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README.md

@@ -4,7 +4,7 @@ emoji: 🖥️
 colorFrom: yellow
 colorTo: pink
 sdk: gradio
-sdk_version: 5.7.0
+sdk_version: 4.44.1
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -6,6 +6,7 @@ import torch
 from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
 from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
 from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
+from gradio_imageslider import ImageSlider
 
 dtype = torch.bfloat16
 #model_id = "black-forest-labs/FLUX.1-dev"
@@ -23,14 +24,24 @@ MAX_IMAGE_SIZE = 2048
 
 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
 
-@spaces.GPU(duration=75)
+@spaces.GPU(duration=90)
 def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, sigmas=0.95, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
     
     #for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-    for img in pipe(
+    image_def = pipe(
+            prompt=prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+            output_type="pil",
+        ).images[0]
+    #        yield img, seed
+    image_sigmas = pipe(
             prompt=prompt,
             guidance_scale=guidance_scale,
             num_inference_steps=num_inference_steps,
@@ -39,8 +50,8 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidan
             generator=generator,
             output_type="pil",
             mul_sigmas=sigmas
-        ).images:
-            yield img, seed
+        ).images[0]
+    return [image_def, image_sigmas], seed
     
 examples = [
     "a tiny astronaut hatching from an egg on the moon",
@@ -75,7 +86,8 @@ with gr.Blocks(css=css) as demo:
             
             run_button = gr.Button("Run", scale=0)
         
-        result = gr.Image(label="Result", show_label=False)
+        #result = gr.Image(label="Result", show_label=False)
+        result = ImageSlider(label="Result", show_label=False, type="pil", slider_color="pink")
         
         with gr.Accordion("Advanced Settings", open=True):
             sigmas = gr.Slider(
@@ -97,7 +109,7 @@ with gr.Blocks(css=css) as demo:
             randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
             
             with gr.Row():
-                
+
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
@@ -147,4 +159,4 @@ with gr.Blocks(css=css) as demo:
         outputs = [result, seed]
     )
 
-demo.launch(ssr_mode=False)
+demo.launch()

requirements.txt

@@ -4,4 +4,5 @@ git+https://github.com/huggingface/diffusers.git@6b1c4a766b7f83fe06ddb9bbb58c112
 torch
 transformers==4.42.4
 xformers
-sentencepiece
+sentencepiece
+gradio_imageslider