Update app.py

app.py

@@ -2,9 +2,9 @@ import gradio as gr
 import numpy as np
 import random
 import os
-
-import spaces #[uncomment to use ZeroGPU]
-from diffusers import AutoPipelineForText2Image, AutoencoderTiny
+from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
+import spaces
+DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -17,7 +17,7 @@ else:
     dtype = torch.float32
 
 taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-pipe = pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", token=hf_token, torch_dtype=torch.bfloat16)
+pipe = pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", token=hf_token, torch_dtype=torch.bfloat16)
 pipe.load_lora_weights('aleksa-codes/flux-ghibsky-illustration', weight_name='lora.safetensors')
 pipe = pipe.to(device)
 
@@ -25,7 +25,7 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 
-@spaces.GPU #[uncomment to use ZeroGPU]
+@spaces.GPU
 def infer(
     prompt,
     seed=42,
@@ -41,16 +41,17 @@ def infer(
 
     generator = torch.Generator().manual_seed(seed)
 
-    image = pipe(
-        prompt=prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
+    for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+            prompt=prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+            output_type="pil",
+            good_vae=good_vae,
+        ):
+            yield img, seed
 
 
 examples = [
@@ -100,7 +101,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
 
                 height = gr.Slider(
@@ -108,7 +109,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
 
             with gr.Row():
@@ -117,7 +118,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=3.5,  # Replace with defaults that work for your model
+                    value=3.5,
                 )
 
                 num_inference_steps = gr.Slider(
@@ -125,7 +126,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=28,  # Replace with defaults that work for your model
+                    value=28,
                 )
 
         gr.Examples(examples=examples, inputs=[prompt])