Spaces:
Running
on
Zero
Running
on
Zero
upload img to s3
Browse files- app.py +41 -3
- requirements.txt +2 -1
app.py
CHANGED
@@ -17,6 +17,8 @@ from transformers import DPTFeatureExtractor, DPTForDepthEstimation, DPTImagePro
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from transformers import CLIPImageProcessor
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from diffusers.utils import load_image
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from gradio_imageslider import ImageSlider
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device = "cuda"
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base_model_id = "SG161222/RealVisXL_V4.0"
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@@ -82,9 +84,25 @@ def get_depth_map(image):
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return image
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@spaces.GPU(enable_queue=True)
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-
def process(image, image_url, prompt, n_prompt, num_steps, guidance_scale, control_strength, seed, progress=gr.Progress()):
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if image_url:
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orginal_image = load_image(image_url)
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@@ -106,7 +124,14 @@ def process(image, image_url, prompt, n_prompt, num_steps, guidance_scale, contr
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generator=generator,
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image=depth_image
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).images[0]
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-
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with gr.Blocks() as demo:
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@@ -128,6 +153,14 @@ with gr.Blocks() as demo:
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label="Negative prompt",
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value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
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)
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with gr.Column():
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result = ImageSlider(label="Generate image", type="pil", slider_color="pink")
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logs = gr.Textbox(label="logs")
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@@ -140,7 +173,12 @@ with gr.Blocks() as demo:
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num_steps,
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guidance_scale,
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control_strength,
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seed
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]
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run_button.click(
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fn=randomize_seed_fn,
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from transformers import CLIPImageProcessor
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from diffusers.utils import load_image
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from gradio_imageslider import ImageSlider
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import boto3
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from io import BytesIO
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device = "cuda"
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base_model_id = "SG161222/RealVisXL_V4.0"
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return image
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+
def upload_to_s3(image, region, access_key, secret_key, bucket_name):
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s3 = boto3.client(
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's3',
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region_name=region,
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aws_access_key_id=access_key,
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aws_secret_access_key=secret_key
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)
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image_key = f"generated_images/{random.randint(0, MAX_SEED)}.png"
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buffer = BytesIO()
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image.save(buffer, "PNG")
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buffer.seek(0)
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s3.upload_fileobj(buffer, bucket_name, image_key)
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return image_key
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@spaces.GPU(enable_queue=True)
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def process(image, image_url, prompt, n_prompt, num_steps, guidance_scale, control_strength, seed, upload_to_s3, region, access_key, secret_key, progress=gr.Progress()):
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if image_url:
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orginal_image = load_image(image_url)
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generator=generator,
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image=depth_image
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).images[0]
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+
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if upload_to_s3:
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url = upload_to_s3(generated_image, region, access_key, secret_key, bucket)
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result = {"status": "success", "url": url}
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else:
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result = {"status": "success", "message": "Image generated but not uploaded"}
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return [[depth_image, generated_image], json.dumps(result)]
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with gr.Blocks() as demo:
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label="Negative prompt",
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value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
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)
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upload_to_s3 = gr.Checkbox(label="Upload to S3", value=False)
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region = gr.Textbox(label="S3 Region", placeholder="Enter S3 region here")
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access_key = gr.Textbox(label="Access Key", placeholder="Enter S3 access key here")
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secret_key = gr.Textbox(label="Secret Key", placeholder="Enter S3 secret key here")
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bucket = gr.Textbox(label="Bucket Name", placeholder="Enter S3 bucket name here")
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with gr.Column():
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result = ImageSlider(label="Generate image", type="pil", slider_color="pink")
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logs = gr.Textbox(label="logs")
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num_steps,
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guidance_scale,
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control_strength,
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seed,
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upload_to_s3,
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region,
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access_key,
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secret_key,
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bucket
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]
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run_button.click(
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fn=randomize_seed_fn,
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requirements.txt
CHANGED
@@ -9,4 +9,5 @@ requests
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spaces
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huggingface_hub
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controlnet-aux
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-
safetensors
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spaces
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huggingface_hub
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controlnet-aux
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safetensors
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boto3
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