initial

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,8 +1,8 @@
 ---
 title: Multi Controlnet
-emoji: 💻
-colorFrom: blue
-colorTo: pink
+emoji: 😻
+colorFrom: green
+colorTo: gray
 sdk: gradio
 sdk_version: 3.20.1
 app_file: app.py

app.py

@@ -0,0 +1,66 @@
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from diffusers import UniPCMultistepScheduler
+import gradio as gr
+import torch
+
+# Constants
+low_threshold = 100
+high_threshold = 200
+
+# Models
+controlnet_pose = ControlNetModel.from_pretrained(
+    "lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16
+)
+controlnet_canny = ControlNetModel.from_pretrained(
+    "lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16
+)
+
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", 
+    controlnet=[controlnet_pose,controlnet_canny], 
+    safety_checker=None, torch_dtype=torch.float16
+)
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+# This command loads the individual model components on GPU on-demand. So, we don't
+# need to explicitly call pipe.to("cuda").
+pipe.enable_model_cpu_offload()
+
+# xformers
+pipe.enable_xformers_memory_efficient_attention()
+
+# Generator seed,
+generator = torch.manual_seed(0)
+
+def generate_images(pose_image, canny_image, prompt):
+    output = pipe(
+        prompt,
+        [pose_image, canny_image],
+        generator=generator,
+        num_images_per_prompt=3,
+        num_inference_steps=20,
+    )
+    all_outputs = []
+    all_outputs.append(pose_image, canny_image)
+    for image in output.images:
+        all_outputs.append(image)
+    return all_outputs
+
+
+gr.Interface(
+    generate_images,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Image(type="pil"),
+        gr.Textbox(
+            label="Enter your prompt",
+            max_lines=1,
+            placeholder="best quality, extremely detailed",
+        ),
+    ],
+    outputs=gr.Gallery().style(grid=[2], height="auto"),
+    title="Generate controlled outputs with Mult-ControlNet and Stable Diffusion using 🤗Diffusers",
+    description="This Space uses pose lines and canny edged image as the additional conditioning. Please refer to the \"Examples\" for what kind of images are appropriate.",
+    examples=[["sample_pose_body.png", "sample_canny_hand.png", "best quality, extremely detailed"]],
+    allow_flagging=False,
+).launch(enable_queue=True)

requirements.txt

@@ -0,0 +1,7 @@
+git+https://github.com/takuma104/diffusers.git@multi-controlnet-ext
+transformers
+git+https://github.com/huggingface/accelerate
+torch==1.13.1
+numpy
+#controlnet_aux
+xformers==0.0.16