fix fp8 for I2V

nodes.py

@@ -57,8 +57,8 @@ class DownloadAndLoadCogVideoModel:
         offload_device = mm.unet_offload_device()
         mm.soft_empty_cache()
 
-        if "I2V" in model and fp8_transformer != "disabled":
+        #if "I2V" in model and fp8_transformer != "disabled":
-            raise NotImplementedError("fp8_transformer is not implemented yet for I2V -model")
+        #    raise NotImplementedError("fp8_transformer is not implemented yet for I2V -model")
 
         dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
 
@@ -99,7 +99,15 @@ class DownloadAndLoadCogVideoModel:
                     if name != "pos_embedding":
                         param.data = param.data.to(torch.float8_e4m3fn)
             else:
-                transformer.to(torch.float8_e4m3fn)
+                for name, param in transformer.named_parameters():
+                    
+                    if "patch_embed" not in name:
+                        param.data = param.data.to(torch.float8_e4m3fn)
+                        
+                    else:
+                        print(name)
+                        print(param.data.dtype)
+                #transformer.to(torch.float8_e4m3fn)
         
             if fp8_transformer == "fastmode":
                 from .fp8_optimization import convert_fp8_linear
@@ -238,6 +246,9 @@ class CogVideoTextEncode:
         return {"required": {
             "clip": ("CLIP",),
             "prompt": ("STRING", {"default": "", "multiline": True} ),
+            },
+            "optional": {
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
             }
         }
 
@@ -246,7 +257,7 @@ class CogVideoTextEncode:
     FUNCTION = "process"
     CATEGORY = "CogVideoWrapper"
 
-    def process(self, clip, prompt):
+    def process(self, clip, prompt, strength=1.0):
         load_device = mm.text_encoder_device()
         offload_device = mm.text_encoder_offload_device()
         clip.tokenizer.t5xxl.pad_to_max_length = True
@@ -255,6 +266,7 @@ class CogVideoTextEncode:
         tokens = clip.tokenize(prompt, return_word_ids=True)
 
         embeds = clip.encode_from_tokens(tokens, return_pooled=False, return_dict=False)
+        embeds *= strength
         clip.cond_stage_model.to(offload_device)
 
         return (embeds, )

pipeline_cogvideox.py

@@ -412,7 +412,7 @@ class CogVideoXPipeline(DiffusionPipeline):
 
         if do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-        prompt_embeds = prompt_embeds.to(self.transformer.dtype)
+        prompt_embeds = prompt_embeds.to(self.vae.dtype)
 
         # 4. Prepare timesteps
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
@@ -442,14 +442,11 @@ class CogVideoXPipeline(DiffusionPipeline):
             num_inference_steps,
             latents
         )
-        latents = latents.to(self.transformer.dtype)
+        latents = latents.to(self.vae.dtype)
         print("latents", latents.shape)
 
         # 5.5.
         if image_cond_latents is not None:
-            print("image_cond_latents", image_cond_latents.shape)
-            #image_cond_latents = torch.cat(image_cond_latents, dim=0).to(self.transformer.dtype)#.permute(0, 2, 1, 3, 4)  # [B, F, C, H, W]
-
             padding_shape = (
                 batch_size,
                 (latents.shape[1] - 1),
@@ -457,10 +454,8 @@ class CogVideoXPipeline(DiffusionPipeline):
                 height // self.vae_scale_factor_spatial,
                 width // self.vae_scale_factor_spatial,
             )
-            print("padding_shape", padding_shape)
             latent_padding = torch.zeros(padding_shape, device=device, dtype=self.transformer.dtype)
             image_cond_latents = torch.cat([image_cond_latents, latent_padding], dim=1)
-            print("image_cond_latents", image_cond_latents.shape)
        
         # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
@@ -602,11 +597,7 @@ class CogVideoXPipeline(DiffusionPipeline):
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
                     if image_cond_latents is not None:
-                        
-                        
                         latent_image_input = torch.cat([image_cond_latents] * 2) if do_classifier_free_guidance else image_cond_latents
-                        print("latent_model_input",latent_model_input.shape)
-                        print("image_cond_latents",image_cond_latents.shape)
                         latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=2)
 
                     # broadcast to batch dimension in a way that's compatible with ONNX/Core ML