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| from diffusers import StableDiffusionXLPipeline, AutoencoderKL | |
| from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput | |
| from typing import Any, Callable, Dict, List, Optional, Tuple, Union | |
| import torch | |
| from PIL import Image, ImageOps | |
| import gradio as gr | |
| import user_history | |
| vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) | |
| pipe = StableDiffusionXLPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-xl-base-1.0", | |
| vae=vae, | |
| torch_dtype=torch.float16, | |
| variant="fp16", | |
| use_safetensors=True, | |
| ) | |
| pipe.to("cuda") | |
| pipe.unet.to(memory_format=torch.channels_last) | |
| pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| def call( | |
| pipe, | |
| prompt: Union[str, List[str]] = None, | |
| prompt2: Union[str, List[str]] = None, | |
| height: Optional[int] = None, | |
| width: Optional[int] = None, | |
| num_inference_steps: int = 50, | |
| denoising_end: Optional[float] = None, | |
| guidance_scale: float = 5.0, | |
| guidance_scale2: float = 5.0, | |
| negative_prompt: Optional[Union[str, List[str]]] = None, | |
| negative_prompt2: Optional[Union[str, List[str]]] = None, | |
| num_images_per_prompt: Optional[int] = 1, | |
| eta: float = 0.0, | |
| generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
| latents: Optional[torch.FloatTensor] = None, | |
| prompt_embeds: Optional[torch.FloatTensor] = None, | |
| negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| pooled_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, | |
| callback_steps: int = 1, | |
| cross_attention_kwargs: Optional[Dict[str, Any]] = None, | |
| guidance_rescale: float = 0.0, | |
| original_size: Optional[Tuple[int, int]] = None, | |
| crops_coords_top_left: Tuple[int, int] = (0, 0), | |
| target_size: Optional[Tuple[int, int]] = None, | |
| negative_original_size: Optional[Tuple[int, int]] = None, | |
| negative_crops_coords_top_left: Tuple[int, int] = (0, 0), | |
| negative_target_size: Optional[Tuple[int, int]] = None, | |
| ): | |
| # 0. Default height and width to unet | |
| height = height or pipe.default_sample_size * pipe.vae_scale_factor | |
| width = width or pipe.default_sample_size * pipe.vae_scale_factor | |
| original_size = original_size or (height, width) | |
| target_size = target_size or (height, width) | |
| # 1. Check inputs. Raise error if not correct | |
| pipe.check_inputs( | |
| prompt, | |
| None, | |
| height, | |
| width, | |
| callback_steps, | |
| negative_prompt, | |
| None, | |
| prompt_embeds, | |
| negative_prompt_embeds, | |
| pooled_prompt_embeds, | |
| negative_pooled_prompt_embeds, | |
| ) | |
| # 2. Define call parameters | |
| if prompt is not None and isinstance(prompt, str): | |
| batch_size = 1 | |
| elif prompt is not None and isinstance(prompt, list): | |
| batch_size = len(prompt) | |
| else: | |
| batch_size = prompt_embeds.shape[0] | |
| device = pipe._execution_device | |
| # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) | |
| # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` | |
| # corresponds to doing no classifier free guidance. | |
| do_classifier_free_guidance = guidance_scale > 1.0 | |
| # 3. Encode input prompt | |
| text_encoder_lora_scale = ( | |
| cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None | |
| ) | |
| ( | |
| prompt_embeds, | |
| negative_prompt_embeds, | |
| pooled_prompt_embeds, | |
| negative_pooled_prompt_embeds, | |
| ) = pipe.encode_prompt( | |
| prompt=prompt, | |
| device=device, | |
| num_images_per_prompt=num_images_per_prompt, | |
| do_classifier_free_guidance=do_classifier_free_guidance, | |
| negative_prompt=negative_prompt, | |
| prompt_embeds=None, | |
| negative_prompt_embeds=None, | |
| pooled_prompt_embeds=None, | |
| negative_pooled_prompt_embeds=None, | |
| lora_scale=text_encoder_lora_scale, | |
| ) | |
| ( | |
| prompt2_embeds, | |
| negative_prompt2_embeds, | |
| pooled_prompt2_embeds, | |
| negative_pooled_prompt2_embeds, | |
| ) = pipe.encode_prompt( | |
| prompt=prompt2, | |
| device=device, | |
| num_images_per_prompt=num_images_per_prompt, | |
| do_classifier_free_guidance=do_classifier_free_guidance, | |
| negative_prompt=negative_prompt2, | |
| prompt_embeds=None, | |
| negative_prompt_embeds=None, | |
| pooled_prompt_embeds=None, | |
| negative_pooled_prompt_embeds=None, | |
| lora_scale=text_encoder_lora_scale, | |
| ) | |
| # 4. Prepare timesteps | |
| pipe.scheduler.set_timesteps(num_inference_steps, device=device) | |
| timesteps = pipe.scheduler.timesteps | |
| # 5. Prepare latent variables | |
| num_channels_latents = pipe.unet.config.in_channels | |
| latents = pipe.prepare_latents( | |
| batch_size * num_images_per_prompt, | |
| num_channels_latents, | |
| height, | |
| width, | |
| prompt_embeds.dtype, | |
| device, | |
| generator, | |
| latents, | |
| ) | |
| # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline | |
| extra_step_kwargs = pipe.prepare_extra_step_kwargs(generator, eta) | |
| # 7. Prepare added time ids & embeddings | |
| add_text_embeds = pooled_prompt_embeds | |
| add_text2_embeds = pooled_prompt2_embeds | |
| add_time_ids = pipe._get_add_time_ids( | |
| original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype | |
| ) | |
| add_time2_ids = pipe._get_add_time_ids( | |
| original_size, crops_coords_top_left, target_size, dtype=prompt2_embeds.dtype | |
| ) | |
| if negative_original_size is not None and negative_target_size is not None: | |
| negative_add_time_ids = pipe._get_add_time_ids( | |
| negative_original_size, | |
| negative_crops_coords_top_left, | |
| negative_target_size, | |
| dtype=prompt_embeds.dtype, | |
| ) | |
| else: | |
| negative_add_time_ids = add_time_ids | |
| negative_add_time2_ids = add_time2_ids | |
| if do_classifier_free_guidance: | |
| prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) | |
| add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) | |
| add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) | |
| prompt2_embeds = torch.cat([negative_prompt2_embeds, prompt2_embeds], dim=0) | |
| add_text2_embeds = torch.cat([negative_pooled_prompt2_embeds, add_text2_embeds], dim=0) | |
| add_time2_ids = torch.cat([negative_add_time2_ids, add_time2_ids], dim=0) | |
| prompt_embeds = prompt_embeds.to(device) | |
| add_text_embeds = add_text_embeds.to(device) | |
| add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) | |
| prompt2_embeds = prompt2_embeds.to(device) | |
| add_text2_embeds = add_text2_embeds.to(device) | |
| add_time2_ids = add_time2_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) | |
| # 8. Denoising loop | |
| num_warmup_steps = max(len(timesteps) - num_inference_steps * pipe.scheduler.order, 0) | |
| # 7.1 Apply denoising_end | |
| if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1: | |
| discrete_timestep_cutoff = int( | |
| round( | |
| pipe.scheduler.config.num_train_timesteps | |
| - (denoising_end * pipe.scheduler.config.num_train_timesteps) | |
| ) | |
| ) | |
| num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) | |
| timesteps = timesteps[:num_inference_steps] | |
| with pipe.progress_bar(total=num_inference_steps) as progress_bar: | |
| for i, t in enumerate(timesteps): | |
| if i % 2 == 0: | |
| # expand the latents if we are doing classifier free guidance | |
| latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents | |
| latent_model_input = pipe.scheduler.scale_model_input(latent_model_input, t) | |
| # predict the noise residual | |
| added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} | |
| noise_pred = pipe.unet( | |
| latent_model_input, | |
| t, | |
| encoder_hidden_states=prompt_embeds, | |
| cross_attention_kwargs=cross_attention_kwargs, | |
| added_cond_kwargs=added_cond_kwargs, | |
| return_dict=False, | |
| )[0] | |
| # perform guidance | |
| if do_classifier_free_guidance: | |
| noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) | |
| noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | |
| else: | |
| # expand the latents if we are doing classifier free guidance | |
| latent_model_input2 = torch.cat([latents.flip(2)] * 2) if do_classifier_free_guidance else latents | |
| latent_model_input2 = pipe.scheduler.scale_model_input(latent_model_input2, t) | |
| # predict the noise residual | |
| added_cond2_kwargs = {"text_embeds": add_text2_embeds, "time_ids": add_time2_ids} | |
| noise_pred2 = pipe.unet( | |
| latent_model_input2, | |
| t, | |
| encoder_hidden_states=prompt2_embeds, | |
| cross_attention_kwargs=cross_attention_kwargs, | |
| added_cond_kwargs=added_cond2_kwargs, | |
| return_dict=False, | |
| )[0] | |
| # perform guidance | |
| if do_classifier_free_guidance: | |
| noise_pred2_uncond, noise_pred2_text = noise_pred2.chunk(2) | |
| noise_pred2 = noise_pred2_uncond + guidance_scale2 * (noise_pred2_text - noise_pred2_uncond) | |
| noise_pred = noise_pred if i % 2 == 0 else noise_pred2.flip(2) | |
| # compute the previous noisy sample x_t -> x_t-1 | |
| latents = pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] | |
| # call the callback, if provided | |
| if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % pipe.scheduler.order == 0): | |
| progress_bar.update() | |
| if callback is not None and i % callback_steps == 0: | |
| callback(i, t, latents) | |
| if not output_type == "latent": | |
| # make sure the VAE is in float32 mode, as it overflows in float16 | |
| needs_upcasting = pipe.vae.dtype == torch.float16 and pipe.vae.config.force_upcast | |
| if needs_upcasting: | |
| pipe.upcast_vae() | |
| latents = latents.to(next(iter(pipe.vae.post_quant_conv.parameters())).dtype) | |
| image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0] | |
| # cast back to fp16 if needed | |
| if needs_upcasting: | |
| pipe.vae.to(dtype=torch.float16) | |
| else: | |
| image = latents | |
| if not output_type == "latent": | |
| # apply watermark if available | |
| if pipe.watermark is not None: | |
| image = pipe.watermark.apply_watermark(image) | |
| image = pipe.image_processor.postprocess(image, output_type=output_type) | |
| # Offload all models | |
| pipe.maybe_free_model_hooks() | |
| if not return_dict: | |
| return (image,) | |
| return StableDiffusionXLPipelineOutput(images=image) | |
| NEGATIVE_PROMPTS = "text, watermark, low-quality, signature, moiré pattern, downsampling, aliasing, distorted, blurry, glossy, blur, jpeg artifacts, compression artifacts, poorly drawn, low-resolution, bad, distortion, twisted, excessive, exaggerated pose, exaggerated limbs, grainy, symmetrical, duplicate, error, pattern, beginner, pixelated, fake, hyper, glitch, overexposed, high-contrast, bad-contrast" | |
| def rotate_output(has_flipped): | |
| if(has_flipped): | |
| return gr.Image(elem_classes="not_rotated"), gr.Button("Rotate to see prompt 2!"), not has_flipped | |
| else: | |
| return gr.Image(elem_classes="rotated"), gr.Button("Rotate to see prompt 1!"), not has_flipped | |
| def simple_call(prompt1, prompt2, profile: gr.OAuthProfile | None=None): | |
| generator = [torch.Generator(device="cuda").manual_seed(5)] | |
| res = call( | |
| pipe, | |
| prompt1, | |
| prompt2, | |
| width=768, | |
| height=768, | |
| num_images_per_prompt=1, | |
| num_inference_steps=50, | |
| guidance_scale=5.0, | |
| guidance_scale2=8.0, | |
| negative_prompt=NEGATIVE_PROMPTS, | |
| negative_prompt2=NEGATIVE_PROMPTS, | |
| generator=generator | |
| ) | |
| image1 = res.images[0] | |
| # save generated images (if logged in) | |
| user_history.save_image(label=f"{prompt1} / {prompt2}", image=image1, profile=profile, metadata={ | |
| "prompt2": prompt1, | |
| "prompt1": prompt2, | |
| "seed": seed, | |
| }) | |
| return image1 | |
| css = ''' | |
| #result_image{ transition: transform 2s ease-in-out } | |
| #result_image.rotated{transform: rotate(180deg)} | |
| ''' | |
| with gr.Blocks() as app: | |
| gr.Markdown( | |
| ''' | |
| <center> | |
| <h1>Upside Down Diffusion</h1> | |
| <p>Code by Alex Carlier, <a href="https://colab.research.google.com/drive/1rjDQOn11cTHAf3Oeq87Hfl_Vh41NbTl4?usp=sharing">Google Colab</a>, follow them on <a href="https://twitter.com/alexcarliera">Twitter</a></p> | |
| <p>A space by <a href="https://twitter.com/angrypenguinPNG">AP</a> with contributions from <a href="https://twitter.com/multimodalart">MultimodalArt</a></p> | |
| </center> | |
| <hr> | |
| <p> | |
| Enter your first prompt to craft an image that will show when upright. Then, add a second prompt to reveal a mesmerizing surprise when you flip the image upside down! ✨ | |
| </p> | |
| <p> | |
| <em>For best results, please include the prompt in the following format: Art Style and Object. Here is an example: Prompt 1: A sketch of a turtle, Prompt 2: A sketch of a tree. Both prompts need to have the same style!</em> | |
| </p> | |
| ''' | |
| ) | |
| has_flipped = gr.State(value=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt1 = gr.Textbox(label="Prompt 1", info="Prompt for the side up", placeholder="A sketch of a...") | |
| prompt2 = gr.Textbox(label="Prompt 2", info="Prompt for the side down", placeholder="A sketch of a...") | |
| run_btn = gr.Button("Run") | |
| with gr.Column(): | |
| result_image1 = gr.Image(label="Output", elem_id="result_image", elem_classes="not_rotated") | |
| rotate_button = gr.Button("Rotate to see prompt 2!") | |
| run_btn.click( | |
| simple_call, | |
| inputs=[prompt1, prompt2], | |
| outputs=[result_image1] | |
| ) | |
| rotate_button.click( | |
| rotate_output, | |
| inputs=[has_flipped], | |
| outputs=[result_image1, rotate_button, has_flipped], | |
| queue=False, | |
| show_progress=False | |
| ) | |
| with gr.Blocks(css=css) as app_with_history: | |
| with gr.Tab("Upside Down Diffusion"): | |
| app.render() | |
| with gr.Tab("Past generations"): | |
| user_history.render() | |
| app_with_history.queue(max_size=20) | |
| if __name__ == "__main__": | |
| app_with_history.launch(debug=True) |