Add interpolation option

README.md

@@ -4,7 +4,10 @@
 Get the model from here, put it in ComfyUI/models/checkpoints and name it `dynamicrafter_1024_v1.ckpt`
 https://huggingface.co/Doubiiu/DynamiCrafter_1024
 
-With fp16 1024x576 uses bit under 12GB VRAM
+Interpolation model should be named `dynamicrafter_512_interp_v1.ckpt`
+https://huggingface.co/Doubiiu/DynamiCrafter_512_Interp/
+
+With fp16 1024x576 uses bit under 12GB VRAM, and interpolation at 512p can be done with 8GB
 
 # ORIGINAL REPO:
 

configs/dynamicrafter_256_v1.yaml

@@ -1,5 +1,5 @@
 model:
-  target: lvdm.models.ddpm3d.LatentVisualDiffusion
+  target: .lvdm.models.ddpm3d.LatentVisualDiffusion
   params:
     linear_start: 0.00085
     linear_end: 0.012
@@ -16,7 +16,7 @@ model:
     use_ema: False
     uncond_type: 'empty_seq'
     unet_config:
-      target: lvdm.modules.networks.openaimodel3d.UNetModel
+      target: .lvdm.modules.networks.openaimodel3d.UNetModel
       params:
         in_channels: 8
         out_channels: 4
@@ -50,7 +50,7 @@ model:
         fs_condition: true
 
     first_stage_config:
-      target: lvdm.models.autoencoder.AutoencoderKL
+      target: .lvdm.models.autoencoder.AutoencoderKL
       params:
         embed_dim: 4
         monitor: val/rec_loss
@@ -73,18 +73,18 @@ model:
           target: torch.nn.Identity
 
     cond_stage_config:
-      target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
+      target: .lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
       params:
         freeze: true
         layer: "penultimate"
 
     img_cond_stage_config:
-      target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2
+      target: .lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2
       params:
         freeze: true
     
     image_proj_stage_config:
-      target: lvdm.modules.encoders.resampler.Resampler
+      target: .lvdm.modules.encoders.resampler.Resampler
       params:
         dim: 1024
         depth: 4

configs/dynamicrafter_512_interp_v1.yaml

@@ -0,0 +1,103 @@
+model:
+  target: .lvdm.models.ddpm3d.LatentVisualDiffusion
+  params:
+    rescale_betas_zero_snr: True
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.012
+    num_timesteps_cond: 1
+    timesteps: 1000
+    first_stage_key: video
+    cond_stage_key: caption
+    cond_stage_trainable: False
+    conditioning_key: hybrid
+    image_size: [40, 64]
+    channels: 4
+    scale_by_std: False
+    scale_factor: 0.18215
+    use_ema: False
+    uncond_type: 'empty_seq'
+    use_dynamic_rescale: true
+    base_scale: 0.7
+    fps_condition_type: 'fps'
+    perframe_ae: True
+    unet_config:
+      target: .lvdm.modules.networks.openaimodel3d.UNetModel
+      params:
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions:
+        - 4
+        - 2
+        - 1
+        num_res_blocks: 2
+        channel_mult:
+        - 1
+        - 2
+        - 4
+        - 4
+        dropout: 0.1
+        num_head_channels: 64
+        transformer_depth: 1
+        context_dim: 1024
+        use_linear: true
+        use_checkpoint: True
+        temporal_conv: True
+        temporal_attention: True
+        temporal_selfatt_only: true
+        use_relative_position: false
+        use_causal_attention: False
+        temporal_length: 16
+        addition_attention: true
+        image_cross_attention: true
+        default_fs: 24
+        fs_condition: true
+
+    first_stage_config:
+      target: .lvdm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: True
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: .lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
+      params:
+        freeze: true
+        layer: "penultimate"
+
+    img_cond_stage_config:
+      target: .lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2
+      params:
+        freeze: true
+    
+    image_proj_stage_config:
+      target: .lvdm.modules.encoders.resampler.Resampler
+      params:
+        dim: 1024
+        depth: 4
+        dim_head: 64
+        heads: 12
+        num_queries: 16
+        embedding_dim: 1280
+        output_dim: 1024
+        ff_mult: 4
+        video_length: 16
+

nodes.py

@@ -50,6 +50,10 @@ class DynamiCrafterI2V:
                         "default": 'fp16'
                     }),
             "keep_model_loaded": ("BOOLEAN", {"default": True}),
+            
+            },
+            "optional": {
+               "optional_image2": ("IMAGE",),     
             }
         }
 
@@ -58,7 +62,7 @@ class DynamiCrafterI2V:
     FUNCTION = "process"
     CATEGORY = "DynamiCrafter"
 
-    def process(self, image, dtype, ckpt_name, prompt, cfg, steps, eta, seed, fs, keep_model_loaded):
+    def process(self, image, dtype, ckpt_name, prompt, cfg, steps, eta, seed, fs, keep_model_loaded, optional_image2=None):
         device = mm.get_torch_device()
         mm.unload_all_models()
         mm.soft_empty_cache()
@@ -84,18 +88,31 @@ class DynamiCrafterI2V:
         channels = self.model.model.diffusion_model.out_channels
         frames = self.model.temporal_length
         B, H, W, C = image.shape
-
+        image2 = optional_image2
         noise_shape = [B, channels, frames, H // 8, W // 8]
-        
-        image = image * 2 - 1
-        image = image.permute(0, 3, 1, 2).to(dtype).to(device)
+  
         autocast_condition = (dtype != torch.float32) and not comfy.model_management.is_device_mps(device)
         with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
             text_emb = self.model.get_learned_conditioning([prompt])
 
+            image = image * 2 - 1
+            image = image.permute(0, 3, 1, 2).to(dtype).to(device)
             z = get_latent_z(self.model, image.unsqueeze(2)) #bc,1,hw
-            image
+
+            if image2 is not None:
+                image2 = image2 * 2 - 1
+                image2 = image2.permute(0, 3, 1, 2).to(dtype).to(device)
+                z2 = get_latent_z(self.model, image2.unsqueeze(2)) #bc,1,hw
+
             img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames)
+            img_tensor_repeat = torch.zeros_like(img_tensor_repeat)
+
+            img_tensor_repeat[:,:,:1,:,:] = z
+            if image2 is not None:
+                img_tensor_repeat[:,:,-1:,:,:] = z2
+            else:
+                img_tensor_repeat[:,:,-1:,:,:] = z
+
             cond_images = self.model.embedder(image)
             img_emb = self.model.image_proj_model(cond_images)
             imtext_cond = torch.cat([text_emb, img_emb], dim=1)
@@ -103,6 +120,9 @@ class DynamiCrafterI2V:
             cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]}
             ## inference
             batch_samples = batch_ddim_sampling(self.model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg)
+            ## remove the last frame
+            if image2 is None:
+                batch_samples = batch_samples[:,:,:,:-1,...]
             ## b,samples,c,t,h,w
             prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt
             prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str