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82
+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
README.md CHANGED
@@ -1,420 +1,61 @@
1
- # Flux FP8 (true) Matmul Implementation with FastAPI
 
 
2
 
3
- This repository contains an implementation of the Flux model, along with an API that allows you to generate images based on text prompts. And also a simple single line of code to use the generator as a single object, similar to diffusers pipelines.
4
 
5
- ## Speed Comparison
6
 
7
- Note:
8
 
9
- - The "bfl codebase" refers to the original [BFL codebase](https://github.com/black-forest-labs/flux), not this repo.
10
- - The "fp8 wo quant" refers to the original BFL codebase using fp8 weight only quantization, not using fp8 matmul which is default in this repo.
11
- - The "compile blocks & extras" refers to the option within this repo setting the config values `"compile_blocks" true` & `"compile_extras": true`. ❌ means both were set to false, ✅ means both were set to true.
12
- - All generations which including a ❌ or ✅ are using this repo.
13
 
14
- | Resolution | Device | Test | Average it/s |
15
- | ---------- | ---------- | -------------------------- | ------------ |
16
- | 1024x1024 | RTX4090 | bfl codebase fp8 wo quant | 1.7 |
17
- | 1024x1024 | RTX4090 | ❌ compile blocks & extras | 2.55 |
18
- | 1024x1024 | RTX4090 | ✅ compile blocks & extras | 3.51 |
19
- | 1024x1024 | RTX4000ADA | ❌ compile blocks & extras | 0.79 |
20
- | 1024x1024 | RTX4000ADA | ✅ compile blocks & extras | 1.26 |
21
- | 1024x1024 | RTX6000ADA | bfl codebase | 1.74 |
22
- | 1024x1024 | RTX6000ADA | ❌ compile blocks & extras | 2.08 |
23
- | 1024x1024 | RTX6000ADA | ✅ compile blocks & extras | 2.8 |
24
- | 1024x1024 | H100 | ❌ compile blocks & extras | 6.1 |
25
- | 1024x1024 | H100 | ✅ compile blocks & extras | 11.5 |
26
- | 768x768 | RTX4090 | bfl codebase fp8 wo quant | 2.32 |
27
- | 768x768 | RTX4090 | ❌ compile blocks & extras | 4.47 |
28
- | 768x768 | RTX4090 | ✅ compile blocks & extras | 6.2 |
29
- | 768x768 | RTX4000 | ❌ compile blocks & extras | 1.41 |
30
- | 768x768 | RTX4000 | ✅ compile blocks & extras | 2.19 |
31
- | 768x768 | RTX6000ADA | bfl codebase | 3.01 |
32
- | 768x768 | RTX6000ADA | ❌ compile blocks & extras | 3.43 |
33
- | 768x768 | RTX6000ADA | ✅ compile blocks & extras | 4.46 |
34
- | 768x768 | H100 | ❌ compile blocks & extras | 10.3 |
35
- | 768x768 | H100 | ✅ compile blocks & extras | 20.8 |
36
- | 1024x720 | RTX4090 | bfl codebase fp8 wo quant | 3.01 |
37
- | 1024x720 | RTX4090 | ❌ compile blocks & extras | 3.6 |
38
- | 1024x720 | RTX4090 | ✅ compile blocks & extras | 4.96 |
39
- | 1024x720 | RTX4000 | ❌ compile blocks & extras | 1.14 |
40
- | 1024x720 | RTX4000 | ✅ compile blocks & extras | 1.78 |
41
- | 1024x720 | RTX6000ADA | bfl codebase | 2.37 |
42
- | 1024x720 | RTX6000ADA | ❌ compile blocks & extras | 2.87 |
43
- | 1024x720 | RTX6000ADA | ✅ compile blocks & extras | 3.78 |
44
- | 1024x720 | H100 | ❌ compile blocks & extras | 8.2 |
45
- | 1024x720 | H100 | ✅ compile blocks & extras | 15.7 |
46
 
47
- ## Table of Contents
48
-
49
- - [Installation](#installation)
50
- - [Usage](#usage)
51
- - [Configuration](#configuration)
52
- - [API Endpoints](#api-endpoints)
53
- - [Examples](#examples)
54
- - [License](https://github.com/aredden/flux-fp8-api/blob/main/LICENSE)
55
-
56
- ### Updates 08/24/24
57
-
58
- - Add config options for levels of quantization for the flow transformer:
59
- - `quantize_modulation`: Quantize the modulation layers in the flow model. If false, adds ~2GB vram usage for moderate precision improvements `(default: true)`
60
- - `quantize_flow_embedder_layers`: Quantize the flow embedder layers in the flow model. If false, adds ~512MB vram usage, but precision improves considerably. `(default: false)`
61
- - Override default config values when loading FluxPipeline, e.g. `FluxPipeline.load_pipeline_from_config_path(config_path, **config_overrides)`
62
-
63
- #### Fixes
64
-
65
- - Fix bug where loading text encoder from HF with bnb will error if device is not set to cuda:0
66
-
67
- **note:** prequantized flow models will only work with the specified quantization levels as when they were created. e.g. if you create a prequantized flow model with `quantize_modulation` set to false, it will only work with `quantize_modulation` set to false, same with `quantize_flow_embedder_layers`.
68
-
69
- ### Updates 08/25/24
70
-
71
- - Added LoRA loading functionality to FluxPipeline. Simple example:
72
-
73
- ```python
74
- from flux_pipeline import FluxPipeline
75
-
76
- config_path = "path/to/config/file.json"
77
- config_overrides = {
78
- #...
79
- }
80
-
81
- lora_path = "path/to/lora/file.safetensors"
82
-
83
- pipeline = FluxPipeline.load_pipeline_from_config_path(config_path, **config_overrides)
84
-
85
- pipeline.load_lora(lora_path, scale=1.0)
86
- ```
87
-
88
- ### Updates 09/07/24
89
-
90
- - Improve quality by ensuring that the RMSNorm layers use fp32
91
- - Raise the clamp range for single blocks & double blocks to +/-32000 to reduce deviation from expected outputs.
92
- - Make BF16 _not_ clamp, which improves quality and isn't needed because bf16 is the expected dtype for flux. **I would now recommend always using `"flow_dtype": "bfloat16"` in the config**, though it will slow things down on consumer gpus- but not by much at all since most of the compute still happens via fp8.
93
- - Allow for the T5 Model to be run without any quantization, by specifying `"text_enc_quantization_dtype": "bfloat16"` in the config - or also `"float16"`, though not recommended since t5 deviates a bit when running with float16. I noticed that even with qint8/qfloat8 there is a bit of deviation from bf16 text encoder outputs- so for those who want more accurate / expected text encoder outputs, you can use this option.
94
-
95
- ### Updates 10/3/24
96
-
97
- - #### Adding configurable clip model path
98
- Now you can specify the clip model's path in the config, using the `clip_path` parameter in a config file.
99
- - #### Improved lora loading
100
- I believe I have fixed the lora loading bug that was causing the lora to not apply properly, or when not all of the linear weights in the q/k/v/o had loras attached (it wouldn't be able to apply if only some of them did).
101
- - #### Lora loading via api endpoint
102
-
103
- You can now post to the `/lora` endpoint with a json file containing a `scale`, `path`, `name`, and `action` parameters.
104
-
105
- The `path` should be the path to the lora safetensors file either absolute or relative to the root of this repo.
106
-
107
- The `name` is an optional parameter, mainly just for checking purposes to see if the correct lora was being loaded, it's used as an identifier to check whether it's already been loaded or which lora to unload if `action` is `unload` (you can also use the exact same path which was loaded previously to unload the same lora).
108
-
109
- The `action` should be either `load` or `unload`, to load or unload the lora.
110
-
111
- The `scale` should be a float, which is the scale of the lora.
112
-
113
- e.g.
114
-
115
- ```json
116
- {
117
- <!-- If you have a lora directory like 'fluxloras' in the root of this repo -->
118
- "path": "./fluxloras/loras/aidmaImageUpgrader-FLUX-V0.2.safetensors",
119
- <!-- name is optional -->
120
- "name": "imgupgrade",
121
- <!-- action (load or unload) is required -->
122
- "action": "load",
123
- <!-- lora scale to use -->
124
- "scale": 0.6
125
- }
126
- ```
127
-
128
- ## Installation
129
-
130
- This repo _requires_ at least pytorch with cuda=12.4 and an ADA gpu with fp8 support, otherwise `torch._scaled_mm` will throw a CUDA error saying it's not supported. To install with conda/mamba:
131
-
132
- ```bash
133
- mamba create -n flux-fp8-matmul-api python=3.11 pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
134
- mamba activate flux-fp8-matmul-api
135
-
136
- # or with conda
137
- conda create -n flux-fp8-matmul-api python=3.11 pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
138
- conda activate flux-fp8-matmul-api
139
-
140
- # or with nightly... (which is what I am using) - also, just switch 'mamba' to 'conda' if you are using conda
141
- mamba create -n flux-fp8-matmul-api python=3.11 pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch-nightly -c nvidia
142
- mamba activate flux-fp8-matmul-api
143
-
144
- # or with pip
145
- python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
146
- # or pip nightly
147
- python -m pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124
148
- ```
149
-
150
- To install the required dependencies, run:
151
-
152
- ```bash
153
- python -m pip install -r requirements.txt
154
- ```
155
-
156
- If you get errors installing `torch-cublas-hgemm`, feel free to comment it out in requirements.txt, since it's not necessary, but will speed up inference for non-fp8 linear layers.
157
-
158
- ## Usage
159
-
160
- For a single ADA GPU with less than 24GB vram, and more than 16GB vram, you should use the `configs/config-dev-offload-1-4080.json` config file as a base, and then tweak the parameters to fit your needs. It offloads all models to CPU when not in use, compiles the flow model with extra optimizations, and quantizes the text encoder to nf4 and the autoencoder to qfloat8.
161
-
162
- For a single ADA GPU with more than ~32GB vram, you should use the `configs/config-dev-1-RTX6000ADA.json` config file as a base, and then tweak the parameters to fit your needs. It does not offload any models to CPU, compiles the flow model with extra optimizations, and quantizes the text encoder to qfloat8 and the autoencoder to stays as bfloat16.
163
-
164
- For a single 4090 GPU, you should use the `configs/config-dev-offload-1-4090.json` config file as a base, and then tweak the parameters to fit your needs. It offloads the text encoder and the autoencoder to CPU, compiles the flow model with extra optimizations, and quantizes the text encoder to nf4 and the autoencoder to float8.
165
-
166
- **NOTE:** For all of these configs, you must change the `ckpt_path`, `ae_path`, and `text_enc_path` parameters to the path to your own checkpoint, autoencoder, and text encoder.
167
-
168
- You can run the API server using the following command:
169
 
170
  ```bash
171
- python main.py --config-path <path_to_config> --port <port_number> --host <host_address>
172
  ```
173
 
174
- ### API Command-Line Arguments
175
-
176
- - `--config-path`: Path to the configuration file. If not provided, the model will be loaded from the command line arguments.
177
- - `--port`: Port to run the server on (default: 8088).
178
- - `--host`: Host to run the server on (default: 0.0.0.0).
179
- - `--flow-model-path`: Path to the flow model.
180
- - `--text-enc-path`: Path to the text encoder.
181
- - `--autoencoder-path`: Path to the autoencoder.
182
- - `--model-version`: Choose model version (`flux-dev` or `flux-schnell`).
183
- - `--flux-device`: Device to run the flow model on (default: cuda:0).
184
- - `--text-enc-device`: Device to run the text encoder on (default: cuda:0).
185
- - `--autoencoder-device`: Device to run the autoencoder on (default: cuda:0).
186
- - `--compile`: Compile the flow model with extra optimizations (default: False).
187
- - `--quant-text-enc`: Quantize the T5 text encoder to the given dtype (`qint4`, `qfloat8`, `qint2`, `qint8`, `bf16`), if `bf16`, will not quantize (default: `qfloat8`).
188
- - `--quant-ae`: Quantize the autoencoder with float8 linear layers, otherwise will use bfloat16 (default: False).
189
- - `--offload-flow`: Offload the flow model to the CPU when not being used to save memory (default: False).
190
- - `--no-offload-ae`: Disable offloading the autoencoder to the CPU when not being used to increase e2e inference speed (default: True [implies it will offload, setting this flag sets it to False]).
191
- - `--no-offload-text-enc`: Disable offloading the text encoder to the CPU when not being used to increase e2e inference speed (default: True [implies it will offload, setting this flag sets it to False]).
192
- - `--prequantized-flow`: Load the flow model from a prequantized checkpoint, which reduces the size of the checkpoint by about 50% & reduces startup time (default: False).
193
- - `--no-quantize-flow-modulation`: Disable quantization of the modulation layers in the flow transformer, which improves precision _moderately_ but adds ~2GB vram usage.
194
- - `--quantize-flow-embedder-layers`: Quantize the flow embedder layers in the flow transformer, reduces precision _considerably_ but saves ~512MB vram usage.
195
-
196
- ## Configuration
197
-
198
- The configuration files are located in the `configs` directory. You can specify different configurations for different model versions and devices.
199
-
200
- Example configuration file for a single 4090 (`configs/config-dev-offload-1-4090.json`):
201
-
202
- ```js
203
- {
204
- "version": "flux-dev", // or flux-schnell
205
- "params": {
206
- "in_channels": 64,
207
- "vec_in_dim": 768,
208
- "context_in_dim": 4096,
209
- "hidden_size": 3072,
210
- "mlp_ratio": 4.0,
211
- "num_heads": 24,
212
- "depth": 19,
213
- "depth_single_blocks": 38,
214
- "axes_dim": [16, 56, 56],
215
- "theta": 10000,
216
- "qkv_bias": true,
217
- "guidance_embed": true // if you are using flux-schnell, set this to false
218
- },
219
- "ae_params": {
220
- "resolution": 256,
221
- "in_channels": 3,
222
- "ch": 128,
223
- "out_ch": 3,
224
- "ch_mult": [1, 2, 4, 4],
225
- "num_res_blocks": 2,
226
- "z_channels": 16,
227
- "scale_factor": 0.3611,
228
- "shift_factor": 0.1159
229
- },
230
- "ckpt_path": "/your/path/to/flux1-dev.sft", // local path to original bf16 BFL flux checkpoint
231
- "ae_path": "/your/path/to/ae.sft", // local path to original bf16 BFL autoencoder checkpoint
232
- "repo_id": "black-forest-labs/FLUX.1-dev", // can ignore
233
- "repo_flow": "flux1-dev.sft", // can ignore
234
- "repo_ae": "ae.sft", // can ignore
235
- "text_enc_max_length": 512, // use 256 if you are using flux-schnell
236
- "text_enc_path": "city96/t5-v1_1-xxl-encoder-bf16", // or custom HF full bf16 T5EncoderModel repo id
237
- "text_enc_device": "cuda:0",
238
- "ae_device": "cuda:0",
239
- "flux_device": "cuda:0",
240
- "flow_dtype": "float16",
241
- "ae_dtype": "bfloat16",
242
- "text_enc_dtype": "bfloat16",
243
- "flow_quantization_dtype": "qfloat8", // will always be qfloat8, so can ignore
244
- "text_enc_quantization_dtype": "qint4", // choose between qint4, qint8, qfloat8, qint2 or delete entry for no quantization
245
- "ae_quantization_dtype": "qfloat8", // can either be qfloat8 or delete entry for no quantization
246
- "compile_extras": true, // compile the layers not included in the single-blocks or double-blocks
247
- "compile_blocks": true, // compile the single-blocks and double-blocks
248
- "offload_text_encoder": true, // offload the text encoder to cpu when not in use
249
- "offload_vae": true, // offload the autoencoder to cpu when not in use
250
- "offload_flow": false, // offload the flow transformer to cpu when not in use
251
- "prequantized_flow": false, // load the flow transformer from a prequantized checkpoint, which reduces the size of the checkpoint by about 50% & reduces startup time (default: false)
252
- "quantize_modulation": true, // quantize the modulation layers in the flow transformer, which reduces precision moderately but saves ~2GB vram usage (default: true)
253
- "quantize_flow_embedder_layers": false, // quantize the flow embedder layers in the flow transformer, if false, improves precision considerably at the cost of adding ~512MB vram usage (default: false)
254
- }
255
- ```
256
-
257
- The only things you should need to change in general are the:
258
-
259
- ```json5
260
- "ckpt_path": "/path/to/your/flux1-dev.sft", // path to your original BFL flow transformer (not diffusers)
261
- "ae_path": "/path/to/your/ae.sft", // path to your original BFL autoencoder (not diffusers)
262
- "text_enc_path": "path/to/your/t5-v1_1-xxl-encoder-bf16", // HF T5EncoderModel - can use "city96/t5-v1_1-xxl-encoder-bf16" for a simple to download version
263
- ```
264
-
265
- Other things to change can be the
266
-
267
- - `"text_enc_max_length": 512`
268
- max length for the text encoder, 256 if you are using flux-schnell
269
-
270
- - `"ae_quantization_dtype": "qfloat8"`
271
- quantization dtype for the autoencoder, can be `qfloat8` or delete entry for no quantization, will use the float8 linear layer implementation included in this repo.
272
-
273
- - `"text_enc_quantization_dtype": "qfloat8"`
274
- quantization dtype for the text encoder, if `qfloat8` or `qint2` will use quanto, `qint4`, `qint8` will use bitsandbytes
275
-
276
- - `"compile_extras": true,`
277
- compiles all modules that are not the single-blocks or double-blocks (default: false)
278
-
279
- - `"compile_blocks": true,`
280
- compiles all single-blocks and double-blocks (default: false)
281
-
282
- - `"text_enc_offload": false,`
283
- offload text encoder to cpu (default: false) - set to true if you only have a single 4090 and no other GPUs, otherwise you can set this to false and reduce latency [NOTE: this will be slow, if you have multiple GPUs, change the text_enc_device to a different device so you can set offloading for text_enc to false]
284
-
285
- - `"ae_offload": false,`
286
- offload autoencoder to cpu (default: false) - set to true if you only have a single 4090 and no other GPUs, otherwise you can set this to false and reduce latency [NOTE: this will be slow, if you have multiple GPUs, change the ae_device to a different device so you can set offloading for ae to false]
287
-
288
- - `"flux_offload": false,`
289
- offload flow transformer to cpu (default: false) - set to true if you only have a single 4090 and no other GPUs, otherwise you can set this to false and reduce latency [NOTE: this will be slow, if you have multiple GPUs, change the flux_device to a different device so you can set offloading for flux to false]
290
-
291
- - `"flux_device": "cuda:0",`
292
- device for flow transformer (default: cuda:0) - this gpu must have fp8 support and at least 16GB of memory, does not need to be the same as text_enc_device or ae_device
293
-
294
- - `"text_enc_device": "cuda:0",`
295
- device for text encoder (default: cuda:0) - set this to a different device - e.g. `"cuda:1"` if you have multiple gpus so you can set offloading for text_enc to false, does not need to be the same as flux_device or ae_device
296
-
297
- - `"ae_device": "cuda:0",`
298
- device for autoencoder (default: cuda:0) - set this to a different device - e.g. `"cuda:1"` if you have multiple gpus so you can set offloading for ae to false, does not need to be the same as flux_device or text_enc_device
299
-
300
- - `"prequantized_flow": false,`
301
- load the flow transformer from a prequantized checkpoint, which reduces the size of the checkpoint by about 50% & reduces startup time (default: false)
302
-
303
- - Note: MUST be a prequantized checkpoint created with the same quantization settings as the current config, and must have been quantized using this repo.
304
-
305
- - `"quantize_modulation": true,`
306
- quantize the modulation layers in the flow transformer, which improves precision at the cost of adding ~2GB vram usage (default: true)
307
-
308
- - `"quantize_flow_embedder_layers": false,`
309
- quantize the flow embedder layers in the flow transformer, which improves precision considerably at the cost of adding ~512MB vram usage (default: false)
310
-
311
- ## API Endpoints
312
-
313
- ### Generate Image
314
-
315
- - **URL**: `/generate`
316
- - **Method**: `POST`
317
- - **Request Body**:
318
-
319
- - `prompt` (str): The text prompt for image generation.
320
- - `width` (int, optional): The width of the generated image (default: 720).
321
- - `height` (int, optional): The height of the generated image (default: 1024).
322
- - `num_steps` (int, optional): The number of steps for the generation process (default: 24).
323
- - `guidance` (float, optional): The guidance scale for the generation process (default: 3.5).
324
- - `seed` (int, optional): The seed for random number generation.
325
- - `init_image` (str, optional): The base64 encoded image to be used as a reference for the generation process.
326
- - `strength` (float, optional): The strength of the diffusion process when image is provided (default: 1.0).
327
-
328
- - **Response**: A JPEG image stream.
329
-
330
- ## Examples
331
-
332
- ### Running the Server
333
-
334
- ```bash
335
- python main.py --config-path configs/config-dev-1-4090.json --port 8088 --host 0.0.0.0
336
- ```
337
-
338
- Or if you need more granular control over the all of the settings, you can run the server with something like this:
339
 
340
  ```bash
341
- python main.py --port 8088 --host 0.0.0.0 \
342
- --flow-model-path /path/to/your/flux1-dev.sft \
343
- --text-enc-path /path/to/your/t5-v1_1-xxl-encoder-bf16 \
344
- --autoencoder-path /path/to/your/ae.sft \
345
- --model-version flux-dev \
346
- --flux-device cuda:0 \
347
- --text-enc-device cuda:0 \
348
- --autoencoder-device cuda:0 \
349
- --compile \
350
- --quant-text-enc qfloat8 \
351
- --quant-ae
 
352
  ```
353
 
354
- ### Generating an image on a client
355
 
356
- Send a POST request to `http://<host>:<port>/generate` with the following JSON body:
357
 
358
- ```json
359
- {
360
- "prompt": "a beautiful asian woman in traditional clothing with golden hairpin and blue eyes, wearing a red kimono with dragon patterns",
361
- "width": 1024,
362
- "height": 1024,
363
- "num_steps": 24,
364
- "guidance": 3.0,
365
- "seed": 13456
366
- }
367
- ```
368
-
369
- For an example of how to generate from a python client using the FastAPI server:
370
 
371
- ```py
372
- import requests
373
- import io
374
 
375
- prompt = "a beautiful asian woman in traditional clothing with golden hairpin and blue eyes, wearing a red kimono with dragon patterns"
376
- res = requests.post(
377
- "http://localhost:8088/generate",
378
- json={
379
- "width": 1024,
380
- "height": 720,
381
- "num_steps": 20,
382
- "guidance": 4,
383
- "prompt": prompt,
384
- },
385
- stream=True,
386
- )
387
 
388
- with open(f"output.jpg", "wb") as f:
389
- f.write(io.BytesIO(res.content).read())
 
 
 
 
 
390
 
391
- ```
392
 
393
- You can also generate an image by directly importing the FluxPipeline class and using it to generate an image. This is useful if you have a custom model configuration and want to generate an image without having to run the server.
394
 
395
- ```py
396
- import io
397
- from flux_pipeline import FluxPipeline
398
 
399
-
400
- pipe = FluxPipeline.load_pipeline_from_config_path(
401
- "configs/config-dev-offload-1-4090.json" # or whatever your config is
402
- )
403
-
404
- output_jpeg_bytes: io.BytesIO = pipe.generate(
405
- # Required args:
406
- prompt="A beautiful asian woman in traditional clothing with golden hairpin and blue eyes, wearing a red kimono with dragon patterns",
407
- # Optional args:
408
- width=1024,
409
- height=1024,
410
- num_steps=20,
411
- guidance=3.5,
412
- seed=13456,
413
- init_image="path/to/your/init_image.jpg",
414
- strength=0.8,
415
- )
416
-
417
- with open("output.jpg", "wb") as f:
418
- f.write(output_jpeg_bytes.getvalue())
419
-
420
- ```
 
1
+ ---
2
+ license: apache-2.0
3
+ ---
4
 
5
+ # FLUX.1 [schnell] -- Flumina Server App (FP8 Version)
6
 
7
+ This repository contains an implementation of the FLUX.1 [schnell] [FP8 version](https://github.com/aredden/flux-fp8-api), which utilizes float8 numerics instead of bfloat16. This update allows for a 2x performance improvement, significantly speeding up inference tasks when deployed via Fireworks AI’s Flumina Server App toolkit.
8
 
9
+ ![Example output](example.png)
10
 
11
+ ## Getting Started -- Serverless deployment on Fireworks
 
 
 
12
 
13
+ This FP8 Server App is deployed to Fireworks as-is in a "serverless" deployment, offering high-speed, hassle-free performance.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
14
 
15
+ Grab an [API Key](https://fireworks.ai/account/api-keys) from Fireworks and set it in your environment variables:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
16
 
17
  ```bash
18
+ export API_KEY=YOUR_API_KEY_HERE
19
  ```
20
 
21
+ ### Text-to-Image Example Call
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
22
 
23
  ```bash
24
+ curl -X POST 'https://api.fireworks.ai/inference/v1/workflows/accounts/fireworks/models/flux-1-schnell-fp8/text_to_image' \
25
+ -H "Authorization: Bearer $API_KEY" \
26
+ -H "Content-Type: application/json" \
27
+ -H "Accept: image/jpeg" \
28
+ -d '{
29
+ "prompt": "Woman laying in the grass",
30
+ "aspect_ratio": "16:9",
31
+ "guidance_scale": 3.5,
32
+ "num_inference_steps": 4,
33
+ "seed": 0
34
+ }' \
35
+ --output output.jpg
36
  ```
37
 
38
+ ![Output of text-to-image](t2i_output.jpg)
39
 
40
+ ## What is Flumina?
41
 
42
+ Flumina is Fireworks.ai’s innovative platform for hosting Server Apps that lets users deploy deep learning inference to production environments in just minutes.
 
 
 
 
 
 
 
 
 
 
 
43
 
44
+ ## What does Flumina offer for FLUX models?
 
 
45
 
46
+ Flumina provides the following advantages for FLUX models:
 
 
 
 
 
 
 
 
 
 
 
47
 
48
+ * Clear, precise definitions of server-side workloads by reviewing the server app implementation (right here).
49
+ * Extensibility interface for dynamic loading and dispatching of add-ons server-side. For FLUX, this includes:
50
+ * ControlNet (Union) adapters
51
+ * LoRA adapters
52
+ * Off-the-shelf support for on-demand capacity scaling with Server Apps on Fireworks.
53
+ * Customization of the deployment logic through modifications to the Server App, with easy redeployment.
54
+ * Support for FP8 numerics, unlocking faster, more efficient inference capabilities.
55
 
56
+ ## Deploying FLUX.1 [schnell] FP8 Version to Fireworks On-Demand
57
 
 
58
 
59
+ ## Deploying Custom FLUX.1 [schnell] FP8 Apps to Fireworks On-demand
 
 
60
 
61
+ Coming soon!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
example.png ADDED

Git LFS Details

  • SHA256: cd39e24ad8376fb89e510950c222d45c3d20c63a5ab816df9b4f8142a407ed1b
  • Pointer size: 132 Bytes
  • Size of remote file: 2.9 MB
flumina.py CHANGED
@@ -95,7 +95,7 @@ def parse_accept_header(accept: str) -> str:
95
  r"(?P<media_type>[^;]+)(;q=(?P<q_factor>\d+(\.\d+)?))?", part.strip()
96
  )
97
  if match:
98
- media_type = match.group("media_type")
99
  q_factor = (
100
  float(match.group("q_factor")) if match.group("q_factor") else 1.0
101
  )
@@ -106,11 +106,19 @@ def parse_accept_header(accept: str) -> str:
106
  # Sort the media types by q-factor, descending
107
  sorted_types = sorted(weighted_types, key=lambda x: x[1], reverse=True)
108
 
 
 
 
109
  for media_type, _ in sorted_types:
110
- if media_type in {"image/jpeg", "image/png"}:
111
  return media_type
 
 
 
 
 
112
 
113
- raise ValueError(f"Accept header did not include ones of supported MIME types: image/jpeg, image/png")
114
 
115
 
116
  # Define request and response schemata
@@ -319,7 +327,7 @@ if __name__ == "__main__":
319
  body=Text2ImageRequest(
320
  prompt="test"
321
  ),
322
- accept="image/png"
323
  ))
324
  with open("out_image.png", "wb") as f:
325
  f.write(out)
 
95
  r"(?P<media_type>[^;]+)(;q=(?P<q_factor>\d+(\.\d+)?))?", part.strip()
96
  )
97
  if match:
98
+ media_type = match.group("media_type").strip()
99
  q_factor = (
100
  float(match.group("q_factor")) if match.group("q_factor") else 1.0
101
  )
 
106
  # Sort the media types by q-factor, descending
107
  sorted_types = sorted(weighted_types, key=lambda x: x[1], reverse=True)
108
 
109
+ # Define a list of supported MIME types
110
+ supported_types = ["image/jpeg", "image/png"]
111
+
112
  for media_type, _ in sorted_types:
113
+ if media_type in supported_types:
114
  return media_type
115
+ elif media_type == "*/*":
116
+ return supported_types[0] # Default to the first supported type
117
+ elif media_type == "image/*":
118
+ # If "image/*" is specified, return the first matching supported image type
119
+ return supported_types[0]
120
 
121
+ raise ValueError(f"Accept header did not include any supported MIME types: {supported_types}")
122
 
123
 
124
  # Define request and response schemata
 
327
  body=Text2ImageRequest(
328
  prompt="test"
329
  ),
330
+ accept="*/*"
331
  ))
332
  with open("out_image.png", "wb") as f:
333
  f.write(out)
t2i_output.jpg ADDED