From e6f0e36f5e856d9651c597d75aed13ae7298d03b Mon Sep 17 00:00:00 2001 From: Andreas Blattmann Date: Tue, 28 Nov 2023 20:33:27 +0100 Subject: [PATCH] SDXL-Turbo --- README.md | 14 +- assets/turbo_tile.png | Bin 0 -> 2165901 bytes model_licenses/LICENSE-SDXL-Turbo | 58 ++++++++ requirements/pt2.txt | 1 + scripts/demo/turbo.py | 214 ++++++++++++++++++++++++++++++ 5 files changed, 285 insertions(+), 2 deletions(-) create mode 100644 assets/turbo_tile.png create mode 100644 model_licenses/LICENSE-SDXL-Turbo create mode 100644 scripts/demo/turbo.py diff --git a/README.md b/README.md index 61fa687b..b717d7d3 100644 --- a/README.md +++ b/README.md @@ -3,12 +3,22 @@ ![sample1](assets/000.jpg) ## News +**November 28, 2023** -**November 21, 2023** +- We are releasing SDXL-Turbo, a lightning fast text-to image model. + Alongside the model, we release a [technical report](https://stability.ai/research/adversarial-diffusion-distillation) + - Usage: + - Follow the installation instructions or update the existing environment with `pip install streamlit-keyup`. + - Download the [weights](https://huggingface.co/stabilityai/sdxl-turbo) and place them in the `checkpoints/` directory. + - Run `streamlit run scripts/demo/turbo.py`. + + ![tile](assets/turbo_tile.png) + +**November 21, 2023** - We are releasing Stable Video Diffusion, an image-to-video model, for research purposes: - [SVD](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid): This model was trained to generate 14 - frames at resolution 576x1024 given a context frame of the same size. + frames at resolution 576x1024 given a context frame of the same size. We use the standard image encoder from SD 2.1, but replace the decoder with a temporally-aware `deflickering decoder`. - [SVD-XT](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt): Same architecture as `SVD` but finetuned for 25 frame generation. diff --git a/assets/turbo_tile.png b/assets/turbo_tile.png new file mode 100644 index 0000000000000000000000000000000000000000..e820fc0ff2879fdeb5324152d92f48049b40506c GIT binary patch literal 2165901 zcmV)_K!3l9P)004R>004l5008;`004mK004C`008P>0026e000+ooVrmw00006 zVoOIv0RI600RN!9r;`8xfB;EEK~#9!%>7xHEJ<=62!0}>X6_!#-Eyg_tU>_ZZ)ofBXHW*LAxwa*)rh+q`|Y)3mA(G!0nhr2s5QJmc3{PFqt z8L;?o`whXE*_;5t9016Oh#>L!fV*)U`EqqA5qUW$+Wo{2@!=RuGb3R$I1$6jZ|djc zY-a1z#i4OFcaMi5u=&};o?j7XM~puak-ED(mvd4bmU6H&0Hgkv*0Z@=nG0>?jR3?@ z#bd^sWe@;ExvO~acq8re7X$U^p1wt2zJkAL_t{^*bY=zsVhKmX#3tJ|9&e)7qyk6z?F^N>{4fiZ1FWXaP!&%O{5 zEz2^`vqG8q!~Fv@Kiu6D(f;9-26kYcPW!tD&C8PWX*x{D!+yVC@|04VkH_Qj2+fDX z0Zg0CW}fHM>BP*N&E{};aOh?jb6%F&+?@pga?ay8y2-LEPBsh!n{PIoG;Vjh-OJ~< 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+class SubstepSampler(EulerAncestralSampler): + def __init__(self, n_sample_steps=1, *args, **kwargs): + super().__init__(*args, **kwargs) + self.n_sample_steps = n_sample_steps + self.steps_subset = [0, 100, 200, 300, 1000] + + def prepare_sampling_loop(self, x, cond, uc=None, num_steps=None): + sigmas = self.discretization( + self.num_steps if num_steps is None else num_steps, device=self.device + ) + sigmas = sigmas[ + self.steps_subset[: self.n_sample_steps] + self.steps_subset[-1:] + ] + uc = cond + x *= torch.sqrt(1.0 + sigmas[0] ** 2.0) + num_sigmas = len(sigmas) + s_in = x.new_ones([x.shape[0]]) + return x, s_in, sigmas, num_sigmas, cond, uc + + +def seeded_randn(shape, seed): + randn = np.random.RandomState(seed).randn(*shape) + randn = torch.from_numpy(randn).to(device="cuda", dtype=torch.float32) + return randn + + +class SeededNoise: + def __init__(self, seed): + self.seed = seed + + def __call__(self, x): + self.seed = self.seed + 1 + return seeded_randn(x.shape, self.seed) + + +def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None): + value_dict = {} + for key in keys: + if key == "txt": + value_dict["prompt"] = prompt + value_dict["negative_prompt"] = "" + + if key == "original_size_as_tuple": + orig_width = init_dict["orig_width"] + orig_height = init_dict["orig_height"] + + value_dict["orig_width"] = orig_width + value_dict["orig_height"] = orig_height + + if key == "crop_coords_top_left": + crop_coord_top = 0 + crop_coord_left = 0 + + value_dict["crop_coords_top"] = crop_coord_top + value_dict["crop_coords_left"] = crop_coord_left + + if key == "aesthetic_score": + value_dict["aesthetic_score"] = 6.0 + value_dict["negative_aesthetic_score"] = 2.5 + + if key == "target_size_as_tuple": + value_dict["target_width"] = init_dict["target_width"] + value_dict["target_height"] = init_dict["target_height"] + + return value_dict + + +def sample( + model, + sampler, + prompt="A lush garden with oversized flowers and vibrant colors, inhabited by miniature animals.", + H=1024, + W=1024, + seed=0, + filter=None, +): + F = 8 + C = 4 + shape = (1, C, H // F, W // F) + + value_dict = init_embedder_options( + keys=get_unique_embedder_keys_from_conditioner(model.conditioner), + init_dict={ + "orig_width": W, + "orig_height": H, + "target_width": W, + "target_height": H, + }, + prompt=prompt, + ) + + if seed is None: + seed = torch.seed() + precision_scope = autocast + with torch.no_grad(): + with precision_scope("cuda"): + batch, batch_uc = get_batch( + get_unique_embedder_keys_from_conditioner(model.conditioner), + value_dict, + [1], + ) + c = model.conditioner(batch) + uc = None + randn = seeded_randn(shape, seed) + + def denoiser(input, sigma, c): + return model.denoiser( + model.model, + input, + sigma, + c, + ) + + samples_z = sampler(denoiser, randn, cond=c, uc=uc) + samples_x = model.decode_first_stage(samples_z) + samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0) + if filter is not None: + samples = filter(samples) + samples = ( + (255 * samples) + .to(dtype=torch.uint8) + .permute(0, 2, 3, 1) + .detach() + .cpu() + .numpy() + ) + return samples + + +def v_spacer(height) -> None: + for _ in range(height): + st.write("\n") + + +if __name__ == "__main__": + st.title("Turbo") + + head_cols = st.columns([1, 1, 1]) + with head_cols[0]: + version = st.selectbox("Model Version", list(VERSION2SPECS.keys()), 0) + version_dict = VERSION2SPECS[version] + + with head_cols[1]: + v_spacer(2) + if st.checkbox("Load Model"): + mode = "txt2img" + else: + mode = "skip" + + if mode != "skip": + state = init_st(version_dict, load_filter=True) + if state["msg"]: + st.info(state["msg"]) + model = state["model"] + load_model(model) + + # seed + if "seed" not in st.session_state: + st.session_state.seed = 0 + + def increment_counter(): + st.session_state.seed += 1 + + def decrement_counter(): + if st.session_state.seed > 0: + st.session_state.seed -= 1 + + with head_cols[2]: + n_steps = st.number_input(label="number of steps", min_value=1, max_value=4) + + sampler = SubstepSampler( + n_sample_steps=1, + num_steps=1000, + eta=1.0, + discretization_config=dict( + target="sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization" + ), + ) + sampler.n_sample_steps = n_steps + default_prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe." + prompt = st_keyup("Enter a value", value=default_prompt, debounce=300, key="interactive_text") + + cols = st.columns([1, 5, 1]) + if mode != "skip": + with cols[0]: + v_spacer(14) + st.button("↩", on_click=decrement_counter) + with cols[2]: + v_spacer(14) + st.button("↪", on_click=increment_counter) + + sampler.noise_sampler = SeededNoise(seed=st.session_state.seed) + out = sample( + model, sampler, H=512, W=512, seed=st.session_state.seed, prompt=prompt, filter=state.get("filter") + ) + with cols[1]: + st.image(out[0])