Upload app.py

app.py

@@ -0,0 +1,269 @@
+import cv2
+import einops
+import gradio as gr
+import numpy as np
+import torch
+
+from pytorch_lightning import seed_everything
+from util import resize_image, HWC3, apply_canny
+from ldm.models.diffusion.ddim import DDIMSampler
+from annotator.openpose import apply_openpose
+from cldm.model import create_model, load_state_dict
+from huggingface_hub import hf_hub_url, cached_download
+
+
+
+REPO_ID = "Thaweewat/ControlNet-Architecture"
+canny_checkpoint = "models/control_sd15_canny.pth"
+scribble_checkpoint = "models/control_sd15_scribble.pth"
+pose_checkpoint = "models/control_sd15_openpose.pth"
+
+
+canny_model = create_model('./models/cldm_v15.yaml').cpu()
+canny_model.load_state_dict(load_state_dict(cached_download(
+    hf_hub_url(REPO_ID, canny_checkpoint)
+), location='cpu'))
+canny_model = canny_model.cuda()
+ddim_sampler = DDIMSampler(canny_model)
+
+pose_model = create_model('./models/cldm_v15.yaml').cpu()
+pose_model.load_state_dict(load_state_dict(cached_download(
+    hf_hub_url(REPO_ID, pose_checkpoint)
+), location='cpu'))
+pose_model = pose_model.cuda()
+ddim_sampler_pose = DDIMSampler(pose_model)
+
+scribble_model = create_model('./models/cldm_v15.yaml').cpu()
+scribble_model.load_state_dict(load_state_dict(cached_download(
+    hf_hub_url(REPO_ID, scribble_checkpoint)
+), location='cpu'))
+scribble_model = scribble_model.cuda()
+ddim_sampler_scribble = DDIMSampler(scribble_model)
+
+save_memory = False
+
+def process(input_image, prompt, input_control, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold):
+    # TODO: Add other control tasks
+    if input_control == "Scribble":
+        return process_scribble(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta)
+    elif input_control == "Pose":
+        return process_pose(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, image_resolution, ddim_steps, scale, seed, eta)
+        
+    return process_canny(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold)
+
+def process_canny(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold):
+    with torch.no_grad():
+        img = resize_image(HWC3(input_image), image_resolution)
+        H, W, C = img.shape
+
+        detected_map = apply_canny(img, low_threshold, high_threshold)
+        detected_map = HWC3(detected_map)
+
+        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
+        control = torch.stack([control for _ in range(num_samples)], dim=0)
+        control = einops.rearrange(control, 'b h w c -> b c h w').clone()
+
+        seed_everything(seed)
+
+        if save_memory:
+            canny_model.low_vram_shift(is_diffusing=False)
+
+        cond = {"c_concat": [control], "c_crossattn": [canny_model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
+        un_cond = {"c_concat": [control], "c_crossattn": [canny_model.get_learned_conditioning([n_prompt] * num_samples)]}
+        shape = (4, H // 8, W // 8)
+
+        if save_memory:
+            canny_model.low_vram_shift(is_diffusing=False)
+
+        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
+                                                     shape, cond, verbose=False, eta=eta,
+                                                     unconditional_guidance_scale=scale,
+                                                     unconditional_conditioning=un_cond)
+
+        if save_memory:
+            canny_model.low_vram_shift(is_diffusing=False)
+            
+        x_samples = canny_model.decode_first_stage(samples)
+        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
+
+        results = [x_samples[i] for i in range(num_samples)]
+    return [255 - detected_map] + results
+    
+def process_scribble(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta):
+    with torch.no_grad():
+        img = resize_image(HWC3(input_image), image_resolution)
+        H, W, C = img.shape
+
+        detected_map = np.zeros_like(img, dtype=np.uint8)
+        detected_map[np.min(img, axis=2) < 127] = 255
+
+        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
+        control = torch.stack([control for _ in range(num_samples)], dim=0)
+        control = einops.rearrange(control, 'b h w c -> b c h w').clone()
+
+        seed_everything(seed)
+
+        if save_memory:
+            scribble_model.low_vram_shift(is_diffusing=False)
+
+        cond = {"c_concat": [control], "c_crossattn": [scribble_model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
+        un_cond = {"c_concat": [control], "c_crossattn": [scribble_model.get_learned_conditioning([n_prompt] * num_samples)]}
+        shape = (4, H // 8, W // 8)
+
+        if save_memory:
+            scribble_model.low_vram_shift(is_diffusing=False)
+            
+        samples, intermediates = ddim_sampler_scribble.sample(ddim_steps, num_samples,
+                                                     shape, cond, verbose=False, eta=eta,
+                                                     unconditional_guidance_scale=scale,
+                                                     unconditional_conditioning=un_cond)
+
+        if save_memory:
+            scribble_model.low_vram_shift(is_diffusing=False)
+                    
+        x_samples = scribble_model.decode_first_stage(samples)
+        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
+
+        results = [x_samples[i] for i in range(num_samples)]
+    return [255 - detected_map] + results
+
+def process_pose(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, scale, seed, eta):
+    with torch.no_grad():
+        input_image = HWC3(input_image)
+        detected_map, _ = apply_openpose(resize_image(input_image, detect_resolution))
+        detected_map = HWC3(detected_map)
+        img = resize_image(input_image, image_resolution)
+        H, W, C = img.shape
+
+        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
+
+        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
+        control = torch.stack([control for _ in range(num_samples)], dim=0)
+        control = einops.rearrange(control, 'b h w c -> b c h w').clone()
+
+        if seed == -1:
+            seed = random.randint(0, 65535)
+        seed_everything(seed)
+
+        if save_memory:
+            pose_model.low_vram_shift(is_diffusing=False)
+
+    
+        cond = {"c_concat": [control], "c_crossattn": [pose_model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
+        un_cond = {"c_concat": [control], "c_crossattn": [pose_model.get_learned_conditioning([n_prompt] * num_samples)]}
+        shape = (4, H // 8, W // 8)
+
+        if save_memory:
+            pose_model.low_vram_shift(is_diffusing=False)
+            
+        samples, intermediates = ddim_sampler_pose.sample(ddim_steps, num_samples,
+                                                     shape, cond, verbose=False, eta=eta,
+                                                     unconditional_guidance_scale=scale,
+                                                     unconditional_conditioning=un_cond)
+
+        if save_memory:
+            pose_model.low_vram_shift(is_diffusing=False)
+            
+        x_samples = pose_model.decode_first_stage(samples)
+        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
+
+        results = [x_samples[i] for i in range(num_samples)]
+    return [detected_map] + results
+    
+def create_canvas(w, h):
+    new_control_options = ["Interactive Scribble"]
+    return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
+
+    
+block = gr.Blocks().queue()
+control_task_list = [
+    "Canny Edge Map",
+    "Scribble", 
+    "Pose"
+]
+with block:
+    gr.Markdown("## Adding Conditional Control to Text-to-Image Diffusion Models")
+    gr.HTML('''
+     <p style="margin-bottom: 10px; font-size: 94%">
+                This is an unofficial demo for ControlNet, which is a neural network structure to control diffusion models by adding extra conditions such as canny edge detection. The demo is based on the <a href="https://github.com/lllyasviel/ControlNet" style="text-decoration: underline;" target="_blank"> Github </a> implementation. 
+              </p>
+              ''')
+    gr.HTML("<p>You can duplicate this Space to run it privately without a queue and load additional checkpoints.  : <a style='display:inline-block' href='https://huggingface.co/spaces/RamAnanth1/ControlNet?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a> <a style='display:inline-block' href='https://colab.research.google.com/github/camenduru/controlnet-colab/blob/main/controlnet-colab.ipynb'><img src = 'https://colab.research.google.com/assets/colab-badge.svg' alt='Open in Colab'></a></p>")
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(source='upload', type="numpy")
+            input_control = gr.Dropdown(control_task_list, value="Scribble", label="Control Task")
+            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=12, value=1, step=1)
+                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=256)
+                low_threshold = gr.Slider(label="Canny low threshold", minimum=1, maximum=255, value=100, step=1)
+                high_threshold = gr.Slider(label="Canny high threshold", minimum=1, maximum=255, value=200, step=1)
+                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
+                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
+                seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True)
+                eta = gr.Slider(label="eta (DDIM)", minimum=0.0,maximum =1.0, value=0.0, step=0.1)
+                a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
+                n_prompt = gr.Textbox(label="Negative Prompt",
+                                      value='longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality')
+        with gr.Column():
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
+    ips = [input_image, prompt, input_control, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold]
+    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
+    examples_list = [
+                [
+            "bird.png", 
+            "bird",
+            "Canny Edge Map",
+            "best quality, extremely detailed",
+            'longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality',
+             1, 
+            512,
+            20, 
+            9.0, 
+            123490213,
+            0.0, 
+            100, 
+            200
+            
+        ],
+        
+                [
+            "turtle.png", 
+            "turtle",
+            "Scribble",
+            "best quality, extremely detailed",
+            'longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality',
+             1, 
+            512,
+            20, 
+            9.0, 
+            123490213,
+            0.0,
+            100,
+            200
+            
+        ],
+                  [
+            "pose1.png", 
+            "Chef in the Kitchen",
+            "Pose",
+            "best quality, extremely detailed",
+            'longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality',
+             1, 
+            512,
+            20, 
+            9.0, 
+            123490213,
+            0.0,
+            100,
+            200
+            
+        ]
+    ]
+    examples = gr.Examples(examples=examples_list,inputs = [input_image, prompt, input_control, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold], outputs = [result_gallery], cache_examples = True, fn = process)
+    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=RamAnanth1.ControlNet)")  
+
+block.launch(debug = True)