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" DESCRIPTION += "\n

Würstchen is a new fast and efficient high resolution text-to-image architecture and model

" if not torch.cuda.is_available(): DESCRIPTION += "\n

Running on CPU 🥶

" 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()