init

README.md

@@ -1,12 +1,11 @@
 ---
-title: Aiste
-emoji: 👁
-colorFrom: red
-colorTo: blue
+title: Fhnw Image Lora
+emoji: 🐠
+colorFrom: indigo
+colorTo: green
 sdk: gradio
-sdk_version: 5.9.1
+sdk_version: 5.6.0
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,97 @@
+import gradio as gr
+import numpy as np
+import random
+import spaces
+import torch
+from diffusers import DiffusionPipeline
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+@spaces.GPU()
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
+    lora = "kratadata/aiste-tiles"
+    prefix = "Abstract glazed, tile pattern of "
+    suffix = ""
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed)
+    full_prompt = prefix + " " + prompt + " " + suffix
+    pipe.load_lora_weights(lora)
+    image = pipe(
+                prompt = full_prompt,
+                width = width,
+                height = height,
+                num_inference_steps = num_inference_steps,
+                generator = generator,
+                guidance_scale=0.0
+    ).images[0]
+    return image, seed
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Image Prompt",
+                show_label = "True",
+                info="Your image prompt",
+                max_lines=4,
+                placeholder="Enter your prompt",
+                container=True,
+            )
+            with gr.Accordion("Advanced Settings", open=False):
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                    info = "Keep at 1024 for best results"
+                )
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                    info = "Keep at 1024 for best results"
+                )
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=8,
+                    step=1,
+                    value=2,
+                    info = "Increase to 4 for better results"
+                )
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True, info = "Keep true to generate a new image each time")
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                    info = "Fix seed if you want to keep generating the same image"
+                )
+            run_button = gr.Button("Run", scale=0)
+
+        with gr.Column():
+            result = gr.Image(label="Result", show_label=False, format="jpeg")
+
+
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
+        outputs = [result, seed]
+    )
+
+demo.launch()

requirements.txt

@@ -0,0 +1,12 @@
+gradio
+spaces
+diffusers
+transformers
+accelerate
+sentencepiece
+protobuf
+peft
+numpy==1.26.1
+#optimum-quanto
+
+--extra-index-url https://download.pytorch.org/whl/cu121