Update app.py

app.py

@@ -1,165 +1,106 @@
 import gradio as gr
 import numpy as np
 import random
-
 import spaces
 import os
-from huggingface_hub import login
-from diffusers import StableDiffusionPipeline
 import torch
 
-# Authenticate with HF token
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
+from huggingface_hub import login
+from live_preview_helpers import flux_pipe_call_that_returns_an_iterable_of_images
+
+# Authenticate for gated repo access
 hf_token = os.environ.get("HF_TOKEN")
 if hf_token:
-    login(token=hf_token)  # sets token for downstream usage
+    login(token=hf_token)
 
+dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
-pipe = StableDiffusionPipeline.from_pretrained(
+# Load VAEs
+taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
+good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
+
+# Load base model with TAEF1 injected
+pipe = DiffusionPipeline.from_pretrained(
     "black-forest-labs/FLUX.1-dev",
-    torch_dtype=torch_dtype,
-    use_auth_token=hf_token  # ← pass your token explicitly
-)
+    torch_dtype=dtype,
+    token=hf_token,
+    vae=taef1
+).to(device)
 
-pipe.unet.load_attn_procs("ZennyKenny/flux_lora_natalie-diffusion")
-pipe = pipe.to(device)
+# Inject method + apply LoRA
+pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
+pipe.load_lora_weights("ZennyKenny/flux_lora_natalie-diffusion")
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-@spaces.GPU
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
+MAX_IMAGE_SIZE = 2048
+
+@spaces.GPU(duration=75)
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-
     generator = torch.Generator().manual_seed(seed)
 
-    # Append "XTON" to the prompt
-    prompt = "XTON " + prompt
+    # Prepend XTON
+    prompt = f"XTON {prompt}"
 
-    image = pipe(
+    for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
         prompt=prompt,
-        negative_prompt=negative_prompt,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
         width=width,
         height=height,
         generator=generator,
-    ).images[0]
-
-    return image, seed
-
+        output_type="pil",
+        good_vae=good_vae,
+    ):
+        yield img, seed
 
 
 examples = [
-    "A man walking in a forest",
-    "A viking ship sailing on a river at night",
-    "A woman dancing in a tavern",
+    "a man walking a in the forest",
+    "a viking ship sailing down a river",
+    "a woman resting by an open fire",
 ]
 
 css = """
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 520px;
 }
 """
 
-with gr.Blocks(css=css) as natalie_diffusion:
+with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown("# FLUX.1 [Natalie LoRA]\nModel prompt-augmented with `XTON`, adapted from [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)")
 
         with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
+            prompt = gr.Text(label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False)
+            run_button = gr.Button("Run", scale=0)
 
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
             with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
+                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
 
             with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
+                guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5)
+                num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=28)
+
+        gr.Examples(examples=examples, fn=infer, inputs=[prompt], outputs=[result, seed], cache_examples="lazy")
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
+        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs=[result, seed],
     )
 
 if __name__ == "__main__":
-    natalie_diffusion.launch()
+    demo.launch()