Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+import spaces
+import torch
+from diffusers import Transformer2DModel
+from scripts.diffusers_patches import pixart_sigma_init_patched_inputs, PixArtSigmaPipeline
+
+assert getattr(Transformer2DModel, '_init_patched_inputs', False), "Need to Upgrade diffusers: pip install git+https://github.com/huggingface/diffusers"
+setattr(Transformer2DModel, '_init_patched_inputs', pixart_sigma_init_patched_inputs)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+weight_dtype = torch.float16
+
+transformer = Transformer2DModel.from_pretrained(
+    "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
+    subfolder='transformer',
+    torch_dtype=weight_dtype,
+    use_safetensors=True,
+)
+pipe = PixArtSigmaPipeline.from_pretrained(
+    "PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers",
+    transformer=transformer,
+    torch_dtype=weight_dtype,
+    use_safetensors=True,
+)
+pipe.to(device)
+
+@spaces.GPU(duration=120)
+def generate(prompt, negative_prompt, num_inference_steps, guidance_scale, height, width):
+    image = pipe(
+        prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        height=height,
+        width=width
+    ).images[0]
+    return image
+
+interface = gr.Interface(
+    fn=generate,
+    inputs=[
+        gr.Text(label="Prompt"),
+        gr.Text(label="Negative Prompt"),
+        gr.Slider(minimum=1, maximum=500, value=100, step=1, label="Number of Inference Steps"),
+        gr.Slider(minimum=1, maximum=20, value=4.5, step=0.1, label="Guidance Scale"),
+        gr.Slider(minimum=64, maximum=1024, value=512, step=64, label="Height"),
+        gr.Slider(minimum=64, maximum=1024, value=512, step=64, label="Width"),
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title="PixArt Sigma Image Generation",
+    description="Generate images using the PixArt Sigma model.",
+)
+
+interface.launch()