NitroFusion demo

app.py

@@ -1,154 +1,106 @@
+import copy
+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)
-
-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;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderKL
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download
+
+
+class TimestepShiftLCMScheduler(LCMScheduler):
+    def __init__(self, *args, shifted_timestep=250, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.register_to_config(shifted_timestep=shifted_timestep)
+
+    def set_timesteps(self, *args, **kwargs):
+        super().set_timesteps(*args, **kwargs)
+        self.origin_timesteps = self.timesteps.clone()
+        self.shifted_timesteps = (self.timesteps * self.config.shifted_timestep / self.config.num_train_timesteps).long()
+        self.timesteps = self.shifted_timesteps
+
+    def step(self, model_output, timestep, sample, generator=None, return_dict=True):
+        if self.step_index is None:
+            self._init_step_index(timestep)
+        self.timesteps = self.origin_timesteps
+        output = super().step(model_output, timestep, sample, generator, return_dict)
+        self.timesteps = self.shifted_timesteps
+        return output
+
+
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+
+base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+pipe = DiffusionPipeline.from_pretrained(
+    base_model_id,
+    vae=vae,
+    torch_dtype=torch.float16,
+    variant="fp16",
+).to("cuda")
+
+repo = "ChenDY/NitroFusion"
+
+unet_realism = pipe.unet
+unet_realism.load_state_dict(load_file(hf_hub_download(repo, "nitrosd-realism_unet.safetensors"), device="cuda"))
+scheduler_realism = TimestepShiftLCMScheduler.from_pretrained(base_model_id, subfolder="scheduler", shifted_timestep=250)
+scheduler_realism.config.original_inference_steps = 4
+
+unet_vibrant = copy.deepcopy(pipe.unet)
+unet_vibrant.load_state_dict(load_file(hf_hub_download(repo, "nitrosd-vibrant_unet.safetensors"), device="cuda"))
+scheduler_vibrant = TimestepShiftLCMScheduler.from_pretrained(base_model_id, subfolder="scheduler", shifted_timestep=500)
+scheduler_vibrant.config.original_inference_steps = 4
+
+
+@spaces.GPU
+def process_image(model_choice, num_images, height, width, prompt, seed):
+    global pipe
+    # Switch to the selected model
+    if model_choice == "NitroSD-Realism":
+        pipe.unet = unet_realism
+        pipe.scheduler = scheduler_realism
+    elif model_choice == "NitroSD-Vibrant":
+        pipe.unet = unet_vibrant
+        pipe.scheduler = scheduler_vibrant
+    else:
+        raise ValueError("Invalid model choice.")
+    # Generate the image
+    with torch.inference_mode(), torch.autocast("cuda", dtype=torch.float16):
+        return pipe(
+            prompt=[prompt] * num_images,
+            generator=torch.manual_seed(int(seed)),
+            num_inference_steps=1,
+            guidance_scale=0.0,
+            height=int(height),
+            width=int(width),
+        ).images
+
+
+# Gradio UI
+with gr.Blocks() as demo:
+    with gr.Column():
         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
+            with gr.Column():
+                model_choice = gr.Dropdown(
+                    label="Choose Model",
+                    choices=["NitroSD-Realism", "NitroSD-Vibrant"],
+                    value="NitroSD-Realism",
+                    interactive=True,
                 )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                num_images = gr.Slider(
+                    label="Number of Images", minimum=1, maximum=4, step=1, value=4, interactive=True
                 )
-
-            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
+                height = gr.Slider(
+                    label="Image Height", minimum=768, maximum=1024, step=8, value=1024, interactive=True
                 )
-
-                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
+                width = gr.Slider(
+                    label="Image Width", minimum=768, maximum=1024, step=8, value=1024, interactive=True
                 )
+                prompt = gr.Text(label="Prompt", value="a photo of a cat", interactive=True)
+                seed = gr.Number(label="Seed", value=2024, interactive=True)
+                btn = gr.Button(value="Generate Image")
+            with gr.Column():
+                output = gr.Gallery(height=1024)
 
-        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],
-    )
+            btn.click(process_image, inputs=[model_choice, num_images, height, width, prompt, seed], outputs=[output])
 
 if __name__ == "__main__":
     demo.launch()