Merge sketch app

Co-authored-by: Suraj Patil <[email protected]>

app.py

@@ -1,13 +1,12 @@
 #!/usr/bin/env python
 
 import os
-import random
 
 import gradio as gr
-import numpy as np
-import PIL.Image
 import torch
 
+from app_base import create_demo as create_demo_base
+from app_sketch import create_demo as create_demo_sketch
 from model import ADAPTER_NAMES, Model
 
 DESCRIPTION = "# T2I-Adapter-SDXL"
@@ -15,45 +14,10 @@ DESCRIPTION = "# T2I-Adapter-SDXL"
 if not torch.cuda.is_available():
     DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
 
-MAX_SEED = np.iinfo(np.int32).max
-
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
 
 model = Model(ADAPTER_NAMES[0])
 
 
-def run(
-    image: PIL.Image.Image,
-    prompt: str,
-    negative_prompt: str,
-    adapter_name: str,
-    num_inference_steps: int = 30,
-    guidance_scale: float = 5.0,
-    adapter_conditioning_scale: float = 1.0,
-    cond_tau: float = 1.0,
-    seed: int = 0,
-    apply_preprocess: bool = True,
-    progress=gr.Progress(track_tqdm=True),
-) -> list[PIL.Image.Image]:
-    return model.run(
-        image=image,
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        adapter_name=adapter_name,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance_scale,
-        adapter_conditioning_scale=adapter_conditioning_scale,
-        cond_tau=cond_tau,
-        seed=seed,
-        apply_preprocess=apply_preprocess,
-    )
-
-
 with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     gr.DuplicateButton(
@@ -61,107 +25,11 @@ with gr.Blocks(css="style.css") as demo:
         elem_id="duplicate-button",
         visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
     )
-
-    with gr.Row():
-        with gr.Column():
-            with gr.Group():
-                image = gr.Image(label="Input image", type="pil", height=600)
-                prompt = gr.Textbox(label="Prompt")
-                adapter_name = gr.Dropdown(label="Adapter", choices=ADAPTER_NAMES, value=ADAPTER_NAMES[0])
-                run_button = gr.Button("Run")
-            with gr.Accordion("Advanced options", open=False):
-                apply_preprocess = gr.Checkbox(label="Apply preprocess", value=True)
-                negative_prompt = gr.Textbox(
-                    label="Negative prompt",
-                    value="anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
-                )
-                num_inference_steps = gr.Slider(
-                    label="Number of steps",
-                    minimum=1,
-                    maximum=Model.MAX_NUM_INFERENCE_STEPS,
-                    step=1,
-                    value=30,
-                )
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.1,
-                    maximum=30.0,
-                    step=0.1,
-                    value=5.0,
-                )
-                adapter_conditioning_scale = gr.Slider(
-                    label="Adapter Conditioning Scale",
-                    minimum=0.5,
-                    maximum=1,
-                    step=0.1,
-                    value=1.0,
-                )
-                cond_tau = gr.Slider(
-                    label="Fraction of timesteps for which adapter should be applied",
-                    minimum=0.5,
-                    maximum=1.0,
-                    step=0.1,
-                    value=1.0,
-                )
-                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.Column():
-            result = gr.Gallery(label="Result", columns=2, height=600, object_fit="scale-down", show_label=False)
-
-    inputs = [
-        image,
-        prompt,
-        negative_prompt,
-        adapter_name,
-        num_inference_steps,
-        guidance_scale,
-        adapter_conditioning_scale,
-        cond_tau,
-        seed,
-        apply_preprocess,
-    ]
-    prompt.submit(
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=run,
-        inputs=inputs,
-        outputs=result,
-        api_name=False,
-    )
-    negative_prompt.submit(
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=run,
-        inputs=inputs,
-        outputs=result,
-        api_name=False,
-    )
-    run_button.click(
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=run,
-        inputs=inputs,
-        outputs=result,
-        api_name="run",
-    )
+    with gr.Tabs():
+        with gr.Tab(label="Base"):
+            create_demo_base(model)
+        with gr.Tab(label="Sketch"):
+            create_demo_sketch(model)
 
 if __name__ == "__main__":
     demo.queue(max_size=20).launch()

app_base.py

@@ -0,0 +1,145 @@
+#!/usr/bin/env python
+
+import gradio as gr
+import PIL.Image
+
+from model import ADAPTER_NAMES, Model
+from utils import MAX_SEED, randomize_seed_fn
+
+
+def create_demo(model: Model) -> gr.Blocks:
+    def run(
+        image: PIL.Image.Image,
+        prompt: str,
+        negative_prompt: str,
+        adapter_name: str,
+        num_inference_steps: int = 30,
+        guidance_scale: float = 5.0,
+        adapter_conditioning_scale: float = 1.0,
+        cond_tau: float = 1.0,
+        seed: int = 0,
+        apply_preprocess: bool = True,
+        progress=gr.Progress(track_tqdm=True),
+    ) -> list[PIL.Image.Image]:
+        return model.run(
+            image=image,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            adapter_name=adapter_name,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            adapter_conditioning_scale=adapter_conditioning_scale,
+            cond_tau=cond_tau,
+            seed=seed,
+            apply_preprocess=apply_preprocess,
+        )
+
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                with gr.Group():
+                    image = gr.Image(label="Input image", type="pil", height=600)
+                    prompt = gr.Textbox(label="Prompt")
+                    adapter_name = gr.Dropdown(label="Adapter", choices=ADAPTER_NAMES, value=ADAPTER_NAMES[0])
+                    run_button = gr.Button("Run")
+                with gr.Accordion("Advanced options", open=False):
+                    apply_preprocess = gr.Checkbox(label="Apply preprocess", value=True)
+                    negative_prompt = gr.Textbox(
+                        label="Negative prompt",
+                        value="anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
+                    )
+                    num_inference_steps = gr.Slider(
+                        label="Number of steps",
+                        minimum=1,
+                        maximum=Model.MAX_NUM_INFERENCE_STEPS,
+                        step=1,
+                        value=30,
+                    )
+                    guidance_scale = gr.Slider(
+                        label="Guidance scale",
+                        minimum=0.1,
+                        maximum=30.0,
+                        step=0.1,
+                        value=5.0,
+                    )
+                    adapter_conditioning_scale = gr.Slider(
+                        label="Adapter Conditioning Scale",
+                        minimum=0.5,
+                        maximum=1,
+                        step=0.1,
+                        value=1.0,
+                    )
+                    cond_tau = gr.Slider(
+                        label="Fraction of timesteps for which adapter should be applied",
+                        minimum=0.5,
+                        maximum=1.0,
+                        step=0.1,
+                        value=1.0,
+                    )
+                    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.Column():
+                result = gr.Gallery(label="Result", columns=2, height=600, object_fit="scale-down", show_label=False)
+
+        inputs = [
+            image,
+            prompt,
+            negative_prompt,
+            adapter_name,
+            num_inference_steps,
+            guidance_scale,
+            adapter_conditioning_scale,
+            cond_tau,
+            seed,
+            apply_preprocess,
+        ]
+        prompt.submit(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=run,
+            inputs=inputs,
+            outputs=result,
+            api_name=False,
+        )
+        negative_prompt.submit(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=run,
+            inputs=inputs,
+            outputs=result,
+            api_name=False,
+        )
+        run_button.click(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=run,
+            inputs=inputs,
+            outputs=result,
+            api_name="run",
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    model = Model(ADAPTER_NAMES[0])
+    demo = create_demo(model)
+    demo.queue(max_size=20).launch()

app_sketch.py

@@ -0,0 +1,206 @@
+#!/usr/bin/env python
+
+import gradio as gr
+import PIL.Image
+import torch
+import torchvision.transforms.functional as TF
+
+from model import Model
+from utils import MAX_SEED, randomize_seed_fn
+
+SKETCH_ADAPTER_NAME = "TencentARC/t2i-adapter-sketch-sdxl-1.0"
+
+style_list = [
+    {
+        "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": "3D Model",
+        "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
+        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
+    },
+    {
+        "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": "Digital Art",
+        "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
+        "negative_prompt": "photo, photorealistic, realism, ugly",
+    },
+    {
+        "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": "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",
+    },
+]
+
+styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
+default_style_name = "Photographic"
+default_style = styles[default_style_name]
+style_names = list(styles.keys())
+
+
+def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
+    p, n = styles.get(style_name, default_style)
+    return p.replace("{prompt}", positive), n + negative
+
+
+def create_demo(model: Model) -> gr.Blocks:
+    def run(
+        image: PIL.Image.Image,
+        prompt: str,
+        negative_prompt: str,
+        style_name: str = default_style_name,
+        num_steps: int = 25,
+        guidance_scale: float = 5,
+        adapter_conditioning_scale: float = 0.8,
+        cond_tau: float = 0.8,
+        seed: int = 0,
+        progress=gr.Progress(track_tqdm=True),
+    ) -> PIL.Image.Image:
+        image = image.convert("RGB")
+        image = TF.to_tensor(image) > 0.5
+        image = TF.to_pil_image(image.to(torch.float32))
+
+        prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
+
+        return model.run(
+            image=image,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            adapter_name=SKETCH_ADAPTER_NAME,
+            num_inference_steps=num_steps,
+            guidance_scale=guidance_scale,
+            adapter_conditioning_scale=adapter_conditioning_scale,
+            cond_tau=cond_tau,
+            seed=seed,
+            apply_preprocess=False,
+        )[1]
+
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                with gr.Group():
+                    image = gr.Image(
+                        source="canvas",
+                        tool="sketch",
+                        type="pil",
+                        image_mode="L",
+                        invert_colors=True,
+                        shape=(1024, 1024),
+                        brush_radius=4,
+                        height=600,
+                    )
+                    prompt = gr.Textbox(label="Prompt")
+                    run_button = gr.Button("Run")
+                with gr.Accordion("Advanced options", open=False):
+                    style = gr.Dropdown(choices=style_names, value=default_style_name, label="Style")
+                    negative_prompt = gr.Textbox(label="Negative prompt")
+                    num_steps = gr.Slider(
+                        label="Number of steps",
+                        minimum=1,
+                        maximum=50,
+                        step=1,
+                        value=25,
+                    )
+                    guidance_scale = gr.Slider(
+                        label="Guidance scale",
+                        minimum=0.1,
+                        maximum=10.0,
+                        step=0.1,
+                        value=5,
+                    )
+                    adapter_conditioning_scale = gr.Slider(
+                        label="Adapter Conditioning Scale",
+                        minimum=0.5,
+                        maximum=1,
+                        step=0.1,
+                        value=0.8,
+                    )
+                    cond_tau = gr.Slider(
+                        label="Fraction of timesteps for which adapter should be applied",
+                        minimum=0.5,
+                        maximum=1,
+                        step=0.1,
+                        value=0.8,
+                    )
+                    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.Column():
+                result = gr.Image(label="Result", height=600)
+
+        inputs = [
+            image,
+            prompt,
+            negative_prompt,
+            style,
+            num_steps,
+            guidance_scale,
+            adapter_conditioning_scale,
+            cond_tau,
+            seed,
+        ]
+        prompt.submit(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=run,
+            inputs=inputs,
+            outputs=result,
+            api_name=False,
+        )
+        negative_prompt.submit(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=run,
+            inputs=inputs,
+            outputs=result,
+            api_name=False,
+        )
+        run_button.click(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=run,
+            inputs=inputs,
+            outputs=result,
+            api_name=False,
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    model = Model(SKETCH_ADAPTER_NAME)
+    demo = create_demo(model)
+    demo.queue(max_size=20).launch()

model.py

@@ -193,13 +193,11 @@ class Model:
                 torch_dtype=torch.float16,
                 varient="fp16",
             ).to(self.device)
-            euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
-            vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
             self.pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
                 model_id,
-                vae=vae,
+                vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16),
                 adapter=adapter,
-                scheduler=euler_a,
+                scheduler=EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler"),
                 torch_dtype=torch.float16,
                 variant="fp16",
             ).to(self.device)

utils.py

@@ -0,0 +1,11 @@
+import random
+
+import numpy as np
+
+MAX_SEED = np.iinfo(np.int32).max
+
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed