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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 14 new columns ({'id', 'tags', 'modelVersions', 'poi', 'allowDerivatives', 'description', 'nsfw', 'allowCommercialUse', 'allowDifferentLicense', 'stats', 'type', 'allowNoCredit', 'name', 'creator'}) and 3 missing columns ({'Tokenizer', 'Model', 'Title'}).
This happened while the json dataset builder was generating data using
hf://datasets/crystantine/koleksiekstensi/miaoshouai-assistant/configs/controlnet.json (at revision 553ec588d84158dd0244bc58e86071ca602bb8a1)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id: int64
tags: list<item: null>
child 0, item: null
modelVersions: list<item: struct<baseModel: string, createdAt: string, description: string, earlyAccessTimeFrame: int64, files: list<item: struct<downloadUrl: string, format: string, hashes: struct<AutoV2: string, BLAKE3: string, CRC32: string, SHA256: string>, id: int64, name: string, pickleScanMessage: string, pickleScanResult: string, primary: bool, scannedAt: string, sizeKB: double, type: string, virusScanResult: string>>, id: int64, images: list<item: struct<url: string>>, modelId: int64, name: string, trainedWords: list<item: null>, updatedAt: string>>
child 0, item: struct<baseModel: string, createdAt: string, description: string, earlyAccessTimeFrame: int64, files: list<item: struct<downloadUrl: string, format: string, hashes: struct<AutoV2: string, BLAKE3: string, CRC32: string, SHA256: string>, id: int64, name: string, pickleScanMessage: string, pickleScanResult: string, primary: bool, scannedAt: string, sizeKB: double, type: string, virusScanResult: string>>, id: int64, images: list<item: struct<url: string>>, modelId: int64, name: string, trainedWords: list<item: null>, updatedAt: string>
child 0, baseModel: string
child 1, createdAt: string
child 2, description: string
child 3, earlyAccessTimeFrame: int64
child 4, files: list<item: struct<downloadUrl: string, format: string, hashes: struct<AutoV2: string, BLAKE3: string, CRC32: string, SHA256: string>, id: int64, name: string,
...
string
child 2, hashes: struct<AutoV2: string, BLAKE3: string, CRC32: string, SHA256: string>
child 0, AutoV2: string
child 1, BLAKE3: string
child 2, CRC32: string
child 3, SHA256: string
child 3, id: int64
child 4, name: string
child 5, pickleScanMessage: string
child 6, pickleScanResult: string
child 7, primary: bool
child 8, scannedAt: string
child 9, sizeKB: double
child 10, type: string
child 11, virusScanResult: string
child 5, id: int64
child 6, images: list<item: struct<url: string>>
child 0, item: struct<url: string>
child 0, url: string
child 7, modelId: int64
child 8, name: string
child 9, trainedWords: list<item: null>
child 0, item: null
child 10, updatedAt: string
poi: bool
allowDerivatives: bool
description: string
nsfw: bool
allowCommercialUse: string
allowDifferentLicense: bool
stats: struct<commentCount: int64, downloadCount: int64, favoriteCount: int64, rating: int64, ratingCount: int64>
child 0, commentCount: int64
child 1, downloadCount: int64
child 2, favoriteCount: int64
child 3, rating: int64
child 4, ratingCount: int64
type: string
allowNoCredit: bool
name: string
creator: struct<image: string, username: string>
child 0, image: string
child 1, username: string
to
{'Tokenizer': Value(dtype='string', id=None), 'Model': Value(dtype='string', id=None), 'Title': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 14 new columns ({'id', 'tags', 'modelVersions', 'poi', 'allowDerivatives', 'description', 'nsfw', 'allowCommercialUse', 'allowDifferentLicense', 'stats', 'type', 'allowNoCredit', 'name', 'creator'}) and 3 missing columns ({'Tokenizer', 'Model', 'Title'}).
This happened while the json dataset builder was generating data using
hf://datasets/crystantine/koleksiekstensi/miaoshouai-assistant/configs/controlnet.json (at revision 553ec588d84158dd0244bc58e86071ca602bb8a1)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Open a discussion for direct support.
Title
string | Tokenizer
string | Model
string | type
string | nsfw
bool | description
string | poi
bool | tags
sequence | id
int64 | allowDifferentLicense
bool | allowCommercialUse
string | creator
dict | modelVersions
list | stats
dict | allowNoCredit
bool | allowDerivatives
bool | name
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Gustavosta | gpt2 | Gustavosta/MagicPrompt-Dalle | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
FredZhang7 | distilgpt2 | FredZhang7/distilgpt2-stable-diffusion-v2 | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | Controlnet | false | <p>This repository hosts pruned .safetensors modules of ControlNet, by lllyasviel and T2I-Adapters, TencentARC Team
The modules are meant for this extension for AUTOMATIC1111/stable-diffusion-webui, but should work for different webuis too if they have it implemented. cheers!🥂</p></li></ol> | false | [] | 1 | true | Rent | {
"image": "https://huggingface.co/webui/ControlNet-modules-safetensors",
"username": "lllyasviel"
} | [
{
"baseModel": "SD 1.5",
"createdAt": "2023-02-06T13:52:34.605Z",
"description": "<p></p>",
"earlyAccessTimeFrame": 0,
"files": [
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_canny-fp16.safetensors",
"format": "Pth",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 1,
"name": "control_canny-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 161261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_depth-fp16.safetensors",
"format": "Pth",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 2,
"name": "control_depth-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": true,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 741261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_hed-fp16.safetensors",
"format": "Pth",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 3,
"name": "control_hed-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 741261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_mlsd-fp16.safetensors",
"format": "Pth",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 4,
"name": "control_mlsd-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 741261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_normal-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 5,
"name": "control_normal-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 741261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_openpose-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 6,
"name": "control_openpose-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 741261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_scribble-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 7,
"name": "control_scribble-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 741261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_seg-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 8,
"name": "control_seg-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 161261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_canny-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 9,
"name": "t2iadapter_canny-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 38261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_color-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 10,
"name": "t2iadapter_color-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 161261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_depth-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 11,
"name": "t2iadapter_depth-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 161261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_keypose-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 12,
"name": "t2iadapter_keypose-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 161261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_openpose-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 13,
"name": "t2iadapter_openpose-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 161261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_seg-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 14,
"name": "t2iadapter_seg-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 161261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_sketch-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 15,
"name": "t2iadapter_sketch-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 161261.130859375,
"type": "Model",
"virusScanResult": "Success"
},
{
"downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_style-fp16.safetensors",
"format": "Checkpoint",
"hashes": {
"AutoV2": "833E816807",
"BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266",
"CRC32": "87F68C12",
"SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E"
},
"id": 16,
"name": "t2iadapter_style-fp16.safetensors",
"pickleScanMessage": "No Pickle imports",
"pickleScanResult": "Success",
"primary": false,
"scannedAt": "2023-02-06T13:55:58.281Z",
"sizeKB": 78261.130859375,
"type": "Model",
"virusScanResult": "Success"
}
],
"id": 1,
"images": [
{
"url": "https://msdn.miaoshouai.com/msai/kt/ez/9900922.png"
}
],
"modelId": 7019,
"name": "Controlnet 1.0",
"trainedWords": [],
"updatedAt": "2023-02-06T13:52:34.605Z"
}
] | {
"commentCount": 7,
"downloadCount": 865,
"favoriteCount": 190,
"rating": 5,
"ratingCount": 2
} | true | true | Controlnet 1.0 |
null | null | null | Controlnet | false | <p>Stable Diffusion v2-1 Model Card\nThis model card focuses on the model associated with the Stable Diffusion v2-1 model, codebase available here.\n\nThis stable-diffusion-2-1 model is fine-tuned from stable-diffusion-2 (768-v-ema.ckpt) with an additional 55k steps on the same dataset (with punsafe=0.1), and then fine-tuned for another 155k extra steps with punsafe=0.98.\n\nUse it with the stablediffusion repository: download the v2-1_768-ema-pruned.ckpt here.\nUse it with \uD83E\uDDE8 diffusers\nModel Details\nDeveloped by: Robin Rombach, Patrick Esser\n\nModel type: Diffusion-based text-to-image generation model\n\nLanguage(s): English\n\nLicense: CreativeML Open RAIL++-M License\n\nModel Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H).\n\nResources for more information: GitHub Repository.</p></li></ol>",</p></li></ol> | false | [] | 2 | true | Rent | {
"image": "https://huggingface.co/lllyasviel/ControlNet-v1-1",
"username": "lllyasviel"
} | [
{
"baseModel": "SD 1.5",
"createdAt": "2023-02-06T13:52:34.605Z",
"description": "<p></p>",
"earlyAccessTimeFrame": 0,
"files": [
{
"downloadUrl": "https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p.pth",
"format": "Pth",
"hashes": {
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null | null | null | TextualInversion | false | <p>This is a collection of negative embeddings for Stable Diffusion negative prompts</p> | false | [] | 1 | true | Rent | {
"image": "",
"username": "multiple authors"
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null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | Checkpoint | false | <p>Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 🤗's Stable Diffusion blog.
The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling.
You can use this both with the 🧨Diffusers library and the RunwayML GitHub repository.</p></li></ol> | false | [
"horror",
"dzislaw beksinskidzi",
"kappa_neuro",
"paintings"
] | 1 | true | Rent | {
"image": "https://huggingface.co/runwayml/stable-diffusion-v1-5",
"username": "creativeml-openrail-m"
} | [
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null | null | null | Checkpoint | false | <p>Stable Diffusion v2-1 Model Card
This model card focuses on the model associated with the Stable Diffusion v2-1 model, codebase available here.
This stable-diffusion-2-1 model is fine-tuned from stable-diffusion-2 (768-v-ema.ckpt) with an additional 55k steps on the same dataset (with punsafe=0.1), and then fine-tuned for another 155k extra steps with punsafe=0.98.
Use it with the stablediffusion repository: download the v2-1_768-ema-pruned.ckpt here.
Use it with 🧨 diffusers
Model Details
Developed by: Robin Rombach, Patrick Esser
Model type: Diffusion-based text-to-image generation model
Language(s): English
License: CreativeML Open RAIL++-M License
Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H).
Resources for more information: GitHub Repository.</p></li></ol> | false | [] | 2 | true | Rent | {
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null | null | null | Checkpoint | false | SDXL consists of a two-step pipeline for latent diffusion: First, we use a base model to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. | false | [] | 4 | true | Rent | {
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null | null | null | VAE | false | <p>Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 🤗's Stable Diffusion blog.
The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling.
You can use this both with the 🧨Diffusers library and the RunwayML GitHub repository.</p></li></ol> | false | [] | 3 | true | Rent | {
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