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be16ca0
1
Parent(s):
f9270e9
updated
Browse files
app.py
CHANGED
@@ -5,6 +5,7 @@ from huggingface_hub import hf_hub_download
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import numpy as np
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from PIL import Image
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import os
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# Suppress symlink warnings
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os.environ['HF_HUB_DISABLE_SYMLINKS_WARNING'] = "1"
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@@ -38,15 +39,26 @@ styles = {
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}
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}
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def load_pipeline():
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"""Load and prepare the pipeline with all style embeddings"""
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# Check if CUDA is available
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device = "cuda" if torch.cuda.is_available() else "cpu"
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dtype = torch.float16 if device == "cuda" else torch.float32
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pipe = StableDiffusionPipeline.from_pretrained(
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torch_dtype=dtype
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).to(device)
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# Load all embeddings
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@@ -69,6 +81,28 @@ def apply_purple_guidance(image, strength=0.5):
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def generate_image(prompt, style, seed, apply_guidance, guidance_strength=0.5):
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"""Generate an image with selected style and optional purple guidance"""
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if style not in styles:
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return None
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@@ -82,20 +116,39 @@ def generate_image(prompt, style, seed, apply_guidance, guidance_strength=0.5):
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# Create styled prompt
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styled_prompt = f"{prompt} {style_info['token']}"
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# Generate image
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# Apply purple guidance if requested
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if apply_guidance:
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image = apply_purple_guidance(image, guidance_strength)
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return image
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# Initialize the pipeline globally
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print("Loading pipeline and embeddings...")
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pipe = load_pipeline()
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@@ -113,14 +166,35 @@ demo = gr.Interface(
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outputs=gr.Image(label="Generated Image"),
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title="Style-Guided Image Generation with Purple Enhancement",
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description="""Generate images in different styles with optional purple color guidance.
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Choose a style, enter a prompt, and optionally apply purple color enhancement.
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examples=[
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["A serene mountain landscape with a lake at sunset", "glitch", 42, True, 0.5],
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["A magical forest at twilight", "anime80s", 789, True, 0.7],
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["A cyberpunk city at night", "night", 456, False, 0.5],
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],
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cache_examples=True
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)
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if __name__ == "__main__":
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import numpy as np
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from PIL import Image
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import os
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import gc
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# Suppress symlink warnings
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os.environ['HF_HUB_DISABLE_SYMLINKS_WARNING'] = "1"
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}
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}
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# Pre-generate example images
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example_images = {
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"glitch": "examples/glitch_example.jpg",
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"anime80s": "examples/anime80s_example.jpg",
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"night": "examples/night_example.jpg"
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}
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def load_pipeline():
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"""Load and prepare the pipeline with all style embeddings"""
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# Check if CUDA is available
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device = "cuda" if torch.cuda.is_available() else "cpu"
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dtype = torch.float16 if device == "cuda" else torch.float32
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# Use smaller model for CPU
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model_id = "runwayml/stable-diffusion-v1-5" if device == "cuda" else "CompVis/stable-diffusion-v1-4"
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pipe = StableDiffusionPipeline.from_pretrained(
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model_id,
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torch_dtype=dtype,
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low_cpu_mem_usage=True
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).to(device)
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# Load all embeddings
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def generate_image(prompt, style, seed, apply_guidance, guidance_strength=0.5):
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"""Generate an image with selected style and optional purple guidance"""
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# Check if this is one of our examples with pre-generated images
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if prompt == "A serene mountain landscape with a lake at sunset" and style == "glitch" and seed == 42:
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if os.path.exists(example_images["glitch"]):
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image = Image.open(example_images["glitch"])
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if apply_guidance:
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image = apply_purple_guidance(image, guidance_strength)
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return image
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if prompt == "A magical forest at twilight" and style == "anime80s" and seed == 789:
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if os.path.exists(example_images["anime80s"]):
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image = Image.open(example_images["anime80s"])
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if apply_guidance:
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image = apply_purple_guidance(image, guidance_strength)
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return image
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if prompt == "A cyberpunk city at night" and style == "night" and seed == 456:
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if os.path.exists(example_images["night"]):
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image = Image.open(example_images["night"])
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if apply_guidance:
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image = apply_purple_guidance(image, guidance_strength)
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return image
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if style not in styles:
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return None
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# Create styled prompt
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styled_prompt = f"{prompt} {style_info['token']}"
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# Generate image with reduced settings for CPU
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if device == "cpu":
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# Use much smaller image size and fewer steps on CPU
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image = pipe(
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styled_prompt,
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generator=generator,
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guidance_scale=7.5,
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num_inference_steps=10, # Reduced steps
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height=256, # Smaller height
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width=256 # Smaller width
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).images[0]
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else:
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image = pipe(
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styled_prompt,
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generator=generator,
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guidance_scale=7.5,
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num_inference_steps=20
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).images[0]
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# Apply purple guidance if requested
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if apply_guidance:
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image = apply_purple_guidance(image, guidance_strength)
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# Clean up memory
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gc.collect()
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if device == "cuda":
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torch.cuda.empty_cache()
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return image
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# Create examples directory
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os.makedirs("examples", exist_ok=True)
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# Initialize the pipeline globally
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print("Loading pipeline and embeddings...")
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pipe = load_pipeline()
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outputs=gr.Image(label="Generated Image"),
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title="Style-Guided Image Generation with Purple Enhancement",
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description="""Generate images in different styles with optional purple color guidance.
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Choose a style, enter a prompt, and optionally apply purple color enhancement.
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Note: Generation may take a few minutes on CPU.""",
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examples=[
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["A serene mountain landscape with a lake at sunset", "glitch", 42, True, 0.5],
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["A magical forest at twilight", "anime80s", 789, True, 0.7],
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["A cyberpunk city at night", "night", 456, False, 0.5],
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],
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cache_examples=True,
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allow_flagging="never" # Disable flagging to reduce overhead
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)
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if __name__ == "__main__":
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# Generate and save example images if they don't exist
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if not all(os.path.exists(path) for path in example_images.values()):
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print("Pre-generating example images...")
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# Example 1
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if not os.path.exists(example_images["glitch"]):
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img = generate_image("A serene mountain landscape with a lake at sunset", "glitch", 42, False, 0.5)
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img.save(example_images["glitch"])
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# Example 2
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if not os.path.exists(example_images["anime80s"]):
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img = generate_image("A magical forest at twilight", "anime80s", 789, False, 0.7)
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img.save(example_images["anime80s"])
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# Example 3
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if not os.path.exists(example_images["night"]):
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img = generate_image("A cyberpunk city at night", "night", 456, False, 0.5)
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img.save(example_images["night"])
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# Launch the app
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demo.launch(share=False, show_error=True)
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