Spaces:
Running
on
Zero
Running
on
Zero
update separate_cfg_infer,
Browse files
app.py
CHANGED
@@ -11,7 +11,7 @@ pipe = OmniGenPipeline.from_pretrained(
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@spaces.GPU(duration=180)
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# 示例处理函数:生成图像
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-
def generate_image(text, img1, img2, img3, height, width, guidance_scale, img_guidance_scale, inference_steps, seed):
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input_images = [img1, img2, img3]
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# 去除 None
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input_images = [img for img in input_images if img is not None]
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@@ -29,6 +29,7 @@ def generate_image(text, img1, img2, img3, height, width, guidance_scale, img_gu
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separate_cfg_infer=True, # set False can speed up the inference process
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use_kv_cache=False,
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seed=seed,
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)
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img = output[0]
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return img
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@@ -57,6 +58,7 @@ def get_example():
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1.6,
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50,
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0,
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],
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[
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"The woman in <img><|image_1|></img> waves her hand happily in the crowd",
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@@ -69,6 +71,7 @@ def get_example():
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1.9,
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50,
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128,
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],
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[
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"A man in a black shirt is reading a book. The man is the right man in <img><|image_1|></img>.",
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@@ -81,6 +84,7 @@ def get_example():
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1.6,
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50,
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0,
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],
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[
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"Two woman are raising fried chicken legs in a bar. A woman is <img><|image_1|></img>. The other woman is <img><|image_2|></img>.",
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@@ -93,6 +97,7 @@ def get_example():
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1.8,
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50,
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168,
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],
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[
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"A man and a short-haired woman with a wrinkled face are standing in front of a bookshelf in a library. The man is the man in the middle of <img><|image_1|></img>, and the woman is oldest woman in <img><|image_2|></img>",
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@@ -105,6 +110,7 @@ def get_example():
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1.6,
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50,
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60,
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],
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[
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"A man and a woman are sitting at a classroom desk. The man is the man with yellow hair in <img><|image_1|></img>. The woman is the woman on the left of <img><|image_2|></img>",
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@@ -117,6 +123,7 @@ def get_example():
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1.8,
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50,
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66,
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],
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[
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"The flower <img><|image_1|><\/img> is placed in the vase which is in the middle of <img><|image_2|><\/img> on a wooden table of a living room",
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@@ -129,6 +136,7 @@ def get_example():
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1.6,
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50,
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0,
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],
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[
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"<img><|image_1|><img>\n Remove the woman's earrings. Replace the mug with a clear glass filled with sparkling iced cola.",
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@@ -141,6 +149,7 @@ def get_example():
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1.6,
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50,
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222,
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],
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[
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"Detect the skeleton of human in this image: <img><|image_1|></img>.",
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@@ -153,6 +162,7 @@ def get_example():
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1.6,
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50,
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0,
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],
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[
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"Generate a new photo using the following picture and text as conditions: <img><|image_1|><img>\n A young boy is sitting on a sofa in the library, holding a book. His hair is neatly combed, and a faint smile plays on his lips, with a few freckles scattered across his cheeks. The library is quiet, with rows of shelves filled with books stretching out behind him.",
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@@ -165,6 +175,7 @@ def get_example():
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1.6,
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50,
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42,
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],
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[
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"Following the pose of this image <img><|image_1|><img>, generate a new photo: A young boy is sitting on a sofa in the library, holding a book. His hair is neatly combed, and a faint smile plays on his lips, with a few freckles scattered across his cheeks. The library is quiet, with rows of shelves filled with books stretching out behind him.",
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@@ -177,6 +188,7 @@ def get_example():
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1.6,
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50,
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123,
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],
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[
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"Following the depth mapping of this image <img><|image_1|><img>, generate a new photo: A young girl is sitting on a sofa in the library, holding a book. His hair is neatly combed, and a faint smile plays on his lips, with a few freckles scattered across his cheeks. The library is quiet, with rows of shelves filled with books stretching out behind him.",
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@@ -189,6 +201,7 @@ def get_example():
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1.6,
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50,
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1,
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],
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[
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"<img><|image_1|><\/img> What item can be used to see the current time? Please remove it.",
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@@ -201,6 +214,7 @@ def get_example():
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1.6,
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50,
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0,
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],
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[
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"According to the following examples, generate an output for the input.\nInput: <img><|image_1|></img>\nOutput: <img><|image_2|></img>\n\nInput: <img><|image_3|></img>\nOutput: ",
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@@ -213,12 +227,13 @@ def get_example():
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1.6,
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50,
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1,
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],
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]
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return case
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-
def run_for_examples(text, img1, img2, img3, height, width, guidance_scale, img_guidance_scale, inference_steps, seed):
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return generate_image(text, img1, img2, img3, height, width, guidance_scale, img_guidance_scale, inference_steps, seed)
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description = """
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OmniGen is a unified image generation model that you can use to perform various tasks, including but not limited to text-to-image generation, subject-driven generation, Identity-Preserving Generation, and image-conditioned generation.
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@@ -235,6 +250,8 @@ Tips:
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- For image editing tasks, we recommend placing the image before the editing instruction. For example, use `<img><|image_1|></img> remove suit`, rather than `remove suit <img><|image_1|></img>`.
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"""
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# Gradio 接口
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with gr.Blocks() as demo:
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gr.Markdown("# OmniGen: Unified Image Generation [paper](https://arxiv.org/abs/2409.11340) [code](https://github.com/VectorSpaceLab/OmniGen)")
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@@ -277,6 +294,10 @@ with gr.Blocks() as demo:
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label="Seed", minimum=0, maximum=2147483647, value=42, step=1
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)
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# 生成按钮
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generate_button = gr.Button("Generate Image")
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@@ -298,6 +319,7 @@ with gr.Blocks() as demo:
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img_guidance_scale_input,
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num_inference_steps,
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seed_input,
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],
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outputs=output_image,
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)
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@@ -316,6 +338,7 @@ with gr.Blocks() as demo:
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img_guidance_scale_input,
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num_inference_steps,
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seed_input,
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],
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outputs=output_image,
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)
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@spaces.GPU(duration=180)
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# 示例处理函数:生成图像
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+
def generate_image(text, img1, img2, img3, height, width, guidance_scale, img_guidance_scale, inference_steps, seed, separate_cfg_infer):
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input_images = [img1, img2, img3]
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# 去除 None
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input_images = [img for img in input_images if img is not None]
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separate_cfg_infer=True, # set False can speed up the inference process
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use_kv_cache=False,
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seed=seed,
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separate_cfg_infer=separate_cfg_infer,
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)
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img = output[0]
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return img
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1.6,
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50,
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0,
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+
False,
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],
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[
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"The woman in <img><|image_1|></img> waves her hand happily in the crowd",
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1.9,
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50,
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128,
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+
False,
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],
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[
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"A man in a black shirt is reading a book. The man is the right man in <img><|image_1|></img>.",
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1.6,
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50,
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0,
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+
False,
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],
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[
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"Two woman are raising fried chicken legs in a bar. A woman is <img><|image_1|></img>. The other woman is <img><|image_2|></img>.",
|
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1.8,
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50,
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168,
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+
False,
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],
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[
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"A man and a short-haired woman with a wrinkled face are standing in front of a bookshelf in a library. The man is the man in the middle of <img><|image_1|></img>, and the woman is oldest woman in <img><|image_2|></img>",
|
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1.6,
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50,
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60,
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+
False,
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],
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[
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"A man and a woman are sitting at a classroom desk. The man is the man with yellow hair in <img><|image_1|></img>. The woman is the woman on the left of <img><|image_2|></img>",
|
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1.8,
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50,
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66,
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+
False,
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],
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[
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"The flower <img><|image_1|><\/img> is placed in the vase which is in the middle of <img><|image_2|><\/img> on a wooden table of a living room",
|
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1.6,
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50,
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0,
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+
False,
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],
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[
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"<img><|image_1|><img>\n Remove the woman's earrings. Replace the mug with a clear glass filled with sparkling iced cola.",
|
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1.6,
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50,
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222,
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+
False,
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],
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[
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"Detect the skeleton of human in this image: <img><|image_1|></img>.",
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1.6,
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50,
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0,
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+
False,
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],
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[
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"Generate a new photo using the following picture and text as conditions: <img><|image_1|><img>\n A young boy is sitting on a sofa in the library, holding a book. His hair is neatly combed, and a faint smile plays on his lips, with a few freckles scattered across his cheeks. The library is quiet, with rows of shelves filled with books stretching out behind him.",
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1.6,
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50,
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177 |
42,
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+
False,
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],
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[
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"Following the pose of this image <img><|image_1|><img>, generate a new photo: A young boy is sitting on a sofa in the library, holding a book. His hair is neatly combed, and a faint smile plays on his lips, with a few freckles scattered across his cheeks. The library is quiet, with rows of shelves filled with books stretching out behind him.",
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1.6,
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50,
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123,
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+
False,
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],
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[
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"Following the depth mapping of this image <img><|image_1|><img>, generate a new photo: A young girl is sitting on a sofa in the library, holding a book. His hair is neatly combed, and a faint smile plays on his lips, with a few freckles scattered across his cheeks. The library is quiet, with rows of shelves filled with books stretching out behind him.",
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1.6,
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50,
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1,
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+
False,
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],
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[
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"<img><|image_1|><\/img> What item can be used to see the current time? Please remove it.",
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1.6,
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50,
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0,
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+
False,
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],
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[
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"According to the following examples, generate an output for the input.\nInput: <img><|image_1|></img>\nOutput: <img><|image_2|></img>\n\nInput: <img><|image_3|></img>\nOutput: ",
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1.6,
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50,
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1,
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+
False,
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],
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]
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return case
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+
def run_for_examples(text, img1, img2, img3, height, width, guidance_scale, img_guidance_scale, inference_steps, seed, separate_cfg_infer,):
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return generate_image(text, img1, img2, img3, height, width, guidance_scale, img_guidance_scale, inference_steps, seed, separate_cfg_infer,)
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description = """
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OmniGen is a unified image generation model that you can use to perform various tasks, including but not limited to text-to-image generation, subject-driven generation, Identity-Preserving Generation, and image-conditioned generation.
|
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- For image editing tasks, we recommend placing the image before the editing instruction. For example, use `<img><|image_1|></img> remove suit`, rather than `remove suit <img><|image_1|></img>`.
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"""
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+
separate_cfg_infer_arg = False
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+
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# Gradio 接口
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with gr.Blocks() as demo:
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gr.Markdown("# OmniGen: Unified Image Generation [paper](https://arxiv.org/abs/2409.11340) [code](https://github.com/VectorSpaceLab/OmniGen)")
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label="Seed", minimum=0, maximum=2147483647, value=42, step=1
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)
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separate_cfg_infer = gr.Checkbox(
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label="separate_cfg_infer", info="enable separate cfg infer"
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)
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# 生成按钮
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generate_button = gr.Button("Generate Image")
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img_guidance_scale_input,
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num_inference_steps,
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seed_input,
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separate_cfg_infer,
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],
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outputs=output_image,
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)
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img_guidance_scale_input,
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num_inference_steps,
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seed_input,
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separate_cfg_infer,
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],
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outputs=output_image,
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)
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