Seiichi Uchida, Takahiro Shirakawa
We lifted 9 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| univ-esuty/noisecollage | canonical | 5 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| batch_per_cosine_similarity | Ran | univ-esuty/noisecollage/evaluation/model.py pointer only (licence: NONE) · get_code("01ff60f53da84692") |
| batch_per_mse | Ran | univ-esuty/noisecollage/evaluation/model.py pointer only (licence: NONE) · get_code("1bd79f01f560a239") |
| eval_result | Ran | univ-esuty/noisecollage/gen_img_by_noisecollage-with-controlnet.py pointer only (licence: NONE) · get_code("06c26d7b1073325f") |
| get_activation | Ran | univ-esuty/noisecollage/diffusers/models/activations.py pointer only (licence: NONE) · get_code("125f7ee5f275ed2a") |
| rescale_noise_cfg | Ran | univ-esuty/noisecollage/pipeline_custom/pipeline_noise_collage_controlnet.py pointer only (licence: NONE) · get_code("bea2d776a332f2b0") |
| mse | Not yet run | univ-esuty/noisecollage/evaluation/model.py pointer only (licence: NONE) · get_code("edf5d54bc7a50c95") |
| text_encoder_attn_modules | Not yet run | univ-esuty/noisecollage/diffusers/loaders.py pointer only (licence: NONE) · get_code("f4ac91315a94774b") |
| text_encoder_lora_state_dict | Not yet run | univ-esuty/noisecollage/diffusers/loaders.py pointer only (licence: NONE) · get_code("2c476d130bd89887") |
| text_encoder_mlp_modules | Not yet run | univ-esuty/noisecollage/diffusers/loaders.py pointer only (licence: NONE) · get_code("ccca296c90beae91") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Layout-aware text-to-image generation is a task to generate multi-object images that reflect layout conditions in addition to text conditions. The current layout-aware textto-image diffusion models still have several issues, including mismatches between the text and layout conditions and quality degradation of generated images. This paper proposes a novel layout-aware text-to-image diffusion model called NoiseCollage to tackle these issues. During the denoising process, NoiseCollage independently estimates noises for individual objects and then crops and merges them into a single noise. This operation helps avoid condition mismatches; in other words, it can put the right objects in the right places. Qualitative and quantitative evaluations show that NoiseCollage outperforms several state-of-theart models. These successful results indicate that the cropand-merge operation of noises is a reasonable strategy to control image generation. We also show that NoiseCollage can be integrated with ControlNet to use edges, sketches, and pose skeletons as additional conditions. Experimental results show that this integration boosts the layout accuracy of ControlNet. The code is available at https: //github.com/univ-esuty/noisecollage.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.03485")
get_code_for_paper("2403.03485")
have("2403.03485")
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