Spaces:
Runtime error
Runtime error
add styles in base app
Browse files- app_base.py +53 -0
app_base.py
CHANGED
@@ -6,12 +6,61 @@ import PIL.Image
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from model import ADAPTER_NAMES, Model
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from utils import MAX_SEED, randomize_seed_fn
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def create_demo(model: Model) -> gr.Blocks:
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def run(
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image: PIL.Image.Image,
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prompt: str,
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negative_prompt: str,
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adapter_name: str,
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num_inference_steps: int = 30,
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guidance_scale: float = 5.0,
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@@ -21,6 +70,8 @@ def create_demo(model: Model) -> gr.Blocks:
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apply_preprocess: bool = True,
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progress=gr.Progress(track_tqdm=True),
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) -> list[PIL.Image.Image]:
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return model.run(
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image=image,
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prompt=prompt,
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@@ -48,6 +99,7 @@ def create_demo(model: Model) -> gr.Blocks:
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label="Negative prompt",
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value="anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
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)
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num_inference_steps = gr.Slider(
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label="Number of steps",
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minimum=1,
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@@ -91,6 +143,7 @@ def create_demo(model: Model) -> gr.Blocks:
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image,
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prompt,
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negative_prompt,
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adapter_name,
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num_inference_steps,
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guidance_scale,
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from model import ADAPTER_NAMES, Model
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from utils import MAX_SEED, randomize_seed_fn
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style_list = [
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{
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"name": "Cinematic",
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"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
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"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
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},
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{
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"name": "3D Model",
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"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
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"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
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},
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{
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"name": "Anime",
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"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed",
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"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
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},
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{
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"name": "Digital Art",
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"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
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"negative_prompt": "photo, photorealistic, realism, ugly",
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},
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{
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"name": "Photographic",
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"prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
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"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
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},
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{
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"name": "Pixel art",
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"prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
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"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
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},
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{
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"name": "Fantasy art",
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"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
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"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",
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},
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]
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styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
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default_style_name = "Photographic"
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default_style = styles[default_style_name]
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style_names = list(styles.keys())
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def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
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p, n = styles.get(style_name, default_style)
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return p.replace("{prompt}", positive), n + negative
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def create_demo(model: Model) -> gr.Blocks:
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def run(
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image: PIL.Image.Image,
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prompt: str,
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negative_prompt: str,
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style_name: str = default_style_name,
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adapter_name: str,
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num_inference_steps: int = 30,
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guidance_scale: float = 5.0,
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apply_preprocess: bool = True,
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progress=gr.Progress(track_tqdm=True),
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) -> list[PIL.Image.Image]:
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prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
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return model.run(
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image=image,
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prompt=prompt,
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label="Negative prompt",
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value="anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
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)
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style = gr.Dropdown(choices=style_names, value=default_style_name, label="Style")
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num_inference_steps = gr.Slider(
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label="Number of steps",
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minimum=1,
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image,
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prompt,
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negative_prompt,
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style,
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adapter_name,
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num_inference_steps,
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guidance_scale,
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