SDXL-Turbo

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.

model_licenses/LICENSE-SDXL-Turbo

@@ -0,0 +1,58 @@
+STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE AGREEMENT        
+Dated: November 28, 2023
+
+
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requirements/pt2.txt

@@ -37,3 +37,4 @@ wandb>=0.15.6
 webdataset>=0.2.33
 wheel>=0.41.0
 xformers>=0.0.20
+streamlit-keyup==0.2.0

scripts/demo/turbo.py

@@ -0,0 +1,214 @@
+from streamlit_helpers import *
+from st_keyup import st_keyup
+from sgm.modules.diffusionmodules.sampling import EulerAncestralSampler
+
+VERSION2SPECS = {
+    "SDXL-Turbo": {
+        "H": 512,
+        "W": 512,
+        "C": 4,
+        "f": 8,
+        "is_legacy": False,
+        "config": "configs/inference/sd_xl_base.yaml",
+        "ckpt": "checkpoints/sd_xl_turbo_1.0.safetensors",
+    },
+}
+
+
+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])