbar:
for frame in frame_list:
frame = cv2.resize(frame, save_size)
faces = face_aligner.forward(frame, reset=reset)
if len(faces) > 0:
face = sorted(faces, key=lambda x: (x['face_rect'][2] - x['face_rect'][0]) * (
x['face_rect'][3] - x['face_rect'][1]))[-1]
rect_list.append(face['face_rect'])
new_frame_list.append(frame)
pbar.set_description('DET stage1')
pbar.update()
face_aligner.reset_track()
save_frame_list = []
save_landmark_list = []
for frame, rect in zip(new_frame_list, rect_list):
faces = face_aligner.forward(frame, pre_rect=rect, reset=reset)
if len(faces) > 0:
face = sorted(faces, key=lambda x: (x['face_rect'][2] - x['face_rect'][0]) * (
x['face_rect'][3] - x['face_rect'][1]))[-1]
landmarks = face['pre_kpt_222']
save_frame_list.append(frame)
save_landmark_list.append(landmarks)
with tqdm(total=len(new_frame_list)) as pbar:
for frame, rect in zip(new_frame_list, rect_list):
faces = face_aligner.forward(frame, pre_rect=rect, reset=reset)
if len(faces) > 0:
face = sorted(faces, key=lambda x: (x['face_rect'][2] - x['face_rect'][0]) * (
x['face_rect'][3] - x['face_rect'][1]))[-1]
landmarks = face['pre_kpt_222']
save_frame_list.append(frame)
save_landmark_list.append(landmarks)
pbar.set_description('DET stage2')
pbar.update()
save_landmark_list = np.stack(save_landmark_list, axis=0).astype(np.float16)
face_aligner.reset_track()
meme.py
+69 -19
View File
@@ -38,7 +38,7 @@ from .hellomeme.utils import (face_params_to_tensor,
load_unet_from_safetensors)
from .hellomeme.tools import Hello3DMMPred, HelloARKitBSPred, HelloFaceAlignment, HelloCameraDemo
from .hellomeme import HMImagePipeline, HMVideoPipeline
from .hellomeme import HMImagePipeline, HMVideoPipeline, HMVideoSimplePipeline
from transformers import CLIPVisionModelWithProjection
class HMImagePipelineLoader:
@@ -138,6 +138,56 @@ class HMVideoPipelineLoader:
return (pipeline,)
class HMVideoSimplePipelineLoader:
@classmethod
def INPUT_TYPES(s):
checkpoint_files = sorted(load_data_list(osp.join(cur_dir, '../../models/checkpoints'), '.pt;.pth;.ckpt;.safetensors'))
lora_files = sorted(load_data_list(osp.join(cur_dir, '../../models/loras'), '.safetensors'))
return {
"optional": {
"checkpoint_path": (['None'] + checkpoint_files, ),
"lora_path": (['None'] + lora_files, ),
"gpu_id": ("INT", {"default": 0}),
}
}
RETURN_TYPES = ("HMVIDEOPIPELINE",)
RETURN_NAMES = ("hm_video_pipeline",)
FUNCTION = "load_pipeline"
CATEGORY = "hellomeme"
def load_pipeline(self, checkpoint_path=None, lora_path=None, gpu_id=0):
dtype = torch.float16
if gpu_id >= 0:
device = torch.device("cuda:{}".format(gpu_id))
else:
device = torch.device("cpu")
pipeline = HMVideoSimplePipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5").to(dtype=dtype,
device=device)
pipeline.caryomitosis()
### load customized checkpoint or lora here:
## checkpoints
if checkpoint_path and osp.isfile(checkpoint_path):
if checkpoint_path.endswith('.safetensors'):
state_dict = load_unet_from_safetensors(checkpoint_path, pipeline.unet_ref.config)
pipeline.unet.load_state_dict(state_dict, strict=False)
elif osp.splitext(checkpoint_path)[-1] in ['.pt', '.pth', '.ckpt']:
state_dict = torch.load(checkpoint_path)
pipeline.unet.load_state_dict(state_dict, strict=False)
else:
print("Invalid checkpoint path", checkpoint_path)
### lora
if lora_path and osp.isfile(lora_path):
pipeline.load_lora_weights(osp.dirname(lora_path), weight_name=osp.basename(lora_path), adapter_name="lora")
pipeline.insert_hm_modules(dtype=dtype, device=device)
return (pipeline,)
class HMFaceToolkitsLoader:
@classmethod
def INPUT_TYPES(s):
@@ -180,7 +230,7 @@ class GetReferenceImageRT:
def get_reference_image_rt(self, face_toolkits, image):
image_np = cv2.cvtColor((image[0] * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB)
image_np = cv2.resize(image_np, (512, 512))
print(image_np.shape)
# print(image_np.shape)
face_toolkits['face_aligner'].reset_track()
faces = face_toolkits['face_aligner'].forward(image_np)
assert len(faces) > 0
@@ -208,7 +258,7 @@ class CropReferenceImage:
def crop_reference_image(self, image, face_toolkits):
image_np = cv2.cvtColor((image[0] * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB)
image_np = cv2.resize(image_np, (512, 512))
print(image_np.shape)
# print(image_np.shape)
face_toolkits['face_aligner'].reset_track()
faces = face_toolkits['face_aligner'].forward(image_np)
assert len(faces) > 0
@@ -228,7 +278,6 @@ class GetImageDriveParams:
"face_toolkits": ("FACE_TOOLKITS",),
"image": ("IMAGE",),
"ref_rt": ("REFRT",),
"gpu_id": ("INT", {"default": 0})
}
}
@@ -236,9 +285,8 @@ class GetImageDriveParams:
RETURN_NAMES = ("drive_params",)
FUNCTION = "get_face_params"
CATEGORY = "hellomeme"
def get_face_params(self, face_toolkits, image, ref_rt, gpu_id):
def get_face_params(self, face_toolkits, image, ref_rt):
dtype = torch.float16
device = torch.device(f'cuda:{gpu_id}')
image_np = cv2.cvtColor((image[0] * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB)
@@ -263,9 +311,9 @@ class GetImageDriveParams:
save_size=512, trans_ratio=0.0)
drive_params = dict(
face_parts=face_parts_embedding.unsqueeze(0).to(dtype=dtype, device=device),
drive_coeff=drive_coeff.unsqueeze(0).to(dtype=dtype, device=device),
condition=control_heatmaps.unsqueeze(0).to(dtype=dtype, device=device),
face_parts=face_parts_embedding.unsqueeze(0).to(dtype=dtype),
drive_coeff=drive_coeff.unsqueeze(0).to(dtype=dtype),
condition=control_heatmaps.unsqueeze(0).to(dtype=dtype),
)
return (drive_params, )
@@ -274,8 +322,8 @@ class HMPipelineImage:
def INPUT_TYPES(s):
return {
"required": {
"pipeline": ("HMIMAGEPIPELINE",),
"image": ("IMAGE",),
"hm_image_pipeline": ("HMIMAGEPIPELINE",),
"ref_image": ("IMAGE",),
"drive_params": ("DRIVE_PARAMS",),
"prompt": ("STRING", {"default": '(best quality), highly detailed, ultra-detailed, headshot, person, well-placed five sense organs, looking at the viewer, centered composition, sharp focus, realistic skin texture'}),
"negative_prompt": ("STRING", {"default": ''}),
@@ -289,8 +337,8 @@ class HMPipelineImage:
FUNCTION = "sample"
CATEGORY = "hellomeme"
def sample(self, pipeline, image, drive_params, prompt, negative_prompt, steps=25, seed=-1, guidance_scale=2.0):
image_np = (image[0] * 255).cpu().numpy().astype(np.uint8)
def sample(self, hm_image_pipeline, ref_image, drive_params, prompt, negative_prompt, steps=25, seed=-1, guidance_scale=2.0):
image_np = (ref_image[0] * 255).cpu().numpy().astype(np.uint8)
image_np = cv2.resize(image_np, (512, 512))
image_pil = Image.fromarray(image_np)
@@ -299,7 +347,7 @@ class HMPipelineImage:
else:
generator = torch.Generator().manual_seed(seed)
result_img = pipeline(
result_img = hm_image_pipeline(
prompt=[prompt],
strength=1.0,
image=image_pil,
@@ -358,8 +406,8 @@ class HMPipelineVideo:
def INPUT_TYPES(s):
return {"required":
{
"pipeline": ("HMVIDEOPIPELINE",),
"image": ("IMAGE",),
"hm_video_pipeline": ("HMVIDEOPIPELINE",),
"ref_image": ("IMAGE",),
"drive_params": ("DRIVE_PARAMS",),
"prompt": ("STRING", {"default": '(best quality), highly detailed, ultra-detailed, headshot, person, well-placed five sense organs, looking at the viewer, centered composition, sharp focus, realistic skin texture'}),
"negative_prompt": ("STRING", {"default": ''}),
@@ -373,8 +421,8 @@ class HMPipelineVideo:
FUNCTION = "sample"
CATEGORY = "hellomeme"
def sample(self, pipeline, image, drive_params, prompt, negative_prompt, steps=25, seed=-1, guidance_scale=2.0):
image_np = (image[0] * 255).cpu().numpy().astype(np.uint8)
def sample(self, hm_video_pipeline, ref_image, drive_params, prompt, negative_prompt, steps=25, seed=-1, guidance_scale=2.0):
image_np = (ref_image[0] * 255).cpu().numpy().astype(np.uint8)
image_np = cv2.resize(image_np, (512, 512))
image_pil = Image.fromarray(image_np)
if seed < 0:
@@ -382,7 +430,7 @@ class HMPipelineVideo:
else:
generator = torch.Generator().manual_seed(seed)
res_frames = pipeline(
res_frames = hm_video_pipeline(
prompt=[prompt],
strength=1.0,
image=image_pil,
@@ -399,6 +447,7 @@ class HMPipelineVideo:
NODE_CLASS_MAPPINGS = {
"HMImagePipelineLoader": HMImagePipelineLoader,
"HMVideoPipelineLoader": HMVideoPipelineLoader,
"HMVideoSimplePipelineLoader": HMVideoSimplePipelineLoader,
"HMFaceToolkitsLoader": HMFaceToolkitsLoader,
"GetReferenceImageRT": GetReferenceImageRT,
"GetImageDriveParams": GetImageDriveParams,
@@ -411,6 +460,7 @@ NODE_CLASS_MAPPINGS = {
NODE_DISPLAY_NAME_MAPPINGS = {
"HMImagePipelineLoader": "Load HelloMemeImage Pipeline",
"HMVideoPipelineLoader": "Load HelloMemeVideo Pipeline",
"HMVideoSimplePipelineLoader": "Load HelloMemeVideoSimple Pipeline",
"HMFaceToolkitsLoader": "Load Face Toolkits",
"GetReferenceImageT": "Get Reference Image Translation",
"GetImageDriveParams": "Get Drive Image Parameters",
workflows/hellomeme_image_cropref_workflow.json
-497
View File
@@ -1,497 +0,0 @@
{
"last_node_id": 31,
"last_link_id": 40,
"nodes": [
{
"id": 28,
"type": "HMImagePipelineLoader",
"pos": {
"0": 111,
"1": 102
},
"size": {
"0": 352.79998779296875,
"1": 106
},
"flags": {},
"order": 0,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "HMIMAGEPIPELINE",
"type": "HMIMAGEPIPELINE",
"links": [
34
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "HMImagePipelineLoader"
},
"widgets_values": [
"None",
"None",
0
]
},
{
"id": 29,
"type": "PreviewImage",
"pos": {
"0": 1825,
"1": 420
},
"size": [
210,
246
],
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 35
}
],
"outputs": [],
"properties": {
"Node name for S&R": "PreviewImage"
},
"widgets_values": []
},
{
"id": 26,
"type": "HMPipelineImage",
"pos": {
"0": 1407.3828125,
"1": 432.0332336425781
},
"size": {
"0": 315,
"1": 218
},
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"name": "pipeline",
"type": "HMIMAGEPIPELINE",
"link": 34
},
{
"name": "image",
"type": "IMAGE",
"link": 39
},
{
"name": "drive_params",
"type": "DRIVE_PARAMS",
"link": 32
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
35
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "HMPipelineImage"
},
"widgets_values": [
"(best quality), highly detailed, ultra-detailed, headshot, person, well-placed five sense organs, looking at the viewer, centered composition, sharp focus, realistic skin texture",
"",
25,
467,
"randomize",
2
]
},
{
"id": 25,
"type": "LoadImage",
"pos": {
"0": 120,
"1": 796
},
"size": {
"0": 315,
"1": 314
},
"flags": {},
"order": 1,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
31
],
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": null
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"yao.jpg",
"image"
]
},
{
"id": 8,
"type": "LoadImage",
"pos": {
"0": 120,
"1": 414
},
"size": {
"0": 315,
"1": 314
},
"flags": {},
"order": 2,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
36
],
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": null
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"majicmix2.jpg",
"image"
]
},
{
"id": 2,
"type": "HMFaceToolkitsLoader",
"pos": {
"0": 124,
"1": 276
},
"size": {
"0": 315,
"1": 58
},
"flags": {},
"order": 3,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "FACE_TOOLKITS",
"type": "FACE_TOOLKITS",
"links": [
13,
27,
37
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "HMFaceToolkitsLoader"
},
"widgets_values": [
0
]
},
{
"id": 11,
"type": "GetReferenceImageRT",
"pos": {
"0": 639,
"1": 512
},
"size": {
"0": 241.79998779296875,
"1": 46
},
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "face_toolkits",
"type": "FACE_TOOLKITS",
"link": 13
},
{
"name": "image",
"type": "IMAGE",
"link": 38
}
],
"outputs": [
{
"name": "REFRT",
"type": "REFRT",
"links": [
26
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "GetReferenceImageRT"
},
"widgets_values": []
},
{
"id": 22,
"type": "GetVideoDriveParams",
"pos": {
"0": 960,
"1": 552
},
"size": {
"0": 330,
"1": 98
},
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "face_toolkits",
"type": "FACE_TOOLKITS",
"link": 27
},
{
"name": "images",
"type": "IMAGE",
"link": 31
},
{
"name": "ref_rt",
"type": "REFRT",
"link": 26
}
],
"outputs": [
{
"name": "drive_params",
"type": "DRIVE_PARAMS",
"links": [
32
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "GetVideoDriveParams"
},
"widgets_values": [
0
]
},
{
"id": 31,
"type": "PreviewImage",
"pos": {
"0": 934,
"1": 134
},
"size": [
210,
246
],
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 40
}
],
"outputs": [],
"properties": {
"Node name for S&R": "PreviewImage"
}
},
{
"id": 30,
"type": "CropReferenceImage",
"pos": {
"0": 488,
"1": 306
},
"size": {
"0": 252,
"1": 46
},
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "image",
"type": "IMAGE",
"link": 36
},
{
"name": "face_toolkits",
"type": "FACE_TOOLKITS",
"link": 37
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
38,
39,
40
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "CropReferenceImage"
}
}
],
"links": [
[
13,
2,
0,
11,
0,
"FACE_TOOLKITS"
],
[
26,
11,
0,
22,
2,
"REFRT"
],
[
27,
2,
0,
22,
0,
"FACE_TOOLKITS"
],
[
31,
25,
0,
22,
1,
"IMAGE"
],
[
32,
22,
0,
26,
2,
"DRIVE_PARAMS"
],
[
34,
28,
0,
26,
0,
"HMIMAGEPIPELINE"
],
[
35,
26,
0,
29,
0,
"IMAGE"
],
[
36,
8,
0,
30,
0,
"IMAGE"
],
[
37,
2,
0,
30,
1,
"FACE_TOOLKITS"
],
[
38,
30,
0,
11,
1,
"IMAGE"
],
[
39,
30,
0,
26,
1,
"IMAGE"
],
[
40,
30,
0,
31,
0,
"IMAGE"
]
],
"groups": [],
"config": {},
"extra": {
"ds": {
"scale": 0.9090909090909091,
"offset": [
328.0506633321691,
3.5667581298829703
]
}
},
"version": 0.4
}
workflows/hellomeme_image_workflow.json
+336 -242
View File
@@ -1,47 +1,116 @@
{
"last_node_id": 29,
"last_link_id": 35,
"last_node_id": 31,
"last_link_id": 40,
"nodes": [
{
"id": 11,
"type": "GetReferenceImageRT",
"id": 28,
"type": "HMImagePipelineLoader",
"pos": {
"0": 536,
"1": 516
"0": 111,
"1": 102
},
"size": {
"0": 241.79998779296875,
"1": 46
"0": 352.79998779296875,
"1": 106
},
"flags": {},
"order": 4,
"order": 0,
"mode": 0,
"inputs": [
{
"name": "face_toolkits",
"type": "FACE_TOOLKITS",
"link": 13
},
{
"name": "image",
"type": "IMAGE",
"link": 12
}
],
"inputs": [],
"outputs": [
{
"name": "REFRT",
"type": "REFRT",
"name": "hm_image_pipeline",
"type": "HMIMAGEPIPELINE",
"links": [
26
34
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "GetReferenceImageRT"
"Node name for S&R": "HMImagePipelineLoader"
},
"widgets_values": []
"widgets_values": [
"None",
"None",
0
]
},
{
"id": 25,
"type": "LoadImage",
"pos": {
"0": 120,
"1": 796
},
"size": {
"0": 315,
"1": 314
},
"flags": {},
"order": 1,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
31
],
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": null
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"yao.jpg",
"image"
]
},
{
"id": 8,
"type": "LoadImage",
"pos": {
"0": 120,
"1": 414
},
"size": {
"0": 315,
"1": 314
},
"flags": {},
"order": 2,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
36
],
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": null
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"majicmix2.jpg",
"image"
]
},
{
"id": 2,
@@ -55,16 +124,17 @@
"1": 58
},
"flags": {},
"order": 0,
"order": 3,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "FACE_TOOLKITS",
"name": "face_toolkits",
"type": "FACE_TOOLKITS",
"links": [
13,
27
27,
37
],
"slot_index": 0
}
@@ -76,19 +146,210 @@
0
]
},
{
"id": 30,
"type": "CropReferenceImage",
"pos": {
"0": 506,
"1": 235
},
"size": {
"0": 252,
"1": 46
},
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "image",
"type": "IMAGE",
"link": 36
},
{
"name": "face_toolkits",
"type": "FACE_TOOLKITS",
"link": 37
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
38,
39,
40
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "CropReferenceImage"
},
"widgets_values": []
},
{
"id": 31,
"type": "PreviewImage",
"pos": {
"0": 858,
"1": 37
},
"size": {
"0": 210,
"1": 246
},
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 40
}
],
"outputs": [],
"properties": {
"Node name for S&R": "PreviewImage"
},
"widgets_values": []
},
{
"id": 11,
"type": "GetReferenceImageRT",
"pos": {
"0": 807,
"1": 370
},
"size": {
"0": 241.79998779296875,
"1": 46
},
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "face_toolkits",
"type": "FACE_TOOLKITS",
"link": 13
},
{
"name": "image",
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@@ -123,208 +384,9 @@
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workflows/hellomeme_video_cropref_workflow.json → workflows/hellomeme_video_simple_workflow.json
+239 -239
View File
@@ -1,93 +1,20 @@
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@@ -192,19 +119,84 @@
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@@ -234,72 +227,19 @@
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@@ -466,7 +331,7 @@
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@@ -475,17 +340,144 @@
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workflows/hellomeme_video_workflow.json
+421 -327
View File
@@ -1,66 +1,13 @@
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