Add depth2img Gradio demo

README.md

@@ -136,12 +136,18 @@ To augment the well-established [img2img](https://github.com/CompVis/stable-diff
 Note that the original method for image modification introduces significant semantic changes w.r.t. the initial image.
 If that is not desired, download our [depth-conditional stable diffusion](https://huggingface.co/stabilityai/stable-diffusion-2-depth) model and the `dpt_hybrid` MiDaS [model weights](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt), place the latter in a folder `midas_models` and sample via 
 ```
-python scripts/streamlit/depth2img.py streamlit run scripts/demo/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>
+python scripts/gradio/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>
+```
+
+or
+
+```
+streamlit run scripts/streamlit/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>
 ```
 
 This method can be used on the samples of the base model itself.
 For example, take [this sample](assets/stable-samples/depth2img/old_man.png) generated by an anonymous discord user.
-Using the [streamlit](https://streamlit.io/) script `depth2img.py`, the MiDaS model first infers a monocular depth estimate given this input, 
+Using the [gradio](https://gradio.app) or [streamlit](https://streamlit.io/) script `depth2img.py`, the MiDaS model first infers a monocular depth estimate given this input, 
 and the diffusion model is then conditioned on the (relative) depth output.
 
 <p align="center">

scripts/gradio/depth2img.py

@@ -0,0 +1,184 @@
+import sys
+import torch
+import numpy as np
+import gradio as gr
+from PIL import Image
+from omegaconf import OmegaConf
+from einops import repeat, rearrange
+from pytorch_lightning import seed_everything
+from imwatermark import WatermarkEncoder
+
+from scripts.txt2img import put_watermark
+from ldm.util import instantiate_from_config
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.data.util import AddMiDaS
+
+torch.set_grad_enabled(False)
+
+
+def initialize_model(config, ckpt):
+    config = OmegaConf.load(config)
+    model = instantiate_from_config(config.model)
+    model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False)
+
+    device = torch.device(
+        "cuda") if torch.cuda.is_available() else torch.device("cpu")
+    model = model.to(device)
+    sampler = DDIMSampler(model)
+    return sampler
+
+
+def make_batch_sd(
+        image,
+        txt,
+        device,
+        num_samples=1,
+        model_type="dpt_hybrid"
+):
+    image = np.array(image.convert("RGB"))
+    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+    # sample['jpg'] is tensor hwc in [-1, 1] at this point
+    midas_trafo = AddMiDaS(model_type=model_type)
+    batch = {
+        "jpg": image,
+        "txt": num_samples * [txt],
+    }
+    batch = midas_trafo(batch)
+    batch["jpg"] = rearrange(batch["jpg"], 'h w c -> 1 c h w')
+    batch["jpg"] = repeat(batch["jpg"].to(device=device),
+                          "1 ... -> n ...", n=num_samples)
+    batch["midas_in"] = repeat(torch.from_numpy(batch["midas_in"][None, ...]).to(
+        device=device), "1 ... -> n ...", n=num_samples)
+    return batch
+
+
+def paint(sampler, image, prompt, t_enc, seed, scale, num_samples=1, callback=None,
+          do_full_sample=False):
+    device = torch.device(
+        "cuda") if torch.cuda.is_available() else torch.device("cpu")
+    model = sampler.model
+    seed_everything(seed)
+
+    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
+    wm = "SDV2"
+    wm_encoder = WatermarkEncoder()
+    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
+
+    with torch.no_grad(),\
+            torch.autocast("cuda"):
+        batch = make_batch_sd(
+            image, txt=prompt, device=device, num_samples=num_samples)
+        z = model.get_first_stage_encoding(model.encode_first_stage(
+            batch[model.first_stage_key]))  # move to latent space
+        c = model.cond_stage_model.encode(batch["txt"])
+        c_cat = list()
+        for ck in model.concat_keys:
+            cc = batch[ck]
+            cc = model.depth_model(cc)
+            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
+                                                                                           keepdim=True)
+            display_depth = (cc - depth_min) / (depth_max - depth_min)
+            depth_image = Image.fromarray(
+                (display_depth[0, 0, ...].cpu().numpy() * 255.).astype(np.uint8))
+            cc = torch.nn.functional.interpolate(
+                cc,
+                size=z.shape[2:],
+                mode="bicubic",
+                align_corners=False,
+            )
+            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
+                                                                                           keepdim=True)
+            cc = 2. * (cc - depth_min) / (depth_max - depth_min) - 1.
+            c_cat.append(cc)
+        c_cat = torch.cat(c_cat, dim=1)
+        # cond
+        cond = {"c_concat": [c_cat], "c_crossattn": [c]}
+
+        # uncond cond
+        uc_cross = model.get_unconditional_conditioning(num_samples, "")
+        uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
+        if not do_full_sample:
+            # encode (scaled latent)
+            z_enc = sampler.stochastic_encode(
+                z, torch.tensor([t_enc] * num_samples).to(model.device))
+        else:
+            z_enc = torch.randn_like(z)
+        # decode it
+        samples = sampler.decode(z_enc, cond, t_enc, unconditional_guidance_scale=scale,
+                                 unconditional_conditioning=uc_full, callback=callback)
+        x_samples_ddim = model.decode_first_stage(samples)
+        result = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+        result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255
+    return [depth_image] + [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]
+
+
+def pad_image(input_image):
+    pad_w, pad_h = np.max(((2, 2), np.ceil(
+        np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
+    im_padded = Image.fromarray(
+        np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
+    return im_padded
+
+
+def predict(input_image, prompt, steps, num_samples, scale, seed, eta, strength):
+    init_image = input_image.convert("RGB")
+    image = pad_image(init_image)  # resize to integer multiple of 32
+
+    sampler.make_schedule(steps, ddim_eta=eta, verbose=True)
+    assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]'
+    do_full_sample = strength == 1.
+    t_enc = min(int(strength * steps), steps-1)
+    result = paint(
+        sampler=sampler,
+        image=image,
+        prompt=prompt,
+        t_enc=t_enc,
+        seed=seed,
+        scale=scale,
+        num_samples=num_samples,
+        callback=None,
+        do_full_sample=do_full_sample
+    )
+    return result
+
+
+sampler = initialize_model(sys.argv[1], sys.argv[2])
+
+block = gr.Blocks().queue()
+with block:
+    with gr.Row():
+        gr.Markdown("## Stable Diffusion Depth2Img")
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(source='upload', type="pil")
+            prompt = gr.Textbox(label="Prompt")
+            run_button = gr.Button(label="Run")
+            with gr.Accordion("Advanced options", open=False):
+                num_samples = gr.Slider(
+                    label="Images", minimum=1, maximum=4, value=1, step=1)
+                ddim_steps = gr.Slider(label="Steps", minimum=1,
+                                       maximum=50, value=50, step=1)
+                scale = gr.Slider(
+                    label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1
+                )
+                strength = gr.Slider(
+                    label="Strength", minimum=0.0, maximum=1.0, value=0.9, step=0.01
+                )
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=2147483647,
+                    step=1,
+                    randomize=True,
+                )
+                eta = gr.Number(label="eta (DDIM)", value=0.0)
+        with gr.Column():
+            gallery = gr.Gallery(label="Generated images", show_label=False).style(
+                grid=[2], height="auto")
+
+    run_button.click(fn=predict, inputs=[
+                     input_image, prompt, ddim_steps, num_samples, scale, seed, eta, strength], outputs=[gallery])
+
+
+block.launch()