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| import os | |
| import random | |
| import gradio as gr | |
| import numpy as np | |
| import PIL.Image | |
| import torch | |
| from typing import List | |
| from diffusers.utils import numpy_to_pil | |
| from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline | |
| from diffusers.pipelines.wuerstchen import WuerstchenPrior, default_stage_c_timesteps | |
| from previewer.modules import Previewer | |
| os.environ['TOKENIZERS_PARALLELISM'] = 'false' | |
| DESCRIPTION = "# Würstchen" | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
| MAX_SEED = np.iinfo(np.int32).max | |
| CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1" | |
| MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536")) | |
| USE_TORCH_COMPILE = False | |
| ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1" | |
| PREVIEW_IMAGES = True | |
| dtype = torch.float16 | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| if torch.cuda.is_available(): | |
| prior_pipeline = WuerstchenPriorPipeline.from_pretrained("warp-ai/wuerstchen-prior", torch_dtype=dtype) | |
| decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained("warp-ai/wuerstchen", torch_dtype=dtype) | |
| if ENABLE_CPU_OFFLOAD: | |
| prior_pipeline.enable_model_cpu_offload() | |
| decoder_pipeline.enable_model_cpu_offload() | |
| else: | |
| prior_pipeline.to(device) | |
| decoder_pipeline.to(device) | |
| if USE_TORCH_COMPILE: | |
| prior_pipeline.prior = torch.compile(prior_pipeline.prior, mode="reduce-overhead", fullgraph=True) | |
| decoder_pipeline.decoder = torch.compile(decoder_pipeline.decoder, mode="reduce-overhead", fullgraph=True) | |
| if PREVIEW_IMAGES: | |
| previewer = Previewer() | |
| previewer.load_state_dict(torch.load("previewer/text2img_wurstchen_b_v1_previewer_100k.pt")["state_dict"]) | |
| previewer.eval().requires_grad_(False).to(device).to(dtype) | |
| def callback_prior(i, t, latents): | |
| output = previewer(latents) | |
| output = numpy_to_pil(output.clamp(0, 1).permute(0, 2, 3, 1).cpu().numpy()) | |
| return output | |
| else: | |
| previewer = None | |
| callback_prior = None | |
| else: | |
| prior_pipeline = None | |
| decoder_pipeline = None | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| def generate( | |
| prompt: str, | |
| negative_prompt: str = "", | |
| seed: int = 0, | |
| width: int = 1024, | |
| height: int = 1024, | |
| prior_num_inference_steps: int = 60, | |
| # prior_timesteps: List[float] = None, | |
| prior_guidance_scale: float = 4.0, | |
| decoder_num_inference_steps: int = 12, | |
| # decoder_timesteps: List[float] = None, | |
| decoder_guidance_scale: float = 0.0, | |
| num_images_per_prompt: int = 2, | |
| ) -> PIL.Image.Image: | |
| generator = torch.Generator().manual_seed(seed) | |
| prior_output = prior_pipeline( | |
| prompt=prompt, | |
| height=height, | |
| width=width, | |
| timesteps=default_stage_c_timesteps, | |
| negative_prompt=negative_prompt, | |
| guidance_scale=prior_guidance_scale, | |
| num_images_per_prompt=num_images_per_prompt, | |
| generator=generator, | |
| callback=callback_prior, | |
| ) | |
| if PREVIEW_IMAGES: | |
| for _ in range(len(default_stage_c_timesteps)): | |
| r = next(prior_output) | |
| if isinstance(r, list): | |
| yield r | |
| prior_output = r | |
| decoder_output = decoder_pipeline( | |
| image_embeddings=prior_output.image_embeddings, | |
| prompt=prompt, | |
| num_inference_steps=decoder_num_inference_steps, | |
| # timesteps=decoder_timesteps, | |
| guidance_scale=decoder_guidance_scale, | |
| negative_prompt=negative_prompt, | |
| num_images_per_prompt=num_images_per_prompt, | |
| generator=generator, | |
| output_type="pil", | |
| ).images | |
| yield decoder_output | |
| examples = [ | |
| "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
| "An astronaut riding a green horse", | |
| ] | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| gr.DuplicateButton( | |
| value="Duplicate Space for private use", | |
| elem_id="duplicate-button", | |
| visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
| ) | |
| with gr.Group(): | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| run_button = gr.Button("Run", scale=0) | |
| result = gr.Gallery(label="Result", show_label=False) | |
| with gr.Accordion("Advanced options", open=False): | |
| negative_prompt = gr.Text( | |
| label="Negative prompt", | |
| max_lines=1, | |
| placeholder="Enter a Negative Prompt", | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=768, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=128, | |
| value=1024, | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=768, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=128, | |
| value=1024, | |
| ) | |
| num_images_per_prompt = gr.Slider( | |
| label="Number of Images", | |
| minimum=1, | |
| maximum=6, | |
| step=1, | |
| value=2, | |
| ) | |
| with gr.Row(): | |
| prior_guidance_scale = gr.Slider( | |
| label="Prior Guidance Scale", | |
| minimum=0, | |
| maximum=20, | |
| step=0.1, | |
| value=4.0, | |
| ) | |
| prior_num_inference_steps = gr.Slider( | |
| label="Prior Inference Steps", | |
| minimum=10, | |
| maximum=100, | |
| step=1, | |
| value=60, | |
| ) | |
| decoder_guidance_scale = gr.Slider( | |
| label="Decoder Guidance Scale", | |
| minimum=0, | |
| maximum=20, | |
| step=0.1, | |
| value=0.0, | |
| ) | |
| decoder_num_inference_steps = gr.Slider( | |
| label="Decoder Inference Steps", | |
| minimum=10, | |
| maximum=100, | |
| step=1, | |
| value=12, | |
| ) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=prompt, | |
| outputs=result, | |
| fn=generate, | |
| cache_examples=CACHE_EXAMPLES, | |
| ) | |
| inputs = [ | |
| prompt, | |
| negative_prompt, | |
| seed, | |
| width, | |
| height, | |
| prior_num_inference_steps, | |
| # prior_timesteps, | |
| prior_guidance_scale, | |
| decoder_num_inference_steps, | |
| # decoder_timesteps, | |
| decoder_guidance_scale, | |
| num_images_per_prompt, | |
| ] | |
| prompt.submit( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=generate, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name="run", | |
| ) | |
| negative_prompt.submit( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=generate, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name=False, | |
| ) | |
| run_button.click( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=generate, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name=False, | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() |