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app.py

@@ -1,128 +1,186 @@
+import spaces
 import gradio as gr
 import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+import random
+import logging
+import utils
+from diffusers.models import AutoencoderKL
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
+MIN_IMAGE_SIZE = 512
+MAX_IMAGE_SIZE = 2048
+
+# Enhanced logging configuration
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S'
+)
+logger = logging.getLogger(__name__)
+
+# PyTorch settings for better performance and determinism
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False
+torch.backends.cuda.matmul.allow_tf32 = True
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+logger.info(f"Using device: {device}")
+
+# Model initialization
+# if torch.cuda.is_available():
+#     try:
+#         logger.info("Loading VAE and pipeline...")
+#         vae = AutoencoderKL.from_pretrained(
+#             "madebyollin/sdxl-vae-fp16-fix",
+#             torch_dtype=torch.float16,
+#         )
+#         pipe = utils.load_pipeline("cagliostrolab/animagine-xl-4.0", device, vae=vae)
+#         logger.info("Pipeline loaded successfully on GPU!")
+#     except Exception as e:
+#         logger.error(f"Error loading VAE, falling back to default: {e}")
+#         pipe = utils.load_pipeline("cagliostrolab/animagine-xl-4.0", device)
+# else:
+#     logger.warning("CUDA not available, running on CPU")
+#     pipe = None
+
+
+
+@spaces.GPU
+def generate(
+    prompt: str,
+    negative_prompt: str,
+    width: int,
+    height: int,
+    scheduler: str,
+    upscaler_strength:float,
+    upscale_by:float,
+    seed: int,
+    randomize_seed: bool,
+    guidance_scale: float,
+    num_inference_steps: int,
+    progress:gr.Progress=gr.Progress(track_tqdm=True),
 ):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
+    # generator = torch.Generator().manual_seed(seed)
+
+    # image = pipe(
+    #     prompt=prompt,
+    #     negative_prompt=negative_prompt,
+    #     guidance_scale=guidance_scale,
+    #     num_inference_steps=num_inference_steps,
+    #     width=width,
+    #     height=height,
+    #     generator=generator,
+    # ).images[0]
 
-    image = pipe(
-        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
 
-    return image, seed
+    return None, seed
 
 
+
+scheduler_list = [
+    "DPM++ 2M Karras",
+    "DPM++ SDE Karras",
+    "DPM++ 2M SDE Karras",
+    "Euler",
+    "Euler a",
+    "DDIM"
+]
+
+
+title = "# Animagine XL 4.0 Demo"
+
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
     "A delicious ceviche cheesecake slice",
 ]
 
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
+custom_css = """
+#row-container {
+    align-items: stretch;
+}
+#output-image{
+    flex-grow: 1;
 }
 """
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        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")
-
-        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,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
+with gr.Blocks(css=custom_css).queue() as demo:
+    gr.Markdown(title)
+    with gr.Row(
+        elem_id="row-container"
+    ):
+        with gr.Column():
+            gr.Markdown("### Input")
+            with gr.Column():
+                prompt = gr.Text(
+                    label="Prompt",
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                )
+                negative_prompt = gr.Text(
+                    label="Negative prompt",
+                    max_lines=1,
+                    placeholder="Enter a negative prompt",
+                )
             with gr.Row():
                 width = gr.Slider(
                     label="Width",
-                    minimum=256,
+                    minimum=MIN_IMAGE_SIZE,
                     maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    step=8,
+                    value=832,  
                 )
-
                 height = gr.Slider(
                     label="Height",
-                    minimum=256,
+                    minimum=MIN_IMAGE_SIZE,
                     maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    step=8,
+                    value=1216, 
                 )
-
+            with gr.Row():
+                upscaler_strength = gr.Slider(
+                    label="Upscaler strength",
+                    minimum=0,
+                    maximum=1,
+                    step=0.05,
+                    value=0.55,  
+                )
+                upscale_by = gr.Slider(
+                    label="Upscale",
+                    minimum=1,
+                    maximum=1.5,
+                    step=0.1,
+                    value=1.5, 
+                )
+            with gr.Column():
+                scheduler = gr.Dropdown(
+                            label="scheduler",
+                            choices=scheduler_list,
+                            interactive=True,
+                            value="Euler a",
+                        )
+            with gr.Column():
+                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():
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
+                    minimum=1.0,
+                    maximum=12.0,
                     step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    value=6.0, 
                 )
 
                 num_inference_steps = gr.Slider(
@@ -130,25 +188,27 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=25, 
                 )
+            run_button = gr.Button("Run", variant="primary")
 
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
+        with gr.Column():
+            gr.Markdown("### Output")
+            result = gr.Image(
+                label="Generated Image",
+                elem_id="output-image"
+            )
+    run_button.click(
+        fn=generate,
         inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
+            prompt, negative_prompt,
+            width, height, 
+            scheduler,
+            upscaler_strength,upscale_by,
+            seed,randomize_seed,
+            guidance_scale,num_inference_steps
+            ], 
         outputs=[result, seed],
-    )
+    )  
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()

requirements.txt

@@ -1,6 +1,8 @@
-accelerate
-diffusers
-invisible_watermark
-torch
-transformers
-xformers
+accelerate>=1.2.1
+diffusers>=0.32.1
+gradio==4.44.1
+hf-transfer>=0.1.9
+spaces>=0.32.0
+torch>=2.4.0
+transformers>=4.48.0
+tomli>=2.0.1

utils.py

@@ -0,0 +1,35 @@
+import torch
+from typing import  Optional, Any
+from diffusers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    DPMSolverSinglestepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    AutoencoderKL,
+    StableDiffusionXLPipeline,
+)
+import logging
+
+def load_pipeline(model_name: str, device: torch.device, hf_token: Optional[str] = None, vae: Optional[AutoencoderKL] = None) -> Any:
+    """Load the Stable Diffusion pipeline."""
+    try:
+        pipeline = (
+            StableDiffusionXLPipeline.from_single_file
+            if model_name.endswith(".safetensors")
+            else StableDiffusionXLPipeline.from_pretrained
+        )
+
+        pipe = pipeline(
+            model_name,
+            vae=vae,
+            torch_dtype=torch.float16,
+            custom_pipeline="lpw_stable_diffusion_xl",
+            use_safetensors=True,
+            add_watermarker=False
+        )
+        pipe.to(device)
+        return pipe
+    except Exception as e:
+        logging.error(f"Failed to load pipeline: {str(e)}", exc_info=True)
+        raise