Downgrade versions

README.md

@@ -4,7 +4,8 @@ emoji: 🏃
 colorFrom: gray
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.42.0
+sdk_version: 4.44.1
+python_version: 3.9.13
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -1,20 +1,101 @@
+import os
+import random
+import torch
 import gradio as gr
+from diffusers import StableDiffusionPipeline
 
-def greet(name):
-    """
-    A simple function that takes a name as input and returns a greeting.
-    """
-    return "Hello " + name + "!"
-
-# Create a Gradio Interface
-# - fn: The Python function to wrap with a UI
-# - inputs: The type of input component (e.g., "text" for a text box)
-# - outputs: The type of output component (e.g., "text" for a text box)
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-
-# Launch the Gradio application
-# When deploying to Hugging Face Spaces, demo.launch() is often not explicitly called in app.py
-# as Spaces handles the launching automatically based on the presence of gr.Interface.
-# However, for local testing, demo.launch() is necessary.
-if __name__ == "__main__":
-    demo.launch()
+MODEL_ID = os.getenv("MODEL_ID", "runwayml/stable-diffusion-v1-5")
+
+# Load pipeline with a dtype that fits the device
+use_cuda = torch.cuda.is_available()
+dtype = torch.float16 if use_cuda else torch.float32
+pipe = StableDiffusionPipeline.from_pretrained(
+    MODEL_ID,
+    torch_dtype=dtype,
+    safety_checker=None,     # set to None to disable built-in safety checker; remove this to enable it
+)
+if use_cuda:
+    pipe.to("cuda")
+    try:
+        pipe.enable_xformers_memory_efficient_attention()
+    except Exception:
+        pipe.enable_attention_slicing()
+else:
+    pipe.enable_attention_slicing()
+
+def generate(prompt, negative_prompt, steps, guidance, width, height, seed):
+    if not prompt or prompt.strip() == "":
+        raise gr.Error("Please enter a prompt.")
+
+    # force valid dims (diffusers expects multiples of 8)
+    width = int(max(256, min(1024, (width // 8) * 8)))
+    height = int(max(256, min(1024, (height // 8) * 8)))
+
+    if seed is None or seed < 0:
+        seed = random.randint(0, 2**32 - 1)
+
+    generator = torch.Generator(device="cuda" if use_cuda else "cpu").manual_seed(seed)
+
+    result = pipe(
+        prompt=prompt,
+        negative_prompt=(negative_prompt or None),
+        num_inference_steps=int(steps),
+        guidance_scale=float(guidance),
+        width=width,
+        height=height,
+        generator=generator,
+    )
+    image = result.images[0]
+    return image, seed
+
+with gr.Blocks(css="footer {visibility: hidden}") as demo:
+    gr.Markdown(
+        "# 🌸 Flowerfy — Text → Image\n"
+        "Type a prompt, tweak settings, and generate!"
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            prompt = gr.Textbox(
+                label="Prompt",
+                value="a tasteful home flower arrangement in a ceramic vase, soft morning light, minimalist",
+                lines=2,
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative prompt (optional)",
+                value="low quality, blurry, deformed, text, watermark",
+                lines=2,
+            )
+            steps = gr.Slider(1, 50, value=25, step=1, label="Inference steps")
+            guidance = gr.Slider(0.0, 15.0, value=7.5, step=0.1, label="Guidance scale")
+            with gr.Row():
+                width = gr.Slider(256, 1024, value=768, step=8, label="Width")
+                height = gr.Slider(256, 1024, value=768, step=8, label="Height")
+            seed = gr.Number(value=-1, precision=0, label="Seed (-1 = random)")
+            run = gr.Button("Generate", variant="primary")
+
+        with gr.Column(scale=1):
+            out_image = gr.Image(label="Result", type="pil")
+            out_seed = gr.Number(label="Used seed", interactive=False, precision=0)
+
+    run.click(
+        fn=generate,
+        inputs=[prompt, negative_prompt, steps, guidance, width, height, seed],
+        outputs=[out_image, out_seed],
+        api_name="generate"
+    )
+
+    gr.Examples(
+        examples=[
+            ["a modern ikebana-style flower arrangement with orchids and branches, natural light, elegant, clean background",
+             "low quality, extra fingers, text", 25, 7.5, 768, 768, -1],
+            ["a cozy bouquet on a kitchen table, soft bokeh background, film photography, muted colors",
+             "blurry, noisy, text", 24, 6.5, 768, 512, -1],
+            ["close-up macro shot of dew on rose petals, dramatic lighting, high detail",
+             "cartoon, lowres", 30, 8.0, 768, 768, -1],
+        ],
+        inputs=[prompt, negative_prompt, steps, guidance, width, height, seed],
+        label="Try these"
+    )
+
+demo.queue(max_size=32).launch()

packages.txt

@@ -1,9 +0,0 @@
-git
-git-lfs
-ffmpeg
-libsm6
-libxext6
-cmake
-rsync
-libgl1-mesa-dri
-mesa-utils

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=4.44.0
+diffusers>=0.30.0
+transformers>=4.44.0
+accelerate>=0.33.0
+safetensors>=0.4.3
+torch