Update app.py

app.py

@@ -1,154 +1,114 @@
+import torch
 import gradio as gr
-import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
-import torch
+from sdnq import SDNQConfig
+from sdnq.loader import apply_sdnq_options_to_model
 
+# --- Model Configuration ---
+MODEL_ID = "Abrahamm3r/Z-Image-SDNQ-uint4-svd-r32"
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "Abrahamm3r/Z-Image-SDNQ-uint4-svd-r32"  # 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)
-
-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),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_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]
-
-    return image, seed
-
-
-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;
+print(f"Loading model: {MODEL_ID} on {device}...")
+
+# 1. Load the pipeline with trust_remote_code=True for Z-Image architecture
+# We use bfloat16 as it is standard for these newer flux/z-image models
+pipe = DiffusionPipeline.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True
+)
+
+# 2. Apply SDNQ quantization hooks to the transformer
+# This is critical for the model to run with the compressed weights
+pipe.transformer = apply_sdnq_options_to_model(pipe.transformer)
+
+# 3. Optimize memory
+if device == "cuda":
+    pipe.to(device)
+    # Enable if you are on a smaller GPU (e.g., T4 16GB) to save VRAM
+    # pipe.enable_model_cpu_offload() 
+    
+print("Model loaded successfully!")
+
+# --- Helper Functions ---
+
+# Preset resolutions for Aspect Ratios
+# Z-Image handles various resolutions, but these are safe standard presets
+ASPECT_RATIOS = {
+    "1:1 (Square)": (1024, 1024),
+    "16:9 (Cinematic)": (1280, 720),
+    "9:16 (Portrait)": (720, 1280),
+    "4:3 (Photo)": (1152, 864),
+    "3:4 (Portrait Photo)": (864, 1152),
+    "21:9 (Ultrawide)": (1536, 640)
 }
+
+def generate_image(prompt, negative_prompt, steps, aspect_ratio_choice, seed, guidance_scale):
+    # Determine Width/Height from preset
+    width, height = ASPECT_RATIOS.get(aspect_ratio_choice, (1024, 1024))
+    
+    # Handle Seed
+    if seed == -1:
+        seed = random.randint(0, 2**32 - 1)
+    generator = torch.Generator(device=device).manual_seed(int(seed))
+    
+    print(f"Generating: '{prompt}' | Steps: {steps} | Size: {width}x{height} | Seed: {seed}")
+    
+    try:
+        image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            width=width,
+            height=height,
+            num_inference_steps=steps,
+            guidance_scale=guidance_scale,
+            generator=generator
+        ).images[0]
+        return image, seed
+    except Exception as e:
+        raise gr.Error(f"Generation failed: {str(e)}")
+
+# --- Gradio UI ---
+
+custom_css = """
+#col-container { max-width: 800px; margin: 0 auto; }
+#generate-btn { background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%); border: none; color: white; }
 """
 
-with gr.Blocks(css=css) as demo:
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+        gr.Markdown(f"# ⚡ Z-Image SDNQ (uint4-svd-r32) Generator")
+        gr.Markdown(f"Running `{MODEL_ID}`. This uses Structured Decomposable Neural Quantization for high efficiency.")
+        
         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.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+            with gr.Column(scale=2):
+                prompt = gr.Textbox(label="Prompt", placeholder="Describe the image you want...", lines=3)
+                negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Low quality, blurry, ugly...", value="low quality, bad anatomy, worst quality, distortion, blurry")
+                
+                with gr.Row():
+                    aspect_ratio = gr.Dropdown(
+                        label="Aspect Ratio", 
+                        choices=list(ASPECT_RATIOS.keys()), 
+                        value="1:1 (Square)"
+                    )
+                    steps = gr.Slider(label="Inference Steps", minimum=4, maximum=50, step=1, value=25)
+                
+                with gr.Row():
+                    guidance = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=3.5)
+                    seed = gr.Number(label="Seed (-1 for Random)", value=-1, precision=0)
+                
+                run_btn = gr.Button("Generate Image", elem_id="generate-btn", size="lg")
+            
+            with gr.Column(scale=2):
+                result_image = gr.Image(label="Generated Image", type="pil")
+                seed_output = gr.Label(label="Used Seed")
+
+    run_btn.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, steps, aspect_ratio, seed, guidance],
+        outputs=[result_image, seed_output]
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue().launch()