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| # -*- coding: utf-8 -*- | |
| """Copy of compose_glide.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/19xx6Nu4FeiGj-TzTUFxBf-15IkeuFx_F | |
| """ | |
| import streamlit as st | |
| import gradio as gr | |
| import torch as th | |
| from composable_diffusion.download import download_model | |
| from composable_diffusion.model_creation import create_model_and_diffusion as create_model_and_diffusion_for_clevr | |
| from composable_diffusion.model_creation import model_and_diffusion_defaults as model_and_diffusion_defaults_for_clevr | |
| from composable_diffusion.composable_stable_diffusion.pipeline_composable_stable_diffusion import ComposableStableDiffusionPipeline | |
| # This notebook supports both CPU and GPU. | |
| # On CPU, generating one sample may take on the order of 20 minutes. | |
| # On a GPU, it should be under a minute. | |
| has_cuda = th.cuda.is_available() | |
| device = th.device('cpu' if not th.cuda.is_available() else 'cuda') | |
| # init stable diffusion model | |
| pipe = ComposableStableDiffusionPipeline.from_pretrained( | |
| "CompVis/stable-diffusion-v1-4", | |
| ).to(device) | |
| pipe.safety_checker = None | |
| # create model for CLEVR Objects | |
| clevr_options = model_and_diffusion_defaults_for_clevr() | |
| flags = { | |
| "image_size": 128, | |
| "num_channels": 192, | |
| "num_res_blocks": 2, | |
| "learn_sigma": True, | |
| "use_scale_shift_norm": False, | |
| "raw_unet": True, | |
| "noise_schedule": "squaredcos_cap_v2", | |
| "rescale_learned_sigmas": False, | |
| "rescale_timesteps": False, | |
| "num_classes": '2', | |
| "dataset": "clevr_pos", | |
| "use_fp16": has_cuda, | |
| "timestep_respacing": '100' | |
| } | |
| for key, val in flags.items(): | |
| clevr_options[key] = val | |
| clevr_model, clevr_diffusion = create_model_and_diffusion_for_clevr(**clevr_options) | |
| clevr_model.eval() | |
| if has_cuda: | |
| clevr_model.convert_to_fp16() | |
| clevr_model.to(device) | |
| clevr_model.load_state_dict(th.load(download_model('clevr_pos'), device)) | |
| print('total clevr_pos parameters', sum(x.numel() for x in clevr_model.parameters())) | |
| def compose_clevr_objects(prompt, weights, steps): | |
| weights = [float(x.strip()) for x in weights.split('|')] | |
| weights = th.tensor(weights, device=device).reshape(-1, 1, 1, 1) | |
| coordinates = [ | |
| [ | |
| float(x.split(',')[0].strip()), float(x.split(',')[1].strip())] | |
| for x in prompt.split('|') | |
| ] | |
| coordinates += [[-1, -1]] # add unconditional score label | |
| batch_size = 1 | |
| clevr_options['timestep_respacing'] = str(int(steps)) | |
| _, clevr_diffusion = create_model_and_diffusion_for_clevr(**clevr_options) | |
| def model_fn(x_t, ts, **kwargs): | |
| half = x_t[:1] | |
| combined = th.cat([half] * kwargs['y'].size(0), dim=0) | |
| model_out = clevr_model(combined, ts, **kwargs) | |
| eps, rest = model_out[:, :3], model_out[:, 3:] | |
| masks = kwargs.get('masks') | |
| cond_eps = eps[masks] | |
| uncond_eps = eps[~masks] | |
| half_eps = uncond_eps + (weights * (cond_eps - uncond_eps)).sum(dim=0, keepdims=True) | |
| eps = th.cat([half_eps] * x_t.size(0), dim=0) | |
| return th.cat([eps, rest], dim=1) | |
| def sample(coordinates): | |
| masks = [True] * (len(coordinates) - 1) + [False] | |
| model_kwargs = dict( | |
| y=th.tensor(coordinates, dtype=th.float, device=device), | |
| masks=th.tensor(masks, dtype=th.bool, device=device) | |
| ) | |
| samples = clevr_diffusion.p_sample_loop( | |
| model_fn, | |
| (len(coordinates), 3, clevr_options["image_size"], clevr_options["image_size"]), | |
| device=device, | |
| clip_denoised=True, | |
| progress=True, | |
| model_kwargs=model_kwargs, | |
| cond_fn=None, | |
| )[:batch_size] | |
| return samples | |
| samples = sample(coordinates) | |
| out_img = samples[0].permute(1, 2, 0) | |
| out_img = (out_img + 1) / 2 | |
| out_img = (out_img.detach().cpu() * 255.).to(th.uint8) | |
| out_img = out_img.numpy() | |
| return out_img | |
| def stable_diffusion_compose(prompt, steps, weights, seed): | |
| generator = th.Generator("cuda").manual_seed(int(seed)) | |
| image = pipe(prompt, guidance_scale=7.5, num_inference_steps=steps, | |
| weights=weights, generator=generator).images[0] | |
| image.save(f'{"_".join(prompt.split())}.png') | |
| return image | |
| def compose(prompt, weights, version, steps, seed): | |
| try: | |
| with th.no_grad(): | |
| if version == 'Stable_Diffusion_1v_4': | |
| res = stable_diffusion_compose(prompt, steps, weights, seed) | |
| return res | |
| else: | |
| return compose_clevr_objects(prompt, weights, steps) | |
| except Exception as e: | |
| print(e) | |
| return None | |
| examples_1 = "A castle in a forest | grainy, fog" | |
| examples_3 = '0.1, 0.5 | 0.3, 0.5 | 0.5, 0.5 | 0.7, 0.5 | 0.9, 0.5' | |
| examples_5 = 'a white church | lightning in the background' | |
| examples_6 = 'mystical trees | A dark magical pond | dark' | |
| examples_7 = 'A lake | A mountain | Cherry Blossoms next to the lake' | |
| examples = [ | |
| [examples_6, "7.5 | 7.5 | -7.5", 'Stable_Diffusion_1v_4', 50, 8], | |
| [examples_6, "7.5 | 7.5 | 7.5", 'Stable_Diffusion_1v_4', 50, 8], | |
| [examples_1, "7.5 | -7.5", 'Stable_Diffusion_1v_4', 50, 0], | |
| [examples_7, "7.5 | 7.5 | 7.5", 'Stable_Diffusion_1v_4', 50, 3], | |
| [examples_5, "7.5 | 7.5", 'Stable_Diffusion_1v_4', 50, 0], | |
| [examples_3, "7.5 | 7.5 | 7.5 | 7.5 | 7.5", 'CLEVR Objects', 100, 0] | |
| ] | |
| title = 'Compositional Visual Generation with Composable Diffusion Models' | |
| description = '<p>Our conjunction and negation (a.k.a. negative prompts) operators are also added into stable diffusion webui! (<a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Negative-prompt">Negation</a> and <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/c26732fbee2a57e621ac22bf70decf7496daa4cd">Conjunction</a>)</p></p><p>See more information from our <a href="https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/">Project Page</a>.</p><ul><li>One version is based on the released <a href="https://github.com/openai/glide-text2im">GLIDE</a> and <a href="https://github.com/CompVis/stable-diffusion/">Stable Diffusion</a> for composing natural language description.</li><li>Another is based on our pre-trained CLEVR Object Model for composing objects. <br>(<b>Note</b>: We recommend using <b><i>x</i></b> in range <b><i>[0.1, 0.9]</i></b> and <b><i>y</i></b> in range <b><i>[0.25, 0.7]</i></b>, since the training dataset labels are in given ranges.)</li></ul><p>When composing multiple sentences, use `|` as the delimiter, see given examples below.</p><p>You can also specify the weight of each text by using `|` as the delimiter. When the weight is negative, it will use Negation Operator (NOT), which indicates the corresponding prompt is a negative prompt. Otherwise it will use Conjunction operator (AND).</p><p><b>Only Conjunction operator is enabled for CLEVR Object.</b></p><p><b>Note: When using Stable Diffusion, black images will be returned if the given prompt is detected as problematic. For composing GLIDE model, we recommend using the Colab demo in our <a href="https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/">Project Page</a>.</b></p>' | |
| iface = gr.Interface(compose, | |
| inputs=[ | |
| gr.Textbox(label='prompt', value='mystical trees | A dark magical pond | dark'), | |
| gr.Textbox(label='weights', value='7.5 | 7.5 | -7.5'), | |
| gr.Radio(['Stable_Diffusion_1v_4', 'CLEVR Objects'], type="value", label='version', value='Stable_Diffusion_1v_4'), | |
| gr.Slider(10, 200, value=50), | |
| gr.Number(2) | |
| ], | |
| outputs='image', cache_examples=False, | |
| title=title, description=description, examples=examples) | |
| iface.launch() |