Update app.py

app.py

@@ -4,59 +4,46 @@ import random
 from diffusers import DiffusionPipeline
 import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+torch_dtype, device = torch.float16, torch.device("cuda") if torch.cuda.is_available() else torch.float32, torch.device("cpu")
+
 model_repo_id = "black-forest-labs/FLUX.1-dev"
 
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype).to(device)
 pipe.load_lora_weights("pepper13/flux-anime")
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator().manual_seed(seed)
-    
+def infer(prompt, randomize_seed, width, height, guidance_scale, num_inference_steps):
     image = pipe(
-        prompt=prompt, 
-        negative_prompt=negative_prompt,
+        prompt=prompt,
         guidance_scale=guidance_scale, 
         num_inference_steps=num_inference_steps, 
         width=width, 
-        height=height,
-        generator=generator
+        height=height
     ).images[0] 
     
-    return image, seed
+    return image
 
 with gr.Blocks() as demo:
     with gr.Column(elem_id="col-container"):
         with gr.Row():
-            prompt = gr.Text(label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False)
-            run_button = gr.Button("Run", scale=0)
+            prompt = gr.Text(label="Prompt", show_label=False, placeholder="Enter your prompt")
+            run_button = gr.Button("Generate", scale=0)
         
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", visible=False)
-            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
             with gr.Row():
-                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
-                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
+                width = gr.Slider(label="Width", minimum=256, maximum=1024, step=32, value=512)
+                height = gr.Slider(label="Height", minimum=256, maximum=1024, step=32, value=512)
             
             with gr.Row():
-                guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=0.0)
+                guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=10.0, step=0.1, value=7.0)
                 num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=20)
     
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
-        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result, seed]
+        inputs=[prompt, negative_prompt, width, height, guidance_scale, num_inference_steps],
+        outputs=[result]
     )
 
 demo.launch()