Update app.py

app.py

@@ -3,13 +3,17 @@ 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 DiffusionPipeline, FlowMatchEulerDiscreteScheduler
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 
 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)
+# Include your Hugging Face access token
+hf_token = "waffles"
+
+# Load the diffusion pipeline with the access token
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, use_auth_token=hf_token).to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
@@ -20,11 +24,11 @@ def infer(prompt, seed=42, randomize_seed=False, width=512, height=512, guidance
         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
@@ -83,7 +87,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,
+                    value=512,
                 )
                 
                 height = gr.Slider(
@@ -91,7 +95,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,
+                    value=512,
                 )
             
             with gr.Row():
@@ -101,7 +105,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=15,
                     step=0.1,
-                    value=3.5,
+                    value=5.0,
                 )
   
                 num_inference_steps = gr.Slider(
@@ -127,4 +131,4 @@ with gr.Blocks(css=css) as demo:
         outputs = [result, seed]
     )
 
-demo.launch()
+demo.launch()