udpate path

examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py

@@ -10,8 +10,9 @@ def main():
     # model.
     # If a local path is provided, FastVideo will make a best effort
     # attempt to identify the optimal arguments.
+    model_id = "FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers"
     generator = VideoGenerator.from_pretrained(
-        "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
+        model_id,
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
         use_fsdp_inference=True,
@@ -25,7 +26,7 @@ def main():
         # image_encoder_cpu_offload=False,
     )
 
-    sampling_param = SamplingParam.from_pretrained("FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers")
+    sampling_param = SamplingParam.from_pretrained(model_id)
     sampling_param.num_frames = 81
     sampling_param.width = 832
     sampling_param.height = 480

examples/inference/gradio/serving/gradio_frontend.py

@@ -6,8 +6,10 @@ import time
 import json
 from pathlib import Path
 import tempfile
+from io import BytesIO
 
 import gradio as gr
+from PIL import Image
 
 from fastvideo.configs.sample.base import SamplingParam
 
@@ -15,7 +17,7 @@ from fastvideo.configs.sample.base import SamplingParam
 MODEL_PATH_MAPPING = {
     "FastWan2.1-T2V-1.3B": "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
     "FastWan2.2-TI2V-5B-FullAttn": "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
-    "CausalWan2.2-I2V-A14B-Preview": "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
+    "CausalWan2.2-I2V-A14B-Preview": "FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers",
 }
 
 
@@ -85,28 +87,39 @@ def save_video_from_base64(video_data: str, output_dir: str, prompt: str) -> str
         return None
 
 
-def encode_image_to_base64(image_path: str) -> str:
-    """Encode an image file to base64 string."""
-    if not image_path or not os.path.exists(image_path):
+def encode_image_to_base64(image_input) -> str:
+    """Encode an image file path or in-memory image to a base64 string."""
+    if image_input is None:
         return None
     
+    mime_types = {
+        '.jpg': 'image/jpeg',
+        '.jpeg': 'image/jpeg',
+        '.png': 'image/png',
+        '.gif': 'image/gif',
+        '.webp': 'image/webp',
+    }
+    
     try:
-        with open(image_path, 'rb') as f:
-            image_bytes = f.read()
+        if isinstance(image_input, str):
+            if not os.path.exists(image_input):
+                return None
+            
+            with open(image_input, 'rb') as f:
+                image_bytes = f.read()
+            
+            ext = os.path.splitext(image_input)[1].lower()
+            mime_type = mime_types.get(ext, 'image/jpeg')
+        elif isinstance(image_input, Image.Image):
+            buffer = BytesIO()
+            image_to_save = image_input.convert("RGB")
+            image_to_save.save(buffer, format="PNG")
+            image_bytes = buffer.getvalue()
+            mime_type = 'image/png'
+        else:
+            return None
         
         image_base64 = base64.b64encode(image_bytes).decode('utf-8')
-        
-        # Determine image type from extension
-        ext = os.path.splitext(image_path)[1].lower()
-        mime_types = {
-            '.jpg': 'image/jpeg',
-            '.jpeg': 'image/jpeg',
-            '.png': 'image/png',
-            '.gif': 'image/gif',
-            '.webp': 'image/webp',
-        }
-        mime_type = mime_types.get(ext, 'image/jpeg')
-        
         return f"data:{mime_type};base64,{image_base64}"
         
     except Exception as e:
@@ -426,7 +439,7 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
                         gr.Markdown("**Please make sure you upload a 480x832 image**")
                         input_image = gr.Image(
                             label="",
-                            type="filepath",
+                            type="pil",
                             height=400,
                         )
                     
@@ -512,7 +525,17 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
             if example_label and example_label in example_labels:
                 index = example_labels.index(example_label)
                 selected_prompt = examples[index]
-                selected_image = example_images[index] if index < len(example_images) else None
+                selected_image_path = example_images[index] if index < len(example_images) else None
+                
+                if selected_image_path and os.path.exists(selected_image_path):
+                    try:
+                        with Image.open(selected_image_path) as img:
+                            selected_image = img.convert("RGB")
+                    except Exception:
+                        selected_image = None
+                else:
+                    selected_image = None
+                
                 return selected_prompt, selected_image
             return "", None
         
@@ -615,7 +638,7 @@ def main():
                         default="",
                         help="Comma separated list of paths to the T2V model(s)")
     parser.add_argument("--i2v_model_paths", type=str,
-                        default="FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
+                        default="FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers",
                         help="Comma separated list of paths to the I2V model(s)")
     parser.add_argument("--host", type=str, default="0.0.0.0",
                         help="Host to bind to")
@@ -733,6 +756,7 @@ def main():
         app, 
         demo, 
         path="/gradio",
+        root_path="/gradio",
         allowed_paths=[
             os.path.abspath("outputs"), 
             os.path.abspath("fastvideo-logos"),

examples/inference/gradio/serving/prompts.txt

@@ -12,4 +12,4 @@ Man dressed in 80's style dances very happily in his kitchen while listening to
 Pair of jazz musicians performing a song with their saxophone and trombone on an abandoned train.
 A young woman with short hair wearing pink sunglasses chews gum and makes a bubble gum with the city in the background.
 Natural aerial landscape with a relief covered with abundant trees and vegetation and a thick layer of mist.
-Loving couple sitting on a log on the shore of a lake outside, sharing an affectionate hug.
+Loving couple sitting on a log on the shore of a lake outside, sharing an affectionate hug.

examples/inference/gradio/serving/ray_serve_backend.py

@@ -26,7 +26,7 @@ SEED_RANGE_MAX = 1_000_000
 SUPPORTED_MODELS = [
     "FastVideo/FastWan2.1-T2V-1.3B-Diffusers", 
     "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
-    "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
+    "FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers",
 ]
 
 MODEL_CONFIGS = {
@@ -272,7 +272,7 @@ class T2VModelDeployment(BaseModelDeployment):
 
 
 @serve.deployment(
-    ray_actor_options={"num_cpus": 16, "num_gpus": 1, "runtime_env": {"conda": "demo-fv"}},
+    ray_actor_options={"num_cpus": 2, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
 )
 class T2V14BModelDeployment(BaseModelDeployment):
     def __init__(self, t2v_14b_model_path: str, output_path: str = "outputs"):
@@ -284,7 +284,7 @@ class T2V14BModelDeployment(BaseModelDeployment):
 
 
 @serve.deployment(
-    ray_actor_options={"num_cpus": 15, "num_gpus": 1, "runtime_env": {"conda": "demo-fv"}},
+    ray_actor_options={"num_cpus": 2, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
 )
 class I2VModelDeployment(BaseModelDeployment):
     def __init__(self, i2v_model_path: str, output_path: str = "outputs"):
@@ -445,7 +445,7 @@ if __name__ == "__main__":
                         help="Comma separated list of number of replicas for the T2V model(s)")
     parser.add_argument("--i2v_model_paths",
                         type=str,
-                        default="FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
+                        default="FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers",
                         help="Comma separated list of paths to the I2V model(s)")
     parser.add_argument("--i2v_model_replicas",
                         type=str,

examples/inference/gradio/serving/start.sh

@@ -1,5 +1,5 @@
 python examples/inference/gradio/serving/start_ray_serve_app.py \
 --t2v_model_paths "" \
 --t2v_model_replicas "" \
---i2v_model_paths "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers" \
+--i2v_model_paths "FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers" \
 --i2v_model_replicas "1"

fastvideo/pipelines/stages/causal_denoising.py

@@ -159,7 +159,7 @@ class CausalDMDDenosingStage(DenoisingStage):
             # Causal video gen directly replaces the first frame of the latent with
             # the image latent instead of appending along the channel dim
             assert self.vae is not None, "VAE is not provided for causal video gen task"
-            self.vae = self.vae.to(get_local_torch_device())
+           self.vae = self.vae.to(get_local_torch_device())
             first_frame_latent = self.vae.encode(batch.pil_image).mean.float()
             if (hasattr(self.vae, "shift_factor")
                     and self.vae.shift_factor is not None):
@@ -201,6 +201,7 @@ class CausalDMDDenosingStage(DenoisingStage):
                     **pos_cond_kwargs,
                 )
                 if boundary_timestep is not None:
+                    assert self.transformer_2 is not None, "transformer_2 is not provided, but boundary_timestep is not None"
                     self.transformer_2(
                         first_frame_latent.to(target_dtype),
                         prompt_embeds,
@@ -283,6 +284,7 @@ class CausalDMDDenosingStage(DenoisingStage):
                             (latent_model_input.shape[0], 1),
                             device=latent_model_input.device,
                             dtype=torch.long)
+                        assert current_model is not None
                         pred_noise_btchw = current_model(
                             latent_model_input,
                             prompt_embeds,
@@ -372,6 +374,7 @@ class CausalDMDDenosingStage(DenoisingStage):
                     t_expanded_context = t_context.unsqueeze(1)
 
                     if boundary_timestep is not None:
+                        assert self.transformer_2 is not None, "transformer_2 is not provided, but boundary_timestep is not None"
                         self.transformer_2(
                             context_bcthw,
                             prompt_embeds,
@@ -384,7 +387,6 @@ class CausalDMDDenosingStage(DenoisingStage):
                             **image_kwargs,
                             **pos_cond_kwargs,
                         )
-
                     self.transformer(
                         context_bcthw,
                         prompt_embeds,
@@ -397,7 +399,6 @@ class CausalDMDDenosingStage(DenoisingStage):
                         **image_kwargs,
                         **pos_cond_kwargs,
                     )
-
                 start_index += current_num_frames
 
         if boundary_timestep is not None:

fastvideo/pipelines/stages/input_validation.py

@@ -5,7 +5,6 @@ Input validation stage for diffusion pipelines.
 
 import torch
 import torchvision.transforms.functional as TF
-from PIL import Image
 
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
@@ -14,7 +13,6 @@ from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
 from fastvideo.pipelines.stages.base import PipelineStage
 from fastvideo.pipelines.stages.validators import (StageValidators,
                                                    VerificationResult)
-from fastvideo.utils import best_output_size
 
 logger = init_logger(__name__)