random seed

api.py

@@ -25,7 +25,8 @@ async def text_to_image(
     model: str = Form(config.DEFAULT_TEXT2IMG_MODEL),
     negative_prompt: str = Form(config.DEFAULT_NEGATIVE_PROMPT),
     guidance_scale: float = Form(7.5),
-    num_inference_steps: int = Form(50)
+    num_inference_steps: int = Form(50),
+    seed: str = Form(None)
 ):
     """
     Generate an image from a text prompt
@@ -39,13 +40,24 @@ async def text_to_image(
         if not negative_prompt or negative_prompt.strip() == '':
             negative_prompt = config.DEFAULT_NEGATIVE_PROMPT
             
+        # Process seed parameter
+        # We'll pass seed=None to inference.text_to_image if no valid seed is provided
+        # The random seed will be generated in the inference module
+        seed_value = None
+        if seed is not None and seed.strip() != '':
+            try:
+                seed_value = int(seed)
+            except (ValueError, TypeError):
+                # Let the inference module handle invalid seed
+                
         # Call the inference module
         image = inference.text_to_image(
             prompt=prompt,
             model_name=model,
             negative_prompt=negative_prompt,
             guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps
+            num_inference_steps=num_inference_steps,
+            seed=seed_value
         )
         
         # Convert PIL image to bytes

app.py

@@ -11,18 +11,28 @@ inference = DiffusionInference()
 # Initialize the ControlNet pipeline
 controlnet = ControlNetPipeline()
 
-def text_to_image_fn(prompt, model, negative_prompt=None, guidance_scale=7.5, num_inference_steps=50):
+def text_to_image_fn(prompt, model, negative_prompt=None, guidance_scale=7.5, num_inference_steps=50, seed=None):
     try:
         # Model validation - fallback to default if empty
         if not model or model.strip() == '':
             model = config.DEFAULT_TEXT2IMG_MODEL
             
+        # Prepare seed parameter
+        seed_value = None
+        if seed and seed.strip() != '':
+            try:
+                seed_value = int(seed)
+            except (ValueError, TypeError):
+                # Let inference handle invalid seed
+                pass
+            
         # Create kwargs dictionary for parameters
         kwargs = {
             "prompt": prompt,
             "model_name": model,
             "guidance_scale": guidance_scale,
-            "num_inference_steps": num_inference_steps
+            "num_inference_steps": num_inference_steps,
+            "seed": seed_value
         }
         
         # Only add negative_prompt if it's not None
@@ -102,6 +112,7 @@ with gr.Blocks(title="Diffusion Models") as app:
                 txt2img_model = gr.Textbox(label="Model", placeholder=f"Enter model name", value=config.DEFAULT_TEXT2IMG_MODEL)
                 txt2img_guidance = gr.Slider(minimum=1.0, maximum=20.0, value=7.5, step=0.5, label="Guidance Scale")
                 txt2img_steps = gr.Slider(minimum=10, maximum=100, value=50, step=1, label="Inference Steps")
+                txt2img_seed = gr.Textbox(label="Seed (Optional)", placeholder="Leave empty for random seed", value="")
                 txt2img_button = gr.Button("Generate Image")
             
             with gr.Column():
@@ -110,7 +121,7 @@ with gr.Blocks(title="Diffusion Models") as app:
         
         txt2img_button.click(
             fn=text_to_image_fn,
-            inputs=[txt2img_prompt, txt2img_model, txt2img_negative, txt2img_guidance, txt2img_steps],
+            inputs=[txt2img_prompt, txt2img_model, txt2img_negative, txt2img_guidance, txt2img_steps, txt2img_seed],
             outputs=[txt2img_output, txt2img_error]
         )
     

inference.py

@@ -2,6 +2,7 @@ from huggingface_hub import InferenceClient
 from PIL import Image
 import io
 import config
+import random
 
 
 class DiffusionInference:
@@ -15,7 +16,7 @@ class DiffusionInference:
             api_key=self.api_key,
         )
 
-    def text_to_image(self, prompt, model_name=None, negative_prompt=None, **kwargs):
+    def text_to_image(self, prompt, model_name=None, negative_prompt=None, seed=None, **kwargs):
         """
         Generate an image from a text prompt.
         
@@ -36,6 +37,22 @@ class DiffusionInference:
             "model": model
         }
         
+        # Handle seed parameter
+        if seed is not None:
+            try:
+                # Convert to integer and add to params
+                params["seed"] = int(seed)
+            except (ValueError, TypeError):
+                # Use random seed if conversion fails
+                random_seed = random.randint(0, 3999999999)  # Max 32-bit integer
+                params["seed"] = random_seed
+                print(f"Warning: Invalid seed value: {seed}, using random seed {random_seed} instead")
+        else:
+            # Generate random seed when none is provided
+            random_seed = random.randint(0, 3999999999)  # Max 32-bit integer
+            params["seed"] = random_seed
+            print(f"Using random seed: {random_seed}")
+        
         # Add negative prompt if provided
         if negative_prompt is not None:
             params["negative_prompt"] = negative_prompt