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extensions_built_in/diffusion_models/wan22/wan22_14b_i2v_model.py

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+import torch
+from toolkit.models.wan21.wan_utils import add_first_frame_conditioning
+from toolkit.prompt_utils import PromptEmbeds
+from PIL import Image
+import torch
+from toolkit.config_modules import GenerateImageConfig
+from .wan22_pipeline import Wan22Pipeline
+
+from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
+from diffusers import WanImageToVideoPipeline
+from torchvision.transforms import functional as TF
+
+from .wan22_14b_model import Wan2214bModel
+
+class Wan2214bI2VModel(Wan2214bModel):
+    arch = "wan22_14b_i2v"
+    
+    
+    def generate_single_image(
+        self,
+        pipeline: Wan22Pipeline,
+        gen_config: GenerateImageConfig,
+        conditional_embeds: PromptEmbeds,
+        unconditional_embeds: PromptEmbeds,
+        generator: torch.Generator,
+        extra: dict,
+    ):
+        
+        # todo 
+        # reactivate progress bar since this is slooooow
+        pipeline.set_progress_bar_config(disable=False)
+
+        num_frames = (
+            (gen_config.num_frames - 1) // 4
+        ) * 4 + 1  # make sure it is divisible by 4 + 1
+        gen_config.num_frames = num_frames
+
+        height = gen_config.height
+        width = gen_config.width
+        first_frame_n1p1 = None
+        if gen_config.ctrl_img is not None:
+            control_img = Image.open(gen_config.ctrl_img).convert("RGB")
+
+            d = self.get_bucket_divisibility()
+
+            # make sure they are divisible by d
+            height = height // d * d
+            width = width // d * d
+
+            # resize the control image
+            control_img = control_img.resize((width, height), Image.LANCZOS)
+
+            # 5. Prepare latent variables
+            # num_channels_latents = self.transformer.config.in_channels
+            num_channels_latents = 16
+            latents = pipeline.prepare_latents(
+                1,
+                num_channels_latents,
+                height,
+                width,
+                gen_config.num_frames,
+                torch.float32,
+                self.device_torch,
+                generator,
+                None,
+            ).to(self.torch_dtype)
+
+            first_frame_n1p1 = (
+                TF.to_tensor(control_img)
+                .unsqueeze(0)
+                .to(self.device_torch, dtype=self.torch_dtype)
+                * 2.0
+                - 1.0
+            )  # normalize to [-1, 1]
+            
+            # Add conditioning using the standalone function
+            gen_config.latents = add_first_frame_conditioning(
+                latent_model_input=latents,
+                first_frame=first_frame_n1p1,
+                vae=self.vae
+            )
+
+        output = pipeline(
+            prompt_embeds=conditional_embeds.text_embeds.to(
+                self.device_torch, dtype=self.torch_dtype
+            ),
+            negative_prompt_embeds=unconditional_embeds.text_embeds.to(
+                self.device_torch, dtype=self.torch_dtype
+            ),
+            height=height,
+            width=width,
+            num_inference_steps=gen_config.num_inference_steps,
+            guidance_scale=gen_config.guidance_scale,
+            latents=gen_config.latents,
+            num_frames=gen_config.num_frames,
+            generator=generator,
+            return_dict=False,
+            output_type="pil",
+            **extra,
+        )[0]
+
+        # shape = [1, frames, channels, height, width]
+        batch_item = output[0]  # list of pil images
+        if gen_config.num_frames > 1:
+            return batch_item  # return the frames.
+        else:
+            # get just the first image
+            img = batch_item[0]
+        return img
+    
+    def get_noise_prediction(
+        self,
+        latent_model_input: torch.Tensor,
+        timestep: torch.Tensor,  # 0 to 1000 scale
+        text_embeddings: PromptEmbeds,
+        batch: DataLoaderBatchDTO,
+        **kwargs
+    ):
+        # videos come in (bs, num_frames, channels, height, width)
+        # images come in (bs, channels, height, width)
+        with torch.no_grad():
+            frames = batch.tensor
+            if len(frames.shape) == 4:
+                first_frames = frames
+            elif len(frames.shape) == 5:
+                first_frames = frames[:, 0]
+            else:
+                raise ValueError(f"Unknown frame shape {frames.shape}")
+            
+            # Add conditioning using the standalone function
+            conditioned_latent = add_first_frame_conditioning(
+                latent_model_input=latent_model_input,
+                first_frame=first_frames,
+                vae=self.vae
+            )
+        
+        noise_pred = self.model(
+            hidden_states=conditioned_latent,
+            timestep=timestep,
+            encoder_hidden_states=text_embeddings.text_embeds,
+            return_dict=False,
+            **kwargs
+        )[0]
+        return noise_pred