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import os |
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import random |
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import gradio as gr |
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import numpy as np |
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import PIL.Image |
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import torch |
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from typing import List |
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from diffusers.utils import numpy_to_pil |
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from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline |
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from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS |
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from previewer.modules import Previewer |
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os.environ['TOKENIZERS_PARALLELISM'] = 'false' |
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DESCRIPTION = "ImMagician🪄" |
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DESCRIPTION += "\n<p style=\"text-align: center\"><a href='https://huggingface.co/warp-ai/wuerstchen' target='_blank'>ImMagician🪄</a> is a new fast and efficient high resolution text-to-image architecture and model</p>" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running 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" |
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MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536")) |
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USE_TORCH_COMPILE = False |
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ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1" |
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PREVIEW_IMAGES = True |
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dtype = torch.float16 |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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if torch.cuda.is_available(): |
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prior_pipeline = WuerstchenPriorPipeline.from_pretrained("warp-ai/wuerstchen-prior", torch_dtype=dtype) |
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decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained("warp-ai/wuerstchen", torch_dtype=dtype) |
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if ENABLE_CPU_OFFLOAD: |
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prior_pipeline.enable_model_cpu_offload() |
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decoder_pipeline.enable_model_cpu_offload() |
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else: |
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prior_pipeline.to(device) |
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decoder_pipeline.to(device) |
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if USE_TORCH_COMPILE: |
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prior_pipeline.prior = torch.compile(prior_pipeline.prior, mode="reduce-overhead", fullgraph=True) |
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decoder_pipeline.decoder = torch.compile(decoder_pipeline.decoder, mode="reduce-overhead", fullgraph=True) |
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if PREVIEW_IMAGES: |
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previewer = Previewer() |
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previewer.load_state_dict(torch.load("previewer/text2img_wurstchen_b_v1_previewer_100k.pt")["state_dict"]) |
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previewer.eval().requires_grad_(False).to(device).to(dtype) |
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def callback_prior(i, t, latents): |
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output = previewer(latents) |
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output = numpy_to_pil(output.clamp(0, 1).permute(0, 2, 3, 1).cpu().numpy()) |
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return output |
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else: |
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previewer = None |
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callback_prior = None |
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else: |
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prior_pipeline = None |
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decoder_pipeline = None |
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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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def generate( |
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prompt: str, |
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negative_prompt: str = "", |
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seed: int = 0, |
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width: int = 1024, |
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height: int = 1024, |
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prior_num_inference_steps: int = 60, |
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prior_guidance_scale: float = 4.0, |
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decoder_num_inference_steps: int = 12, |
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decoder_guidance_scale: float = 0.0, |
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num_images_per_prompt: int = 2, |
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) -> PIL.Image.Image: |
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generator = torch.Generator().manual_seed(seed) |
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prior_output = prior_pipeline( |
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prompt=prompt, |
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height=height, |
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width=width, |
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timesteps=DEFAULT_STAGE_C_TIMESTEPS, |
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negative_prompt=negative_prompt, |
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guidance_scale=prior_guidance_scale, |
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num_images_per_prompt=num_images_per_prompt, |
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generator=generator, |
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callback=callback_prior, |
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) |
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if PREVIEW_IMAGES: |
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for _ in range(len(DEFAULT_STAGE_C_TIMESTEPS)): |
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r = next(prior_output) |
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if isinstance(r, list): |
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yield r |
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prior_output = r |
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decoder_output = decoder_pipeline( |
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image_embeddings=prior_output.image_embeddings, |
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prompt=prompt, |
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num_inference_steps=decoder_num_inference_steps, |
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guidance_scale=decoder_guidance_scale, |
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negative_prompt=negative_prompt, |
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generator=generator, |
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output_type="pil", |
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).images |
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yield decoder_output |
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examples = [ |
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", |
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"An astronaut riding a green horse", |
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] |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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gr.DuplicateButton( |
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value="Duplicate Space for private use", |
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elem_id="duplicate-button", |
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visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", |
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) |
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with gr.Group(): |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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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", show_label=False) |
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with gr.Accordion("Advanced options", open=False): |
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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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placeholder="Enter a Negative Prompt", |
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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(): |
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width = gr.Slider( |
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label="Width", |
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minimum=1024, |
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maximum=MAX_IMAGE_SIZE, |
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step=512, |
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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=1024, |
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maximum=MAX_IMAGE_SIZE, |
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step=512, |
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value=1024, |
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) |
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num_images_per_prompt = gr.Slider( |
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label="Number of Images", |
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minimum=1, |
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maximum=6, |
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step=1, |
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value=2, |
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) |
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with gr.Row(): |
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prior_guidance_scale = gr.Slider( |
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label="Prior Guidance Scale", |
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minimum=0, |
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maximum=20, |
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step=0.1, |
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value=4.0, |
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) |
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prior_num_inference_steps = gr.Slider( |
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label="Prior Inference Steps", |
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minimum=30, |
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maximum=30, |
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step=1, |
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value=30, |
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) |
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decoder_guidance_scale = gr.Slider( |
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label="Decoder Guidance Scale", |
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minimum=0, |
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maximum=0, |
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step=0.1, |
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value=0.0, |
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) |
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decoder_num_inference_steps = gr.Slider( |
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label="Decoder Inference Steps", |
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minimum=4, |
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maximum=12, |
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step=1, |
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value=12, |
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) |
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gr.Examples( |
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examples=examples, |
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inputs=prompt, |
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outputs=result, |
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fn=generate, |
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cache_examples=CACHE_EXAMPLES, |
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) |
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inputs = [ |
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prompt, |
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negative_prompt, |
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seed, |
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width, |
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height, |
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prior_num_inference_steps, |
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prior_guidance_scale, |
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decoder_num_inference_steps, |
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decoder_guidance_scale, |
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num_images_per_prompt, |
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] |
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prompt.submit( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=generate, |
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inputs=inputs, |
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outputs=result, |
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api_name="run", |
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) |
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negative_prompt.submit( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=generate, |
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inputs=inputs, |
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outputs=result, |
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api_name=False, |
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) |
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run_button.click( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=generate, |
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inputs=inputs, |
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outputs=result, |
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api_name=False, |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
