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README.md

@@ -2,7 +2,7 @@
 title: Stable Depth2Image V2
 emoji: 🦷
 colorFrom: green
-colorTo: indigo
+colorTo: purple
 sdk: gradio
 sdk_version: 4.36.0
 app_file: app.py

app.py

@@ -1,123 +1,58 @@
 import gradio as gr
-import numpy as np
-import random
 import os
-from PIL import Image
+from diffusers.utils import load_image
 import spaces
-import torch
-from transformers import pipeline
-from diffusers import StableDiffusionDepth2ImgPipeline
+from panna import Depth2Image, DepthAnythingV2
 
-
-model_id_depth = "depth-anything/Depth-Anything-V2-Large-hf"
-if torch.cuda.is_available():
-    pipe_depth = pipeline(task="depth-estimation", model=model_id_depth, device="cuda")
-else:
-    pipe_depth = pipeline(task="depth-estimation", model=model_id_depth)
-model_id_depth2image = "stabilityai/stable-diffusion-2-depth"
-if torch.cuda.is_available():
-    pipe_depth2image = StableDiffusionDepth2ImgPipeline.from_pretrained(model_id_depth2image, torch_dtype=torch.float16).to("cuda")
-else:
-    pipe_depth2image = StableDiffusionDepth2ImgPipeline.from_pretrained(model_id_depth2image)
-max_seed = np.iinfo(np.int32).max
-max_image_size = 1344
-example_files = [os.path.join('assets/examples', filename) for filename in sorted(os.listdir('assets/examples'))]
+model_image = Depth2Image("stabilityai/stable-diffusion-2-depth")
+model_depth = DepthAnythingV2("depth-anything/Depth-Anything-V2-Large-hf")
+title = ("# [Depth2Image](https://huggingface.co/stabilityai/stable-diffusion-2-depth) with [DepthAnythingV2](https://huggingface.co/depth-anything/Depth-Anything-V2-Large-hf)\n"
+         "Depth2Image with depth map predicted by DepthAnything V2. The demo is part of [panna](https://github.com/abacws-abacus/panna) project.")
+example_files = []
+for n in range(10):
+    url = f"https://huggingface.co/spaces/depth-anything/Depth-Anything-V2/resolve/main/assets/examples/demo{n:0>2}.jpg"
+    load_image(url).save(os.path.basename(url))
+    example_files += [os.path.basename(url)]
 
 
 @spaces.GPU
-def infer(
-        init_image,
-        prompt,
-        negative_prompt,
-        seed,
-        randomize_seed,
-        width,
-        height,
-        guidance_scale,
-        num_inference_steps):
-    if randomize_seed:
-        seed = random.randint(0, max_seed)
-    init_image = Image.fromarray(np.uint8(init_image))
-    predicted_depth = pipe_depth(init_image)["predicted_depth"]
-    image = pipe_depth2image(
-        prompt=prompt,
-        image=init_image,
-        depth_map=predicted_depth,
-        negative_prompt=negative_prompt,
+def infer(init_image, prompt, negative_prompt, seed, width, height, guidance_scale, num_inference_steps):
+    depth = model_depth.image2depth([init_image])
+    return model_image.text2image(
+        [init_image],
+        depth_maps=depth,
+        prompt=[prompt],
+        negative_prompt=[negative_prompt],
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
         height=height,
         width=width,
-        generator=torch.Generator().manual_seed(seed)
-    ).images[0]
-    return image, seed
+        seed=seed
+    )[0]
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Demo [Depth2Image](https://huggingface.co/stabilityai/stable-diffusion-2-depth) with depth map estimated by [Depth Anything V2](https://huggingface.co/depth-anything/Depth-Anything-V2-Large-hf)")
+    gr.Markdown(title)
     with gr.Row():
-        prompt = gr.Text(
-            label="Prompt",
-            show_label=True,
-            max_lines=1,
-            placeholder="Enter your prompt",
-            container=False,
-        )
+        prompt = gr.Text(label="Prompt", show_label=True, max_lines=1, placeholder="Enter your prompt", container=False)
         run_button = gr.Button("Run", scale=0)
     with gr.Row():
-        init_image = gr.Image(label="Input Image", type='numpy')
+        init_image = gr.Image(label="Input Image", type='pil')
         result = gr.Image(label="Result")
     with gr.Accordion("Advanced Settings", open=False):
-        negative_prompt = gr.Text(
-            label="Negative Prompt",
-            max_lines=1,
-            placeholder="Enter a negative prompt",
-        )
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=max_seed,
-            step=1,
-            value=0,
-        )
-        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        negative_prompt = gr.Text(label="Negative Prompt", max_lines=1, placeholder="Enter a negative prompt")
+        seed = gr.Slider(label="Seed", minimum=0, maximum=1_000_000, step=1, value=0)
         with gr.Row():
-            width = gr.Slider(
-                label="Width",
-                minimum=256,
-                maximum=max_image_size,
-                step=64,
-                value=1024,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=256,
-                maximum=max_image_size,
-                step=64,
-                value=1024,
-            )
+            width = gr.Slider(label="Width", minimum=256, maximum=1344, step=64, value=1024)
+            height = gr.Slider(label="Height", minimum=256, maximum=1344, step=64, value=1024)
         with gr.Row():
-            guidance_scale = gr.Slider(
-                label="Guidance scale",
-                minimum=0.0,
-                maximum=10.0,
-                step=0.1,
-                value=7.5,
-            )
-            num_inference_steps = gr.Slider(
-                label="Number of inference steps",
-                minimum=1,
-                maximum=50,
-                step=1,
-                value=50,
-            )
+            guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=7.5)
+            num_inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=50)
+    examples = gr.Examples(examples=example_files, inputs=[init_image])
     gr.on(
         triggers=[run_button.click, prompt.submit, negative_prompt.submit],
         fn=infer,
-        inputs=[init_image, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result, seed]
-    )
-    examples = gr.Examples(
-        examples=example_files, inputs=[init_image], outputs=[result, seed]
+        inputs=[init_image, prompt, negative_prompt, seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result]
     )
-demo.queue().launch()
+demo.launch()

requirements.txt

@@ -1,6 +1,4 @@
 git+https://github.com/huggingface/diffusers.git
-transformers
 accelerate
 sentencepiece
-gradio==4.36.0
-torch
+panna