Fluminato

.gitattributes

@@ -4,3 +4,4 @@ t5-v1_1-xxl-encoder-bf16//*.safetensor filter=lfs diff=lfs merge=lfs -text
 t5-v1_1-xxl-encoder-bf16//spiece.model filter=lfs diff=lfs merge=lfs -text
 t5-v1_1-xxl-encoder-bf16/model.safetensors filter=lfs diff=lfs merge=lfs -text
 flux1-schnell.safetensors filter=lfs diff=lfs merge=lfs -text
+example.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -1,201 +1,82 @@
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+**FLUX.1 \[dev\] Non-Commercial License**
+
+Black Forest Labs, Inc. (“**we**” or “**our**” or “**Company**”) is pleased to make available the weights, parameters and inference code for the FLUX.1 \[dev\] Model (as defined below) freely available for your non-commercial and non-production use as set forth in this FLUX.1 \[dev\] Non-Commercial License (“**License**”).  The “**FLUX.1 \[dev\] Model**” means the FLUX.1 \[dev\] text-to-image AI model and its elements which includes algorithms, software, checkpoints, parameters, source code (inference code, evaluation code, and if applicable, fine-tuning code) and any other materials associated with the FLUX.1 \[dev\] AI model made available by Company under this License, including if any, the technical documentation, manuals and instructions for the use and operation thereof (collectively, “**FLUX.1 \[dev\] Model**”).
+
+By downloading, accessing, use, Distributing (as defined below), or creating a Derivative (as defined below) of the FLUX.1 \[dev\] Model, you agree to the terms of this License. If you do not agree to this License, then you do not have any rights to access, use, Distribute or create a Derivative of the FLUX.1 \[dev\] Model and you must immediately cease using the FLUX.1 \[dev\] Model. If you are agreeing to be bound by the terms of this License on behalf of your employer or other entity, you represent and warrant to us that you have full legal authority to bind your employer or such entity to this License. If you do not have the requisite authority, you may not accept the License or access the FLUX.1 \[dev\] Model on behalf of your employer or other entity.
+
+1. **Definitions**. Capitalized terms used in this License but not defined herein have the following meanings:
+
+   1. “**Derivative**”  means any (i) modified version of the FLUX.1 \[dev\] Model (including but not limited to any customized or fine-tuned version thereof), (ii) work based on the FLUX.1 \[dev\] Model, or (iii) any other derivative work thereof. For the avoidance of doubt, Outputs are not considered Derivatives under this License.
+
+   1. “**Distribution**” or “**Distribute**” or “**Distributing**” means providing or making available, by any means, a copy of the FLUX.1 \[dev\] Models and/or the Derivatives as the case may be.
+
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+
+   1. “**Outputs**” means any content generated by the operation of the FLUX.1 \[dev\] Models or the Derivatives from a prompt (i.e., text instructions) provided by users. For the avoidance of doubt, Outputs do not include any components of a FLUX.1 \[dev\] Models, such as any fine-tuned versions of the FLUX.1 \[dev\] Models, the weights, or parameters.
+
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+
+   1. Non-Commercial Use Only.  You may only access, use, Distribute, or creative Derivatives of or the FLUX.1 \[dev\] Model or Derivatives for Non-Commercial Purposes.  If You want to use a FLUX.1 \[dev\] Model a Derivative for any purpose that is not expressly authorized under this License, such as for a commercial activity, you must request a license from Company, which Company may grant to you in Company’s sole discretion and which additional use may be subject to a fee, royalty or other revenue share. Please contact Company at the following e-mail address if you want to discuss such a license: [email protected].
+
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+
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+
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+
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+
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+
+   1. alter or remove copyright and other proprietary notices which appear on or in any portion of the FLUX.1 \[dev\] Model;
+
+   1. utilize any equipment, device, software, or other means to circumvent or remove any security or protection used by Company in connection with the FLUX.1 \[dev\] Model, or to circumvent or remove any usage restrictions, or to enable functionality disabled by FLUX.1 \[dev\] Model; or
+
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+
+   1. violate any applicable U.S. and non-U.S. export control and trade sanctions laws (“**Export Laws**”) in connection with your use or Distribution of any FLUX.1 \[dev\] Model;
+
+   1. directly or indirectly Distribute, export, or otherwise transfer FLUX.1 \[dev\] Model  (a) to any individual, entity, or country prohibited by Export Laws; (b) to anyone on U.S. or non-U.S. government restricted parties lists; or (c) for any purpose prohibited by Export Laws, including nuclear, chemical or biological weapons, or missile technology applications; 3\) use or download FLUX.1 \[dev\] Model if you or they are  (a) located in a comprehensively sanctioned jurisdiction, (b) currently listed on any U.S. or non-U.S. restricted parties list, or (c) for any purpose prohibited by Export Laws; and (4) will not disguise your location through IP proxying or other methods.
+
+1. **DISCLAIMERS**.  THE FLUX.1 \[dev\] MODEL IS PROVIDED “AS IS” AND “WITH ALL FAULTS” WITH NO WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. COMPANY EXPRESSLY DISCLAIMS ALL REPRESENTATIONS AND WARRANTIES, EXPRESS OR IMPLIED, WHETHER BY STATUTE, CUSTOM, USAGE OR OTHERWISE AS TO ANY MATTERS RELATED TO THE FLUX.1 \[dev\] MODEL, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, SATISFACTORY QUALITY, OR NON-INFRINGEMENT. COMPANY MAKES NO WARRANTIES OR REPRESENTATIONS THAT THE FLUX.1 \[dev\] MODEL WILL BE ERROR FREE OR FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS, OR PRODUCE ANY PARTICULAR RESULTS.
+
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+
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+
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+
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+
+   1. This License will automatically terminate upon any breach by you of the terms of this License.
+
+   1. We may terminate this License, in whole or in part, at any time upon notice (including electronic) to you.
+
+   1. If You initiate any legal action or proceedings against Company or any other entity (including a cross-claim or counterclaim in a lawsuit), alleging that the FLUX.1 \[dev\] Model or any Derivative, or any part thereof, infringe upon intellectual property or other rights owned or licensable by you, then any licenses granted to you under this License will immediately terminate as of the date such legal action or claim is filed or initiated.
+
+   1. Upon termination of this License, you must cease all use, access or Distribution of the FLUX.1 \[dev\] Model and any Derivatives.  The following sections survive termination of this License  2(c), 2(d), 4-11.
+
+1. **Third Party Materials**. The FLUX.1 \[dev\] Model may contain third-party software or other components (including free and open source software) (all of the foregoing, “**Third Party Materials**”), which are subject to the license terms of the respective third-party licensors. Your dealings or correspondence with third parties and your use of or interaction with any Third Party Materials are solely between you and the third party. Company does not control or endorse, and makes no representations or warranties regarding, any Third Party Materials, and your access to and use of such Third Party Materials are at your own risk.
+
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+
+1. **General**. This License will be governed and construed under the laws of the State of Delaware without regard to conflicts of law provisions. If any provision or part of a provision of this License is unlawful, void or unenforceable, that provision or part of the provision is deemed severed from this License, and will not affect the validity and enforceability of any remaining provisions. The failure of Company to exercise or enforce any right or provision of this License will not operate as a waiver of such right or provision. This License does not confer any third-party beneficiary rights upon any other person or entity. This License, together with the Documentation, contains the entire understanding between you and Company regarding the subject matter of this License, and supersedes all other written or oral agreements and understandings between you and Company regarding such subject matter. No change or addition to any provision of this License will be binding unless it is in writing and signed by an authorized representative of both you and Company.
+

README.md

@@ -1,420 +1,61 @@
-# Flux FP8 (true) Matmul Implementation with FastAPI
+---
+license: apache-2.0
+---
 
-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.
+# FLUX.1 [schnell] -- Flumina Server App (FP8 Version)
 
-## Speed Comparison
+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.
 
-Note:
+![Example output](example.png)
 
--   The "bfl codebase" refers to the original [BFL codebase](https://github.com/black-forest-labs/flux), not this repo.
--   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.
--   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.
--   All generations which including a ❌ or ✅ are using this repo.
+## Getting Started -- Serverless deployment on Fireworks
 
-| Resolution | Device     | Test                       | Average it/s |
-| ---------- | ---------- | -------------------------- | ------------ |
-| 1024x1024  | RTX4090    | bfl codebase fp8 wo quant  | 1.7          |
-| 1024x1024  | RTX4090    | ❌ compile blocks & extras | 2.55         |
-| 1024x1024  | RTX4090    | ✅ compile blocks & extras | 3.51         |
-| 1024x1024  | RTX4000ADA | ❌ compile blocks & extras | 0.79         |
-| 1024x1024  | RTX4000ADA | ✅ compile blocks & extras | 1.26         |
-| 1024x1024  | RTX6000ADA | bfl codebase               | 1.74         |
-| 1024x1024  | RTX6000ADA | ❌ compile blocks & extras | 2.08         |
-| 1024x1024  | RTX6000ADA | ✅ compile blocks & extras | 2.8          |
-| 1024x1024  | H100       | ❌ compile blocks & extras | 6.1          |
-| 1024x1024  | H100       | ✅ compile blocks & extras | 11.5         |
-| 768x768    | RTX4090    | bfl codebase fp8 wo quant  | 2.32         |
-| 768x768    | RTX4090    | ❌ compile blocks & extras | 4.47         |
-| 768x768    | RTX4090    | ✅ compile blocks & extras | 6.2          |
-| 768x768    | RTX4000    | ❌ compile blocks & extras | 1.41         |
-| 768x768    | RTX4000    | ✅ compile blocks & extras | 2.19         |
-| 768x768    | RTX6000ADA | bfl codebase               | 3.01         |
-| 768x768    | RTX6000ADA | ❌ compile blocks & extras | 3.43         |
-| 768x768    | RTX6000ADA | ✅ compile blocks & extras | 4.46         |
-| 768x768    | H100       | ❌ compile blocks & extras | 10.3         |
-| 768x768    | H100       | ✅ compile blocks & extras | 20.8         |
-| 1024x720   | RTX4090    | bfl codebase fp8 wo quant  | 3.01         |
-| 1024x720   | RTX4090    | ❌ compile blocks & extras | 3.6          |
-| 1024x720   | RTX4090    | ✅ compile blocks & extras | 4.96         |
-| 1024x720   | RTX4000    | ❌ compile blocks & extras | 1.14         |
-| 1024x720   | RTX4000    | ✅ compile blocks & extras | 1.78         |
-| 1024x720   | RTX6000ADA | bfl codebase               | 2.37         |
-| 1024x720   | RTX6000ADA | ❌ compile blocks & extras | 2.87         |
-| 1024x720   | RTX6000ADA | ✅ compile blocks & extras | 3.78         |
-| 1024x720   | H100       | ❌ compile blocks & extras | 8.2          |
-| 1024x720   | H100       | ✅ compile blocks & extras | 15.7         |
+This FP8 Server App is deployed to Fireworks as-is in a "serverless" deployment, offering high-speed, hassle-free performance.
 
-## Table of Contents
-
--   [Installation](#installation)
--   [Usage](#usage)
--   [Configuration](#configuration)
--   [API Endpoints](#api-endpoints)
--   [Examples](#examples)
--   [License](https://github.com/aredden/flux-fp8-api/blob/main/LICENSE)
-
-### Updates 08/24/24
-
--   Add config options for levels of quantization for the flow transformer:
-    -   `quantize_modulation`: Quantize the modulation layers in the flow model. If false, adds ~2GB vram usage for moderate precision improvements `(default: true)`
-    -   `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)`
--   Override default config values when loading FluxPipeline, e.g. `FluxPipeline.load_pipeline_from_config_path(config_path, **config_overrides)`
-
-#### Fixes
-
--   Fix bug where loading text encoder from HF with bnb will error if device is not set to cuda:0
-
-**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`.
-
-### Updates 08/25/24
-
--   Added LoRA loading functionality to FluxPipeline. Simple example:
-
-```python
-from flux_pipeline import FluxPipeline
-
-config_path = "path/to/config/file.json"
-config_overrides = {
-    #...
-}
-
-lora_path = "path/to/lora/file.safetensors"
-
-pipeline = FluxPipeline.load_pipeline_from_config_path(config_path, **config_overrides)
-
-pipeline.load_lora(lora_path, scale=1.0)
-```
-
-### Updates 09/07/24
-
--   Improve quality by ensuring that the RMSNorm layers use fp32
--   Raise the clamp range for single blocks & double blocks to +/-32000 to reduce deviation from expected outputs.
--   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.
--   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.
-
-### Updates 10/3/24
-
--   #### Adding configurable clip model path
-    Now you can specify the clip model's path in the config, using the `clip_path` parameter in a config file.
--   #### Improved lora loading
-    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).
--   #### Lora loading via api endpoint
-
-    You can now post to the `/lora` endpoint with a json file containing a `scale`, `path`, `name`, and `action` parameters.
-
-    The `path` should be the path to the lora safetensors file either absolute or relative to the root of this repo.
-
-    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).
-
-    The `action` should be either `load` or `unload`, to load or unload the lora.
-
-    The `scale` should be a float, which is the scale of the lora.
-
-    e.g.
-
-    ```json
-    {
-        <!-- If you have a lora directory like 'fluxloras' in the root of this repo -->
-        "path": "./fluxloras/loras/aidmaImageUpgrader-FLUX-V0.2.safetensors",
-        <!-- name is optional -->
-        "name": "imgupgrade",
-        <!-- action (load or unload) is required -->
-        "action": "load",
-        <!-- lora scale to use -->
-        "scale": 0.6
-    }
-    ```
-
-## Installation
-
-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:
-
-```bash
-mamba create -n flux-fp8-matmul-api python=3.11 pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
-mamba activate flux-fp8-matmul-api
-
-# or with conda
-conda create -n flux-fp8-matmul-api python=3.11 pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
-conda activate flux-fp8-matmul-api
-
-# or with nightly... (which is what I am using) - also, just switch 'mamba' to 'conda' if you are using conda
-mamba create -n flux-fp8-matmul-api python=3.11 pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch-nightly -c nvidia
-mamba activate flux-fp8-matmul-api
-
-# or with pip
-python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
-# or pip nightly
-python -m pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124
-```
-
-To install the required dependencies, run:
-
-```bash
-python -m pip install -r requirements.txt
-```
-
-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.
-
-## Usage
-
-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.
-
-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.
-
-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.
-
-**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.
-
-You can run the API server using the following command:
+Grab an [API Key](https://fireworks.ai/account/api-keys) from Fireworks and set it in your environment variables:
 
 ```bash
-python main.py --config-path <path_to_config> --port <port_number> --host <host_address>
+export API_KEY=YOUR_API_KEY_HERE
 ```
 
-### API Command-Line Arguments
-
--   `--config-path`: Path to the configuration file. If not provided, the model will be loaded from the command line arguments.
--   `--port`: Port to run the server on (default: 8088).
--   `--host`: Host to run the server on (default: 0.0.0.0).
--   `--flow-model-path`: Path to the flow model.
--   `--text-enc-path`: Path to the text encoder.
--   `--autoencoder-path`: Path to the autoencoder.
--   `--model-version`: Choose model version (`flux-dev` or `flux-schnell`).
--   `--flux-device`: Device to run the flow model on (default: cuda:0).
--   `--text-enc-device`: Device to run the text encoder on (default: cuda:0).
--   `--autoencoder-device`: Device to run the autoencoder on (default: cuda:0).
--   `--compile`: Compile the flow model with extra optimizations (default: False).
--   `--quant-text-enc`: Quantize the T5 text encoder to the given dtype (`qint4`, `qfloat8`, `qint2`, `qint8`, `bf16`), if `bf16`, will not quantize (default: `qfloat8`).
--   `--quant-ae`: Quantize the autoencoder with float8 linear layers, otherwise will use bfloat16 (default: False).
--   `--offload-flow`: Offload the flow model to the CPU when not being used to save memory (default: False).
--   `--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]).
--   `--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]).
--   `--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).
--   `--no-quantize-flow-modulation`: Disable quantization of the modulation layers in the flow transformer, which improves precision _moderately_ but adds ~2GB vram usage.
--   `--quantize-flow-embedder-layers`: Quantize the flow embedder layers in the flow transformer, reduces precision _considerably_ but saves ~512MB vram usage.
-
-## Configuration
-
-The configuration files are located in the `configs` directory. You can specify different configurations for different model versions and devices.
-
-Example configuration file for a single 4090 (`configs/config-dev-offload-1-4090.json`):
-
-```js
-{
-    "version": "flux-dev", // or flux-schnell
-    "params": {
-        "in_channels": 64,
-        "vec_in_dim": 768,
-        "context_in_dim": 4096,
-        "hidden_size": 3072,
-        "mlp_ratio": 4.0,
-        "num_heads": 24,
-        "depth": 19,
-        "depth_single_blocks": 38,
-        "axes_dim": [16, 56, 56],
-        "theta": 10000,
-        "qkv_bias": true,
-        "guidance_embed": true // if you are using flux-schnell, set this to false
-    },
-    "ae_params": {
-        "resolution": 256,
-        "in_channels": 3,
-        "ch": 128,
-        "out_ch": 3,
-        "ch_mult": [1, 2, 4, 4],
-        "num_res_blocks": 2,
-        "z_channels": 16,
-        "scale_factor": 0.3611,
-        "shift_factor": 0.1159
-    },
-    "ckpt_path": "/your/path/to/flux1-dev.sft", // local path to original bf16 BFL flux checkpoint
-    "ae_path": "/your/path/to/ae.sft", // local path to original bf16 BFL autoencoder checkpoint
-    "repo_id": "black-forest-labs/FLUX.1-dev", // can ignore
-    "repo_flow": "flux1-dev.sft", // can ignore
-    "repo_ae": "ae.sft", // can ignore
-    "text_enc_max_length": 512, // use 256 if you are using flux-schnell
-    "text_enc_path": "city96/t5-v1_1-xxl-encoder-bf16", // or custom HF full bf16 T5EncoderModel repo id
-    "text_enc_device": "cuda:0",
-    "ae_device": "cuda:0",
-    "flux_device": "cuda:0",
-    "flow_dtype": "float16",
-    "ae_dtype": "bfloat16",
-    "text_enc_dtype": "bfloat16",
-    "flow_quantization_dtype": "qfloat8", // will always be qfloat8, so can ignore
-    "text_enc_quantization_dtype": "qint4", // choose between qint4, qint8, qfloat8, qint2 or delete entry for no quantization
-    "ae_quantization_dtype": "qfloat8", // can either be qfloat8 or delete entry for no quantization
-    "compile_extras": true, // compile the layers not included in the single-blocks or double-blocks
-    "compile_blocks": true, // compile the single-blocks and double-blocks
-    "offload_text_encoder": true, // offload the text encoder to cpu when not in use
-    "offload_vae": true, // offload the autoencoder to cpu when not in use
-    "offload_flow": false, // offload the flow transformer to cpu when not in use
-    "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)
-    "quantize_modulation": true, // quantize the modulation layers in the flow transformer, which reduces precision moderately but saves ~2GB vram usage (default: true)
-    "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)
-}
-```
-
-The only things you should need to change in general are the:
-
-```json5
-    "ckpt_path": "/path/to/your/flux1-dev.sft", // path to your original BFL flow transformer (not diffusers)
-    "ae_path": "/path/to/your/ae.sft", // path to your original BFL autoencoder (not diffusers)
-    "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
-```
-
-Other things to change can be the
-
--   `"text_enc_max_length": 512`
-    max length for the text encoder, 256 if you are using flux-schnell
-
--   `"ae_quantization_dtype": "qfloat8"`
-    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.
-
--   `"text_enc_quantization_dtype": "qfloat8"`
-    quantization dtype for the text encoder, if `qfloat8` or `qint2` will use quanto, `qint4`, `qint8` will use bitsandbytes
-
--   `"compile_extras": true,`
-    compiles all modules that are not the single-blocks or double-blocks (default: false)
-
--   `"compile_blocks": true,`
-    compiles all single-blocks and double-blocks (default: false)
-
--   `"text_enc_offload": false,`
-    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]
-
--   `"ae_offload": false,`
-    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]
-
--   `"flux_offload": false,`
-    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]
-
--   `"flux_device": "cuda:0",`
-    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
-
--   `"text_enc_device": "cuda:0",`
-    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
-
--   `"ae_device": "cuda:0",`
-    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
-
--   `"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)
-
-        - Note: MUST be a prequantized checkpoint created with the same quantization settings as the current config, and must have been quantized using this repo.
-
--   `"quantize_modulation": true,`
-    quantize the modulation layers in the flow transformer, which improves precision at the cost of adding ~2GB vram usage (default: true)
-
--   `"quantize_flow_embedder_layers": false,`
-    quantize the flow embedder layers in the flow transformer, which improves precision considerably at the cost of adding ~512MB vram usage (default: false)
-
-## API Endpoints
-
-### Generate Image
-
--   **URL**: `/generate`
--   **Method**: `POST`
--   **Request Body**:
-
-    -   `prompt` (str): The text prompt for image generation.
-    -   `width` (int, optional): The width of the generated image (default: 720).
-    -   `height` (int, optional): The height of the generated image (default: 1024).
-    -   `num_steps` (int, optional): The number of steps for the generation process (default: 24).
-    -   `guidance` (float, optional): The guidance scale for the generation process (default: 3.5).
-    -   `seed` (int, optional): The seed for random number generation.
-    -   `init_image` (str, optional): The base64 encoded image to be used as a reference for the generation process.
-    -   `strength` (float, optional): The strength of the diffusion process when image is provided (default: 1.0).
-
--   **Response**: A JPEG image stream.
-
-## Examples
-
-### Running the Server
-
-```bash
-python main.py --config-path configs/config-dev-1-4090.json --port 8088 --host 0.0.0.0
-```
-
-Or if you need more granular control over the all of the settings, you can run the server with something like this:
+### Text-to-Image Example Call
 
 ```bash
-python main.py --port 8088 --host 0.0.0.0 \
-    --flow-model-path /path/to/your/flux1-dev.sft \
-    --text-enc-path /path/to/your/t5-v1_1-xxl-encoder-bf16 \
-    --autoencoder-path /path/to/your/ae.sft \
-    --model-version flux-dev \
-    --flux-device cuda:0 \
-    --text-enc-device cuda:0 \
-    --autoencoder-device cuda:0 \
-    --compile \
-    --quant-text-enc qfloat8 \
-    --quant-ae
+curl -X POST 'https://api.fireworks.ai/inference/v1/workflows/accounts/fireworks/models/flux-1-schnell-fp8/text_to_image' \
+    -H "Authorization: Bearer $API_KEY" \
+    -H "Content-Type: application/json" \
+    -H "Accept: image/jpeg" \
+    -d '{
+        "prompt": "Woman laying in the grass",
+        "aspect_ratio": "16:9",
+        "guidance_scale": 3.5,
+        "num_inference_steps": 4,
+        "seed": 0
+    }' \
+    --output output.jpg
 ```
 
-### Generating an image on a client
+![Output of text-to-image](t2i_output.jpg)
 
-Send a POST request to `http://<host>:<port>/generate` with the following JSON body:
+## What is Flumina?
 
-```json
-{
-    "prompt": "a beautiful asian woman in traditional clothing with golden hairpin and blue eyes, wearing a red kimono with dragon patterns",
-    "width": 1024,
-    "height": 1024,
-    "num_steps": 24,
-    "guidance": 3.0,
-    "seed": 13456
-}
-```
-
-For an example of how to generate from a python client using the FastAPI server:
+Flumina is Fireworks.ai’s innovative platform for hosting Server Apps that lets users deploy deep learning inference to production environments in just minutes.
 
-```py
-import requests
-import io
+## What does Flumina offer for FLUX models?
 
-prompt = "a beautiful asian woman in traditional clothing with golden hairpin and blue eyes, wearing a red kimono with dragon patterns"
-res = requests.post(
-    "http://localhost:8088/generate",
-    json={
-        "width": 1024,
-        "height": 720,
-        "num_steps": 20,
-        "guidance": 4,
-        "prompt": prompt,
-    },
-    stream=True,
-)
+Flumina provides the following advantages for FLUX models:
 
-with open(f"output.jpg", "wb") as f:
-    f.write(io.BytesIO(res.content).read())
+* Clear, precise definitions of server-side workloads by reviewing the server app implementation (right here).
+* Extensibility interface for dynamic loading and dispatching of add-ons server-side. For FLUX, this includes:
+  * ControlNet (Union) adapters
+  * LoRA adapters
+* Off-the-shelf support for on-demand capacity scaling with Server Apps on Fireworks.
+  * Customization of the deployment logic through modifications to the Server App, with easy redeployment.
+* Support for FP8 numerics, unlocking faster, more efficient inference capabilities.
 
-```
+## Deploying FLUX.1 [schnell] FP8 Version to Fireworks On-Demand
 
-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.
 
-```py
-import io
-from flux_pipeline import FluxPipeline
+## Deploying Custom FLUX.1 [schnell] FP8 Apps to Fireworks On-demand
 
-
-pipe = FluxPipeline.load_pipeline_from_config_path(
-    "configs/config-dev-offload-1-4090.json"  # or whatever your config is
-)
-
-output_jpeg_bytes: io.BytesIO = pipe.generate(
-    # Required args:
-    prompt="A beautiful asian woman in traditional clothing with golden hairpin and blue eyes, wearing a red kimono with dragon patterns",
-    # Optional args:
-    width=1024,
-    height=1024,
-    num_steps=20,
-    guidance=3.5,
-    seed=13456,
-    init_image="path/to/your/init_image.jpg",
-    strength=0.8,
-)
-
-with open("output.jpg", "wb") as f:
-    f.write(output_jpeg_bytes.getvalue())
-
-```
+Coming soon!

flumina.py

@@ -95,7 +95,7 @@ def parse_accept_header(accept: str) -> str:
             r"(?P<media_type>[^;]+)(;q=(?P<q_factor>\d+(\.\d+)?))?", part.strip()
         )
         if match:
-            media_type = match.group("media_type")
+            media_type = match.group("media_type").strip()
             q_factor = (
                 float(match.group("q_factor")) if match.group("q_factor") else 1.0
             )
@@ -106,11 +106,19 @@ def parse_accept_header(accept: str) -> str:
     # Sort the media types by q-factor, descending
     sorted_types = sorted(weighted_types, key=lambda x: x[1], reverse=True)
 
+    # Define a list of supported MIME types
+    supported_types = ["image/jpeg", "image/png"]
+
     for media_type, _ in sorted_types:
-        if media_type in {"image/jpeg", "image/png"}:
+        if media_type in supported_types:
             return media_type
+        elif media_type == "*/*":
+            return supported_types[0]  # Default to the first supported type
+        elif media_type == "image/*":
+            # If "image/*" is specified, return the first matching supported image type
+            return supported_types[0]
 
-    raise ValueError(f"Accept header did not include ones of supported MIME types: image/jpeg, image/png")
+    raise ValueError(f"Accept header did not include any supported MIME types: {supported_types}")
 
 
 # Define request and response schemata
@@ -319,7 +327,7 @@ if __name__ == "__main__":
         body=Text2ImageRequest(
             prompt="test"
         ),
-        accept="image/png"
+        accept="*/*"
     ))
     with open("out_image.png", "wb") as f:
         f.write(out)