Fix mim command

okotaku · okotaku · Jan 5, 2024 · Jan 5, 2024 · Jan 5, 2024 · Jan 5, 2024
commit c5d3ef3524ed8bd16ccd5ec7503cdad23128a3a4
diff --git a/.gitignore b/.gitignore
@@ -125,7 +125,6 @@ venv.bak/
 *.pkl.json
 *.log.json
 /work_dirs
-/mmcls/.mim
 .DS_Store
 
 # Pytorch
@@ -153,7 +152,6 @@ artifacts/
 wandb/
 data/
 dump/
-diffengine/.mim/
 build/
 .dist_test/
 .netrc

diff --git a/MANIFEST.in b/MANIFEST.in
@@ -1,11 +1,5 @@
-include diffengine/.mim/model-index.yml
-recursive-include diffengine/.mim/configs *.py *.yml
-recursive-include diffengine/.mim/tools *.sh *.py
-recursive-include diffengine/.mim/demo *.sh *.py
 recursive-exclude tests *
-recursive-exclude demo *
 recursive-exclude data *
-recursive-exclude configs *
 recursive-exclude docs *
 recursive-exclude work_dirs *
 recursive-exclude dist *

diff --git a/README.md b/README.md
@@ -42,7 +42,6 @@ Before installing DiffEngine, please ensure that PyTorch >= v2.0 has been succes
 Install DiffEngine
 
 ```
-pip install openmim
 pip install git+https://github.com/okotaku/diffengine.git
 ```
 
@@ -55,7 +54,7 @@ DiffEngine makes training easy through its pre-defined configs. These configs pr
 2. **Start Training**: Open a terminal and run the following command to start training with the selected config:
 
 ```bash
-mim train diffengine stable_diffusion_v15_dreambooth_lora_dog.py
+diffengine train stable_diffusion_v15_dreambooth_lora_dog.py
 ```
 
 3. **Monitor Progress and get results**: The training process will begin, and you can track its progress. The outputs of the training will be located in the `work_dirs/stable_diffusion_v15_dreambooth_lora_dog` directory, specifically when using the `stable_diffusion_v15_dreambooth_lora_dog` config.
@@ -319,7 +318,6 @@ Also, please check the following openmmlab and huggingface projects and the corr
 
 - [OpenMMLab](https://openmmlab.com/)
 - [HuggingFace](https://huggingface.co/)
-- [MIM](https://github.com/open-mmlab/mim): MIM Installs OpenMMLab Packages.
 
 ```
 @article{mmengine2022,

diff --git a/diffengine/configs/debias_estimation_loss/README.md b/diffengine/configs/debias_estimation_loss/README.md
@@ -21,12 +21,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/debias_estimation_loss/stable_diffusion_xl_pokemon_blip_debias_estimation_loss.py
+$ diffengine train stable_diffusion_xl_pokemon_blip_debias_estimation_loss
 ```
 
 ## Inference with diffusers

diff --git a/diffengine/configs/deepfloyd_if/README.md b/diffengine/configs/deepfloyd_if/README.md
@@ -21,12 +21,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/deepfloyd_if/deepfloyd_if_pokemon_blip.py
+$ diffengine train deepfloyd_if_pokemon_blip
 ```
 
 ## Inference with diffusers
@@ -36,9 +36,9 @@ Once you have trained a model, specify the path to the saved model and utilize i
 Before inferencing, we should convert weights for diffusers format,
 
 ```bash
-$ mim run diffengine publish_model2diffusers ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
+$ diffengine convert ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
 # Example
-$ mim run diffengine publish_model2diffusers configs/deepfloyd_if/deepfloyd_if_l_pokemon_blip.py work_dirs/deepfloyd_if_l_pokemon_blip/epoch_50.pth work_dirs/deepfloyd_if_l_pokemon_blip --save-keys unet
+$ diffengine convert deepfloyd_if_l_pokemon_blip work_dirs/deepfloyd_if_l_pokemon_blip/epoch_50.pth work_dirs/deepfloyd_if_l_pokemon_blip --save-keys unet
 ```
 
 Then we can run inference.
@@ -63,12 +63,6 @@ image = pipe(
 image.save('demo.png')
 ```
 
-We also provide inference demo scripts:
-
-```bash
-$ mim run diffengine demo_if "yoda pokemon" work_dirs/deepfloyd_if_l_pokemon_blip
-```
-
 ## Results Example
 
 #### deepfloyd_if_l_pokemon_blip

diff --git a/diffengine/configs/deepfloyd_if_dreambooth/README.md b/diffengine/configs/deepfloyd_if_dreambooth/README.md
@@ -27,12 +27,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/deepfloyd_if_dreambooth/deepfloyd_if_xl_dreambooth_lora_dog.py
+$ diffengine train deepfloyd_if_xl_dreambooth_lora_dog
 ```
 
 ## Inference with diffusers
@@ -67,12 +67,6 @@ image = pipe(
 image.save('demo.png')
 ```
 
-We also provide inference demo scripts:
-
-```bash
-$ mim run diffengine demo_if_lora "A photo of sks dog in a bucket" work_dirs/deepfloyd_if_xl_dreambooth_lora_dog/step999
-```
-
 ## Results Example
 
 #### deepfloyd_if_xl_dreambooth_lora_dog

diff --git a/diffengine/configs/distill_sd/README.md b/diffengine/configs/distill_sd/README.md
@@ -30,12 +30,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/distill_sd/small_sd_xl_pokemon_blip.py
+$ diffengine train small_sd_xl_pokemon_blip
 ```
 
 ## Inference with diffusers
@@ -45,9 +45,9 @@ Once you have trained a model, specify the path to the saved model and utilize i
 Before inferencing, we should convert weights for diffusers format,
 
 ```bash
-$ mim run diffengine publish_model2diffusers ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
+$ diffengine convert ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
 # Example
-$ mim run diffengine publish_model2diffusers configs/distill_sd/small_sd_xl_pokemon_blip.py work_dirs/small_sd_xl_pokemon_blip/epoch_50.pth work_dirs/small_sd_xl_pokemon_blip --save-keys unet
+$ diffengine convert small_sd_xl_pokemon_blip work_dirs/small_sd_xl_pokemon_blip/epoch_50.pth work_dirs/small_sd_xl_pokemon_blip --save-keys unet
 ```
 
 Then we can run inference.

diff --git a/diffengine/configs/distill_sd_dreambooth/README.md b/diffengine/configs/distill_sd_dreambooth/README.md
@@ -28,12 +28,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/distill_sd_dreambooth/small_sd_dreambooth_lora_dog.py
+$ diffengine train small_sd_dreambooth_lora_dog
 ```
 
 ## Training Speed
@@ -84,12 +84,6 @@ image = pipe(
 image.save('demo.png')
 ```
 
-We also provide inference demo scripts:
-
-```bash
-$ mim run diffengine demo_lora "A photo of sks dog in a bucket" work_dirs/small_sd_dreambooth_lora_dog/step999 --sdmodel segmind/small-sd
-```
-
 ## Results Example
 
 #### small_sd_dreambooth_lora_dog

diff --git a/diffengine/configs/esd/README.md b/diffengine/configs/esd/README.md
@@ -27,12 +27,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/esd/stable_diffusion_xl_gogh_esd.py
+$ diffengine train stable_diffusion_xl_gogh_esd
 ```
 
 ## Inference with diffusers
@@ -42,9 +42,9 @@ Once you have trained a model, specify the path to the saved model and utilize i
 Before inferencing, we should convert weights for diffusers format,
 
 ```bash
-$ mim run diffengine publish_model2diffusers ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
+$ diffengine convert ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
 # Example
-$ mim run diffengine publish_model2diffusers configs/esd/stable_diffusion_xl_gogh_esd.py w
+$ diffengine convert stable_diffusion_xl_gogh_esd w
 ork_dirs/stable_diffusion_xl_gogh_esd/iter_500.pth work_dirs/stable_diffusion_xl_gogh_esd --save-keys unet
 ```
 

diff --git a/diffengine/configs/input_perturbation/README.md b/diffengine/configs/input_perturbation/README.md
@@ -27,12 +27,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/input_perturbation/stable_diffusion_xl_pokemon_blip_input_perturbation.py
+$ diffengine train stable_diffusion_xl_pokemon_blip_input_perturbation
 ```
 
 ## Inference with diffusers

diff --git a/diffengine/configs/instruct_pix2pix/README.md b/diffengine/configs/instruct_pix2pix/README.md
@@ -27,12 +27,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/instruct_pix2pix/stable_diffusion_xl_instruct_pix2pix.py
+$ diffengine train stable_diffusion_xl_instruct_pix2pix
 ```
 
 ## Inference with diffusers
@@ -42,9 +42,9 @@ Once you have trained a model, specify the path to the saved model and utilize i
 Before inferencing, we should convert weights for diffusers format,
 
 ```bash
-$ mim run diffengine publish_model2diffusers ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
+$ diffengine convert ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
 # Example
-$ mim run diffengine publish_model2diffusers configs/instruct_pix2pix/stable_diffusion_xl_instruct_pix2pix.py work_dirs/stable_diffusion_xl_instruct_pix2pix/epoch_3.pth work_dirs/stable_diffusion_xl_instruct_pix2pix --save-keys unet
+$ diffengine convert stable_diffusion_xl_instruct_pix2pix work_dirs/stable_diffusion_xl_instruct_pix2pix/epoch_3.pth work_dirs/stable_diffusion_xl_instruct_pix2pix --save-keys unet
 ```
 
 Then we can run inference.

diff --git a/diffengine/configs/ip_adapter/README.md b/diffengine/configs/ip_adapter/README.md
@@ -27,12 +27,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/ip_adapter/stable_diffusion_xl_pokemon_blip_ip_adapter.py
+$ diffengine train stable_diffusion_xl_pokemon_blip_ip_adapter
 ```
 
 ## Inference with diffusers

diff --git a/diffengine/configs/kandinsky_v22/README.md b/diffengine/configs/kandinsky_v22/README.md
@@ -21,12 +21,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/kandinsky_v22/kandinsky_v22_prior_pokemon_blip.py
+$ diffengine train kandinsky_v22_prior_pokemon_blip
 ```
 
 ## Inference prior with diffusers

diff --git a/diffengine/configs/kandinsky_v3/README.md b/diffengine/configs/kandinsky_v3/README.md
@@ -29,12 +29,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/kandinsky_v3/kandinsky_v3_pokemon_blip.py
+$ diffengine train kandinsky_v3_pokemon_blip
 ```
 
 ## Inference prior with diffusers
@@ -44,10 +44,10 @@ Once you have trained a model, specify the path to the saved model and utilize i
 Before inferencing, we should convert weights for diffusers format,
 
 ```bash
-$ mim run diffengine publish_model2diffusers ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
+$ diffengine convert ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
 # Example
 # Note that when training colossalai, use `--colossalai` and set `INPUT_FILENAME` to index file.
-$ mim run diffengine publish_model2diffusers configs/kandinsky_v3/kandinsky_v3_pokemon_blip.py work_dirs/kandinsky_v3_pokemon_blip/epoch_50.pth/model/pytorch_model.bin.index.json work_dirs/kandinsky_v3_pokemon_blip --save-keys unet --colossalai
+$ diffengine convert kandinsky_v3_pokemon_blip work_dirs/kandinsky_v3_pokemon_blip/epoch_50.pth/model/pytorch_model.bin.index.json work_dirs/kandinsky_v3_pokemon_blip --save-keys unet --colossalai
 ```
 
 Then we can run inference.

diff --git a/diffengine/configs/lcm/README.md b/diffengine/configs/lcm/README.md
@@ -29,12 +29,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/lcm/lcm_xl_pokemon_blip.py
+$ diffengine train lcm_xl_pokemon_blip
 ```
 
 ## Inference with diffusers
@@ -44,9 +44,9 @@ Once you have trained a model, specify the path to the saved model and utilize i
 Before inferencing, we should convert weights for diffusers format,
 
 ```bash
-$ mim run diffengine publish_model2diffusers ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
+$ diffengine convert ${CONFIG_FILE} ${INPUT_FILENAME} ${OUTPUT_DIR} --save-keys ${SAVE_KEYS}
 # Example
-$ mim run diffengine publish_model2diffusers configs/lcm/lcm_xl_pokemon_blip.py work_dirs/lcm_xl_pokemon_blip/epoch_50.pth work_dirs/lcm_xl_pokemon_blip --save-keys unet
+$ diffengine convert lcm_xl_pokemon_blip work_dirs/lcm_xl_pokemon_blip/epoch_50.pth work_dirs/lcm_xl_pokemon_blip --save-keys unet
 ```
 
 Then we can run inference.

diff --git a/diffengine/configs/lcm_lora/README.md b/diffengine/configs/lcm_lora/README.md
@@ -27,12 +27,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/lcm_lora/lcm_xl_lora_pokemon_blip.py
+$ diffengine train lcm_xl_lora_pokemon_blip
 ```
 
 ## Inference with diffusers

diff --git a/diffengine/configs/loha/README.md b/diffengine/configs/loha/README.md
@@ -21,12 +21,12 @@ Run Training
 
 ```
 # single gpu
-$ mim train diffengine ${CONFIG_FILE}
+$ diffengine train ${CONFIG_FILE}
 # multi gpus
-$ mim train diffengine ${CONFIG_FILE} --gpus 2 --launcher pytorch
+$ NPROC_PER_NODE=${GPU_NUM} diffengine train ${CONFIG_FILE}
 
 # Example.
-$ mim train diffengine configs/stable_diffusion_xl_loha/stable_diffusion_xl_loha_pokemon_blip.py
+$ diffengine train stable_diffusion_xl_loha_pokemon_blip
 ```
 
 ## Inference with diffusers