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Update app.py
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app.py
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@@ -1,4 +1,3 @@
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import os
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import random
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import uuid
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@@ -8,18 +7,28 @@ import numpy as np
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from PIL import Image
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import spaces
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import torch
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from diffusers import
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from huggingface_hub import snapshot_download
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huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
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repo_id="stabilityai/stable-diffusion-3-medium",
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repo_type="model",
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ignore_patterns=["*.md", "*..gitattributes"],
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local_dir="stable-diffusion-3-medium",
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token=huggingface_token,
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DESCRIPTION = """# Stable Diffusion 3"""
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if not torch.cuda.is_available():
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@@ -32,23 +41,24 @@ USE_TORCH_COMPILE = False
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ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
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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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return seed
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@spaces.GPU(enable_queue=True)
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def generate(
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prompt: str,
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use_resolution_binning: bool = True,
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progress=gr.Progress(track_tqdm=True),
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):
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pipe.to(device)
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seed = int(randomize_seed_fn(seed, randomize_seed))
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generator = torch.Generator().manual_seed(seed)
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#pipe.scheduler = DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++").from_config(pipe.scheduler.config)
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if not use_negative_prompt:
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negative_prompt = None # type: ignore
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return output
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examples = [
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"neon holography crystal cat",
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"a cat eating a piece of cheese",
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@@ -101,6 +107,7 @@ css = '''
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.gradio-container{max-width: 1000px !important}
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h1{text-align:center}
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'''
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with gr.Blocks(css=css) as demo:
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with gr.Row():
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with gr.Column():
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@@ -136,7 +143,7 @@ with gr.Blocks(css=css) as demo:
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=1,
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value
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visible=True,
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)
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seed = gr.Slider(
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@@ -225,4 +232,4 @@ with gr.Blocks(css=css) as demo:
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)
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if __name__ == "__main__":
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demo.queue().launch()
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import os
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import random
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import uuid
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from PIL import Image
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import spaces
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import torch
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from diffusers import StableDiffusionPipeline, DPMSolverSinglestepScheduler, AutoencoderKL
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from huggingface_hub import snapshot_download
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# Çevre değişkeninden token'ı al
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huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
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if huggingface_token is None:
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raise ValueError("HUGGINGFACE_TOKEN environment variable is not set.")
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# Model ve VAE dosyalarını indir
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model_path = snapshot_download(
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repo_id="stabilityai/stable-diffusion-3-medium",
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repo_type="model",
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ignore_patterns=["*.md", "*..gitattributes"],
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local_dir="stable-diffusion-3-medium",
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token=huggingface_token,
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)
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vae_path = snapshot_download(
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repo_id="madebyollin/sdxl-vae-fp16-fix",
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token=huggingface_token
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)
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DESCRIPTION = """# Stable Diffusion 3"""
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if not torch.cuda.is_available():
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ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# VAE modelini yükle
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vae = AutoencoderKL.from_pretrained(vae_path, torch_dtype=torch.float16)
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# Stable Diffusion pipeline'ı yükle
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pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
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pipe.to(device)
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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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return seed
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@spaces.GPU(enable_queue=True)
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def generate(
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prompt: str,
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use_resolution_binning: bool = True,
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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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generator = torch.Generator().manual_seed(seed)
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if not use_negative_prompt:
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negative_prompt = None # type: ignore
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return output
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examples = [
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"neon holography crystal cat",
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"a cat eating a piece of cheese",
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.gradio-container{max-width: 1000px !important}
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h1{text-align:center}
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'''
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with gr.Blocks(css=css) as demo:
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with gr.Row():
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with gr.Column():
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=1,
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value="deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
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visible=True,
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)
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seed = gr.Slider(
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)
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if __name__ == "__main__":
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demo.queue().launch()
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