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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/rocket.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,116 @@
+import random
+
+import numpy as np
+import torch
+import spaces
+import gradio as gr
+from diffusers import FluxFillPipeline
+
+MAX_SEED = np.iinfo(np.int32).max
+
+pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16)
+lora_repo = "strangerzonehf/Flux-Super-Realism-LoRA"
+trigger_word = "Super Realism"
+pipe.load_lora_weights(lora_repo)
+pipe.to("cuda")
+
+# reference https://huggingface.co/spaces/black-forest-labs/FLUX.1-Fill-dev/blob/main/app.py 
+def calculate_optimal_dimensions(image):
+    # Extract the original dimensions
+    original_width, original_height = image.size
+    
+    # Set constants
+    MIN_ASPECT_RATIO = 9 / 16
+    MAX_ASPECT_RATIO = 16 / 9
+    FIXED_DIMENSION = 1024
+
+    # Calculate the aspect ratio of the original image
+    original_aspect_ratio = original_width / original_height
+
+    # Determine which dimension to fix
+    if original_aspect_ratio > 1:  # Wider than tall
+        width = FIXED_DIMENSION
+        height = round(FIXED_DIMENSION / original_aspect_ratio)
+    else:  # Taller than wide
+        height = FIXED_DIMENSION
+        width = round(FIXED_DIMENSION * original_aspect_ratio)
+
+    # Ensure dimensions are multiples of 8
+    width = (width // 8) * 8
+    height = (height // 8) * 8
+
+    # Enforce aspect ratio limits
+    calculated_aspect_ratio = width / height
+    if calculated_aspect_ratio > MAX_ASPECT_RATIO:
+        width = (height * MAX_ASPECT_RATIO // 8) * 8
+    elif calculated_aspect_ratio < MIN_ASPECT_RATIO:
+        height = (width / MIN_ASPECT_RATIO // 8) * 8
+
+    # Ensure width and height remain above the minimum dimensions
+    width = max(width, 576) if width == FIXED_DIMENSION else width
+    height = max(height, 576) if height == FIXED_DIMENSION else height
+
+    return width, height
+
+
+@spaces.GPU(duration=30)
+def inpaint(
+    image,
+    mask,
+    prompt="",
+    seed=0,
+    num_inference_steps=28,
+    guidance_scale=50,
+):
+    image = image.convert("RGB")
+    mask = mask.convert("L")
+    width, height = calculate_optimal_dimensions(image)
+
+    if trigger_word:
+        prompt = f"{trigger_word} {prompt}"
+        
+    if not isinstance(seed, int) or seed <= 0:        
+        seed = random.randint(0, MAX_SEED)
+        
+    result = pipe(
+        image=image,
+        mask_image=mask,
+        prompt=prompt,
+        width=width,        
+        height=height,        
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        generator = torch.Generator().manual_seed(seed)
+    ).images[0]
+
+    result = result.convert("RGBA")
+
+    return result, seed
+
+demo = gr.Interface(
+    fn=inpaint,
+    inputs=[
+        gr.Image(label="image", type="pil"),
+        gr.Image(label="mask", type="pil"),
+        gr.Text(label="prompt", lines=4),
+        gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+            info="(0 = Random)"
+        ),
+        gr.Number(value=40, label="num_inference_steps"),
+        gr.Number(value=28, label="guidance_scale"),
+    ],
+    outputs=[
+        gr.Image(label="Result"),
+        gr.Number(label="Seed")
+    ],
+    api_name="inpaint",
+    examples=[["./assets/rocket.png", "./assets/Inpainting mask.png"]],
+    cache_examples=False
+)
+
+demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+diffusers
+transformers
+git+https://github.com/huggingface/peft.git
+git+https://github.com/huggingface/accelerate.git
+spaces
+sentencepiece
+accelerate