Update app.py

app.py

@@ -3,36 +3,35 @@ import numpy as np
 import random
 import spaces
 import torch
-from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler
-from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+from diffusers import FluxPipeline
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
+pipe = FluxPipeline.from_pretrained("Shakker-Labs/AWPortrait-FL", torch_dtype=torch.bfloat16).to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
 @spaces.GPU(duration=190)
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+def infer(prompt, seed=42, randomize_seed=False, width=768, height=1024, guidance_scale=3.5, num_inference_steps=24, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
     image = pipe(
-        prompt = prompt, 
-        width = width,
-        height = height,
-        num_inference_steps = num_inference_steps, 
-        generator = generator,
+        prompt=prompt, 
+        width=width,
+        height=height,
+        num_inference_steps=num_inference_steps, 
+        generator=generator,
         guidance_scale=guidance_scale
     ).images[0] 
     return image, seed
- 
+
 examples = [
-    "a tiny astronaut hatching from an egg on the moon",
-    "a cat holding a sign that says hello world",
-    "an anime illustration of a wiener schnitzel",
+    "close up portrait, Amidst the interplay of light and shadows in a photography studio,a soft spotlight traces the contours of a face,highlighting a figure clad in a sleek black turtleneck...",
+    "a cinematic portrait of an elegant elderly man with a wise expression...",
+    "a dramatic close-up shot of a warrior princess with fierce eyes..."
 ]
 
 css="""
@@ -45,9 +44,9 @@ css="""
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev]
-12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
-[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
+        gr.Markdown(f"""# AWPortrait-FL
+12B param flow transformer guidance-distilled from [AWPortrait-FL](https://huggingface.co/Shakker-Labs/AWPortrait-FL)  
+[[non-commercial license](https://huggingface.co/Shakker-Labs/AWPortrait-FL/blob/main/LICENSE.md)]
         """)
         
         with gr.Row():
@@ -83,7 +82,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,
+                    value=768,
                 )
                 
                 height = gr.Slider(
@@ -109,22 +108,22 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=28,
+                    value=24,
                 )
         
         gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
+            examples=examples,
+            fn=infer,
+            inputs=[prompt],
+            outputs=[result, seed],
             cache_examples="lazy"
         )
 
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        fn=infer,
+        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed]
     )
 
-demo.launch()
+demo.launch()