feat: set seed

app.py

@@ -1,6 +1,7 @@
 import torch
 import gradio as gr
 import spaces
+import random
 from diffusers.utils import export_to_video
 from diffusers import AutoencoderKLWan, WanPipeline
 from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
@@ -33,11 +34,21 @@ def generate_video(
     num_frames,
     guidance_scale,
     num_inference_steps,
-    output_fps
+    output_fps,
+    seed
 ):
     # Get model ID from selection
     model_id = MODEL_OPTIONS[model_choice]
     
+    # Set seed for reproducibility
+    if seed == -1 or seed is None or seed == "":
+        seed = random.randint(0, 2147483647)
+    else:
+        seed = int(seed)
+    
+    # Set the seed
+    torch.manual_seed(seed)
+    
     # Load model
     vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
     pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
@@ -69,9 +80,9 @@ def generate_video(
         except ValueError as e:
             # Return informative error if there are multiple safetensors and no weight name
             if "more than one weights file" in str(e):
-                return f"Error: The repository '{lora_id}' contains multiple safetensors files. Please specify a weight name using the 'LoRA Weight Name' field."
+                return f"Error: The repository '{lora_id}' contains multiple safetensors files. Please specify a weight name using the 'LoRA Weight Name' field.", seed
             else:
-                return f"Error loading LoRA weights: {str(e)}"
+                return f"Error loading LoRA weights: {str(e)}", seed
     
     # Enable CPU offload for low VRAM
     pipe.enable_model_cpu_offload()
@@ -84,24 +95,21 @@ def generate_video(
         width=width,
         num_frames=num_frames,
         guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps
+        num_inference_steps=num_inference_steps,
+        generator=torch.Generator("cuda").manual_seed(seed)
     ).frames[0]
     
     # Export to video
     temp_file = "output.mp4"
     export_to_video(output, temp_file, fps=output_fps)
     
-    return temp_file
+    return temp_file, seed
 
 # Create the Gradio interface
 with gr.Blocks() as demo:
     gr.HTML("""
     <p align="center">
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-    </svg>
-    <p align="center">
-    💻 <a href="https://www.markury.dev/"><b>Website</b></a> &nbsp&nbsp | &nbsp&nbsp 🤗 <a href="https://huggingface.co/markury"><b>Hugging Face</b></a> &nbsp&nbsp | &nbsp&nbsp 💿 <a href="https://thebulge.xyz"><b>Discord</b></a>
+    ... 
     </p>
     """)
     gr.Markdown("# Wan 2.1 T2V 1.3B with LoRA")
@@ -209,10 +217,18 @@ with gr.Blocks() as demo:
                     step=1
                 )
             
+            seed = gr.Number(
+                label="Seed (-1 for random)",
+                value=-1,
+                precision=0,
+                info="Set a specific seed for deterministic results"
+            )
+            
             generate_btn = gr.Button("Generate Video")
         
         with gr.Column(scale=1):
             output_video = gr.Video(label="Generated Video")
+            used_seed = gr.Number(label="Seed)", precision=0)
             
     generate_btn.click(
         fn=generate_video,
@@ -230,9 +246,10 @@ with gr.Blocks() as demo:
             num_frames,
             guidance_scale,
             num_inference_steps,
-            output_fps
+            output_fps,
+            seed
         ],
-        outputs=output_video
+        outputs=[output_video, used_seed]
     )
     
     gr.Markdown("""