Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -9,17 +9,14 @@ import spaces
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import torch
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from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
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DESCRIPTIONz = """## SDXL-LoRA-DLC β‘
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"""
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# Function to save generated images
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def save_image(img):
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unique_name = str(uuid.uuid4()) + ".png"
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img.save(unique_name)
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return unique_name
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# Function to handle seed randomization
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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@@ -27,42 +24,41 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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MAX_SEED = np.iinfo(np.int32).max
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# Warning if running on CPU
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if not torch.cuda.is_available():
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DESCRIPTIONz += "\n<p>β οΈRunning on CPU, This may not work on CPU. If it runs for an extended time or if you encounter errors, try running it on a GPU by duplicating the space using @spaces.GPU(). +import spaces.π</p>"
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# Configuration flags (unchanged)
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USE_TORCH_COMPILE = 0
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ENABLE_CPU_OFFLOAD = 0
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# Define style options
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style_list = [
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{
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"name": "3840 x 2160",
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@@ -87,20 +83,20 @@ style_list = [
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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 = "3840 x 2160"
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STYLE_NAMES = list(styles.keys())
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# Function to apply selected style
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def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
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if style_name in styles:
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p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
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else:
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p, n = styles[DEFAULT_STYLE_NAME]
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if not negative:
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negative = ""
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return p.replace("{prompt}", positive), n + negative
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# Generation function with model selection
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@spaces.GPU(duration=180, enable_queue=True)
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def generate(
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prompt: str,
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@@ -113,39 +109,19 @@ def generate(
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randomize_seed: bool = False,
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style_name: str = DEFAULT_STYLE_NAME,
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lora_model: str = "Realism (face/character)π¦π»",
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base_model: str = "RealVisXL V5.0 Lightning",
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progress=gr.Progress(track_tqdm=True),
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):
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global current_base_model, current_pipeline
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# Load the pipeline if the base model has changed
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if base_model != current_base_model:
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model_id = base_models[base_model]
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current_pipeline = StableDiffusionXLPipeline.from_pretrained(
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model_id, torch_dtype=torch.float16, use_safetensors=True
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)
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current_pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
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current_pipeline.scheduler.config
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)
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for lora_display_name, (lora_model, lora_weight, adapter_name) in LORA_OPTIONS.items():
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current_pipeline.load_lora_weights(
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lora_model, weight_name=lora_weight, adapter_name=adapter_name
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)
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current_pipeline.to("cuda")
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current_base_model = base_model
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# Handle seed and prompts
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seed = int(randomize_seed_fn(seed, randomize_seed))
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positive_prompt, effective_negative_prompt = apply_style(style_name, prompt, negative_prompt)
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if not use_negative_prompt:
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effective_negative_prompt = ""
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current_pipeline.set_adapters(adapter_name)
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images = current_pipeline(
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prompt=positive_prompt,
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negative_prompt=effective_negative_prompt,
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width=width,
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@@ -159,14 +135,12 @@ def generate(
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image_paths = [save_image(img) for img in images]
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return image_paths, seed
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# Example prompts
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examples = [
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"Realism: Man in the style of dark beige and brown, uhd image, youthful protagonists, nonrepresentational ",
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"Pixar: A young man with light brown wavy hair and light brown eyes sitting in an armchair and looking directly at the camera, pixar style, disney pixar, office background, ultra detailed, 1 man",
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"Hoodie: Front view, capture a urban style, Superman Hoodie, technical materials, fabric small point label on text Blue theory, the design is minimal, with a raised collar, fabric is a Light yellow, low angle to capture the Hoodies form and detailing, f/5.6 to focus on the hoodies craftsmanship, solid grey background, studio light setting, with batman logo in the chest region of the t-shirt",
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]
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# CSS styling
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css = '''
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.gradio-container{max-width: 545px !important}
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h1{text-align:center}
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@@ -174,10 +148,10 @@ footer {
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visibility: hidden
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}
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'''
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# Function to load predefined images
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def load_predefined_images():
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predefined_images = [
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"assets/1.png",
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"assets/2.png",
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"assets/3.png",
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@@ -187,10 +161,10 @@ def load_predefined_images():
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"assets/7.png",
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"assets/8.png",
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"assets/9.png",
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]
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return predefined_images
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# Gradio interface
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with gr.Blocks(css=css) as demo:
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gr.Markdown(DESCRIPTIONz)
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with gr.Group():
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visible=True
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row(visible=True):
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width = gr.Slider(
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label="Width",
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step=8,
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value=1024,
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance Scale",
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step=0.1,
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value=3.0,
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)
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style_selection = gr.Radio(
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show_label=True,
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container=True,
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label="Quality Style",
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)
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with gr.Row():
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base_model_choice = gr.Dropdown(
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label="Base Model",
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choices=list(base_models.keys()),
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value="RealVisXL V5.0 Lightning"
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)
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model_choice = gr.Dropdown(
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label="LoRA Selection",
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choices=list(LORA_OPTIONS.keys()),
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randomize_seed,
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style_selection,
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model_choice,
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base_model_choice,
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],
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outputs=[result, seed],
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api_name="run",
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)
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with gr.Column(scale=3):
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gr.Markdown("### Image Gallery")
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predefined_gallery = gr.Gallery(
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columns=3,
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show_label=False,
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value=load_predefined_images()
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)
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if __name__ == "__main__":
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demo.queue(max_size=30).launch()
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import torch
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from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
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DESCRIPTIONz= """## SDXL-LoRA-DLC β‘
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"""
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def save_image(img):
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unique_name = str(uuid.uuid4()) + ".png"
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img.save(unique_name)
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return unique_name
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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MAX_SEED = np.iinfo(np.int32).max
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if not torch.cuda.is_available():
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DESCRIPTIONz += "\n<p>β οΈRunning on CPU, This may not work on CPU. If it runs for an extended time or if you encounter errors, try running it on a GPU by duplicating the space using @spaces.GPU(). +import spaces.π</p>"
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USE_TORCH_COMPILE = 0
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ENABLE_CPU_OFFLOAD = 0
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if torch.cuda.is_available():
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pipe = StableDiffusionXLPipeline.from_pretrained(
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"SG161222/RealVisXL_V4.0_Lightning",
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torch_dtype=torch.float16,
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use_safetensors=True,
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)
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pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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LORA_OPTIONS = {
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"Realism (face/character)π¦π»": ("prithivMLmods/Canopus-Realism-LoRA", "Canopus-Realism-LoRA.safetensors", "rlms"),
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"Pixar (art/toons)π": ("prithivMLmods/Canopus-Pixar-Art", "Canopus-Pixar-Art.safetensors", "pixar"),
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"Photoshoot (camera/film)πΈ": ("prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA", "Canopus-Photo-Shoot-Mini-LoRA.safetensors", "photo"),
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"Clothing (hoodies/pant/shirts)π": ("prithivMLmods/Canopus-Clothing-Adp-LoRA", "Canopus-Dress-Clothing-LoRA.safetensors", "clth"),
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"Interior Architecture (house/hotel)π ": ("prithivMLmods/Canopus-Interior-Architecture-0.1", "Canopus-Interior-Architecture-0.1Ξ΄.safetensors", "arch"),
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"Fashion Product (wearing/usable)π": ("prithivMLmods/Canopus-Fashion-Product-Dilation", "Canopus-Fashion-Product-Dilation.safetensors", "fashion"),
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"Minimalistic Image (minimal/detailed)ποΈ": ("prithivMLmods/Pegasi-Minimalist-Image-Style", "Pegasi-Minimalist-Image-Style.safetensors", "minimalist"),
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"Modern Clothing (trend/new)π": ("prithivMLmods/Canopus-Modern-Clothing-Design", "Canopus-Modern-Clothing-Design.safetensors", "mdrnclth"),
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"Animaliea (farm/wild)π«": ("prithivMLmods/Canopus-Animaliea-Artism", "Canopus-Animaliea-Artism.safetensors", "Animaliea"),
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"Liquid Wallpaper (minimal/illustration)πΌοΈ": ("prithivMLmods/Canopus-Liquid-Wallpaper-Art", "Canopus-Liquid-Wallpaper-Minimalize-LoRA.safetensors", "liquid"),
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"Canes Cars (realistic/futurecars)π": ("prithivMLmods/Canes-Cars-Model-LoRA", "Canes-Cars-Model-LoRA.safetensors", "car"),
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"Pencil Art (characteristic/creative)βοΈ": ("prithivMLmods/Canopus-Pencil-Art-LoRA", "Canopus-Pencil-Art-LoRA.safetensors", "Pencil Art"),
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"Art Minimalistic (paint/semireal)π¨": ("prithivMLmods/Canopus-Art-Medium-LoRA", "Canopus-Art-Medium-LoRA.safetensors", "mdm"),
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}
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for model_name, weight_name, adapter_name in LORA_OPTIONS.values():
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pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name)
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pipe.to("cuda")
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style_list = [
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{
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"name": "3840 x 2160",
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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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+
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DEFAULT_STYLE_NAME = "3840 x 2160"
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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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if style_name in styles:
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p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
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else:
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p, n = styles[DEFAULT_STYLE_NAME]
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if not negative:
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negative = ""
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return p.replace("{prompt}", positive), n + negative
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@spaces.GPU(duration=180, enable_queue=True)
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def generate(
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prompt: str,
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randomize_seed: bool = False,
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style_name: str = DEFAULT_STYLE_NAME,
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lora_model: str = "Realism (face/character)π¦π»",
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progress=gr.Progress(track_tqdm=True),
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):
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seed = int(randomize_seed_fn(seed, randomize_seed))
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positive_prompt, effective_negative_prompt = apply_style(style_name, prompt, negative_prompt)
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if not use_negative_prompt:
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effective_negative_prompt = "" # type: ignore
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model_name, weight_name, adapter_name = LORA_OPTIONS[lora_model]
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pipe.set_adapters(adapter_name)
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images = pipe(
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prompt=positive_prompt,
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negative_prompt=effective_negative_prompt,
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width=width,
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image_paths = [save_image(img) for img in images]
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return image_paths, seed
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examples = [
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"Realism: Man in the style of dark beige and brown, uhd image, youthful protagonists, nonrepresentational ",
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"Pixar: A young man with light brown wavy hair and light brown eyes sitting in an armchair and looking directly at the camera, pixar style, disney pixar, office background, ultra detailed, 1 man",
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"Hoodie: Front view, capture a urban style, Superman Hoodie, technical materials, fabric small point label on text Blue theory, the design is minimal, with a raised collar, fabric is a Light yellow, low angle to capture the Hoodies form and detailing, f/5.6 to focus on the hoodies craftsmanship, solid grey background, studio light setting, with batman logo in the chest region of the t-shirt",
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]
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css = '''
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.gradio-container{max-width: 545px !important}
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h1{text-align:center}
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visibility: hidden
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}
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'''
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def load_predefined_images():
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predefined_images = [
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"assets/1.png",
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"assets/2.png",
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"assets/3.png",
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"assets/7.png",
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"assets/8.png",
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"assets/9.png",
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]
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return predefined_images
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with gr.Blocks(css=css) as demo:
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gr.Markdown(DESCRIPTIONz)
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with gr.Group():
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visible=True
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row(visible=True):
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width = gr.Slider(
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label="Width",
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step=8,
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value=1024,
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance Scale",
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step=0.1,
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value=3.0,
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)
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style_selection = gr.Radio(
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show_label=True,
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container=True,
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label="Quality Style",
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)
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with gr.Row(visible=True):
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model_choice = gr.Dropdown(
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label="LoRA Selection",
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choices=list(LORA_OPTIONS.keys()),
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randomize_seed,
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style_selection,
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model_choice,
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],
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outputs=[result, seed],
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api_name="run",
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)
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with gr.Column(scale=3):
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gr.Markdown("### Image Gallery")
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predefined_gallery = gr.Gallery(label="Image Gallery", columns=3, show_label=False, value=load_predefined_images())
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if __name__ == "__main__":
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demo.queue(max_size=30).launch()
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