5 min duration

app.py

@@ -3,8 +3,7 @@ import numpy as np
 import random
 import spaces
 import torch
-from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, FluxTransformer2DModel, FluxPipeline
-from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+from diffusers import FluxPipeline
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -15,10 +14,23 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
 
-@spaces.GPU()
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=8, output_format="png", progress=gr.Progress(track_tqdm=True)):
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=8, output_format="png"):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
+    if width*height*num_inference_steps <= 1024*1024*8:
+        return infer_in_1min(prompt=prompt, seed=seed, randomize_seed=randomize_seed, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, output_format=output_format)
+    else:
+        return infer_in_5min(prompt=prompt, seed=seed, randomize_seed=randomize_seed, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, output_format=output_format)
+
+@spaces.GPU(duration=60)
+def infer_in_1min(prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, output_format):
+    return infer_on_gpu(prompt=prompt, seed=seed, randomize_seed=randomize_seed, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, output_format=output_format)
+
+@spaces.GPU(duration=300)
+def infer_in_5min(prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, output_format):
+    return infer_on_gpu(prompt=prompt, seed=seed, randomize_seed=randomize_seed, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, output_format=output_format)
+
+def infer_on_gpu(prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, output_format, progress=gr.Progress(track_tqdm=True)):
     generator = torch.Generator().manual_seed(seed)
     image = pipe(
         prompt = prompt, 
@@ -51,17 +63,9 @@ Merge by [Sayak Paul](https://huggingface.co/sayakpaul) of 2 of the 12B param re
             placeholder = "Enter your prompt",
             container = False
         )
-        
-        run_button = gr.Button(value = "Run", variant="primary")
-        
-        num_inference_steps = gr.Slider(
-            label="Number of inference steps",
-            minimum=1,
-            maximum=50,
-            step=1,
-            value=4,
-        )
-        
+
+        output_format = gr.Radio([["*.png", "png"], ["*.webp", "webp"], ["*.jpeg", "jpeg"], ["*.gif", "gif"], ["*.bmp", "bmp"]], label="Image format for result", info="File extention", value="png", interactive=True)
+
         with gr.Accordion("Advanced Settings", open=False):
             
             with gr.Row():
@@ -81,6 +85,14 @@ Merge by [Sayak Paul](https://huggingface.co/sayakpaul) of 2 of the 12B param re
                     step=32,
                     value=1024,
                 )
+        
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
+                minimum=1,
+                maximum=50,
+                step=1,
+                value=4,
+            )
             
             guidance_scale = gr.Slider(
                 label="Guidance Scale",
@@ -90,8 +102,6 @@ Merge by [Sayak Paul](https://huggingface.co/sayakpaul) of 2 of the 12B param re
                 value=3.5,
             )
             
-            output_format = gr.Radio([["*.png", "png"], ["*.webp", "webp"], ["*.jpeg", "jpeg"], ["*.gif", "gif"], ["*.bmp", "bmp"]], label="Image format for result", info="File extention", value="png", interactive=True)
-            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -102,6 +112,8 @@ Merge by [Sayak Paul](https://huggingface.co/sayakpaul) of 2 of the 12B param re
             
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
         
+        run_button = gr.Button(value = "🚀 Generate", variant="primary")
+        
         result = gr.Image(label="Result", show_label=False, format="png")
         
         gr.Examples(