modify space

app.py

@@ -1,214 +1,80 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
 import os
 import random
 
 import gradio as gr
 import numpy as np
-import PIL.Image
 import torch
-from diffusers import AutoencoderKL, StableDiffusionXLPipeline
+from diffusers import DiffusionPipeline, UNet2DConditionModel
+import spaces
 import uuid
 
-DESCRIPTION = '''# Segmind Stable Diffusion: SSD-1B
-#### [Segmind's SSD-1B](https://huggingface.co/segmind/SSD-1B) is a distilled, 50% smaller version of SDXL, offering up to 60% speedup
-'''
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+DESCRIPTION = """# SPRIGHT T2I
+#### [SPRIGHT T2I](https://spright.github.io/) is a framework to improve the spatial consistency of text-to-image models WITHOUT compromising their fidelity aspects.
+"""
 
 MAX_SEED = np.iinfo(np.int32).max
-CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "1") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
-ENABLE_REFINER = os.getenv("ENABLE_REFINER", "0") == "1"
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-style_list = [
-    {
-        "name": "(No style)",
-        "prompt": "{prompt}",
-        "negative_prompt": "",
-    },
-    {
-        "name": "Cinematic",
-        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
-        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
-    },
-    {
-        "name": "Photographic",
-        "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
-        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
-    },
-    {
-        "name": "Anime",
-        "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime,  highly detailed",
-        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
-    },
-    {
-        "name": "Manga",
-        "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
-        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
-    },
-    {
-        "name": "Digital Art",
-        "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
-        "negative_prompt": "photo, photorealistic, realism, ugly",
-    },
-    {
-        "name": "Pixel art",
-        "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
-        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
-    },
-    {
-        "name": "Fantasy art",
-        "prompt": "ethereal fantasy concept art of  {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
-        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
-    },
-    {
-        "name": "Neonpunk",
-        "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
-        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
-    },
-    {
-        "name": "3D Model",
-        "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
-        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
-    },
-]
+CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES", "1") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768"))
+TOKEN = os.getenv("HF_TOKEN")
 
-styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
-STYLE_NAMES = list(styles.keys())
-DEFAULT_STYLE_NAME = "Cinematic"
 
+pipe_id = "SPRIGHT-T2I/spright-t2i-v1"
+unet = UNet2DConditionModel.from_pretrained(pipe_id, subfolder="unet_ema", torch_dtype=torch.float16)
+pipe = DiffusionPipeline.from_pretrained(
+    pipe_id,
+    unet=unet,
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+    token=TOKEN,
+).to("cuda")
 
-def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
-    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
-    if not negative:
-        negative = ""
-    return p.replace("{prompt}", positive), n + negative
-
-
-if torch.cuda.is_available():
-    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-    pipe = StableDiffusionXLPipeline.from_pretrained(
-        "segmind/SSD-1B",
-        vae=vae,
-        torch_dtype=torch.float16,
-        use_safetensors=True,
-        variant="fp16",
-    )
-    if ENABLE_REFINER:
-        refiner = DiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-xl-refiner-1.0",
-            vae=vae,
-            torch_dtype=torch.float16,
-            use_safetensors=True,
-            variant="fp16",
-        )
-
-    if ENABLE_CPU_OFFLOAD:
-        pipe.enable_model_cpu_offload()
-        if ENABLE_REFINER:
-            refiner.enable_model_cpu_offload()
-    else:
-        pipe.to(device)
-        if ENABLE_REFINER:
-            refiner.to(device)
-        print("Loaded on Device!")
-
-    if USE_TORCH_COMPILE:
-        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-        if ENABLE_REFINER:
-            refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
-        print("Model Compiled!")
 
 def save_image(img):
-    unique_name = str(uuid.uuid4()) + '.png'
+    unique_name = str(uuid.uuid4()) + ".png"
     img.save(unique_name)
     return unique_name
 
+
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     return seed
 
+
+@spaces.gpu
 def generate(
     prompt: str,
-    negative_prompt: str = "",
-    style: str = DEFAULT_STYLE_NAME,
-    prompt_2: str = "",
-    negative_prompt_2: str = "",
-    use_negative_prompt: bool = False,
-    use_prompt_2: bool = False,
-    use_negative_prompt_2: bool = False,
     seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale_base: float = 5.0,
-    guidance_scale_refiner: float = 5.0,
-    num_inference_steps_base: int = 25,
-    num_inference_steps_refiner: int = 25,
-    apply_refiner: bool = False,
+    width: int = 768,
+    height: int = 768,
+    guidance_scale: float = 7.5,
+    num_inference_steps: int = 50,
     randomize_seed: bool = False,
-    progress = gr.Progress(track_tqdm=True)
+    progress=gr.Progress(track_tqdm=True),
 ):
     seed = randomize_seed_fn(seed, randomize_seed)
     generator = torch.Generator().manual_seed(seed)
 
-    if not use_negative_prompt:
-        negative_prompt = None  # type: ignore
-    if not use_prompt_2:
-        prompt_2 = None  # type: ignore
-    if not use_negative_prompt_2:
-        negative_prompt_2 = None  # type: ignore
-    prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
-    if not apply_refiner:
-        image = pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            prompt_2=prompt_2,
-            negative_prompt_2=negative_prompt_2,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale_base,
-            num_inference_steps=num_inference_steps_base,
-            generator=generator,
-            output_type="pil",
-        ).images[0]
-    else:
-        latents = pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            prompt_2=prompt_2,
-            negative_prompt_2=negative_prompt_2,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale_base,
-            num_inference_steps=num_inference_steps_base,
-            generator=generator,
-            output_type="latent",
-        ).images
-        image = refiner(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            prompt_2=prompt_2,
-            negative_prompt_2=negative_prompt_2,
-            guidance_scale=guidance_scale_refiner,
-            num_inference_steps=num_inference_steps_refiner,
-            image=latents,
-            generator=generator,
-        ).images[0]
-    
+    image = pipe(
+        prompt=prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+    ).images[0]
+
     image_path = save_image(image)
     print(image_path)
     return [image_path], seed
 
 
-examples = ['3d digital art of an adorable ghost, glowing within, holding a heart shaped pumpkin, Halloween, super cute, spooky haunted house background', 'beautiful lady, freckles, big smile, blue eyes, short ginger hair, dark makeup, wearing a floral blue vest top, soft light, dark grey background', 'professional portrait photo of an anthropomorphic cat wearing fancy gentleman hat and jacket walking in autumn forest.', 'an astronaut sitting in a diner, eating fries, cinematic, analog film', 'Albert Einstein in a surrealist Cyberpunk 2077 world, hyperrealistic', 'cinematic film still of Futuristic hero with golden dark armour with machine gun,  muscular body']
+examples = [
+    "A cat next to a suitcase",
+    "A candle on the left of a mouse",
+    "A bag on the right of a dog",
+    "A mouse on the top of a bowl",
+]
 
 with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
@@ -229,34 +95,6 @@ with gr.Blocks(css="style.css") as demo:
             run_button = gr.Button("Run", scale=0)
         result = gr.Gallery(label="Result", columns=1, show_label=False)
     with gr.Accordion("Advanced options", open=False):
-        with gr.Row():
-            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
-            use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
-            use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
-        style_selection = gr.Radio(
-                               show_label=True, container=True, interactive=True,
-                               choices=STYLE_NAMES,
-                               value=DEFAULT_STYLE_NAME,
-                               label='Image Style'
-             )
-        negative_prompt = gr.Text(
-            label="Negative prompt",
-            max_lines=1,
-            placeholder="Enter a negative prompt",
-            visible=False,
-        )
-        prompt_2 = gr.Text(
-            label="Prompt 2",
-            max_lines=1,
-            placeholder="Enter your prompt",
-            visible=False,
-        )
-        negative_prompt_2 = gr.Text(
-            label="Negative prompt 2",
-            max_lines=1,
-            placeholder="Enter a negative prompt",
-            visible=False,
-        )
         seed = gr.Slider(
             label="Seed",
             minimum=0,
@@ -280,36 +118,20 @@ with gr.Blocks(css="style.css") as demo:
                 step=32,
                 value=1024,
             )
-        apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER)
         with gr.Row():
-            guidance_scale_base = gr.Slider(
-                label="Guidance scale for base",
-                minimum=1,
-                maximum=20,
-                step=0.1,
-                value=9.0,
-            )
-            num_inference_steps_base = gr.Slider(
-                label="Number of inference steps for base",
-                minimum=10,
-                maximum=100,
-                step=1,
-                value=25,
-            )
-        with gr.Row(visible=False) as refiner_params:
-            guidance_scale_refiner = gr.Slider(
-                label="Guidance scale for refiner",
+            guidance_scale = gr.Slider(
+                label="Guidance scale",
                 minimum=1,
                 maximum=20,
                 step=0.1,
-                value=5.0,
+                value=7.5,
             )
-            num_inference_steps_refiner = gr.Slider(
-                label="Number of inference steps for refiner",
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
                 minimum=10,
                 maximum=100,
                 step=1,
-                value=25,
+                value=50,
             )
 
     gr.Examples(
@@ -320,66 +142,16 @@ with gr.Blocks(css="style.css") as demo:
         cache_examples=CACHE_EXAMPLES,
     )
 
-    use_negative_prompt.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt,
-        outputs=negative_prompt,
-        queue=False,
-        api_name=False,
-    )
-    use_prompt_2.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_prompt_2,
-        outputs=prompt_2,
-        queue=False,
-        api_name=False,
-    )
-    use_negative_prompt_2.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt_2,
-        outputs=negative_prompt_2,
-        queue=False,
-        api_name=False,
-    )
-    apply_refiner.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=apply_refiner,
-        outputs=refiner_params,
-        queue=False,
-        api_name=False,
-    )
-
     gr.on(
         triggers=[
             prompt.submit,
-            negative_prompt.submit,
-            prompt_2.submit,
-            negative_prompt_2.submit,
             run_button.click,
         ],
         fn=generate,
-        inputs=[
-            prompt,
-            negative_prompt,
-            style_selection,
-            prompt_2,
-            negative_prompt_2,
-            use_negative_prompt,
-            use_prompt_2,
-            use_negative_prompt_2,
-            seed,
-            width,
-            height,
-            guidance_scale_base,
-            guidance_scale_refiner,
-            num_inference_steps_base,
-            num_inference_steps_refiner,
-            apply_refiner,
-            randomize_seed
-        ],
+        inputs=[prompt, seed, width, height, guidance_scale, num_inference_steps, randomize_seed],
         outputs=[result, seed],
         api_name="run",
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=20).launch()
+    demo.queue(max_size=20).launch()

requirements.txt

@@ -1,7 +1,4 @@
-git+https://github.com/huggingface/diffusers.git
-accelerate==0.23.0
-gradio==3.45.2
-invisible-watermark==0.2.0
-Pillow==10.0.1
-torch==2.0.1
-transformers==4.33.3
+diffusers
+accelerate
+torch
+transformers