Each rank should sample a different timestep in dmd_forward and faker_score_forward

Change to fp32 inference
Add i2v images
2025-11-18 20:11:22 +00:00 · 2025-11-18 20:11:21 +00:00 · 2025-11-18 20:11:21 +00:00 · 2025-11-18 20:11:21 +00:00 · 2025-11-18 20:11:21 +00:00
28 changed files with 346 additions and 663 deletions
@@ -10,7 +10,6 @@ exclude: |
    demo/.*|
    predict\.py|
    scripts/.*|
-    prompts/.*|
    fastvideo/data_preprocess/.*|
    fastvideo/dataset/.*|
    fastvideo/models/.*|
@@ -15,7 +15,6 @@ FastVideo features an end-to-end unified pipeline for accelerating diffusion mod
 </div>

 ## NEWS
- ```2025/11/19```: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py)
 - ```2025/08/04```: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd.html) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
 - ```2025/06/14```: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389)
 - ```2025/04/24```: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
@@ -11,7 +11,7 @@ def main():
    # If a local path is provided, FastVideo will make a best effort
    # attempt to identify the optimal arguments.
    generator = VideoGenerator.from_pretrained(
-        "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
+        "rand0nmr/SFWan2.2-T2V-A14B-Diffusers",
        # FastVideo will automatically handle distributed setup
        num_gpus=1,
        use_fsdp_inference=True,
@@ -22,11 +22,13 @@ def main():
        dmd_denoising_steps=[1000, 850, 700, 550, 350, 275, 200, 125],
        # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
        pin_cpu_memory=True,
+        init_weights_from_safetensors="/mnt/sharefs/users/hao.zhang/wei/SFwan2.2_distill_self_forcing_release_x0_CacheAppend_1333/checkpoint-442_weight_only/ema/generator_ema.safetensors",
+        init_weights_from_safetensors_2="/mnt/sharefs/users/hao.zhang/wei/SFwan2.2_distill_self_forcing_release_x0_CacheAppend_1333/checkpoint-442_weight_only/ema/generator_ema_2.safetensors",
        # image_encoder_cpu_offload=False,
    )

-    sampling_param = SamplingParam.from_pretrained("FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers")
-    sampling_param.num_frames = 81
+    sampling_param = SamplingParam.from_pretrained("rand0nmr/SFWan2.2-T2V-A14B-Diffusers")
+    sampling_param.num_frames = 73
    sampling_param.width = 832
    sampling_param.height = 480
    sampling_param.seed = 1000
@@ -41,4 +43,4 @@ def main():


 if __name__ == "__main__":
-    main()
+    main()
@@ -3,9 +3,6 @@ import os
 import requests
 import base64
 import time
-import json
-from pathlib import Path
-import tempfile

 import gradio as gr

@@ -15,7 +12,6 @@ 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",
 }


@@ -41,7 +37,7 @@ class RayServeClient:
                f"{self.backend_url}/generate_video",
                json=request_data,
                headers=headers,
-                timeout=900  # 15 minutes timeout for longer video generation
+                timeout=300
            )
            
            round_trip_time = time.time() - start_time
@@ -85,78 +81,49 @@ 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):
-        return None
+def create_timing_display(inference_time, encoding_time, network_time, total_time, stage_execution_times, num_frames):
+    dit_denoising_time = f"{stage_execution_times[5]:.2f}s" if len(stage_execution_times) > 5 else "N/A"
    
-    try:
-        with open(image_path, 'rb') as f:
-            image_bytes = f.read()
-        
-        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:
-        print(f"Failed to encode image: {e}")
-        return None
-
-
-# def create_timing_display(inference_time, encoding_time, network_time, total_time, stage_execution_times, num_frames):
-#     dit_denoising_time = f"{stage_execution_times[5]:.2f}s" if len(stage_execution_times) > 5 else "N/A"
-#     
-#     timing_html = f"""
-#     <div style="margin: 10px 0;">
-#         <h3 style="text-align: center; margin-bottom: 10px;">⏱️ Timing Breakdown</h3>
-#         <div style="display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; margin-bottom: 10px;">
-#             <div class="timing-card timing-card-highlight">
-#                 <div style="font-size: 20px;">🚀</div>
-#                 <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">DiT Denoising</div>
-#                 <div style="font-size: 16px; color: #ffa200; font-weight: bold;">{dit_denoising_time}</div>
-#             </div>
-#             <div class="timing-card">
-#                 <div style="font-size: 20px;">🧠</div>
-#                 <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">E2E (w. vae/text encoder)</div>
-#                 <div style="font-size: 16px; color: #2563eb;">{inference_time:.2f}s</div>
-#             </div>
-#             <div class="timing-card">
-#                 <div style="font-size: 20px;">🎬</div>
-#                 <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Video Encoding</div>
-#                 <div style="font-size: 16px; color: #dc2626;">{encoding_time:.2f}s</div>
-#             </div>
-#             <div class="timing-card">
-#                 <div style="font-size: 20px;">🌐</div>
-#                 <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Network Transfer</div>
-#                 <div style="font-size: 16px; color: #059669;">{network_time:.2f}s</div>
-#             </div>
-#             <div class="timing-card">
-#                 <div style="font-size: 20px;">📊</div>
-#                 <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Total Processing</div>
-#                 <div style="font-size: 18px; color: #0277bd;">{total_time:.2f}s</div>
-#             </div>
-#         </div>"""
-#     
-#     if inference_time > 0:
-#         fps = num_frames / inference_time
-#         timing_html += f"""
-#         <div class="performance-card" style="margin-top: 15px;">
-#             <span style="font-weight: bold;">Generation Speed: </span>
-#             <span style="font-size: 18px; color: #6366f1; font-weight: bold;">{fps:.1f} frames/second</span>
-#         </div>"""
-#     
-#     return timing_html + "</div>"
+    timing_html = f"""
+    <div style="margin: 10px 0;">
+        <h3 style="text-align: center; margin-bottom: 10px;">⏱️ Timing Breakdown</h3>
+        <div style="display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; margin-bottom: 10px;">
+            <div class="timing-card timing-card-highlight">
+                <div style="font-size: 20px;">🚀</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">DiT Denoising</div>
+                <div style="font-size: 16px; color: #ffa200; font-weight: bold;">{dit_denoising_time}</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">🧠</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">E2E (w. vae/text encoder)</div>
+                <div style="font-size: 16px; color: #2563eb;">{inference_time:.2f}s</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">🎬</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Video Encoding</div>
+                <div style="font-size: 16px; color: #dc2626;">{encoding_time:.2f}s</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">🌐</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Network Transfer</div>
+                <div style="font-size: 16px; color: #059669;">{network_time:.2f}s</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">📊</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Total Processing</div>
+                <div style="font-size: 18px; color: #0277bd;">{total_time:.2f}s</div>
+            </div>
+        </div>"""
+    
+    if inference_time > 0:
+        fps = num_frames / inference_time
+        timing_html += f"""
+        <div class="performance-card" style="margin-top: 15px;">
+            <span style="font-weight: bold;">Generation Speed: </span>
+            <span style="font-size: 18px; color: #6366f1; font-weight: bold;">{fps:.1f} frames/second</span>
+        </div>"""
+    
+    return timing_html + "</div>"


 def load_example_prompts():
@@ -177,83 +144,26 @@ def load_example_prompts():
            print(f"Warning: Could not read {filepath}: {e}")
        return prompts, labels
    
-    # Load prompts from prompts.txt
-    examples, example_labels = load_from_file("examples/inference/gradio/serving/prompts.txt")
+    examples, example_labels = load_from_file("prompts/prompts_final.txt")
    
    if not examples:
        examples = ["A crowded rooftop bar buzzes with energy, the city skyline twinkling like a field of stars in the background."]
        example_labels = ["Crowded rooftop bar at night"]
    
-    # Load image mappings from JSON file
-    prompt_to_image = {}
-    # Try to find the JSON file relative to project root
-    possible_json_paths = [
-        Path("prompts/mixkit_i2v.jsonl"),
-        Path(__file__).parent.parent.parent.parent / "prompts" / "mixkit_i2v.jsonl",
-    ]
-    json_path = None
-    for path in possible_json_paths:
-        if path.exists():
-            json_path = path
-            break
-    
-    if json_path and json_path.exists():
-        try:
-            with open(json_path, "r", encoding='utf-8') as f:
-                data = json.load(f)
-                # Get the project root directory (parent of prompts directory)
-                project_root = json_path.parent.parent
-                for item in data:
-                    prompt_text = item.get("prompt", "").strip()
-                    image_path = item.get("image_path", "")
-                    if prompt_text and image_path:
-                        # Resolve image path relative to project root
-                        full_image_path = project_root / image_path
-                        if full_image_path.exists():
-                            prompt_to_image[prompt_text] = str(full_image_path.absolute())
-        except Exception as e:
-            print(f"Warning: Could not load image mappings from {json_path}: {e}")
-    
-    # Create image paths list matching the prompts
-    example_images = []
-    for prompt in examples:
-        # Try exact match first
-        image_path = prompt_to_image.get(prompt)
-        if not image_path:
-            # Try fuzzy match (case-insensitive, whitespace normalized)
-            normalized_prompt = " ".join(prompt.split())
-            for json_prompt, img_path in prompt_to_image.items():
-                normalized_json = " ".join(json_prompt.split())
-                if normalized_prompt.lower() == normalized_json.lower():
-                    image_path = img_path
-                    break
-        example_images.append(image_path if image_path and os.path.exists(image_path) else None)
-    
-    return examples, example_labels, example_images
+    return examples, example_labels


 def create_gradio_interface(backend_url: str, default_params: dict[str, SamplingParam]):
    
    client = RayServeClient(backend_url)
    
-    def is_i2v_model(model_name: str) -> bool:
-        """Check if the model is an I2V model."""
-        return "I2V" in model_name
-    
    def generate_video(
-        prompt, negative_prompt, use_negative_prompt, guidance_scale,
-        num_frames, height, width, model_selection, input_image, progress
+        prompt, negative_prompt, use_negative_prompt, seed, guidance_scale,
+        num_frames, height, width, randomize_seed, model_selection, progress
    ):
-        # Use default seed value (randomize_seed disabled)
-        seed = 1000
-        randomize_seed = False
        if not client.check_health():
            return None, f"Backend is not available. Please check if Ray Serve is running at {backend_url}", ""
        
-        # Check if I2V model requires an image
-        if is_i2v_model(model_selection) and not input_image:
-            return None, "I2V models require an input image. Please upload an image.", ""
-        
        # Validate dimensions
        max_pixels = 720 * 1280
        if height * width > max_pixels:
@@ -262,15 +172,6 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
        if progress:
            progress(0.1, desc="Checking backend health...")
        
-        # Encode image if provided
-        image_data = None
-        if input_image:
-            if progress:
-                progress(0.2, desc="Encoding input image...")
-            image_data = encode_image_to_base64(input_image)
-            if not image_data:
-                return None, "Failed to encode input image", ""
-        
        request_data = {
            "prompt": prompt,
            "negative_prompt": negative_prompt,
@@ -282,7 +183,7 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
            "width": width,
            "randomize_seed": randomize_seed,
            "return_frames": False,
-            "image_data": image_data,
+            "image_path": None,
            "model_path": MODEL_PATH_MAPPING.get(model_selection, "FastVideo/FastWan2.1-T2V-1.3B-Diffusers")
        }
        
@@ -297,16 +198,16 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
        if response.get("success", False):
            video_data = response.get("video_data", "")
            used_seed = response.get("seed", seed)
-            # inference_time = response.get("inference_time", 0.0)
-            # encoding_time = response.get("encoding_time", 0.0)
-            # total_time = response.get("total_time", 0.0)
-            # network_time = response.get("network_time", 0.0)
-            # stage_execution_times = response.get("stage_execution_times", [])
+            inference_time = response.get("inference_time", 0.0)
+            encoding_time = response.get("encoding_time", 0.0)
+            total_time = response.get("total_time", 0.0)
+            network_time = response.get("network_time", 0.0)
+            stage_execution_times = response.get("stage_execution_times", [])
            
-            # timing_details = create_timing_display(
-            #     inference_time, encoding_time, network_time, total_time, 
-            #     stage_execution_times, num_frames
-            # )
+            timing_details = create_timing_display(
+                inference_time, encoding_time, network_time, total_time, 
+                stage_execution_times, num_frames
+            )
            
            if video_data:
                if progress:
@@ -318,7 +219,7 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
                    progress(1.0, desc="Generation complete!")
                
                if video_path and os.path.exists(video_path):
-                    return video_path, used_seed, ""
+                    return video_path, used_seed, timing_details
                else:
                    return None, "Failed to save video", ""
            else:
@@ -327,7 +228,7 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
            error_msg = response.get("error_message", "Unknown error occurred")
            return None, f"Generation failed: {error_msg}", ""
    
-    examples, example_labels, example_images = load_example_prompts()
+    examples, example_labels = load_example_prompts()
    
    theme = gr.themes.Base().set(
        button_primary_background_fill="#2563eb",
@@ -338,39 +239,33 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
    )
    
    def get_default_values(model_name):
-        # model_path = MODEL_PATH_MAPPING.get(model_name)
-        # if model_path and model_path in default_params:
-        #     params = default_params[model_path]
-        #     return {
-        #         'height': params.height,
-        #         'width': params.width,
-        #         'num_frames': params.num_frames,
-        #         'guidance_scale': params.guidance_scale,
-        #     }
+        model_path = MODEL_PATH_MAPPING.get(model_name)
+        if model_path and model_path in default_params:
+            params = default_params[model_path]
+            return {
+                'height': params.height,
+                'width': params.width,
+                'num_frames': params.num_frames,
+                'guidance_scale': params.guidance_scale,
+                'seed': params.seed,
+            }
        
        return {
-            'height': 480,
+            'height': 448,
            'width': 832,
-            'num_frames': 73,
+            'num_frames': 61,
+            'guidance_scale': 3.0,
+            'seed': 1024,
        }
    
-    # Get available models based on what's loaded
-    available_models = []
-    for model_name, model_path in MODEL_PATH_MAPPING.items():
-        if model_path in default_params:
-            available_models.append(model_name)
+    initial_values = get_default_values("FastWan2.1-T2V-1.3B")
    
-    # Select first available model as default
-    default_model = available_models[0] if available_models else "FastWan2.1-T2V-1.3B"
-    initial_values = get_default_values(default_model)
-    initial_show_image = is_i2v_model(default_model)
-    
-    with gr.Blocks(title="CausalWan", theme=theme) as demo:
+    with gr.Blocks(title="FastWan", theme=theme) as demo:
        gr.Image("assets/logos/logo.svg", show_label=False, container=False, height=80)
        gr.HTML("""
        <div style="text-align: center; margin-bottom: 10px;">
            <p style="font-size: 18px;"> Make Video Generation Go Blurrrrrrr </p>
-            <p style="font-size: 18px;"> <a href="https://github.com/hao-ai-lab/FastVideo/tree/main" target="_blank">Code</a> | <a href="https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/" target="_blank">Blog</a> | <a href="https://hao-ai-lab.github.io/FastVideo/" target="_blank">Docs</a>  </p>
+            <p style="font-size: 18px;"> <a href="https://github.com/hao-ai-lab/FastVideo/tree/main" target="_blank">Code</a> | <a href="https://hao-ai-lab.github.io/blogs/fastvideo_post_training/" target="_blank">Blog</a> | <a href="https://hao-ai-lab.github.io/FastVideo/" target="_blank">Docs</a>  </p>
        </div>
        """)
        
@@ -385,8 +280,8 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
        
        with gr.Row():
            model_selection = gr.Dropdown(
-                choices=available_models,
-                value=default_model,
+                choices=list(MODEL_PATH_MAPPING.keys()),
+                value="FastWan2.1-T2V-1.3B",
                label="Select Model",
                interactive=True
            )
@@ -417,70 +312,69 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
        with gr.Row():
            with gr.Column():
                error_output = gr.Text(label="Error", visible=False)
-                # timing_display = gr.Markdown(label="Timing Breakdown", visible=False)
+                timing_display = gr.Markdown(label="Timing Breakdown", visible=False)

-        with gr.Row(equal_height=False):
-            with gr.Column(scale=1):
-                with gr.Tabs():
-                    with gr.Tab("Input Image", visible=initial_show_image) as image_tab:
-                        gr.Markdown("**Please make sure you upload a 480x832 image**")
-                        input_image = gr.Image(
-                            label="",
-                            type="filepath",
-                            height=400,
+        with gr.Row(equal_height=True, elem_classes="main-content-row"):
+            with gr.Column(scale=1, elem_classes="advanced-options-column"):
+                with gr.Group():
+                    gr.HTML("<div style='margin: 0 0 15px 0; text-align: center; font-size: 16px;'>Advanced Options</div>")
+                    with gr.Row():
+                        height = gr.Number(
+                            label="Height",
+                            value=initial_values['height'],
+                            interactive=False,
+                            container=True
+                        )
+                        width = gr.Number(
+                            label="Width",
+                            value=initial_values['width'],
+                            interactive=False,
+                            container=True
                        )
                    
-                    with gr.Tab("Advanced Options"):
-                        with gr.Group():
-                            with gr.Row():
-                                height = gr.Number(
-                                    label="Height",
-                                    value=initial_values['height'],
-                                    interactive=False,
-                                    container=True
-                                )
-                                width = gr.Number(
-                                    label="Width",
-                                    value=initial_values['width'],
-                                    interactive=False,
-                                    container=True
-                                )
-                            
-                            with gr.Row():
-                                num_frames = gr.Number(
-                                    label="Number of Frames",
-                                    value=initial_values['num_frames'],
-                                    interactive=False,
-                                    container=True
-                                )
-                                guidance_scale = gr.Number(
-                                    label="Guidance Scale",
-                                    value=1.0,
-                                    interactive=False,
-                                    container=True
-                                )
-                            
-                            with gr.Row():
-                                use_negative_prompt = gr.Checkbox(
-                                    label="Use negative prompt", value=False)
-                                negative_prompt = gr.Text(
-                                    label="Negative prompt",
-                                    max_lines=3,
-                                    lines=3,
-                                    placeholder="Enter a negative prompt",
-                                    visible=False,
-                                )
+                    with gr.Row():
+                        num_frames = gr.Number(
+                            label="Number of Frames",
+                            value=initial_values['num_frames'],
+                            interactive=False,
+                            container=True
+                        )
+                        guidance_scale = gr.Slider(
+                            label="Guidance Scale",
+                            minimum=1,
+                            maximum=12,
+                            value=initial_values['guidance_scale'],
+                        )
+                    
+                    with gr.Row():
+                        use_negative_prompt = gr.Checkbox(
+                            label="Use negative prompt", value=False)
+                        negative_prompt = gr.Text(
+                            label="Negative prompt",
+                            max_lines=3,
+                            lines=3,
+                            placeholder="Enter a negative prompt",
+                            visible=False,
+                        )

-                            # randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
-                            seed_output = gr.Number(label="Used Seed", value=1000)
+                    seed = gr.Slider(
+                        label="Seed",
+                        minimum=0,
+                        maximum=1000000,
+                        step=1,
+                        value=initial_values['seed'],
+                    )
+                    randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
+                    seed_output = gr.Number(label="Used Seed")
        
-            with gr.Column(scale=1):
+            with gr.Column(scale=1, elem_classes="video-column"):
                result = gr.Video(
                    label="Generated Video", 
                    show_label=True,
-                    height=500,
+                    height=466,
+                    width=600,
                    container=True,
-                    autoplay=True,
+                    elem_classes="video-component"
                )
        
        gr.HTML("""
@@ -493,10 +387,116 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
        }
        
        .gradio-container {
-            max-width: 1400px !important;
+            max-width: 1200px !important;
            margin: 0 auto !important;
        }
        
+        .main {
+            max-width: 1200px !important;
+            margin: 0 auto !important;
+        }
+        
+        .gr-form, .gr-box, .gr-group {
+            max-width: 1200px !important;
+        }
+        
+        .gr-video {
+            max-width: 500px !important;
+            margin: 0 auto !important;
+        }
+        
+        .main-content-row {
+            display: flex !important;
+            align-items: flex-start !important;
+            min-height: 500px !important;
+            gap: 20px !important;
+        }
+        
+        .advanced-options-column,
+        .video-column {
+            display: flex !important;
+            flex-direction: column !important;
+            flex: 1 !important;
+            min-height: 400px !important;
+            align-items: stretch !important;
+        }
+        
+        .video-column > * {
+            margin-top: 0 !important;
+        }
+        
+        .video-column .gr-video,
+        .video-component {
+            margin-top: 0 !important;
+            padding-top: 0 !important;
+        }
+        
+        .video-column .gr-video .gr-form {
+            margin-top: 0 !important;
+        }
+        
+        .advanced-options-column .gr-group,
+        .video-column .gr-video {
+            margin-top: 0 !important;
+            vertical-align: top !important;
+        }
+        
+        .advanced-options-column > *:last-child,
+        .video-column > *:last-child {
+            flex-grow: 0 !important;
+        }
+        
+        @media (max-width: 1400px) {
+            .main-content-row {
+                min-height: 600px !important;
+            }
+            
+            .advanced-options-column,
+            .video-column {
+                min-height: 600px !important;
+            }
+        }
+        
+        @media (max-width: 1200px) {
+            .main-content-row {
+                flex-direction: column !important;
+                align-items: stretch !important;
+            }
+            
+            .advanced-options-column,
+            .video-column {
+                min-height: auto !important;
+                width: 100% !important;
+            }
+        }
+        
+        .timing-card {
+            background: var(--background-fill-secondary) !important;
+            border: 1px solid var(--border-color-primary) !important;
+            color: var(--body-text-color) !important;
+            padding: 10px;
+            border-radius: 8px;
+            text-align: center;
+            min-height: 80px;
+            display: flex;
+            flex-direction: column;
+            justify-content: center;
+        }
+        
+        .timing-card-highlight {
+            background: var(--background-fill-primary) !important;
+            border: 2px solid var(--color-accent) !important;
+        }
+        
+        .performance-card {
+            background: var(--background-fill-secondary) !important;
+            border: 1px solid var(--border-color-primary) !important;
+            color: var(--body-text-color) !important;
+            padding: 10px;
+            border-radius: 6px;
+            text-align: center;
+        }
+        
        .gr-number input[readonly] {
            background-color: var(--background-fill-secondary) !important;
            border: 1px solid var(--border-color-primary) !important;
@@ -511,20 +511,18 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
        def on_example_select(example_label):
            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
-                return selected_prompt, selected_image
-            return "", None
+                return examples[index]
+            return ""
        
        example_dropdown.change(
            fn=on_example_select,
            inputs=example_dropdown,
-            outputs=[prompt, input_image],
+            outputs=prompt,
        )
        
        gr.HTML("""
        <div style="text-align: center; margin-top: 10px; margin-bottom: 15px;">
-            <p style="font-size: 16px; margin: 0;">The compute for this demo is generously provided by <a href="https://www.gmicloud.ai/" target="_blank">GMI Cloud</a>.  Note that this demo is meant as a preview of our distilled I2V model. Outside of few-step distillation, we have not yet fully optimized it for speed. Stay tuned for updates!</p>
+            <p style="font-size: 16px; margin: 0;">The compute for this demo is generously provided by <a href="https://www.gmicloud.ai/" target="_blank">GMI Cloud</a>. Note that this demo is meant to showcase FastWan's quality and that under a large number of requests, generation speed may be affected. We are also rate-limiting users to 3 requests per minute.</p>
        </div>
        """)
        
@@ -539,7 +537,6 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
                selected_model = "FastWan2.1-T2V-1.3B"
            
            model_path = MODEL_PATH_MAPPING.get(selected_model)
-            show_image_input = is_i2v_model(selected_model)
            
            if model_path and model_path in default_params:
                params = default_params[model_path]
@@ -548,29 +545,29 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
                    gr.update(value=params.width),
                    gr.update(value=params.num_frames),
                    gr.update(value=params.guidance_scale),
-                    gr.update(visible=show_image_input),
+                    gr.update(value=params.seed),
                )
            
            return (
                gr.update(value=448),
                gr.update(value=832),
-                gr.update(value=20),
+                gr.update(value=61),
                gr.update(value=3.0),
-                gr.update(visible=show_image_input),
+                gr.update(value=1024),
            )
        
        model_selection.change(
            fn=on_model_selection_change,
            inputs=model_selection,
-            outputs=[height, width, num_frames, guidance_scale, image_tab],
+            outputs=[height, width, num_frames, guidance_scale, seed],
        )
        
        def handle_generation(*args, progress=None, request: gr.Request = None):
-            model_selection, prompt, negative_prompt, use_negative_prompt, guidance_scale, num_frames, height, width, input_image = args
+            model_selection, prompt, negative_prompt, use_negative_prompt, seed, guidance_scale, num_frames, height, width, randomize_seed = args
            
-            result_path, seed_or_error, _ = generate_video(
-                prompt, negative_prompt, use_negative_prompt, guidance_scale, 
-                num_frames, height, width, model_selection, input_image, progress
+            result_path, seed_or_error, timing_details = generate_video(
+                prompt, negative_prompt, use_negative_prompt, seed, guidance_scale, 
+                num_frames, height, width, randomize_seed, model_selection, progress
            )
            
            if result_path and os.path.exists(result_path):
@@ -578,12 +575,14 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
                    result_path, 
                    seed_or_error, 
                    gr.update(visible=False),
+                    gr.update(visible=True, value=timing_details),
                )
            else:
                return (
                    None, 
                    seed_or_error, 
                    gr.update(visible=True, value=seed_or_error),
+                    gr.update(visible=False),
                )
        
        run_button.click(
@@ -593,14 +592,14 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
                prompt,
                negative_prompt,
                use_negative_prompt,
+                seed,
                guidance_scale,
                num_frames,
                height,
                width,
-                # randomize_seed,
-                input_image,
+                randomize_seed,
            ],
-            outputs=[result, seed_output, error_output],  # timing_display removed
+            outputs=[result, seed_output, error_output, timing_display],
            concurrency_limit=20,
        )
    
@@ -612,11 +611,8 @@ def main():
    parser.add_argument("--backend_url", type=str, default="http://localhost:8000",
                        help="URL of the Ray Serve backend")
    parser.add_argument("--t2v_model_paths", type=str,
-                        default="",
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.1-T2V-14B-Diffusers",
                        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",
-                        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")
    parser.add_argument("--port", type=int, default=7860,
@@ -625,15 +621,8 @@ def main():
    args = parser.parse_args()
    
    default_params = {}
-    
-    # Load T2V model params
-    t2v_paths = [p.strip() for p in args.t2v_model_paths.split(",") if p.strip()]
-    for model_path in t2v_paths:
-        default_params[model_path] = SamplingParam.from_pretrained(model_path)
-    
-    # Load I2V model params
-    i2v_paths = [p.strip() for p in args.i2v_model_paths.split(",") if p.strip()]
-    for model_path in i2v_paths:
+    model_paths = args.t2v_model_paths.split(",")
+    for model_path in model_paths:
        default_params[model_path] = SamplingParam.from_pretrained(model_path)
    
    demo = create_gradio_interface(args.backend_url, default_params)
@@ -641,8 +630,6 @@ def main():
    print(f"Starting Gradio frontend at http://{args.host}:{args.port}")
    print(f"Backend URL: {args.backend_url}")
    print(f"T2V Models: {args.t2v_model_paths}")
-    if args.i2v_model_paths:
-        print(f"I2V Models: {args.i2v_model_paths}")
    
    from fastapi import FastAPI, Request, HTTPException
    from fastapi.responses import HTMLResponse, FileResponse
@@ -687,23 +674,23 @@ def main():
            <meta charset="UTF-8" />
            <meta name="viewport" content="width=device-width, initial-scale=1.0" />
            
-            <title>CausalWan</title>
-            <meta name="title" content="CausalWan">
+            <title>FastWan</title>
+            <meta name="title" content="FastWan">
            <meta name="description" content="Make video generation go blurrrrrrr">
-            <meta name="keywords" content="FastVideo, video generation, AI, machine learning, CausalWan">
+            <meta name="keywords" content="FastVideo, video generation, AI, machine learning, FastWan">
            
            <meta property="og:type" content="website">
            <meta property="og:url" content="{base_url}/">
-            <meta property="og:title" content="CausalWan">
+            <meta property="og:title" content="FastWan">
            <meta property="og:description" content="Make video generation go blurrrrrrr">
            <meta property="og:image" content="{base_url}/logo.svg">
            <meta property="og:image:width" content="1200">
            <meta property="og:image:height" content="630">
-            <meta property="og:site_name" content="CausalWan">
+            <meta property="og:site_name" content="FastWan">
            
            <meta property="twitter:card" content="summary_large_image">
            <meta property="twitter:url" content="{base_url}/">
-            <meta property="twitter:title" content="CausalWan">
+            <meta property="twitter:title" content="FastWan">
            <meta property="twitter:description" content="Make video generation go blurrrrrrr">
            <meta property="twitter:image" content="{base_url}/logo.svg">
            <link rel="icon" type="image/png" sizes="32x32" href="/favicon.ico">
@@ -733,14 +720,7 @@ def main():
        app, 
        demo, 
        path="/gradio",
-        allowed_paths=[
-            os.path.abspath("outputs"), 
-            os.path.abspath("fastvideo-logos"),
-            os.path.abspath("prompts"),
-            os.path.abspath("images"),
-            os.path.abspath(tempfile.gettempdir()),
-            os.path.abspath(os.path.join(tempfile.gettempdir(), "gradio")),
-        ]
+        allowed_paths=[os.path.abspath("outputs"), os.path.abspath("fastvideo-logos")]
    )
    
    uvicorn.run(app, host=args.host, port=args.port)
@@ -1,15 +0,0 @@
-Some friends dancing and having fun together in circles, at a party surrounded by colored lights at a party, in a fancy old place, in a view from below them.
-A man wearing grey shorts jumps rope in a gym, weights and gym equipment in the background.
-Flying over a peninsula covered in bushy trees, while discovering the sea around it, painted a beautiful turquoise blue, on a sunny day.
-Skillful cyclist doing a wheelie on a bike while riding through a forest, on a dirt road, surrounded by many trees, in the morning.
-In the video, a lone rider guides a majestic horse across an expansive, open field as the sun sets in the background. The rider, dressed in a classic blue shirt and wide-brimmed hat, sits confidently in the saddle, silhouetted against the warm glow of the evening sky. The horse moves gracefully, its mane and tail flowing with each step, creating a sense of harmony between horse and rider. Surrounding the pair, towering trees form a natural border, their leaves gently rustling in the breeze. The shadows lengthen on the ground, accentuating the serene and timeless feel of the scene. The distant hills and wooden fences frame the horizon, adding depth to the tranquil landscape. A few horses graze peacefully in the background, blending into the pastoral setting. The overall ambiance evokes a sense of calmness and quietude, capturing a perfect moment in the golden light of dusk.
-A silver SUV drives along a winding, snow-covered mountain road, with dense pine trees blanketed in snow lining both sides. The scene is serene, with the vehicle moving smoothly, possibly on a winter journey or vacation. As the SUV disappears around the bend, another, darker SUV follows, creating a sense of motion and perspective on the snow-dusted asphalt. The towering, snow-laden rock formation to the right contrasts with the dark green of the pines, highlighting the peacefulness of the wintry landscape.
-A man and a woman playing in a field with grass, during a bright afternoon, while cars pass by in the distance.
-A saxophonist wearing a blazer dances while playing a song in a park.
-Romantic couple embracing and looking at each other in the middle of a forest, during a break on a road trip through nature.
-Young woman cleaning her house decorated with plants and decorations, while dancing happily to music in her headphones.
-Man dressed in 80's style dances very happily in his kitchen while listening to music on his radio and drinking wine.
-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.
@@ -26,7 +26,6 @@ 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",
 ]

 MODEL_CONFIGS = {
@@ -43,13 +42,6 @@ MODEL_CONFIGS = {
        "dit_cpu_offload": True,
        "vae_cpu_offload": False,
        "VSA_sparsity": 0.9,
-    },
-    "I2V-A14B": {
-        "num_cpus": 15,
-        "text_encoder_cpu_offload": True,
-        "dit_cpu_offload": True,
-        "vae_cpu_offload": False,
-        "VSA_sparsity": 0.0,
    }
 }

@@ -66,7 +58,6 @@ class VideoGenerationRequest(BaseModel):
    randomize_seed: bool = False
    return_frames: bool = False
    model_path: Optional[str] = None
-    image_data: Optional[str] = None  # Base64 encoded image for I2V


 class VideoGenerationResponse(BaseModel):
@@ -100,38 +91,11 @@ def encode_video_to_base64(frames: List[np.ndarray], fps: int = DEFAULT_FPS) ->
        return ""


-def save_image_from_base64(image_data: str, output_dir: str) -> Optional[str]:
-    """Save base64 image data to a temporary file and return the path."""
-    if not image_data:
-        return None
-    
-    try:
-        # Remove data URL prefix if present
-        if image_data.startswith('data:image/'):
-            image_data = image_data.split(',')[1]
-        
-        image_bytes = base64.b64decode(image_data)
-        
-        # Save to temporary file
-        os.makedirs(output_dir, exist_ok=True)
-        temp_image_path = os.path.join(output_dir, f"temp_input_{int(time.time() * 1000)}.png")
-        
-        with open(temp_image_path, 'wb') as f:
-            f.write(image_bytes)
-        
-        return temp_image_path
-        
-    except Exception as e:
-        print(f"Warning: Failed to save image: {e}")
-        return None
-
-
 def setup_model_environment(model_path: str) -> None:
-    # if "fullattn" in model_path.lower():
-    #     os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
-    # else:
-    #     os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
-    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
+    if "fullattn" in model_path.lower():
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
+    else:
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
    os.environ["FASTVIDEO_STAGE_LOGGING"] = "1"


@@ -193,41 +157,22 @@ class BaseModelDeployment:
            num_gpus=1,
            use_fsdp_inference=True,
            text_encoder_cpu_offload=config["text_encoder_cpu_offload"],
-            dmd_denoising_steps=[1000, 850, 700, 550, 350, 275, 200, 125], # TODO: hardocde for I2V
-            dit_precision="fp32",  # TODO: hardocde for I2V
            dit_cpu_offload=config["dit_cpu_offload"],
            vae_cpu_offload=config["vae_cpu_offload"],
            VSA_sparsity=config["VSA_sparsity"],
            enable_stage_verification=False,
        )
        self.default_params = SamplingParam.from_pretrained(self.model_path)
-        self.default_params.seed = 1000
-        self.default_params.num_frames = 73
-        self.default_params.width = 832
-        self.default_params.height = 480

    def generate_video(self, video_request: VideoGenerationRequest) -> VideoGenerationResponse:
        total_start_time = time.time()
        
        params = prepare_sampling_params(video_request, self.default_params)

-        # Save image if provided (for I2V)
-        image_path = None
-        if video_request.image_data:
-            image_path = save_image_from_base64(video_request.image_data, self.output_path)
-            if image_path is None:
-                return VideoGenerationResponse(
-                    video_data=None,
-                    seed=params.seed,
-                    success=False,
-                    error_message="Failed to save input image",
-                )
-
        inference_start_time = time.time()
        result = self.generator.generate_video(
            prompt=video_request.prompt,
            sampling_param=params,
-            image_path=image_path,
            save_video=False,
            return_frames=False,
        )
@@ -240,13 +185,6 @@ class BaseModelDeployment:
        encoding_time = time.time() - encoding_start_time
        
        total_time = time.time() - total_start_time
-        
-        # Clean up temporary image file
-        if image_path and os.path.exists(image_path):
-            try:
-                os.remove(image_path)
-            except Exception as e:
-                print(f"Warning: Failed to remove temporary image file {image_path}: {e}")

        return VideoGenerationResponse(
            video_data=video_data,
@@ -262,7 +200,7 @@ class BaseModelDeployment:


@serve.deployment(
-    ray_actor_options={"num_cpus": 2, "num_gpus": 1, "runtime_env": {"conda": "demo-fv"}},
+    ray_actor_options={"num_cpus": 2, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
 )
 class T2VModelDeployment(BaseModelDeployment):
    def __init__(self, t2v_model_path: str, output_path: str = "outputs"):
@@ -272,7 +210,7 @@ class T2VModelDeployment(BaseModelDeployment):


@serve.deployment(
-    ray_actor_options={"num_cpus": 16, "num_gpus": 1, "runtime_env": {"conda": "demo-fv"}},
+    ray_actor_options={"num_cpus": 16, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
 )
 class T2V14BModelDeployment(BaseModelDeployment):
    def __init__(self, t2v_14b_model_path: str, output_path: str = "outputs"):
@@ -283,32 +221,18 @@ class T2V14BModelDeployment(BaseModelDeployment):
        print("✅ T2V 14B model initialized successfully")


-@serve.deployment(
-    ray_actor_options={"num_cpus": 15, "num_gpus": 1, "runtime_env": {"conda": "demo-fv"}},
-)
-class I2VModelDeployment(BaseModelDeployment):
-    def __init__(self, i2v_model_path: str, output_path: str = "outputs"):
-        super().__init__(i2v_model_path, output_path)
-        # Override environment for I2V model
-        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
-        self._initialize_generator(MODEL_CONFIGS["I2V-A14B"])
-        print("✅ I2V model initialized successfully")
-
-
 app = FastAPI()
 limiter = Limiter(key_func=get_remote_address)
 app.state.limiter = limiter
 app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)


-@serve.deployment(num_replicas=1, ray_actor_options={"num_cpus": 1})
+@serve.deployment(num_replicas=50, ray_actor_options={"num_cpus": 2})
@serve.ingress(app)
 class FastVideoAPI:

-    def __init__(self, t2v_deployments: Dict[str, DeploymentHandle], i2v_deployments: Dict[str, DeploymentHandle] = None):
+    def __init__(self, t2v_deployments: Dict[str, DeploymentHandle]):
        self.t2v_deployments = t2v_deployments
-        self.i2v_deployments = i2v_deployments or {}
-        self.all_deployments = {**self.t2v_deployments, **self.i2v_deployments}
        
        # Initialize Prometheus metrics
        self.request_count = Counter('fastvideo_requests_total', 'Total FastVideo requests', ['model_type', 'status'])
@@ -333,10 +257,10 @@ class FastVideoAPI:
        model_name = self._get_model_name(video_request.model_path)
        
        try:
-            if video_request.model_path not in self.all_deployments:
+            if video_request.model_path not in self.t2v_deployments:
                raise ValueError(f"Model {video_request.model_path} not found")
            
-            response_ref = self.all_deployments[video_request.model_path].generate_video.remote(video_request)
+            response_ref = self.t2v_deployments[video_request.model_path].generate_video.remote(video_request)
            response = await response_ref
            
            self._record_metrics(model_name, "success", time.time() - start_time, response)
@@ -367,21 +291,18 @@ class FastVideoAPI:


 def validate_configuration(model_paths: List[str], replicas: List[int]) -> None:
-    assert len(model_paths) > 0, "At least one model must be specified"
    assert len(model_paths) == len(replicas), "Number of models and replicas must match"
    assert sum(replicas) <= NUM_GPUS, f"Total replicas ({sum(replicas)}) must be <= {NUM_GPUS}"
    
    for model, replica_count in zip(model_paths, replicas):
-        assert model in SUPPORTED_MODELS, f"Model {model} not supported. Supported models: {SUPPORTED_MODELS}"
+        assert model in SUPPORTED_MODELS, f"Model {model} not supported"
        assert replica_count > 0, f"Replicas must be greater than 0"


 def start_ray_serve(
    *,
-    t2v_model_paths: str = "",
-    t2v_model_replicas: str = "",
-    i2v_model_paths: str = "",
-    i2v_model_replicas: str = "",
+    t2v_model_paths: str,
+    t2v_model_replicas: str,
    output_path: str = "outputs",
    host: str = "0.0.0.0",
    port: int = 8000,
@@ -389,39 +310,21 @@ def start_ray_serve(
    if not ray.is_initialized():
        ray.init()

-    # Parse T2V models
-    t2v_paths = [p.strip() for p in t2v_model_paths.split(",") if p.strip()]
-    t2v_reps = [int(r.strip()) for r in t2v_model_replicas.split(",") if r.strip()] if t2v_model_replicas else []
-    
-    # Parse I2V models
-    i2v_paths = [p.strip() for p in i2v_model_paths.split(",") if p.strip()]
-    i2v_reps = [int(r.strip()) for r in i2v_model_replicas.split(",") if r.strip()] if i2v_model_replicas else []
-    
-    # Validate configurations
-    all_paths = t2v_paths + i2v_paths
-    all_replicas = t2v_reps + i2v_reps
-    validate_configuration(all_paths, all_replicas)
+    model_paths = t2v_model_paths.split(",")
+    replicas = [int(r) for r in t2v_model_replicas.split(",")]
+    validate_configuration(model_paths, replicas)

-    # Create T2V deployments
    t2v_deps = {}
-    for model_path, replica_count in zip(t2v_paths, t2v_reps):
+    for model_path, replica_count in zip(model_paths, replicas):
        t2v_dep = T2VModelDeployment.options(num_replicas=replica_count).bind(model_path, output_path)
        t2v_deps[model_path] = t2v_dep

-    # Create I2V deployments
-    i2v_deps = {}
-    for model_path, replica_count in zip(i2v_paths, i2v_reps):
-        i2v_dep = I2VModelDeployment.options(num_replicas=replica_count).bind(model_path, output_path)
-        i2v_deps[model_path] = i2v_dep
-
-    api = FastVideoAPI.bind(t2v_deps, i2v_deps)
+    api = FastVideoAPI.bind(t2v_deps)
    serve.run(api, route_prefix="/", name="fast_video")

    print(f"Ray Serve backend started at http://{host}:{port}")
-    for model_path, replica_count in zip(t2v_paths, t2v_reps):
+    for model_path, replica_count in zip(model_paths, replicas):
        print(f"T2V Model: {model_path} | Replicas: {replica_count}")
-    for model_path, replica_count in zip(i2v_paths, i2v_reps):
-        print(f"I2V Model: {model_path} | Replicas: {replica_count}")
    print(f"Health check: http://{host}:{port}/health")
    print(f"Video generation endpoint: http://{host}:{port}/generate_video")

@@ -437,20 +340,12 @@ if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="FastVideo Ray Serve Backend")
    parser.add_argument("--t2v_model_paths",
                        type=str,
-                        default="",
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
                        help="Comma separated list of paths to the T2V model(s)")
    parser.add_argument("--t2v_model_replicas",
                        type=str,
-                        default="",
+                        default="4,4",
                        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",
-                        help="Comma separated list of paths to the I2V model(s)")
-    parser.add_argument("--i2v_model_replicas",
-                        type=str,
-                        default="1",
-                        help="Comma separated list of number of replicas for the I2V model(s)")
    parser.add_argument("--output_path",
                        type=str,
                        default="outputs",
@@ -466,21 +361,13 @@ if __name__ == "__main__":
    
    args = parser.parse_args()

-    # Parse and validate all models
-    t2v_paths = [p.strip() for p in args.t2v_model_paths.split(",") if p.strip()]
-    t2v_reps = [int(r.strip()) for r in args.t2v_model_replicas.split(",") if r.strip()] if args.t2v_model_replicas else []
-    i2v_paths = [p.strip() for p in args.i2v_model_paths.split(",") if p.strip()]
-    i2v_reps = [int(r.strip()) for r in args.i2v_model_replicas.split(",") if r.strip()] if args.i2v_model_replicas else []
-    
-    all_paths = t2v_paths + i2v_paths
-    all_replicas = t2v_reps + i2v_reps
-    validate_configuration(all_paths, all_replicas)
+    model_paths = args.t2v_model_paths.split(",")
+    replicas = [int(r) for r in args.t2v_model_replicas.split(",")]
+    validate_configuration(model_paths, replicas)
    
    start_ray_serve(
        t2v_model_paths=args.t2v_model_paths,
        t2v_model_replicas=args.t2v_model_replicas,
-        i2v_model_paths=args.i2v_model_paths,
-        i2v_model_replicas=args.i2v_model_replicas,
        output_path=args.output_path,
        host=args.host,
        port=args.port,
@@ -489,4 +376,4 @@ if __name__ == "__main__":
    setup_signal_handlers()
    print("✅ FastVideo backend is running. Press Ctrl-C to stop.")
    while True:
-        time.sleep(3600) 
+        time.sleep(3600) 
@@ -1,5 +1,3 @@
-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_replicas "1"
+python examples/inference/gradio/start_ray_serve_app.py \
+--t2v_model_paths "FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers" \
+--t2v_model_replicas "4,4"
@@ -20,10 +20,8 @@ DEFAULT_BACKEND_PORT = 8000
 DEFAULT_FRONTEND_HOST = "0.0.0.0"
 DEFAULT_FRONTEND_PORT = 7860
 DEFAULT_OUTPUT_PATH = "outputs"
-DEFAULT_T2V_MODELS = ""
-DEFAULT_T2V_REPLICAS = ""
-DEFAULT_I2V_MODELS = "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers"
-DEFAULT_I2V_REPLICAS = "1"
+DEFAULT_T2V_MODELS = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers"
+DEFAULT_T2V_REPLICAS = "4,4"

 HEALTH_CHECK_TIMEOUT = 5
 HEALTH_CHECK_MAX_RETRIES = 100
@@ -102,12 +100,6 @@ class ServiceManager:
            "port": self.args.backend_port
        }
        
-        # Add I2V parameters if provided
-        if self.args.i2v_model_paths:
-            backend_args["i2v_model_paths"] = self.args.i2v_model_paths
-        if self.args.i2v_model_replicas:
-            backend_args["i2v_model_replicas"] = self.args.i2v_model_replicas
-        
        self.backend_process = self._start_service("ray_serve_backend.py", backend_args, "backend")
        return self.backend_process
    
@@ -119,10 +111,6 @@ class ServiceManager:
            "port": self.args.frontend_port
        }
        
-        # Add I2V parameters if provided
-        if self.args.i2v_model_paths:
-            frontend_args["i2v_model_paths"] = self.args.i2v_model_paths
-        
        self.frontend_process = self._start_service("gradio_frontend.py", frontend_args, "frontend")
        return self.frontend_process
    
@@ -185,9 +173,6 @@ def print_startup_info(args: argparse.Namespace) -> None:
    print("=" * 50)
    print(f"T2V Models: {args.t2v_model_paths}")
    print(f"T2V Model Replicas: {args.t2v_model_replicas}")
-    if args.i2v_model_paths:
-        print(f"I2V Models: {args.i2v_model_paths}")
-        print(f"I2V Model Replicas: {args.i2v_model_replicas}")
    print(f"Output: {args.output_path}")
    print(f"Backend: http://{args.backend_host}:{args.backend_port}")
    print(f"Frontend: http://{args.frontend_host}:{args.frontend_port}")
@@ -205,14 +190,6 @@ def parse_arguments() -> argparse.Namespace:
                        type=str, 
                        default=DEFAULT_T2V_REPLICAS,
                        help="Comma separated list of number of replicas for the T2V model(s)")
-    parser.add_argument("--i2v_model_paths", 
-                        type=str,
-                        default=DEFAULT_I2V_MODELS,
-                        help="Comma separated list of paths to the I2V model(s)")
-    parser.add_argument("--i2v_model_replicas", 
-                        type=str, 
-                        default=DEFAULT_I2V_REPLICAS,
-                        help="Comma separated list of number of replicas for the I2V model(s)")
    parser.add_argument("--output_path", 
                        type=str, 
                        default=DEFAULT_OUTPUT_PATH,
@@ -45,7 +45,6 @@ class PipelineConfig:
    embedded_cfg_scale: float = 6.0
    flow_shift: float | None = None
    disable_autocast: bool = False
-    is_causal: bool = False

    # Model configuration
    dit_config: DiTConfig = field(default_factory=DiTConfig)
@@ -39,8 +39,6 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
    "FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers": WanT2V720PConfig,
    "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
    "rand0nmr/SFWan2.2-T2V-A14B-Diffusers": SelfForcingWan2_2_T2V480PConfig,
-    "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers":
-    SelfForcingWan2_2_T2V480PConfig,
    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_Config,
    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_Config,
    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_Config,
@@ -78,8 +78,6 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
    # Causal Self-Forcing Wan2.2
    "rand0nmr/SFWan2.2-T2V-A14B-Diffusers":
    SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
-    "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers":
-    SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,

    # Cosmos2
    "nvidia/Cosmos-Predict2-2B-Video2World":
@@ -101,19 +101,12 @@ class BaseLayerWithLoRA(nn.Module):
    def set_lora_weights(self,
                         A: torch.Tensor,
                         B: torch.Tensor,
-                         lora_alpha: float | None = None,
                         training_mode: bool = False,
                         lora_path: str | None = None) -> None:
        self.lora_A = torch.nn.Parameter(
            A)  # share storage with weights in the pipeline
        self.lora_B = torch.nn.Parameter(B)
        self.disable_lora = False
-
-        # Store rank and alpha directly
-        rank = A.shape[0]  # rank is the first dimension of A
-        self.lora_rank = rank
-        self.lora_alpha = int(lora_alpha) if lora_alpha is not None else rank
-
        if not training_mode:
            self.merge_lora_weights()
        self.lora_path = lora_path
@@ -141,13 +134,8 @@ class BaseLayerWithLoRA(nn.Module):
            current_device = self.base_layer.weight.data.device
            data = self.base_layer.weight.data.to(
                get_local_torch_device()).full_tensor()
-
-            # Apply LoRA with alpha scaling
-            lora_delta = (self.slice_lora_b_weights(self.lora_B).to(data)
-                          @ self.slice_lora_a_weights(self.lora_A).to(data))
-            if self.lora_alpha and self.lora_rank and self.lora_alpha != self.lora_rank:
-                lora_delta *= (self.lora_alpha / self.lora_rank)
-            data += lora_delta
+            data += (self.slice_lora_b_weights(self.lora_B).to(data)
+                     @ self.slice_lora_a_weights(self.lora_A).to(data))
            unsharded_base_layer.weight = nn.Parameter(data.to(current_device))
            if isinstance(getattr(self.base_layer, "bias", None), DTensor):
                unsharded_base_layer.bias = nn.Parameter(
@@ -166,13 +154,8 @@ class BaseLayerWithLoRA(nn.Module):
        else:
            current_device = self.base_layer.weight.data.device
            data = self.base_layer.weight.data.to(get_local_torch_device())
-
-            # Apply LoRA with alpha scaling
-            lora_delta = (self.slice_lora_b_weights(self.lora_B.to(data))
-                          @ self.slice_lora_a_weights(self.lora_A.to(data)))
-            if self.lora_alpha and self.lora_rank and self.lora_alpha != self.lora_rank:
-                lora_delta *= (self.lora_alpha / self.lora_rank)
-            data += lora_delta
+            data += \
+                (self.slice_lora_b_weights(self.lora_B.to(data)) @ self.slice_lora_a_weights(self.lora_A.to(data)))
            self.base_layer.weight.data = data.to(current_device,
                                                  non_blocking=True)

@@ -28,8 +28,7 @@ class LoRAPipeline(ComposedPipelineBase):
    TODO: support training.
    """
    lora_adapters: dict[str, dict[str, torch.Tensor]] = defaultdict(
-        dict
-    )  # state dicts of loaded lora adapters (includes lora_A, lora_B, and lora_alpha)
+        dict)  # state dicts of loaded lora adapters
    cur_adapter_name: str = ""
    cur_adapter_path: str = ""
    lora_layers: dict[str, BaseLayerWithLoRA] = {}
@@ -184,26 +183,11 @@ class LoRAPipeline(ComposedPipelineBase):
            lora_param_names_mapping_fn = get_param_names_mapping(
                self.modules["transformer"].lora_param_names_mapping)

-            # Extract alpha values and weights in a single pass
            to_merge_params: defaultdict[Hashable,
                                         dict[Any, Any]] = defaultdict(dict)
            for name, weight in lora_state_dict.items():
-                # Extract weights (lora_A, lora_B, and lora_alpha)
                name = name.replace("diffusion_model.", "")
                name = name.replace(".weight", "")
-
-                if "lora_alpha" in name:
-                    # Store alpha with minimal mapping - same processing as lora_A/lora_B
-                    # but store in lora_adapters with ".lora_alpha" suffix
-                    layer_name = name.replace(".lora_alpha", "")
-                    layer_name, _, _ = lora_param_names_mapping_fn(layer_name)
-                    target_name, _, _ = param_names_mapping_fn(layer_name)
-                    # Store alpha alongside weights with same target_name base
-                    alpha_key = target_name + ".lora_alpha"
-                    self.lora_adapters[lora_nickname][alpha_key] = weight.item(
-                    ) if weight.numel() == 1 else float(weight.mean())
-                    continue
-
                name, _, _ = lora_param_names_mapping_fn(name)
                target_name, merge_index, num_params_to_merge = param_names_mapping_fn(
                    name)
@@ -241,20 +225,11 @@ class LoRAPipeline(ComposedPipelineBase):
        for name, layer in self.lora_layers.items():
            lora_A_name = name + ".lora_A"
            lora_B_name = name + ".lora_B"
-            lora_alpha_name = name + ".lora_alpha"
            if lora_A_name in self.lora_adapters[lora_nickname]\
                and lora_B_name in self.lora_adapters[lora_nickname]:
-                # Get alpha value for this layer (defaults to None if not present)
-                lora_A = self.lora_adapters[lora_nickname][lora_A_name]
-                lora_B = self.lora_adapters[lora_nickname][lora_B_name]
-                # Simple lookup - alpha stored with same naming scheme as lora_A/lora_B
-                alpha = self.lora_adapters[lora_nickname].get(
-                    lora_alpha_name) if adapter_updated else None
-
                layer.set_lora_weights(
-                    lora_A,
-                    lora_B,
-                    lora_alpha=alpha,
+                    self.lora_adapters[lora_nickname][lora_A_name],
+                    self.lora_adapters[lora_nickname][lora_B_name],
                    training_mode=self.fastvideo_args.training_mode,
                    lora_path=lora_path)
                adapted_count += 1
@@ -85,10 +85,10 @@ class CausalDMDDenosingStage(DenoisingStage):

        if fastvideo_args.pipeline_config.dit_config.boundary_ratio is not None:
            boundary_timestep = fastvideo_args.pipeline_config.dit_config.boundary_ratio * self.scheduler.num_train_timesteps
-            high_noise_timesteps = timesteps[timesteps >= boundary_timestep]
        else:
            boundary_timestep = None
-            high_noise_timesteps = None
+
+        high_noise_timesteps = timesteps[timesteps >= boundary_timestep]

        # Image kwargs (kept empty unless caller provides compatible args)
        image_kwargs: dict = {}
@@ -123,12 +123,11 @@ class CausalDMDDenosingStage(DenoisingStage):
                                                  dtype=target_dtype,
                                                  device=latents.device)

-        def _get_kv_cache(timestep: float) -> list[dict]:
+        def _get_kv_cache(timestep: float) -> dict:
            if boundary_timestep is not None:
                if timestep >= boundary_timestep:
                    return kv_cache1
                else:
-                    assert kv_cache2 is not None, "kv_cache2 is not initialized"
                    return kv_cache2
            return kv_cache1

@@ -142,18 +141,10 @@ class CausalDMDDenosingStage(DenoisingStage):
        pos_start_base = 0

        # Determine block sizes
-        if t % self.num_frames_per_block != 0:
-            raise ValueError(
-                "num_frames must be divisible by num_frames_per_block for causal DMD denoising"
-            )
-        num_blocks = t // self.num_frames_per_block
-        block_sizes = [self.num_frames_per_block] * num_blocks
+        block_sizes = [self.num_frames_per_block] * 7
+        block_sizes[0] = 1
        start_index = 0

-        # For now hardcode the first block to be 1 frame assuming the model is Wan2.2-MoE
-        if boundary_timestep is not None:
-            block_sizes[0] = 1
-
        first_frame_latent = None
        if batch.pil_image is not None:
            # Causal video gen directly replaces the first frame of the latent with
@@ -400,14 +391,10 @@ class CausalDMDDenosingStage(DenoisingStage):

                start_index += current_num_frames

-        if boundary_timestep is not None:
-            num_frames_to_remove = self.num_frames_per_block - 1
-            latents = latents[:, :, :-num_frames_to_remove, :, :]
-
        batch.latents = latents
        return batch

-    def _initialize_kv_cache(self, batch_size, dtype, device) -> list[dict]:
+    def _initialize_kv_cache(self, batch_size, dtype, device) -> None:
        """
        Initialize a Per-GPU KV cache aligned with the Wan model assumptions.
        """
@@ -444,7 +431,7 @@ class CausalDMDDenosingStage(DenoisingStage):
        return kv_cache1

    def _initialize_crossattn_cache(self, batch_size, max_text_len, dtype,
-                                    device) -> list[dict]:
+                                    device) -> None:
        """
        Initialize a Per-GPU cross-attention cache aligned with the Wan model assumptions.
        """
@@ -494,4 +481,4 @@ class CausalDMDDenosingStage(DenoisingStage):
        result.add_check(
            "negative_prompt_embeds", batch.negative_prompt_embeds, lambda x:
            not batch.do_classifier_free_guidance or V.list_not_empty(x))
-        return result
+        return result
@@ -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__)

@@ -110,25 +108,27 @@ class InputValidationStage(PipelineStage):
                or fastvideo_args.pipeline_config.is_causal
            ) and batch.pil_image is not None:
            img = batch.pil_image
-            ih, iw = img.height, img.width
-            patch_size = fastvideo_args.pipeline_config.dit_config.arch_config.patch_size
-            vae_stride = fastvideo_args.pipeline_config.vae_config.arch_config.scale_factor_spatial
-            dh, dw = patch_size[1] * vae_stride, patch_size[2] * vae_stride
-            max_area = 480 * 832
-            ow, oh = best_output_size(iw, ih, dw, dh, max_area)
+            # ih, iw = img.height, img.width
+            # patch_size = fastvideo_args.pipeline_config.dit_config.arch_config.patch_size
+            # vae_stride = fastvideo_args.pipeline_config.vae_config.arch_config.scale_factor_spatial
+            # dh, dw = patch_size[1] * vae_stride, patch_size[2] * vae_stride
+            # max_area = 720 * 1280
+            # ow, oh = best_output_size(iw, ih, dw, dh, max_area)

-            scale = max(ow / iw, oh / ih)
-            img = img.resize((round(iw * scale), round(ih * scale)),
-                             Image.LANCZOS)
-
-            # center-crop
-            x1 = (img.width - ow) // 2
-            y1 = (img.height - oh) // 2
-            img = img.crop((x1, y1, x1 + ow, y1 + oh))
-            assert img.width == ow and img.height == oh
-            logger.info("final processed img height: %s, img width: %s",
-                        img.height, img.width)
+            # scale = max(ow / iw, oh / ih)
+            # img = img.resize((round(iw * scale), round(ih * scale)),
+            #                  Image.LANCZOS)
+            # logger.info("resized img height: %s, img width: %s", img.height,
+            #             img.width)

+            # # center-crop
+            # x1 = (img.width - ow) // 2
+            # y1 = (img.height - oh) // 2
+            # img = img.crop((x1, y1, x1 + ow, y1 + oh))
+            # assert img.width == ow and img.height == oh
+            logger.info("img height: %s, img width: %s", img.height, img.width)
+            oh = img.height
+            ow = img.width
            # to tensor
            img = TF.to_tensor(img).sub_(0.5).div_(0.5).to(
                self.device).unsqueeze(1)
@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 import json
 import os
-import re

 import pytest

@@ -53,38 +52,12 @@ LORA_CONFIGS = [
        "negative_prompt": "bad quality video,色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
        "ssim_threshold": 0.79
    }
-    # TODO: Add a LoRA with lora_alpha values to test alpha scaling
-    # 
-    # Context: This change is mainly for an in-progress ticket porting over LongCat-Video,
-    # where they used an alpha value that is two times smaller than their rank. This fix
-    # ensures that LoRA weights are correctly scaled by the alpha/rank ratio when merged.
-    #
-    # Issue: Currently, we cannot add a test for LoRA adapters with alpha values because:
-    # - The existing public LoRAs for Wan-AI/Wan2.1-T2V-1.3B-Diffusers don't store lora_alpha
-    # - No publicly available LoRA for this model includes lora_alpha tensors in their weights
-    # - This is why the alpha/rank scaling bug wasn't caught by existing tests
-    #
-    # The fix has been validated with:
-    # - LongCat-Video distilled LoRA (which includes alpha values)
-    # - Manual testing shows correct alpha/rank scaling behavior
-    # - Backward compatibility confirmed with LoRAs without alpha values
-    #
-    # Future work:
-    # - Add a synthetic LoRA test fixture with alpha values when feasible
-    # - Or wait for public Wan LoRAs with alpha to become available
 ]

 MODEL_TO_PARAMS = {
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WAN_LORA_PARAMS,
 }

-def _sanitize_filename_component(name: str) -> str:
-    """Sanitize filename to remove invalid characters (same logic as VideoGenerator)"""
-    sanitized = re.sub(r'[\\/:*?"<>|]', '', name)
-    sanitized = sanitized.strip().strip('.')
-    sanitized = re.sub(r'\s+', ' ', sanitized)
-    return sanitized or "video"
-
@pytest.mark.parametrize("model_id", list(MODEL_TO_PARAMS.keys()))
 def test_merge_lora_weights(model_id):
    lora_config = LORA_CONFIGS[0] # test only one
@@ -164,16 +137,14 @@ def test_lora_inference_similarity(ATTENTION_BACKEND, model_id):
        generation_kwargs["negative_prompt"] = lora_config["negative_prompt"]

        generator.set_lora_adapter(lora_nickname=lora_nickname, lora_path=lora_path)
-        # Sanitize the filename before adding .mp4 extension to match VideoGenerator's behavior
        output_video_name = f"{lora_path.split('/')[-1]}_{prompt[:50]}"
-        output_video_name = _sanitize_filename_component(output_video_name)
-        generated_video_path = os.path.join(output_dir, f"{output_video_name}.mp4")
-        generation_kwargs["output_path"] = generated_video_path
+        generation_kwargs["output_path"] = output_dir
+        generation_kwargs["output_video_name"] = output_video_name

        generator.generate_video(prompt, **generation_kwargs)

        assert os.path.exists(
-            generated_video_path), f"Output video was not generated at {generated_video_path}"
+            output_dir), f"Output video was not generated at {output_dir}"

        reference_folder = os.path.join(script_dir, 'L40S_reference_videos', model_id.split('/')[-1], ATTENTION_BACKEND)
        
@@ -182,25 +153,13 @@ def test_lora_inference_similarity(ATTENTION_BACKEND, model_id):
            raise FileNotFoundError(
                f"Reference video folder does not exist: {reference_folder}")

-        # Find the matching reference video - try exact match first, then fuzzy match
-        # The reference might have different sanitization (e.g., trailing spaces)
+        # Find the matching reference video for the switched LoRA
        reference_video_name = None
-        unsanitized_prefix = f"{lora_path.split('/')[-1]}_{prompt[:50]}"
-        
+
        for filename in os.listdir(reference_folder):
-            if not filename.endswith('.mp4'):
-                continue
-            
-            # Try exact match with sanitized name
-            if filename.startswith(output_video_name):
-                reference_video_name = filename
-                break
-            
-            # Try match with unsanitized prefix (for legacy reference videos)
-            # Remove .mp4 and compare the base names after sanitization
-            base_filename = filename[:-4]  # Remove .mp4
-            if _sanitize_filename_component(base_filename) == output_video_name:
-                reference_video_name = filename
+            # Check if the filename starts with the expected output_video_name and ends with .mp4
+            if filename.startswith(output_video_name) and filename.endswith('.mp4'):
+                reference_video_name = filename  # Remove .mp4 extension to match the logic below
                break

        if not reference_video_name:
@@ -208,6 +167,7 @@ def test_lora_inference_similarity(ATTENTION_BACKEND, model_id):
            raise FileNotFoundError(f"Reference video missing for adapter {lora_path}")

        reference_video_path = os.path.join(reference_folder, reference_video_name)
+        generated_video_path = os.path.join(output_dir, output_video_name + ".mp4")

        logger.info(
            f"Computing SSIM between {reference_video_path} and {generated_video_path}"
@@ -62,49 +62,5 @@
    {
        "prompt": "A solitary boat glides across the expansive, tranquil expanse of a serene lake. The vessel leaves a gentle wake behind, creating delicate ripples across the mirror-like surface. The water appears a rich shade of teal, seamlessly blending with the sky at the horizon. Silhouettes of distant trees are faintly visible, creating a picturesque backdrop that enhances the solitary journey of the boat. The sky is a calm gradient, shifting from soft oranges near the shore to the pale blues above. In the distance, a few slender poles emerge from the water, remnants of an old structure or natural formation. The mood of the scene is one of peace and solitude, with the boat journeying steadily through the quiet landscape. There is a sense of endless possibilities as the boat moves toward the unseen beyond the frame. The simplicity and stillness of the scene invite contemplation and reflection, encapsulating a perfect moment of quietude on the water.",
        "image_path": "images/mixkit-motorboat-on-a-large-lake-with-turquoise-blue-waters-4996.png"
-    },
-    {
-        "prompt": "A man wearing grey shorts jumps rope in a gym, weights and gym equipment in the background.",
-        "image_path": "images/gray_short_man.jpg"
-    },
-    {
-        "prompt": "Flying over a peninsula covered in bushy trees, while discovering the sea around it, painted a beautiful turquoise blue, on a sunny day.",
-        "image_path": "images/peninsula.jpg"
-    },
-    {
-        "prompt": "Skillful cyclist doing a wheelie on a bike while riding through a forest, on a dirt road, surrounded by many trees, in the morning.",
-        "image_path": "images/cyclist.jpg"
-    },
-    {
-        "prompt": "Some friends dancing and having fun together in circles, at a party surrounded by colored lights at a party, in a fancy old place, in a view from below them.",
-        "image_path": "images/friends.jpg"
-    },
-    {
-        "prompt": "A saxophonist wearing a blazer dances while playing a song in a park.",
-        "image_path": "images/saxophonist.jpg"
-    },
-    {
-        "prompt": "Romantic couple embracing and looking at each other in the middle of a forest, during a break on a road trip through nature.",
-        "image_path": "images/romance.jpg"
-    },
-    {
-        "prompt": "Man dressed in 80's style dances very happily in his kitchen while listening to music on his radio and drinking wine.",
-        "image_path": "images/80s_dance.jpg"
-    },
-    {
-        "prompt": "Pair of jazz musicians performing a song with their saxophone and trombone on an abandoned train.",
-        "image_path": "images/jazz.jpg"
-    },
-    {
-        "prompt": "A young woman with short hair wearing pink sunglasses chews gum and makes a bubble gum with the city in the background.",
-        "image_path": "images/pink.jpg"
-    },
-    {
-        "prompt": "Natural aerial landscape with a relief covered with abundant trees and vegetation and a thick layer of mist.",
-        "image_path": "images/natural.jpg"
-    },
-    {
-        "prompt": "Loving couple sitting on a log on the shore of a lake outside, sharing an affectionate hug.",
-        "image_path": "images/couple.jpg"
    }
-]
+]
Author	SHA1	Message	Date
JerryZhou54	fbc0543aa8	Each rank should sample a different timestep in dmd_forward and faker_score_forward	2025-11-18 20:11:22 +00:00
JerryZhou54	94cb9f8880	Change to fp32 inference	2025-11-18 20:11:21 +00:00
JerryZhou54	e181dad528	Add i2v images	2025-11-18 20:11:21 +00:00
JerryZhou54	81dce215c3	Fi lint	2025-11-18 20:11:21 +00:00
RandNMR73	2b22c162be	Add inference for MoE SF	2025-11-18 20:11:21 +00:00