Update README.md

README.md

@@ -11,8 +11,10 @@ base_model: black-forest-labs/FLUX.1-dev
 
 A collection of GGUF models using mixed quantization (different layers quantized to different precision to optimise fidelity v. memory).
 
-They can be loaded in ComfyUI using the [ComfyUI GGUF Nodes](https://github.com/city96/ComfyUI-GGUF). Put the gguf files in your
-model/unet directory.
+They were created using the [convert.py script](https://github.com/chrisgoringe/mixed-gguf-converter).
+
+They can be loaded in ComfyUI using the [ComfyUI GGUF Nodes](https://github.com/city96/ComfyUI-GGUF). Just put the gguf files in your
+models/unet directory.
 
 ## Naming convention (mx for 'mixed')
 
@@ -30,11 +32,13 @@ where NN_N is the approximate reduction in VRAM usage compared the full 16 bit v
 
 The process for optimisation is as follows:
 
-- 240 prompts used for flux images popular at civit.ai were run through the full Flux.1-dev model
-- The hidden states before the start of the double_layer_blocks and after the end of the single_layer_blocks were captured
-- The layer stack was then modified by quantizing one layer to one of Q8_0, Q5_1 or Q4_1
-- The initial hidden states were then processed by the modified layer stack, and the error (MSE) in the final hidden state calculated
-- This gives a 'cost' of each possible layer quantization
+- 240 prompts used for flux images popular at civit.ai were run through the full Flux.1-dev model with randomised resolution and step count.
+- For a randomly selected step in the inference, the hidden states before and after the layer stack were captured.
+- For each layer in turn, and for each of the Q8_0, Q5_1 and Q4_1 quantizations:
+  - A single layer was quantized
+  - The initial hidden states were  processed by the modified layer stack
+  - The error (MSE) in the final hidden state was calculated
+- This gives a 'cost' for each possible layer quantization
 - An optimised quantization is one that gives the desired reduction in size for the smallest total cost
   - A series of recipies for optimization have been created from the calculated costs
 - the various 'in' blocks, the final layer blocks, and all normalization scale parameters are stored in float32