Duplicate from estusgroup/ai-qr-generator-earlybeta

.gitattributes

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.gitignore

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+__pycache__
+venv
+gradio_cached_examples/

README.md

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+---
+title: AI QR Generator BETA V2
+emoji: null
+colorFrom: pink
+colorTo: pink
+sdk: gradio
+sdk_version: 3.35.2
+app_file: app.py
+pinned: false
+suggested_hardware: t4-medium
+startup_duration_timeout: 1h
+duplicated_from: estusgroup/ai-qr-generator-earlybeta
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+#derivative and edit from QR-code-AI-art-generator by patrickvonplaten - customized AND COPYRIGHTED UNDER COMMERCIAL LICENSE
+#ControlNet model is controlnet_qrcode-control_v1p_sd15
+#to do - remove stable diff 2 API and use a different model for generation for init image
+#add init image !!!
+#Change controlnetmodel 
+#--------------changelog-----------------
+#Changed all sizes to 512 to line with sd 1.5
+#removed examples
+#changed description text for model
+#changed sliders to set easier limits 
+#forced QR as INIT image temp
+#removed some options and sliders by commenting out, may reenable
+
+
+import torch
+import gradio as gr
+from PIL import Image
+import qrcode
+from pathlib import Path
+from multiprocessing import cpu_count
+import requests
+import io
+import os
+from PIL import Image
+
+from diffusers import (
+    StableDiffusionPipeline,
+    StableDiffusionControlNetImg2ImgPipeline,
+    ControlNetModel,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    DEISMultistepScheduler,
+    HeunDiscreteScheduler,
+    EulerDiscreteScheduler,
+)
+
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1"
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.content
+
+qrcode_generator = qrcode.QRCode(
+    version=1,
+    error_correction=qrcode.ERROR_CORRECT_H,
+    box_size=10,
+    border=4,
+)
+
+controlnet = ControlNetModel.from_pretrained(
+    "DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
+)
+
+pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    controlnet=controlnet,
+    safety_checker=None,
+    torch_dtype=torch.float16,
+).to("cuda")
+pipe.enable_xformers_memory_efficient_attention()
+
+
+
+def resize_for_condition_image(input_image: Image.Image, resolution: int = 512):
+    input_image = input_image.convert("RGB")
+    W, H = input_image.size
+    k = float(resolution) / min(H, W)
+    H *= k
+    W *= k
+    H = int(round(H / 32.0)) * 32
+    W = int(round(W / 32.0)) * 32
+    img = input_image.resize((W, H), resample=Image.LANCZOS)
+    return img
+
+
+SAMPLER_MAP = {
+    "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
+    "DPM++ Karras": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True),
+    "Heun": lambda config: HeunDiscreteScheduler.from_config(config),
+    "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
+    "DDIM": lambda config: DDIMScheduler.from_config(config),
+    "DEIS": lambda config: DEISMultistepScheduler.from_config(config),
+}
+
+
+def inference(
+    qr_code_content: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: float = 10.0,
+    controlnet_conditioning_scale: float = 2.0,
+    strength: float = 0.8,
+    seed: int = -1,
+    init_image: Image.Image | None = None,
+    qrcode_image: Image.Image | None = None,
+    use_qr_code_as_init_image = True,
+    sampler = "DDIM",
+):
+    if prompt is None or prompt == "":
+        raise gr.Error("Prompt is required")
+
+    if qrcode_image is None and qr_code_content == "":
+        raise gr.Error("QR Code Image or QR Code Content is required")
+
+    pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
+
+    generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
+
+    if qr_code_content != "" or qrcode_image.size == (1, 1):
+        print("Generating QR Code from content")
+        qr = qrcode.QRCode(
+            version=1,
+            error_correction=qrcode.constants.ERROR_CORRECT_H,
+            box_size=10,
+            border=4,
+        )
+        qr.add_data(qr_code_content)
+        qr.make(fit=True)
+
+        qrcode_image = qr.make_image(fill_color="black", back_color="white")
+        qrcode_image = resize_for_condition_image(qrcode_image, 512)
+    else:
+        print("Using QR Code Image")
+        qrcode_image = resize_for_condition_image(qrcode_image, 512)
+
+    # hack due to gradio examples
+    if use_qr_code_as_init_image:
+        init_image = qrcode_image
+    elif init_image is None or init_image.size == (1, 1):
+        print("Generating random image from prompt using Stable Diffusion 2.1 via Inference API")
+        # generate image from prompt
+        image_bytes = query({"inputs": prompt})
+        init_image = Image.open(io.BytesIO(image_bytes))
+    else:
+        print("Using provided init image")
+        init_image = resize_for_condition_image(init_image, 512)
+
+    #promptstart = ""
+    promptend = ", high quality, high resolution"
+    prompt += promptend 
+
+    negative_promptend = ", butt, nipple, nsfw, nude, nudity, naked"
+    negative_prompt += negative_promptend
+    
+    out = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        image=qrcode_image,
+        control_image=qrcode_image,  # type: ignore
+        width=512,  # type: ignore
+        height=512,  # type: ignore
+        guidance_scale=float(guidance_scale),
+        controlnet_conditioning_scale=float(controlnet_conditioning_scale),  # type: ignore
+        generator=generator,
+        strength=float(strength),
+        num_inference_steps=30,
+    )
+    return out.images[0]  # type: ignore
+
+#removed text 
+with gr.Blocks() as blocks:
+    gr.Markdown(
+        """
+====================================================EARLY BETA - PUBLIC ACCESS V1.02===========================================================================  
+        ***DISCLAIMER - By using this model you agree to waive any liability and are assuming all responsibility for generated images.***    
+                        ***IMAGES GENERATED BY THIS PUBLIC VERISON ARE NOT INTENDED FOR COMMERCIAL USE***
+
+First, type in what you want the QR code to look like. Use major subjects seperated by commas like the example below.
+Ex. Mountian, snow, morning, trees  
+  
+Then, type your QR code information such as a website link or enter your own QR code.  
+  
+Hit generate!  
+   
+   
+===========================================================CUSTOM SETTINGS====================================================================================    
+The default settings should work for testing. We are currently working on improving the model and offering more customization!   
+   
+QR High Pass - QR Passover -Change this to affect how much the QR code is overlayed to your image in a second pass.   
+**Higher setting is more QR code, lower setting is less QR code.**   
+   
+QR Initial Weight - this is the initial image - Change this to affect how much your image starts looking like a QR code!   
+**Higher settings mean your image starts with less QR, lower means the QR will appear sharper**  
+  
+Prompt Weight - This determines how much the AI "Listens" to your prompt and try to put what you described into your image.  
+**Lower means it is more absract and higher follows your direction more.**  
+  
+Seed - This is a randomizer! Use the same seed to generate the same image over and over. Change the seed to change up your image!  
+
+                """
+    )
+    prompt = gr.Textbox(
+    label="Prompt",
+    info="Prompt that describes your image - Ex. Mountian, snow, morning, trees",
+    )
+
+    negative_prompt = gr.Textbox(visible=True, label="Negative Prompt", 
+    info="Input things you don't want to see in your image for the model.",
+    value="poorly drawn, blurry image, deformed, low resolution, disfigured, low quality, blurry")
+
+    with gr.Row():
+        with gr.Column():
+            qr_code_content = gr.Textbox(
+                label="QR Code Content",
+                info="QR Code Content or URL",
+                value="",
+            )
+            with gr.Accordion(label="QR Code Image (Optional)", open=False):
+                qr_code_image = gr.Image(
+                    label="QR Code Image (Optional). Leave blank to automatically generate QR code",
+                    type="pil",
+                )
+
+            #negative_prompt = gr.Textbox(
+            #    label="Negative Prompt",
+            #    value="disfigured, low quality, blurry, nsfw",
+            #)
+
+            use_qr_code_as_init_image = gr.Checkbox(visible= False, label="QR Code is used as initial image.", value=True, interactive=False, info="Whether init image should be QR code. Unclick to pass init image or generate init image with Stable Diffusion 2.1")
+
+            with gr.Accordion(label="Init Images (Optional)", open=False, visible=False) as init_image_acc:
+                init_image = gr.Image(visible=False, label="Init Image (Optional). Leave blank to generate image with SD 2.1", type="pil")
+
+            #def change_view(qr_code_as_image: bool):
+            #    if not qr_code_as_image:
+            #        return {init_image_acc: gr.update(visible=True)}
+            #    else:
+            #        return {init_image_acc: gr.update(visible=False)}
+
+            #use_qr_code_as_init_image.change(change_view, inputs=[use_qr_code_as_init_image], outputs=[init_image_acc])
+
+            with gr.Accordion(
+                label="You can modify the generation slightly using the below sliders. See details above. \n ",
+                open=True,
+            ):
+                controlnet_conditioning_scale = gr.Slider(
+                    minimum=0.6,
+                    maximum=2.0,
+                    step=0.01,
+                    value=0.65,
+                    label="QR High Pass",
+                )
+                strength = gr.Slider(
+                    minimum=0.8, maximum=.95, step=0.01, value=0.9, label="QR Initial Weight"
+                )
+                guidance_scale = gr.Slider(
+                    minimum=5.0,
+                    maximum=15.0,
+                    step=0.25,
+                    value=7.5,
+                    label="Prompt Weight",
+                )
+                sampler = gr.Textbox(visible=False, value="DDIM") #gr.Dropdown(choices=list(SAMPLER_MAP.keys()), value="DPM++ Karras SDE")
+                seed = gr.Slider(
+                    minimum=-1,
+                    maximum=9999999999,
+                    step=1,
+                    value=2313123,
+                    label="Seed (-1 is Randomized)",
+                    randomize=True,
+                )
+            with gr.Row():
+                run_btn = gr.Button("Run")
+        with gr.Column():
+            result_image = gr.Image(label="Result Image")
+    run_btn.click(
+        inference,
+        inputs=[
+            qr_code_content,
+            prompt,
+            negative_prompt,
+            guidance_scale,
+            controlnet_conditioning_scale,
+            strength,
+            seed,
+            init_image,
+            qr_code_image,
+            use_qr_code_as_init_image,
+            sampler,
+        ],
+        outputs=[result_image],
+    )
+
+
+blocks.queue(concurrency_count=1, max_size=20)
+blocks.launch(share=False)

requirements.txt

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+diffusers
+transformers
+accelerate
+torch
+xformers
+gradio
+Pillow
+qrcode