Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import StableDiffusionXLPipeline, StableDiffusionPipeline, LCMScheduler
+from diffusers.schedulers import TCDScheduler
+import spaces
+from PIL import Image
+import os
+import re
+from datetime import datetime
+import random
+import glob
+
+SAFETY_CHECKER = True
+
+checkpoints = {
+    "2-Step": ["pcm_{}_smallcfg_2step_converted.safetensors", 2, 0.0],
+    "4-Step": ["pcm_{}_smallcfg_4step_converted.safetensors", 4, 0.0],
+    "8-Step": ["pcm_{}_smallcfg_8step_converted.safetensors", 8, 0.0],
+    "16-Step": ["pcm_{}_smallcfg_16step_converted.safetensors", 16, 0.0],
+    "Normal CFG 4-Step": ["pcm_{}_normalcfg_4step_converted.safetensors", 4, 7.5],
+    "Normal CFG 8-Step": ["pcm_{}_normalcfg_8step_converted.safetensors", 8, 7.5],
+    "Normal CFG 16-Step": ["pcm_{}_normalcfg_16step_converted.safetensors", 16, 7.5],
+    "LCM-Like LoRA": ["pcm_{}_lcmlike_lora_converted.safetensors", 4, 0.0],
+}
+
+loaded = None
+
+if torch.cuda.is_available():
+    pipe_sdxl = StableDiffusionXLPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-xl-base-1.0",
+        torch_dtype=torch.float16,
+        variant="fp16",
+    ).to("cuda")
+    pipe_sd15 = StableDiffusionPipeline.from_pretrained(
+        "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
+    ).to("cuda")
+
+if SAFETY_CHECKER:
+    from safety_checker import StableDiffusionSafetyChecker
+    from transformers import CLIPFeatureExtractor
+
+    safety_checker = StableDiffusionSafetyChecker.from_pretrained(
+        "CompVis/stable-diffusion-safety-checker"
+    ).to("cuda")
+    feature_extractor = CLIPFeatureExtractor.from_pretrained(
+        "openai/clip-vit-base-patch32"
+    )
+
+    def check_nsfw_images(images: list[Image.Image]) -> tuple[list[Image.Image], list[bool]]:
+        safety_checker_input = feature_extractor(images, return_tensors="pt").to("cuda")
+        has_nsfw_concepts = safety_checker(
+            images=[images], clip_input=safety_checker_input.pixel_values.to("cuda")
+        )
+        return images, has_nsfw_concepts
+
+def save_image(image: Image.Image, prompt: str) -> str:
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    clean_prompt = re.sub(r'[^\w\-_\. ]', '_', prompt)[:50]
+    filename = f"{timestamp}_{clean_prompt}.png"
+    image.save(filename)
+    return filename
+
+def get_image_gallery():
+    image_files = glob.glob("*.png")
+    return sorted([(file, file) for file in image_files], key=lambda x: os.path.getmtime(x[0]), reverse=True)
+
+@spaces.GPU(enable_queue=True)
+def generate_image(
+    prompt,
+    ckpt,
+    num_inference_steps,
+    progress=gr.Progress(track_tqdm=True),
+    mode="sdxl",
+):
+    global loaded
+    checkpoint = checkpoints[ckpt][0].format(mode)
+    guidance_scale = checkpoints[ckpt][2]
+    pipe = pipe_sdxl if mode == "sdxl" else pipe_sd15
+
+    if loaded != (ckpt + mode):
+        pipe.load_lora_weights(
+            "wangfuyun/PCM_Weights", weight_name=checkpoint, subfolder=mode
+        )
+        loaded = ckpt + mode
+
+        if ckpt == "LCM-Like LoRA":
+            pipe.scheduler = LCMScheduler()
+        else:
+            pipe.scheduler = TCDScheduler(
+                num_train_timesteps=1000,
+                beta_start=0.00085,
+                beta_end=0.012,
+                beta_schedule="scaled_linear",
+                timestep_spacing="trailing",
+            )
+
+    results = pipe(
+        prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale
+    )
+
+    if SAFETY_CHECKER:
+        images, has_nsfw_concepts = check_nsfw_images(results.images)
+        if any(has_nsfw_concepts):
+            gr.Warning("NSFW content detected.")
+            return Image.new("RGB", (512, 512)), get_image_gallery()
+        filename = save_image(images[0], prompt)
+        return images[0], get_image_gallery()
+    filename = save_image(results.images[0], prompt)
+    return results.images[0], get_image_gallery()
+
+def update_steps(ckpt):
+    num_inference_steps = checkpoints[ckpt][1]
+    if ckpt == "LCM-Like LoRA":
+        return gr.update(interactive=True, value=num_inference_steps)
+    return gr.update(interactive=False, value=num_inference_steps)
+
+css = """
+.gradio-container {
+  max-width: 60rem !important;
+}
+"""
+
+art_styles = ['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism']
+
+examples = [
+    f"{random.choice(art_styles)} painting of a majestic lighthouse on a rocky coast. Use bold brushstrokes and a vibrant color palette to capture the interplay of light and shadow as the lighthouse beam cuts through a stormy night sky.",
+    f"{random.choice(art_styles)} still life featuring a pair of vintage eyeglasses. Focus on the intricate details of the frames and lenses, using a warm color scheme to evoke a sense of nostalgia and wisdom.",
+    f"{random.choice(art_styles)} depiction of a rustic wooden stool in a sunlit artist's studio. Emphasize the texture of the wood and the interplay of light and shadow, using a mix of earthy tones and highlights.",
+    f"{random.choice(art_styles)} scene viewed through an ornate window frame. Contrast the intricate details of the window with a dreamy, soft-focus landscape beyond, using a palette that transitions from cool interior tones to warm exterior hues.",
+    f"{random.choice(art_styles)} close-up study of interlaced fingers. Use a monochromatic color scheme to emphasize the form and texture of the hands, with dramatic lighting to create depth and emotion.",
+    f"{random.choice(art_styles)} composition featuring a set of dice in motion. Capture the energy and randomness of the throw, using a dynamic color palette and blurred lines to convey movement.",
+    f"{random.choice(art_styles)} interpretation of heaven. Create an ethereal atmosphere with soft, billowing clouds and radiant light, using a palette of celestial blues, golds, and whites.",
+    f"{random.choice(art_styles)} portrayal of an ancient, mystical gate. Combine architectural details with elements of fantasy, using a rich, jewel-toned palette to create an air of mystery and magic.",
+    f"{random.choice(art_styles)} portrait of a curious cat. Focus on capturing the feline's expressive eyes and sleek form, using a mix of bold and subtle colors to bring out the cat's personality.",
+    f"{random.choice(art_styles)} abstract representation of toes in sand. Use textured brushstrokes to convey the feeling of warm sand, with a palette inspired by a sun-drenched beach."
+]
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(
+        """
+# Phased Consistency Model  
+
+Phased Consistency Model (PCM) is an image generation technique that addresses the limitations of the Latent Consistency Model (LCM) in high-resolution and text-conditioned image generation.
+PCM outperforms LCM across various generation settings and achieves state-of-the-art results in both image and video generation.
+
+[[paper](https://huggingface.co/papers/2405.18407)] [[arXiv](https://arxiv.org/abs/2405.18407)]  [[code](https://github.com/G-U-N/Phased-Consistency-Model)] [[project page](https://g-u-n.github.io/projects/pcm)]
+"""
+    )
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Textbox(label="Prompt", scale=8)
+            ckpt = gr.Dropdown(
+                label="Select inference steps",
+                choices=list(checkpoints.keys()),
+                value="4-Step",
+            )
+            steps = gr.Slider(
+                label="Number of Inference Steps",
+                minimum=1,
+                maximum=20,
+                step=1,
+                value=4,
+                interactive=False,
+            )
+            ckpt.change(
+                fn=update_steps,
+                inputs=[ckpt],
+                outputs=[steps],
+                queue=False,
+                show_progress=False,
+            )
+
+            submit_sdxl = gr.Button("Run on SDXL", scale=1)
+            submit_sd15 = gr.Button("Run on SD15", scale=1)
+
+    img = gr.Image(label="PCM Image")
+    gallery = gr.Gallery(label="Generated Images", show_label=True, columns=4, height="auto")
+    gr.Examples(
+        examples=examples,
+        inputs=[prompt, ckpt, steps],
+        outputs=[img, gallery],
+        fn=generate_image,
+        cache_examples=True,
+    )
+
+    gr.on(
+        fn=generate_image,
+        triggers=[ckpt.change, prompt.submit, submit_sdxl.click],
+        inputs=[prompt, ckpt, steps],
+        outputs=[img, gallery],
+    )
+    gr.on(
+        fn=lambda *args: generate_image(*args, mode="sd15"),
+        triggers=[submit_sd15.click],
+        inputs=[prompt, ckpt, steps],
+        outputs=[img, gallery],
+    )
+
+    demo.load(fn=get_image_gallery, outputs=gallery)
+
+demo.queue(api_open=False).launch(show_api=False)