update

README.md

@@ -22,8 +22,11 @@
 
 
 ## 🔆 New Features/Updates
-- ✅ `11/14/2024` Added the `HMControlNet2` module, which uses the `PD-FGC` motion module to extract facial expression information (`drive_exp2`); restructured the ComfyUI interface; and optimized VRAM usage.
+- ✅ `11/29/2024` a.Optimize the algorithm; b.Add VAE selection functionality; c.Introduce a super-resolution feature.
 
+     [![YouTube Demo](https://img.youtube.com/vi/fM5nyn6q02Y/0.jpg)](https://www.youtube.com/watch?v=fM5nyn6q02Y)
+
+- ✅ `11/14/2024` Added the `HMControlNet2` module, which uses the `PD-FGC` motion module to extract facial expression information (`drive_exp2`); restructured the ComfyUI interface; and optimized VRAM usage.
 
      [![YouTube Demo](https://img.youtube.com/vi/ZvoMHyRm310/0.jpg)](https://www.youtube.com/watch?v=ZvoMHyRm310)
 
@@ -43,6 +46,19 @@ This repository is the official implementation of the [`HelloMeme`](https://arxi
 > 
 > **Loras** under: `ComfyUI/models/checkpoints`
 
+### workflows
+
+| workflow file | Video Generation | Image Generation | HMControlNet | HMControlNet2 | Super-Resolution |
+|---------------|------------------|------------------|-----------|---------------|-----------------|
+| hellomeme_image_workflow.json |                  | ✅                | ✅ |  |  |
+| hellomeme_video_workflow.json | ✅                |                  | ✅ |  |  |
+| hellomeme_image_sr_workflow.json |                  | ✅                | ✅ |  | ✅ |
+| hellomeme_video_sr_workflow.json | ✅                |                  | ✅ |  | ✅ |
+| hellomeme_image_v2_workflow.json |                  | ✅                |  | ✅ |  |
+| hellomeme_video_v2_workflow.json | ✅                |                  |  | ✅ |  |
+| hellomeme_image_v2_sr_workflow.json |                  | ✅                |  | ✅ | ✅ |
+| hellomeme_video_v2_sr_workflow.json | ✅                |                  |  | ✅ | ✅ |
+
 ### Image Generation Interface
 
 <p align="center">

hellomeme/models/hm_blocks.py

@@ -136,7 +136,9 @@ class SKReferenceAttention(nn.Module):
 
     def forward(self, hidden_states, ref_stats, num_frames):
         h, w = hidden_states.shape[-2:]
-        ref_stats = ref_stats.repeat_interleave(num_frames, dim=0)
+
+        if ref_stats.shape[0] != hidden_states.shape[0]:
+            ref_stats = ref_stats.repeat_interleave(num_frames, dim=0)
         cat_stats = torch.cat([hidden_states, ref_stats], dim=-1)
 
         cat_stats = rearrange(cat_stats.contiguous(), "b c h w -> (b h) w c")

hellomeme/pipelines/pipline_hm_image.py

@@ -9,8 +9,8 @@
 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
-from einops import rearrange
 import torch
 
 from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
@@ -29,28 +29,36 @@ class HMImagePipeline(StableDiffusionImg2ImgPipeline):
     def caryomitosis(self, **kwargs):
         if not hasattr(self, "unet_ref"):
             self.unet_ref = HMDenoising3D.from_unet2d(self.unet)
+            self.unet_ref.cpu()
         if not isinstance(self.unet, HMDenoising3D):
             unet = HMDenoising3D.from_unet2d(unet=self.unet)
             # todo: 不够优雅
             del self.unet
             self.unet = unet
+            self.unet.cpu()
+        self.vae_decode = copy.deepcopy(self.vae)
 
-    def insert_hm_modules(self, dtype, device):
+    def insert_hm_modules(self, dtype):
         if isinstance(self.unet, HMDenoising3D):
             hm_adapter = HMReferenceAdapter.from_pretrained('songkey/hm_reference')
             self.unet.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()
+            self.unet_ref.insert_reference_adapter(hm_adapter)
+            self.unet_ref.to(device='cpu', dtype=dtype).eval()
+            self.unet.to(device='cpu', 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()
+        self.mp_control.to(device='cpu', dtype=dtype).eval()
 
         if hasattr(self, "mp_control2"):
             del self.mp_control2
         self.mp_control2 = HMControlNet2.from_pretrained('songkey/hm_control2')
-        self.mp_control2 = self.mp_control2.to(device=device, dtype=dtype).eval()
+        self.mp_control2.to(device='cpu', dtype=dtype).eval()
+
+        self.vae.to(device='cpu', dtype=dtype).eval()
+        self.vae_decode.to(device='cpu', dtype=dtype).eval()
+        self.text_encoder.to(device='cpu', dtype=dtype).eval()
 
     @torch.no_grad()
     def __call__(
@@ -71,6 +79,7 @@ class HMImagePipeline(StableDiffusionImg2ImgPipeline):
             ip_adapter_image: Optional[PipelineImageInput] = None,
             ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
             output_type: Optional[str] = "pil",
+            device: Optional[str] = "cpu",
             return_dict: bool = True,
             cross_attention_kwargs: Optional[Dict[str, Any]] = None,
             clip_skip: int = None,
@@ -126,13 +135,14 @@ class HMImagePipeline(StableDiffusionImg2ImgPipeline):
         else:
             batch_size = prompt_embeds.shape[0]
 
-        device = self.device
+        # device = self.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
         )
-        print('device:', device)
+
+        self.text_encoder.to(device=device)
         prompt_embeds, negative_prompt_embeds = self.encode_prompt(
             prompt,
             device,
@@ -144,6 +154,8 @@ class HMImagePipeline(StableDiffusionImg2ImgPipeline):
             lora_scale=text_encoder_lora_scale,
             clip_skip=self.clip_skip,
         )
+        self.text_encoder.cpu()
+
         # 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
@@ -169,10 +181,14 @@ class HMImagePipeline(StableDiffusionImg2ImgPipeline):
         timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
+        # 6. Prepare reference latents
+        self.vae.to(device=device)
         ref_latents = [
             retrieve_latents(self.vae.encode(image[i: i + 1].to(device=device)), generator=generator)
             for i in range(batch_size)
         ]
+        self.vae.cpu()
+
         ref_latents = torch.cat(ref_latents, dim=0)
         ref_latents = self.vae.config.scaling_factor * ref_latents
 
@@ -219,6 +235,7 @@ class HMImagePipeline(StableDiffusionImg2ImgPipeline):
             latent_model_input.unsqueeze(2),
             0,
             encoder_hidden_states=prompt_embeds,
+            # control_hidden_states=control_latents,
             return_dict=False,
         )[1]
         self.unet_ref.cpu()
@@ -237,6 +254,7 @@ class HMImagePipeline(StableDiffusionImg2ImgPipeline):
         # 8. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
         self._num_timesteps = len(timesteps)
+        self.unet.to(device=device)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
                 if self.interrupt:
@@ -284,12 +302,16 @@ class HMImagePipeline(StableDiffusionImg2ImgPipeline):
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+        self.unet.cpu()
+
+        self.vae_decode.to(device=device)
         if not output_type == "latent":
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
+            image = self.vae_decode.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
                 0
             ]
         else:
             image = latents
+        self.vae_decode.cpu()
 
         do_denormalize = [True] * image.shape[0]
 
@@ -298,4 +320,4 @@ class HMImagePipeline(StableDiffusionImg2ImgPipeline):
         # Offload all models
         self.maybe_free_model_hooks()
 
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None), latents.detach().cpu() / self.vae.config.scaling_factor

hellomeme/pipelines/pipline_hm_video.py

@@ -8,6 +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
@@ -22,45 +23,53 @@ from diffusers import StableDiffusionImg2ImgPipeline, MotionAdapter, EulerDiscre
 
 from ..models import HMDenoising3D, HMDenoisingMotion, HMControlNet, HMControlNet2
 from ..models import HMReferenceAdapter
-from ..utils import dicts_to_device, cat_dicts
-
+from ..utils import dicts_to_device, cat_dicts, cat_dicts_ref
 
 class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
     def caryomitosis(self, patch_frames=12, **kwargs):
         if hasattr(self, "unet_ref"):
             del self.unet_ref
         self.unet_ref = HMDenoising3D.from_unet2d(self.unet)
+        self.unet_ref.cpu()
+        self.unet_pre = HMDenoising3D.from_unet2d(self.unet)
+        self.unet_pre.cpu()
 
-        if patch_frames >= 14:
-            self.num_frames = 16
-            adapter = MotionAdapter.from_pretrained("songkey/hm_animatediff", torch_dtype=torch.float16)
-        else:
-            self.num_frames = 12
-            adapter = MotionAdapter.from_pretrained("songkey/hm_animatediff_frame12", torch_dtype=torch.float16)
+        self.num_frames = 12
+        adapter = MotionAdapter.from_pretrained("songkey/hm_animatediff_frame12", 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):
+        self.vae_decode = copy.deepcopy(self.vae)
+
+    def insert_hm_modules(self, dtype):
         hm_adapter = HMReferenceAdapter.from_pretrained('songkey/hm_reference')
         if isinstance(self.unet, HMDenoisingMotion):
             self.unet.insert_reference_adapter(hm_adapter)
-            self.unet = self.unet.to(device=device, dtype=dtype).eval()
+            self.unet.to(device='cpu', 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()
+            self.unet_ref.to(device='cpu', dtype=dtype).eval()
+
+        if hasattr(self, "unet_pre"):
+            self.unet_pre.insert_reference_adapter(hm_adapter)
+            self.unet_pre.to(device='cpu', 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()
+        self.mp_control.to(device='cpu', dtype=dtype).eval()
 
         if hasattr(self, "mp_control2"):
             del self.mp_control2
         self.mp_control2 = HMControlNet2.from_pretrained('songkey/hm_control2')
-        self.mp_control2 = self.mp_control2.to(device=device, dtype=dtype).eval()
+        self.mp_control2.to(device='cpu', dtype=dtype).eval()
+
+        self.vae.to(device='cpu', dtype=dtype).eval()
+        self.vae_decode.to(device='cpu', dtype=dtype).eval()
+        self.text_encoder.to(device='cpu', dtype=dtype).eval()
 
     @torch.no_grad()
     def __call__(
@@ -82,6 +91,7 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
             ip_adapter_image: Optional[PipelineImageInput] = None,
             ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
             output_type: Optional[str] = "pil",
+            device: Optional[str] = "cpu",
             return_dict: bool = True,
             cross_attention_kwargs: Optional[Dict[str, Any]] = None,
             clip_skip: int = None,
@@ -137,12 +147,13 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
         else:
             batch_size = prompt_embeds.shape[0]
 
-        device = self.device
+        # device = self.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
         )
+        self.text_encoder.to(device=device)
         prompt_embeds, negative_prompt_embeds = self.encode_prompt(
             prompt,
             device,
@@ -154,6 +165,7 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
             lora_scale=text_encoder_lora_scale,
             clip_skip=self.clip_skip,
         )
+        self.text_encoder.cpu()
         # 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
@@ -172,20 +184,16 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
         # 4. Preprocess image
         image = self.image_processor.preprocess(image).to(device=device, dtype=prompt_embeds.dtype)
 
-        # 5. set timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler, num_inference_steps, device, timesteps, sigmas
-        )
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-
         raw_video_len = drive_params['condition'].size(2)
 
         # 6. Prepare latent variables
+        self.vae.to(device=device)
         ref_latents = [
             retrieve_latents(self.vae.encode(image[i: i + 1].to(device=device)), generator=generator)
             for i in range(batch_size)
         ]
+        self.vae.cpu()
+
         ref_latents = torch.cat(ref_latents, dim=0)
         ref_latents = self.vae.config.scaling_factor * ref_latents
         c, h, w = ref_latents.shape[1:]
@@ -201,14 +209,6 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
         )
 
         ref_latents = ref_latents.unsqueeze(2)
-        self.unet_ref.to(device=device)
-        cached_res = self.unet_ref(
-            torch.cat([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
@@ -218,12 +218,23 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
                 guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
             ).to(device=device, dtype=prompt_embeds.dtype)
 
+        self.unet_ref.to(device=device)
+        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]
+        self.unet_ref.cpu()
+
         pre_latents = []
         control_latents = []
         control_latent1_neg, control_latent2_neg = None, None
         base_noise = randn_tensor([batch_size, c, 1, h, w], dtype=prompt_embeds.dtype, generator=generator).to(device=device)
         self.mp_control.to(device=device)
         self.mp_control2.to(device=device)
+        self.unet_pre.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)
@@ -266,9 +277,7 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
                     beta_schedule="scaled_linear",
                 )
 
-                tmp_timesteps, _ = retrieve_timesteps(
-                    scheduler, 8, device, None, sigmas
-                )
+                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:
@@ -278,7 +287,7 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
                     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_ref(
+                    noise_pred_chunk = self.unet_pre(
                         latent_model_input,
                         t,
                         encoder_hidden_states=prompt_embeds,
@@ -303,6 +312,7 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
         pre_latents = torch.cat(pre_latents, dim=2)
         self.mp_control.cpu()
         self.mp_control2.cpu()
+        self.unet_pre.cpu()
         self.unet_ref.cpu()
 
         chunk_size = self.num_frames
@@ -328,20 +338,25 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
 
         drive_idx_chunks[-1] = list(range(drive_idx_chunks[-1][0], paded_video_len))
 
-        # 5. set timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler, num_inference_steps, device, None, sigmas
+        scheduler = EulerDiscreteScheduler(
+            num_train_timesteps=1000,
+            beta_start=0.00085,
+            beta_end=0.012,
+            beta_schedule="scaled_linear",
         )
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
+
+        # 5. set timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(scheduler, num_inference_steps, device, None, sigmas)
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
         # 8. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        num_warmup_steps = len(timesteps) - num_inference_steps * scheduler.order
         self._num_timesteps = len(timesteps)
 
         base_noise = randn_tensor([batch_size, c, paded_video_len, h, w], dtype=prompt_embeds.dtype, generator=generator)
-        latents = self.scheduler.add_noise(pre_latents, base_noise, latent_timestep)
+        latents = scheduler.add_noise(pre_latents, base_noise, latent_timestep)
 
+        self.unet.to(device=device)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
                 noise_pred = torch.zeros([batch_size * 2 if self.do_classifier_free_guidance
@@ -351,12 +366,13 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
                 latent_model_input_all = torch.cat([latents.clone(), latents.clone()], dim=0) if \
                     self.do_classifier_free_guidance else latents.clone()
 
-                latent_model_input_all = self.scheduler.scale_model_input(latent_model_input_all, t)
+                latent_model_input_all = scheduler.scale_model_input(latent_model_input_all, t)
                 for cidx, chunk in enumerate(drive_idx_chunks):
                     if self.interrupt:
                         continue
 
                     control_latent = cat_dicts([control_latents[ix] for ix in chunk], dim=2)
+                    # cached_res = cat_dicts_ref([cached_latents[ix] for ix in chunk])
                     latent_model_input = latent_model_input_all[:, :, chunk].to(device=device)
 
                     # predict the noise residual
@@ -382,7 +398,7 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
                     noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
 
                 # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred.cpu(), t, latents,
+                latents = scheduler.step(noise_pred.cpu(), t, latents,
                                               **extra_step_kwargs, return_dict=False)[0]
 
                 if callback_on_step_end is not None:
@@ -396,22 +412,24 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
                     negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
 
                 # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % 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)
+                        step_idx = i // getattr(scheduler, "order", 1)
                         callback(step_idx, t, latents)
+        self.unet.cpu()
+        latents_res = latents / self.vae.config.scaling_factor
 
-        latents = latents / self.vae.config.scaling_factor
-
+        self.vae_decode.to(device=device)
         res_frames = []
         with self.progress_bar(total=raw_video_len) as progress_bar:
             for i in range(raw_video_len):
-                ret_tensor = self.vae.decode(latents[:, :, i, :, :].to(device=device),
+                ret_tensor = self.vae_decode.decode(latents_res[:, :, i, :, :].to(device=device),
                                              return_dict=False, generator=generator)[0]
                 ret_image = self.image_processor.postprocess(ret_tensor, output_type=output_type)
                 res_frames.append(ret_image)
                 progress_bar.set_description(f"VAE DECODE")
                 progress_bar.update()
-        return res_frames
+        self.vae_decode.cpu()
+        return res_frames, latents_res

hellomeme/utils.py

@@ -18,10 +18,13 @@ import subprocess
 from einops import rearrange
 from .tools.utils import transform_points
 from .tools.hello_3dmm import get_project_points_rect
-from diffusers.pipelines.stable_diffusion.convert_from_ckpt import convert_ldm_unet_checkpoint
 
 from safetensors import safe_open
 
+def get_torch_device(gpu_id=-1):
+    if gpu_id < 0: return torch.device("cpu")
+    gpu_id = min(max(-1, gpu_id), torch.cuda.device_count())
+    return torch.device(f"cuda:{gpu_id}")
 
 def merge_dicts(dictl, dictr, wl=0.5):
     res = {}
@@ -36,6 +39,11 @@ def cat_dicts(dicts, dim=0):
         res[k] = torch.cat([d[k].clone() for d in dicts], dim=dim)
     return res
 
+def cat_dicts_ref(dicts):
+    res = {}
+    for k in dicts[0].keys():
+        res[k] = rearrange(torch.cat([d[k].clone().unsqueeze(1) for d in dicts], dim=1), "b f c h w -> (b f) c h w ")
+    return res
 
 def dicts_to_device(dicts, device):
     ret = []
@@ -55,11 +63,6 @@ def load_safetensors(model_path):
     return tensors
 
 
-def load_unet_from_safetensors(safetensors_path, unet_config):
-    original_stats = load_safetensors(safetensors_path)
-    return convert_ldm_unet_checkpoint(original_stats, unet_config)
-
-
 def image_preprocess(np_bgr, size, dtype=torch.float32):
     img_np = cv2.resize(np_bgr, size)
     return np_bgr_to_tensor(img_np, dtype)

meme.py

@@ -1,16 +1,18 @@
 import os.path as osp
 import random
 
-import torch
 import numpy as np
 import cv2
 import sys
 
-
 from PIL import Image
 import subprocess
 
+import torch
+from einops import rearrange
+
 import importlib.metadata
+import folder_paths
 
 cur_dir = osp.dirname(osp.abspath(__file__))
 
@@ -33,61 +35,99 @@ from .hellomeme.utils import (get_drive_expression,
                               get_drive_pose,
                               crop_and_resize,
                               det_landmarks,
-                              load_data_list,
-                              load_unet_from_safetensors
+                              get_torch_device,
+                              load_safetensors,
                               )
-
+from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (convert_ldm_unet_checkpoint,
+                                                                    convert_ldm_vae_checkpoint)
 from .hellomeme.tools import Hello3DMMPred, HelloARKitBSPred, HelloFaceAlignment, HelloCameraDemo, FanEncoder
 from .hellomeme import HMImagePipeline, HMVideoPipeline
 from transformers import CLIPVisionModelWithProjection
 
 DEFAULT_PROMPT = '(best quality), highly detailed, ultra-detailed, headshot, person, well-placed five sense organs, looking at the viewer, centered composition, sharp focus, realistic skin texture'
 
+def get_models_files():
+    checkpoint_files = folder_paths.get_filename_list("checkpoints")
+
+    vae_files = folder_paths.get_filename_list("vae")
+    vae_files = ["[vae] " + x for x in vae_files] + \
+                ["[checkpoint] " + x for x in checkpoint_files]
+
+    lora_files = folder_paths.get_filename_list("loras")
+
+    return ['SD1.5'] + checkpoint_files, ['same as checkpoint', 'SD1.5 default vae'] + vae_files, ['None'] + lora_files
+
+def append_pipline_weights(pipeline, checkpoint=None, lora=None, vae=None):
+    ### load customized checkpoint or lora here:
+    ## checkpoints
+
+    raw_stats = None
+    if checkpoint and not checkpoint.startswith('SD1.5'):
+        checkpoint_path = folder_paths.get_full_path_or_raise("checkpoints", checkpoint)
+        if osp.exists(checkpoint_path):
+            print("Loading checkpoint from", checkpoint_path)
+            if checkpoint_path.endswith('.safetensors'):
+                raw_stats = load_safetensors(checkpoint_path)
+            else:
+                raw_stats = torch.load(checkpoint_path)
+
+            if raw_stats:
+                state_dict = convert_ldm_unet_checkpoint(raw_stats, pipeline.unet_ref.config)
+                if hasattr(pipeline, 'unet_pre'):
+                    pipeline.unet_pre.load_state_dict(state_dict, strict=False)
+                pipeline.unet.load_state_dict(state_dict, strict=False)
+
+    if vae and not vae.startswith('SD1.5 default vae'):
+        raw_vae_stats = raw_stats
+        real_vae_path = ''
+        if vae.startswith("[checkpoint] "):
+            real_vae_path = folder_paths.get_full_path_or_raise("checkpoints", vae.replace("[checkpoint] ", ""))
+        elif vae.startswith("[vae] "):
+            real_vae_path = folder_paths.get_full_path_or_raise("vae", vae.replace("[vae] ", ""))
+        if osp.isfile(real_vae_path):
+            print("Loading vae from", real_vae_path)
+            if real_vae_path.endswith('.safetensors'):
+                raw_vae_stats = load_safetensors(real_vae_path)
+            else:
+                raw_vae_stats = torch.load(real_vae_path)
+
+        if raw_vae_stats:
+            vae_state_dict = convert_ldm_vae_checkpoint(raw_vae_stats, pipeline.vae_decode.config)
+            if hasattr(pipeline, 'vae_decode'):
+                pipeline.vae_decode.load_state_dict(vae_state_dict, strict=True)
+
+    ### lora
+    if lora and not lora.startswith('None'):
+        lora_path = folder_paths.get_full_path_or_raise("loras", lora)
+        if osp.exists(lora_path):
+            print("Loading lora from", lora_path)
+            pipeline.load_lora_weights(osp.dirname(lora_path), weight_name=osp.basename(lora_path), adapter_name="lora")
 
 class HMImagePipelineLoader:
     @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'))
+        checkpoint_files, vae_files, lora_files = get_models_files()
 
         return {
             "optional": {
-                "checkpoint_path": (['SD1.5'] + checkpoint_files, ),
-                "lora_path": (['None'] + lora_files, ),
-                "gpu_id": ("INT", {"default": 0}),
+                "checkpoint": (checkpoint_files, ),
+                "lora": (lora_files, ),
+                "vae": (vae_files, ),
             }
         }
     RETURN_TYPES = ("HMIMAGEPIPELINE", )
     RETURN_NAMES = ("hm_image_pipeline", )
     FUNCTION = "load_pipeline"
     CATEGORY = "hellomeme"
-    def load_pipeline(self, checkpoint_path=None, lora_path=None, gpu_id=0):
+    def load_pipeline(self, checkpoint=None, lora=None, vae=None):
         dtype = torch.float16
-        if gpu_id >= 0:
-            device = torch.device("cuda:{}".format(gpu_id))
-        else:
-            device = torch.device("cpu")
-        pipeline = HMImagePipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5").to(dtype=dtype, device=device)
+        pipeline = HMImagePipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
+        pipeline.to(dtype=dtype)
         pipeline.caryomitosis()
 
-        ### load customized checkpoint or lora here:
-        ## checkpoints
+        append_pipline_weights(pipeline, checkpoint=checkpoint, lora=lora, vae=vae)
 
-        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)
+        pipeline.insert_hm_modules(dtype=dtype)
         
         return (pipeline, )
 
@@ -95,15 +135,14 @@ class HMImagePipelineLoader:
 class HMVideoPipelineLoader:
     @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'))
+        checkpoint_files, vae_files, lora_files = get_models_files()
 
         return {
             "optional": {
-                "checkpoint_path": (['SD1.5'] + checkpoint_files, ),
-                "lora_path": (['None'] + lora_files, ),
-                "patch_frames": ([12, 16], ),
-                "gpu_id": ("INT", {"default": 0}, ),
+                "checkpoint": (checkpoint_files, ),
+                "lora": (lora_files, ),
+                "vae": (vae_files, ),
+                "patch_frames": ([12], ),
             }
         }
 
@@ -112,34 +151,15 @@ class HMVideoPipelineLoader:
     FUNCTION = "load_pipeline"
     CATEGORY = "hellomeme"
 
-    def load_pipeline(self, checkpoint_path=None, lora_path=None, patch_frames=12, gpu_id=0):
+    def load_pipeline(self, checkpoint=None, lora=None, vae=None, patch_frames=12):
         dtype = torch.float16
-        if gpu_id >= 0:
-            device = torch.device("cuda:{}".format(gpu_id))
-        else:
-            device = torch.device("cpu")
-        pipeline = HMVideoPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5").to(dtype=dtype,
-                                                                                                     device=device)
+        pipeline = HMVideoPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
+        pipeline.to(dtype=dtype)
         pipeline.caryomitosis(patch_frames=patch_frames)
 
-        ### load customized checkpoint or lora here:
-        ## checkpoints
+        append_pipline_weights(pipeline, checkpoint=checkpoint, lora=lora, vae=vae)
 
-        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)
+        pipeline.insert_hm_modules(dtype=dtype)
 
         return (pipeline,)
 
@@ -149,7 +169,7 @@ class HMFaceToolkitsLoader:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "gpu_id": ("INT", {"default": 0}),
+                "gpu_id": ("INT", {"default": 0, "min": -1, "max": 16}, ),
             }
         }
 
@@ -159,20 +179,20 @@ class HMFaceToolkitsLoader:
     CATEGORY = "hellomeme"
     def load_face_toolkits(self, gpu_id):
         dtype = torch.float16
-        if gpu_id < 0:
-            device = torch.device('cpu')
-        else:
-            device = torch.device(f'cuda:{gpu_id}')
+        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+                'h94/IP-Adapter', subfolder='models/image_encoder')
+        image_encoder.to(dtype=dtype).cpu()
+        pd_fpg_motion = FanEncoder.from_pretrained("songkey/pd_fgc_motion")
+        pd_fpg_motion.to(dtype=dtype).cpu()
         return (
             dict(
-                 device=device,
+                 device=get_torch_device(gpu_id),
                  dtype=dtype,
-                 pd_fpg_motion=FanEncoder.from_pretrained("songkey/pd_fgc_motion").to(dtype=dtype),
+                 pd_fpg_motion=pd_fpg_motion,
                  face_aligner=HelloCameraDemo(face_alignment_module=HelloFaceAlignment(gpu_id=gpu_id), reset=False),
                  harkit_bs=HelloARKitBSPred(gpu_id=gpu_id),
                  h3dmm=Hello3DMMPred(gpu_id=gpu_id),
-                 image_encoder=CLIPVisionModelWithProjection.from_pretrained(
-                    'h94/IP-Adapter', subfolder='models/image_encoder').to(dtype=dtype)
+                 image_encoder=image_encoder
             ),
         )
 
@@ -307,9 +327,12 @@ class HMPipelineImage:
                 "trans_ratio": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                 "prompt": ("STRING", {"default": DEFAULT_PROMPT}),
                 "negative_prompt": ("STRING", {"default": ''}),
-                "steps": ("INT", {"default": 25, "min": 1, "max": 1000}),
-                "seed": ("INT", {"default": -1, "min": -1, "max": 100000}),
+                "steps": ("INT", {"default": 25, "min": 1, "max": 10000,
+                                  "tooltip": "The number of steps used in the denoising process."}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff,
+                                 "tooltip": "The random seed used for creating the noise."}),
                 "guidance_scale": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                "gpu_id": ("INT", {"default": 0, "min": -1, "max": 16}, ),
             },
                 "optional": {
                     "drive_exp": ("DRIVE_EXPRESSION", {"default": None},),
@@ -317,7 +340,7 @@ class HMPipelineImage:
                 }
         }
 
-    RETURN_TYPES = ("IMAGE",)
+    RETURN_TYPES = ("IMAGE", "LATENT", )
     FUNCTION = "sample"
     CATEGORY = "hellomeme"
 
@@ -332,9 +355,12 @@ class HMPipelineImage:
                prompt=DEFAULT_PROMPT,
                negative_prompt='',
                steps=25,
-               seed=-1,
-               guidance_scale=2.0
+               seed=0,
+               guidance_scale=2.0,
+               gpu_id=0
                ):
+        device = get_torch_device(gpu_id)
+
         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)
@@ -357,12 +383,9 @@ class HMPipelineImage:
         if isinstance(drive_exp2, dict):
             drive_params.update(drive_exp2)
 
-        if seed < 0:
-            generator = torch.Generator().manual_seed(random.randint(0, 100000))
-        else:
-            generator = torch.Generator().manual_seed(seed)
+        generator = torch.Generator().manual_seed(seed)
 
-        result_img = hm_image_pipeline(
+        result_img, latents = hm_image_pipeline(
             prompt=[prompt],
             strength=1.0,
             image=image_pil,
@@ -371,9 +394,10 @@ class HMPipelineImage:
             negative_prompt=[negative_prompt],
             guidance_scale=guidance_scale,
             generator=generator,
+            device=device,
             output_type='np'
         )
-        return (torch.from_numpy(np.clip(result_img[0], 0, 1)), )
+        return (torch.from_numpy(np.clip(result_img[0], 0, 1)), dict(samples=latents), )
 
 
 class HMPipelineVideo:
@@ -389,9 +413,12 @@ class HMPipelineVideo:
                         "patch_overlap": ("INT", {"default": 4, "min": 0, "max": 5}),
                         "prompt": ("STRING", {"default": DEFAULT_PROMPT}),
                         "negative_prompt": ("STRING", {"default": ''}),
-                        "steps": ("INT", {"default": 25, "min": 1, "max": 1000}),
-                        "seed": ("INT", {"default": -1, "min": -1, "max": 100000}),
+                        "steps": ("INT", {"default": 25, "min": 1, "max": 10000,
+                                          "tooltip": "The number of steps used in the denoising process."}),
+                        "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff,
+                                         "tooltip": "The random seed used for creating the noise."}),
                         "guidance_scale": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                        "gpu_id": ("INT", {"default": 0, "min": -1, "max": 16}, ),
                      },
                     "optional": {
                         "drive_exp": ("DRIVE_EXPRESSION", {"default": None},),
@@ -399,7 +426,7 @@ class HMPipelineVideo:
                     }
                 }
 
-    RETURN_TYPES = ("IMAGE",)
+    RETURN_TYPES = ("IMAGE", "LATENT", )
     FUNCTION = "sample"
     CATEGORY = "hellomeme"
 
@@ -415,9 +442,12 @@ class HMPipelineVideo:
                 prompt=DEFAULT_PROMPT,
                 negative_prompt="",
                 steps=25,
-                seed=-1,
-                guidance_scale=2.0
+                seed=0,
+                guidance_scale=2.0,
+                gpu_id=0
         ):
+        device = get_torch_device(gpu_id)
+
         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)
@@ -432,10 +462,7 @@ class HMPipelineVideo:
 
         _, ref_trans = face_toolkits['h3dmm'].forward_params(image_np, ref_landmark)
 
-        if seed < 0:
-            generator = torch.Generator().manual_seed(random.randint(0, 100000))
-        else:
-            generator = torch.Generator().manual_seed(seed)
+        generator = torch.Generator().manual_seed(seed)
 
         drive_rot, drive_trans = drive_pose['rot'], drive_pose['trans']
         condition = gen_control_heatmaps(drive_rot, drive_trans, ref_trans, 512, trans_ratio)
@@ -446,7 +473,7 @@ class HMPipelineVideo:
         if isinstance(drive_exp2, dict):
             drive_params.update(drive_exp2)
 
-        res_frames = hm_video_pipeline(
+        res_frames, latents = hm_video_pipeline(
             prompt=[prompt],
             strength=1.0,
             image=image_pil,
@@ -456,10 +483,13 @@ class HMPipelineVideo:
             negative_prompt=[negative_prompt],
             guidance_scale=guidance_scale,
             generator=generator,
+            device=device,
             output_type='np'
         )
         res_frames = [np.clip(x[0], 0, 1) for x in res_frames]
-        return (torch.from_numpy(np.array(res_frames)), )
+        latents = rearrange(latents[0], 'c f h w -> f c h w')
+
+        return (torch.from_numpy(np.array(res_frames)), dict(samples=latents), )
 
 
 NODE_CLASS_MAPPINGS = {

workflows/hellomeme_image_sr_workflow.json

@@ -0,0 +1,663 @@
+{
+  "last_node_id": 65,
+  "last_link_id": 133,
+  "nodes": [
+    {
+      "id": 39,
+      "type": "CropPortrait",
+      "pos": [
+        516,
+        537
+      ],
+      "size": [
+        241.79998779296875,
+        46
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 128
+        },
+        {
+          "name": "face_toolkits",
+          "type": "FACE_TOOLKITS",
+          "link": 57
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            118
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CropPortrait"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 44,
+      "type": "GetDrivePose",
+      "pos": [
+        852,
+        661
+      ],
+      "size": [
+        304.79998779296875,
+        66
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "face_toolkits",
+          "type": "FACE_TOOLKITS",
+          "link": 65
+        },
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 116
+        },
+        {
+          "name": "landmarks",
+          "type": "FACELANDMARKS222",
+          "link": 64
+        }
+      ],
+      "outputs": [
+        {
+          "name": "drive_pose",
+          "type": "DRIVE_POSE",
+          "links": [
+            120
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "GetDrivePose"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 57,
+      "type": "LoadImage",
+      "pos": [
+        45,
+        867
+      ],
+      "size": [
+        315,
+        314
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            115,
+            116,
+            125
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "yao.jpg",
+        "image"
+      ]
+    },
+    {
+      "id": 43,
+      "type": "GetFaceLandmarks",
+      "pos": [
+        465,
+        771
+      ],
+      "size": [
+        292.20001220703125,
+        46
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "face_toolkits",
+          "type": "FACE_TOOLKITS",
+          "link": 62
+        },
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 115
+        }
+      ],
+      "outputs": [
+        {
+          "name": "landmarks",
+          "type": "FACELANDMARKS222",
+          "links": [
+            64,
+            126
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "GetFaceLandmarks"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 2,
+      "type": "HMFaceToolkitsLoader",
+      "pos": [
+        43,
+        699
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "face_toolkits",
+          "type": "FACE_TOOLKITS",
+          "links": [
+            57,
+            62,
+            65,
+            119,
+            127
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "HMFaceToolkitsLoader"
+      },
+      "widgets_values": [
+        0
+      ]
+    },
+    {
+      "id": 61,
+      "type": "GetDriveExpression",
+      "pos": [
+        857,
+        823
+      ],
+      "size": [
+        292.20001220703125,
+        66
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "face_toolkits",
+          "type": "FACE_TOOLKITS",
+          "link": 127
+        },
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 125
+        },
+        {
+          "name": "landmarks",
+          "type": "FACELANDMARKS222",
+          "link": 126
+        }
+      ],
+      "outputs": [
+        {
+          "name": "drive_exp",
+          "type": "DRIVE_EXPRESSION",
+          "links": [
+            124
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "GetDriveExpression"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 62,
+      "type": "LoadImage",
+      "pos": [
+        52,
+        289
+      ],
+      "size": [
+        315,
+        314
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            128
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "majicmix2.jpg",
+        "image"
+      ]
+    },
+    {
+      "id": 63,
+      "type": "HMImagePipelineLoader",
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workflows/hellomeme_image_v2_sr_workflow.json

@@ -0,0 +1,664 @@
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workflows/hellomeme_image_v2_workflow.json

@@ -1,127 +1,7 @@
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-          "link": 62
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-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 115
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-      "outputs": [
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-          "name": "landmarks",
-          "type": "FACELANDMARKS222",
-          "links": [
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-            112
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-          "slot_index": 0
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-        "Node name for S&R": "GetFaceLandmarks"
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-      "type": "LoadImage",
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-      "flags": {},
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-      "mode": 0,
-      "inputs": [],
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-      "widgets_values": [
-        "yao.jpg",
-        "image"
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       "id": 39,
       "type": "CropPortrait",
@@ -140,7 +20,7 @@
         {
           "name": "image",
           "type": "IMAGE",
-          "link": 56
+          "link": 128
         },
         {
           "name": "face_toolkits",
@@ -163,42 +43,6 @@
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       "widgets_values": []
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-      "type": "HMFaceToolkitsLoader",
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     {
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       "type": "GetDrivePose",
@@ -263,7 +107,7 @@
         {
           "name": "hm_image_pipeline",
           "type": "HMIMAGEPIPELINE",
-          "link": 123
+          "link": 129
         },
         {
           "name": "face_toolkits",
@@ -289,7 +133,7 @@
         {
           "name": "drive_exp2",
           "type": "DRIVE_EXPRESSION2",
-          "link": 121,
+          "link": 133,
           "shape": 7
         }
       ],
@@ -311,45 +155,11 @@
         "(best quality), highly detailed, ultra-detailed, headshot, person, well-placed five sense organs, looking at the viewer, centered composition, sharp focus, realistic skin texture",
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         25,
-        55468,
+        38454,
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         2
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-          "type": "HMIMAGEPIPELINE",
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@@ -357,10 +167,10 @@
         "0": 1688,
         "1": 584
       },
-      "size": [
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-        246
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       "mode": 0,
@@ -374,14 +184,206 @@
       "outputs": [],
       "properties": {
         "Node name for S&R": "PreviewImage"
-      }
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     {
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+      "flags": {},
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+      "inputs": [],
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+          "type": "IMAGE",
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+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
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+      "properties": {
+        "Node name for S&R": "LoadImage"
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+      "widgets_values": [
+        "majicmix2.jpg",
+        "image"
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+    {
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+      "type": "HMImagePipelineLoader",
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+        "1": 130
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+      "flags": {},
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+      "mode": 0,
+      "inputs": [],
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+          "type": "HMIMAGEPIPELINE",
+          "links": [
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+          "slot_index": 0
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+      "properties": {
+        "Node name for S&R": "HMImagePipelineLoader"
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+      "widgets_values": [
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+        "same as checkpoint",
+        0
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+      "id": 43,
+      "type": "GetFaceLandmarks",
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+      "inputs": [
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+          "type": "FACE_TOOLKITS",
+          "link": 62
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+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 115
+        }
+      ],
+      "outputs": [
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+          "name": "landmarks",
+          "type": "FACELANDMARKS222",
+          "links": [
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+          "slot_index": 0
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+        "Node name for S&R": "GetFaceLandmarks"
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+      "mode": 0,
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+      "properties": {
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+      "widgets_values": [
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+        "image"
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+    },
+    {
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+      "type": "HMFaceToolkitsLoader",
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+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "face_toolkits",
+          "type": "FACE_TOOLKITS",
+          "links": [
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+            62,
+            65,
+            119,
+            132
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+      "widgets_values": [
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       "type": "GetDriveExpression2",
       "pos": {
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+        "0": 850,
+        "1": 827
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       "size": {
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@@ -394,17 +396,17 @@
         {
           "name": "face_toolkits",
           "type": "FACE_TOOLKITS",
-          "link": 114
+          "link": 132
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         {
           "name": "images",
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         },
         {
           "name": "landmarks",
           "type": "FACELANDMARKS222",
-          "link": 112
+          "link": 130
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       ],
       "outputs": [
@@ -412,7 +414,7 @@
           "name": "drive_exp2",
           "type": "DRIVE_EXPRESSION2",
           "links": [
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+            133
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           "slot_index": 0
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@@ -423,14 +425,6 @@
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@@ -463,22 +457,6 @@
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@@ -495,14 +473,6 @@
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@@ -527,14 +497,6 @@
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@@ -544,22 +506,62 @@
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workflows/hellomeme_image_workflow.json

@@ -1,45 +1,7 @@
 {
-  "last_node_id": 61,
-  "last_link_id": 127,
+  "last_node_id": 63,
+  "last_link_id": 129,
   "nodes": [
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-        "majicmix2.jpg",
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     {
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       "type": "CropPortrait",
@@ -52,13 +14,13 @@
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@@ -145,7 +107,7 @@
         {
           "name": "hm_image_pipeline",
           "type": "HMIMAGEPIPELINE",
-          "link": 123
+          "link": 129
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         {
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@@ -193,45 +155,11 @@
         "(best quality), highly detailed, ultra-detailed, headshot, person, well-placed five sense organs, looking at the viewer, centered composition, sharp focus, realistic skin texture",
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@@ -239,10 +167,10 @@
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         "1": 584
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       "mode": 0,
@@ -256,7 +184,8 @@
       "outputs": [],
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@@ -265,12 +194,12 @@
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@@ -310,7 +239,7 @@
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@@ -352,7 +281,7 @@
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       "outputs": [
@@ -419,18 +348,84 @@
       ],
       "properties": {
         "Node name for S&R": "GetDriveExpression"
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+      "properties": {
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@@ -511,14 +506,6 @@
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@@ -550,16 +537,32 @@
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workflows/hellomeme_video_sr_workflow.json

@@ -0,0 +1,766 @@
+{
+  "last_node_id": 62,
+  "last_link_id": 125,
+  "nodes": [
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+          "type": "FACE_TOOLKITS",
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+      "properties": {
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+      "widgets_values": [
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+    {
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+        {
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+          "type": "FACELANDMARKS222",
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+      "outputs": [
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+          "name": "drive_pose",
+          "type": "DRIVE_POSE",
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+          "slot_index": 0
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workflows/hellomeme_video_v2_sr_workflow.json

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+}

workflows/hellomeme_video_v2_workflow.json

@@ -1,163 +1,7 @@
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@@ -372,7 +97,7 @@
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@@ -397,53 +122,293 @@
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@@ -479,7 +444,7 @@
           "name": "audio",
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           "links": [
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+            118
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           "slot_index": 2
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@@ -517,17 +482,45 @@
           "muted": false
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+      "properties": {
+        "Node name for S&R": "HMVideoPipelineLoader"
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@@ -655,16 +624,48 @@
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workflows/hellomeme_video_workflow.json

@@ -1,13 +1,164 @@
 {
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+  "last_node_id": 60,
+  "last_link_id": 122,
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+          "type": "FACE_TOOLKITS",
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     {
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@@ -49,331 +200,15 @@
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@@ -382,12 +217,12 @@
         {
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         {
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         {
@@ -427,10 +262,10 @@
           "hidden": false,
           "paused": false,
           "params": {
-            "filename": "AnimateDiff_00023.gif",
+            "filename": "AnimateDiff_00054-audio.mp4",
             "subfolder": "",
             "type": "output",
-            "format": "image/gif",
+            "format": "video/h264-mp4",
             "frame_rate": 15
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           "muted": false
@@ -438,11 +273,212 @@
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     {
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@@ -455,17 +491,17 @@
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@@ -473,61 +509,17 @@
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@@ -576,22 +568,6 @@
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     [
       97,
       2,
@@ -600,14 +576,6 @@
       1,
       "FACE_TOOLKITS"
     ],
-    [
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-      49,
-      0,
-      53,
-      0,
-      "HMVIDEOPIPELINE"
-    ],
     [
       99,
       39,
@@ -625,32 +593,64 @@
       "DRIVE_POSE"
     ],
     [
-      107,
-      43,
+      115,
+      57,
       0,
-      55,
-      2,
-      "FACELANDMARKS222"
+      53,
+      0,
+      "HMVIDEOPIPELINE"
     ],
     [
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-      48,
+      116,
+      58,
+      0,
+      39,
       0,
-      55,
-      1,
       "IMAGE"
     ],
     [
-      109,
+      117,
+      53,
+      0,
+      59,
+      0,
+      "IMAGE"
+    ],
+    [
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+      48,
+      2,
+      59,
+      1,
+      "AUDIO"
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+    [
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       2,
       0,
-      55,
+      60,
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       "FACE_TOOLKITS"
     ],
     [
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+      120,
+      43,
+      0,
+      60,
+      2,
+      "FACELANDMARKS222"
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+    [
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+      0,
+      60,
+      1,
+      "IMAGE"
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+    [
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       53,
       4,
@@ -661,10 +661,10 @@
   "config": {},
   "extra": {
     "ds": {
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+      "scale": 0.8264462809917359,
       "offset": [
-        760.8618864750447,
-        107.45573369730707
+        159.4635814609923,
+        -64.01844135245447
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   },