initial 5B support

nodes.py

@@ -16,6 +16,12 @@ class DownloadAndLoadCogVideoModel:
     def INPUT_TYPES(s):
         return {
             "required": {
+                "model": (
+                    [
+                        "THUDM/CogVideoX-2b",
+                        "THUDM/CogVideoX-5b",
+                    ],
+                ),
 
             },
             "optional": {
@@ -35,21 +41,24 @@ class DownloadAndLoadCogVideoModel:
     FUNCTION = "loadmodel"
     CATEGORY = "CogVideoWrapper"
 
-    def loadmodel(self, precision):
+    def loadmodel(self, model, precision):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         mm.soft_empty_cache()
 
         dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
 
-        base_path = os.path.join(folder_paths.models_dir, "CogVideo", "CogVideo2B")
+        if "2b" in model:
+            base_path = os.path.join(folder_paths.models_dir, "CogVideo", "CogVideo2B")
+        elif "5b" in model:
+            base_path = os.path.join(folder_paths.models_dir, "CogVideo", "CogVideoX-5b")
 
         if not os.path.exists(base_path):
             log.info(f"Downloading model to: {base_path}")
             from huggingface_hub import snapshot_download
 
             snapshot_download(
-                repo_id="THUDM/CogVideoX-2b",
+                repo_id=model,
                 ignore_patterns=["*text_encoder*"],
                 local_dir=base_path,
                 local_dir_use_symlinks=False,
@@ -199,14 +208,14 @@ class CogVideoSampler:
                 "negative": ("CONDITIONING", ),
                 "height": ("INT", {"default": 480, "min": 128, "max": 2048, "step": 8}),
                 "width": ("INT", {"default": 720, "min": 128, "max": 2048, "step": 8}),
-                "num_frames": ("INT", {"default": 48, "min": 8, "max": 1024, "step": 8}),
+                "num_frames": ("INT", {"default": 48, "min": 8, "max": 1024, "step": 1}),
                 "fps": ("INT", {"default": 8, "min": 1, "max": 100, "step": 1}),
                 "steps": ("INT", {"default": 25, "min": 1}),
                 "cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}),
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                 "scheduler": (["DDIM", "DPM"],),
-                "t_tile_length": ("INT", {"default": 16, "min": 16, "max": 128, "step": 4}),
+                "t_tile_length": ("INT", {"default": 16, "min": 2, "max": 128, "step": 1}),
-                "t_tile_overlap": ("INT", {"default": 8, "min": 8, "max": 128, "step": 2}),
+                "t_tile_overlap": ("INT", {"default": 8, "min": 2, "max": 128, "step": 1}),
             },
             "optional": {
                 "samples": ("LATENT", ),
@@ -276,10 +285,10 @@ class CogVideoDecode:
 
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("images",)
-    FUNCTION = "process"
+    FUNCTION = "decode"
     CATEGORY = "CogVideoWrapper"
 
-    def process(self, pipeline, samples):
+    def decode(self, pipeline, samples):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         latents = samples["samples"]
@@ -299,19 +308,20 @@ class CogVideoDecode:
 
         frames = []
         pbar = ProgressBar(num_seconds)
-        for i in range(num_seconds):
+        # for i in range(num_seconds):
-            start_frame, end_frame = (0, 3) if i == 0 else (2 * i + 1, 2 * i + 3)
+        #     start_frame, end_frame = (0, 3) if i == 0 else (2 * i + 1, 2 * i + 3)
-            current_frames = vae.decode(latents[:, :, start_frame:end_frame]).sample
+        #     current_frames = vae.decode(latents[:, :, start_frame:end_frame]).sample
-            frames.append(current_frames)
+        #     frames.append(current_frames)
             
-            pbar.update(1)
+        #     pbar.update(1)
-        vae.clear_fake_context_parallel_cache()
+        frames = vae.decode(latents).sample
         vae.to(offload_device)
         mm.soft_empty_cache()
 
-        frames = torch.cat(frames, dim=2)
+        #frames = torch.cat(frames, dim=2)
         video = pipeline["pipe"].video_processor.postprocess_video(video=frames, output_type="pt")
         video = video[0].permute(0, 2, 3, 1).cpu().float()
+        print(video.min(), video.max())
 
         return (video,)
 

pipeline_cogvideox.py

@@ -17,6 +17,7 @@ import inspect
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import torch
+import math
 
 from diffusers.models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
@@ -24,11 +25,29 @@ from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
 from diffusers.utils import logging
 from diffusers.utils.torch_utils import randn_tensor
 from diffusers.video_processor import VideoProcessor
+from diffusers.models.embeddings import get_3d_rotary_pos_embed
 
 from comfy.utils import ProgressBar
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
+    tw = tgt_width
+    th = tgt_height
+    h, w = src
+    r = h / w
+    if r > (th / tw):
+        resize_height = th
+        resize_width = int(round(th / h * w))
+    else:
+        resize_width = tw
+        resize_height = int(round(tw / w * h))
+
+    crop_top = int(round((th - resize_height) / 2.0))
+    crop_left = int(round((tw - resize_width) / 2.0))
+
+    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
 def retrieve_timesteps(
     scheduler,
@@ -229,6 +248,46 @@ class CogVideoXPipeline(DiffusionPipeline):
         weights = weights.unsqueeze(0).unsqueeze(2).unsqueeze(3).unsqueeze(4).repeat(1, t_batch_size,1, 1, 1)
         return weights
     
+    def fuse_qkv_projections(self) -> None:
+        r"""Enables fused QKV projections."""
+        self.fusing_transformer = True
+        self.transformer.fuse_qkv_projections()
+
+    def unfuse_qkv_projections(self) -> None:
+        r"""Disable QKV projection fusion if enabled."""
+        if not self.fusing_transformer:
+            logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.")
+        else:
+            self.transformer.unfuse_qkv_projections()
+            self.fusing_transformer = False
+
+    def _prepare_rotary_positional_embeddings(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        device: torch.device,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+        grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+        base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+        base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+
+        grid_crops_coords = get_resize_crop_region_for_grid(
+            (grid_height, grid_width), base_size_width, base_size_height
+        )
+        freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+            embed_dim=self.transformer.config.attention_head_dim,
+            crops_coords=grid_crops_coords,
+            grid_size=(grid_height, grid_width),
+            temporal_size=num_frames,
+            use_real=True,
+        )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
     @property
     def guidance_scale(self):
         return self._guidance_scale
@@ -374,6 +433,15 @@ class CogVideoXPipeline(DiffusionPipeline):
         t_tile_weights = self._gaussian_weights(t_tile_length=t_tile_length, t_batch_size=1).to(latents.device).to(latents.dtype)
         print("latents.shape", latents.shape)
         print("latents.device", latents.device)
+
+
+        # 6.5. Create rotary embeds if required
+        image_rotary_emb = (
+            self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
+            if self.transformer.config.use_rotary_positional_embeddings
+            else None
+        )
+
         # 7. Denoising loop
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
         comfy_pbar = ProgressBar(num_inference_steps)
@@ -383,94 +451,125 @@ class CogVideoXPipeline(DiffusionPipeline):
             for i, t in enumerate(timesteps):
                 if self.interrupt:
                     continue
+                if not isinstance(self.scheduler, CogVideoXDPMScheduler):
+                    #temporal tiling code based on https://github.com/mayuelala/FollowYourEmoji/blob/main/models/video_pipeline.py
+                    # =====================================================
+                    grid_ts = 0
+                    cur_t = 0
+                    while cur_t < latents.shape[1]:
+                        cur_t = max(grid_ts * t_tile_length - t_tile_overlap * grid_ts, 0) + t_tile_length
+                        grid_ts += 1
 
-                #temporal tiling code based on https://github.com/mayuelala/FollowYourEmoji/blob/main/models/video_pipeline.py
+                    all_t = latents.shape[1]
-                # =====================================================
+                    latents_all_list = []
-                grid_ts = 0
+                    # =====================================================
-                cur_t = 0
-                while cur_t < latents.shape[1]:
-                    cur_t = max(grid_ts * t_tile_length - t_tile_overlap * grid_ts, 0) + t_tile_length
-                    grid_ts += 1
 
-                all_t = latents.shape[1]
+                    for t_i in range(grid_ts):
-                latents_all_list = []
+                        if t_i < grid_ts - 1:
-                # =====================================================
+                            ofs_t = max(t_i * t_tile_length - t_tile_overlap * t_i, 0)
+                        if t_i == grid_ts - 1:
+                            ofs_t = all_t - t_tile_length
 
-                for t_i in range(grid_ts):
+                        input_start_t = ofs_t
-                    if t_i < grid_ts - 1:
+                        input_end_t = ofs_t + t_tile_length
-                        ofs_t = max(t_i * t_tile_length - t_tile_overlap * t_i, 0)
-                    if t_i == grid_ts - 1:
-                        ofs_t = all_t - t_tile_length
 
-                    input_start_t = ofs_t
+                        #latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
-                    input_end_t = ofs_t + t_tile_length
+                        #latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
-                    #latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                        latents_tile = latents[:, input_start_t:input_end_t,:, :, :]
-                    #latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                        latent_model_input_tile = torch.cat([latents_tile] * 2) if do_classifier_free_guidance else latents_tile
+                        latent_model_input_tile = self.scheduler.scale_model_input(latent_model_input_tile, t)
 
-                    latents_tile = latents[:, input_start_t:input_end_t,:, :, :]
+                        #t_input = t[None].to(device)
-                    latent_model_input_tile = torch.cat([latents_tile] * 2) if do_classifier_free_guidance else latents_tile
+                        t_input = t.expand(latent_model_input_tile.shape[0]) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                    latent_model_input_tile = self.scheduler.scale_model_input(latent_model_input_tile, t)
                 
-                    #t_input = t[None].to(device)
+                        # predict noise model_output
-                    t_input = t.expand(latent_model_input_tile.shape[0]) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                        noise_pred = self.transformer(
+                            hidden_states=latent_model_input_tile,
+                            encoder_hidden_states=prompt_embeds,
+                            timestep=t_input,
+                            image_rotary_emb=image_rotary_emb,
+                            return_dict=False,
+                        )[0]
+                        noise_pred = noise_pred.float()                  
+
+                        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)
+
+                        # compute the previous noisy sample x_t -> x_t-1
+                        latents_tile = self.scheduler.step(noise_pred, t, latents_tile, **extra_step_kwargs, return_dict=False)[0]            
+                        latents_all_list.append(latents_tile)
+
+                    # ==========================================
+                    latents_all = torch.zeros(latents.shape, device=latents.device, dtype=latents.dtype)
+                    contributors = torch.zeros(latents.shape, device=latents.device, dtype=latents.dtype)
+                    # Add each tile contribution to overall latents
+                    for t_i in range(grid_ts):
+                        if t_i < grid_ts - 1:
+                            ofs_t = max(t_i * t_tile_length - t_tile_overlap * t_i, 0)
+                        if t_i == grid_ts - 1:
+                            ofs_t = all_t - t_tile_length
+
+                        input_start_t = ofs_t
+                        input_end_t = ofs_t + t_tile_length
+
+                        latents_all[:, input_start_t:input_end_t,:, :, :] += latents_all_list[t_i] * t_tile_weights
+                        contributors[:, input_start_t:input_end_t,:, :, :] += t_tile_weights
+                    
+                    latents_all /= contributors
+
+                    latents = latents_all
+                    
+                    if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                        progress_bar.update()
+                        comfy_pbar.update(1)
+                    # ==========================================
+                else:
+                    latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                    # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                    timestep = t.expand(latent_model_input.shape[0])
 
                     # predict noise model_output
                     noise_pred = self.transformer(
-                        hidden_states=latent_model_input_tile,
+                        hidden_states=latent_model_input,
                         encoder_hidden_states=prompt_embeds,
-                        timestep=t_input,
+                        timestep=timestep,
+                        image_rotary_emb=image_rotary_emb,
                         return_dict=False,
                     )[0]
                     noise_pred = noise_pred.float()
 
-                    if self.do_classifier_free_guidance:
+                   
+                    self._guidance_scale = 1 + guidance_scale * (
+                        (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
+                    )
+                    
+                    if 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)
 
                     # compute the previous noisy sample x_t -> x_t-1
                     if not isinstance(self.scheduler, CogVideoXDPMScheduler):
-                        latents_tile = self.scheduler.step(noise_pred, t, latents_tile, **extra_step_kwargs, return_dict=False)[0]
+                        latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
                     else:
-                        raise NotImplementedError("DPM is not supported with temporal tiling")
+                        latents, old_pred_original_sample = self.scheduler.step(
-                    # else:
+                            noise_pred,
-                    #     latents_tile, old_pred_original_sample = self.scheduler.step(
+                            old_pred_original_sample,
-                    #         noise_pred,
+                            t,
-                    #         old_pred_original_sample,
+                            timesteps[i - 1] if i > 0 else None,
-                    #         t,
+                            latents,
-                    #         t_input[t_i - 1] if t_i > 0 else None,
+                            **extra_step_kwargs,
-                    #         latents_tile,
+                            return_dict=False,
-                    #         **extra_step_kwargs,
+                        )
-                    #         return_dict=False,
+                    latents = latents.to(prompt_embeds.dtype)
-                    #     )
 
-                    latents_all_list.append(latents_tile)
+                    if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                        progress_bar.update()
+                        comfy_pbar.update(1)
             
-                # ==========================================
-                latents_all = torch.zeros(latents.shape, device=latents.device, dtype=latents.dtype)
-                contributors = torch.zeros(latents.shape, device=latents.device, dtype=latents.dtype)
-                # Add each tile contribution to overall latents
-                for t_i in range(grid_ts):
-                    if t_i < grid_ts - 1:
-                        ofs_t = max(t_i * t_tile_length - t_tile_overlap * t_i, 0)
-                    if t_i == grid_ts - 1:
-                        ofs_t = all_t - t_tile_length
-
-                    input_start_t = ofs_t
-                    input_end_t = ofs_t + t_tile_length
-
-                    latents_all[:, input_start_t:input_end_t,:, :, :] += latents_all_list[t_i] * t_tile_weights
-                    contributors[:, input_start_t:input_end_t,:, :, :] += t_tile_weights
-                
-                latents_all /= contributors
-
-                latents = latents_all
-                # ==========================================
-
-
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    comfy_pbar.update(1)
 
         # Offload all models
         self.maybe_free_model_hooks()

requirements.txt

@@ -1,2 +1,2 @@
 huggingface_hub
-diffusers>=0.30.0
+diffusers>=0.30.1