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Update app.py
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app.py
CHANGED
@@ -10,16 +10,25 @@ import spaces
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import torch
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from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
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if not torch.cuda.is_available():
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
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MAX_SEED = np.iinfo(np.int32).max
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
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MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
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USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
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ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
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if torch.cuda.is_available():
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pipe = StableDiffusionXLPipeline.from_pretrained(
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@@ -29,23 +38,14 @@ if torch.cuda.is_available():
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add_watermarker=False
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)
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pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
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pipe.set_adapters("dalle")
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pipe.to("cuda")
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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(duration=30, queue=False)
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def generate(
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prompt: str,
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negative_prompt: str = "",
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@@ -54,32 +54,30 @@ def generate(
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width: int = 1024,
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height: int = 1024,
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guidance_scale: float = 3,
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num_inference_steps: int = 10,
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randomize_seed: bool = False,
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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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options = {
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"prompt":prompt,
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"negative_prompt":negative_prompt,
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"width":width,
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"height":height,
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"guidance_scale":guidance_scale,
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"num_inference_steps":num_inference_steps,
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"generator":generator,
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"use_resolution_binning":use_resolution_binning,
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"output_type":"pil",
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}
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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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@@ -112,55 +110,48 @@ with gr.Blocks(css=css) as demo:
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container=False,
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)
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run_button = gr.Button("Run", scale=0)
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result = gr.Gallery(label="Result", columns=1)
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with gr.Accordion("Advanced options", open=False):
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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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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minimum=512,
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maximum=
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step=
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value=1024,
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)
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height = gr.Slider(
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label="Height",
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minimum=512,
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maximum=
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step=
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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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minimum=0.1,
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maximum=
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step=0.1,
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value=
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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maximum=15,
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step=1,
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value=8,
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)
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gr.Examples(
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@@ -168,7 +159,7 @@ with gr.Blocks(css=css) as demo:
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inputs=prompt,
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outputs=[result, seed],
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fn=generate,
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cache_examples=
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)
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use_negative_prompt.change(
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@@ -193,12 +184,11 @@ with gr.Blocks(css=css) as demo:
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width,
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height,
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guidance_scale,
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num_inference_steps,
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randomize_seed,
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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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if __name__ == "__main__":
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demo.queue(max_size=20).launch()
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import torch
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from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
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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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MAX_SEED = np.iinfo(np.int32).max
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if not torch.cuda.is_available():
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
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MAX_SEED = np.iinfo(np.int32).max
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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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add_watermarker=False
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)
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pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
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pipe.set_adapters("dalle")
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pipe.to("cuda")
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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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negative_prompt: str = "",
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width: int = 1024,
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height: int = 1024,
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guidance_scale: float = 3,
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randomize_seed: bool = False,
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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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if not use_negative_prompt:
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negative_prompt = "" # type: ignore
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images = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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width=width,
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height=height,
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guidance_scale=guidance_scale,
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num_inference_steps=25,
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num_images_per_prompt=1,
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cross_attention_kwargs={"scale": 0.65},
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output_type="pil",
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).images
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image_paths = [save_image(img) for img in images]
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print(image_paths)
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return image_paths, seed
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examples = [
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container=False,
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)
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run_button = gr.Button("Run", scale=0)
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result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False)
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with gr.Accordion("Advanced options", open=False):
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use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
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negative_prompt = gr.Text(
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label="Negative prompt",
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lines=4,
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max_lines=6,
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value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:1.25)""",
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placeholder="Enter a negative prompt",
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visible=True,
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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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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minimum=512,
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maximum=2048,
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step=8,
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value=1024,
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)
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height = gr.Slider(
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label="Height",
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minimum=512,
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maximum=2048,
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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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minimum=0.1,
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maximum=20.0,
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step=0.1,
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value=6,
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)
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gr.Examples(
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inputs=prompt,
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outputs=[result, seed],
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fn=generate,
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cache_examples=False,
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)
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use_negative_prompt.change(
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width,
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height,
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guidance_scale,
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randomize_seed,
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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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if __name__ == "__main__":
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demo.queue(max_size=20).launch(show_api=False, debug=False)
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